Scientific foundations for the
simulation of ecosystem function
and management in FORCYTE-ll
Kimmins
Information Report NOR-X-328
J.P.
Northwest Region
1+1
Forestry
Canada
Forets
Canada
•
Forestry Canada's Northwest Region is responsible for fulfilling the federal role in forestry research, regional
development, and technology transfer in Alberta, Saskatchewan, Manitoba, and the Northwest Territories. The main
objectives are research and region!!1 development in support of improved forest management for the economic, social,
and environmental benefit of all Canadians. The Northwest Region also has responsibility for the implementation
of federal-provincial forestry agreements within its three provinces and territory.
Regional activities are directedfrom the Northern Forestry Centre in Edmonton, Alberta, and there are district
offices in Prince Albert, Saskatchewan, and Winnipeg, Manitoba. The Northwest Region is one of six regions and
two national forestry institutes of Forestry Canada, which has its headquarters in Ottawa, Ontario.
ForNs Canada, region du Nord-Ouest, represente Ie gouvernement federal en Alberta, en Saskatchewan, au
Manitoba et dans les Territoires du Nord-Ouest en ce qui a trait aux recherches forestieres, Ii l'amenagement du
territoire et au trrmsfert de technologie. Cet organisme s'interesse surtout Ii la recherche et Ii l'amenagement du
territoire en vue d'ameliorer l'amenagement forestier afin que tous les Canadiens puissent en profiter aux points de
vue economique, social et environnemental. Le bureau de la region du Nord-Ouest est egalement responsable de la
mise en oeuvre des ententes forestieres federales-provinciales au sein de ces trois provinces et du territoire concerne.
Les activites regionales sont gerees Ii partir du Centre de foresterie du Nord dont Ie bureau est Ii Edmonton
(Alberta); on trouve egalement des bureaux de district Ii Prince Albert (Saskatchewan) et Ii Winnipeg (Manitoba).
La region du Nord-Ouest correspond Ii l'une des six regions de ForNs Canada, dont Ie bureau principal est Ii Ottawa
(Ontario). Elle represente egalement deux des instituts· nationaux de foresterie de ce Ministere.
SCIENTIFIC FOUNDATIONS FOR THE
SIMULATION OF ECOSYSTEM FUNCTION
AND MANAGEMEN T IN FORCYTE-11
J.P. Kimmins1
INFORMATION REPORT NOR-X-328
FORESTRY CANADA
NORTHWEST REGION
NORTHERN FORESTRY CENTRE
1993
I
Department of Forest Sciences, Faculty of Forestry, University of British Columbia. Vancouver, British Columbia V6T 124
© Minister of Supply and Services Canada 1993
Catalogue No. F046-12/328E
ISBN 0-662-20274-0
ISSN 0704-7673
This publication is available at no chargeJrom:
Forestry Canada
Northwest Region
Nortbern Forestry Centre
5320 - 122 Street
Edmonton, Alberta
T6H 3S5
A microfiche edition of this publication may be purchased from:
Micromedia Ltd.
Place du Portage
165, Hotel-de-Ville
Hull, Quebec
J8X 3X2
CANADIAN CATALOGUING IN PUBLICATION DATA
Kimmins, J. P., 1942Scientific foundations for the simulation of ecosystem function and management in
FORCYTE-1 1
(Information report; NOR-X-328)
Includes an abstract in French.
Includes bibliographical references.
ISBN 0-662-20274-0
DSS cat. no. F046-12/328E
1. Forest biomass - Simulation methods. 2. Forest ecology. 3. Forest management­
Environmental aspects. I.' Nortbern Forestry Centre (Canada). II. Title. III. Series:
Information report (Northern Forcstry Centre (Canada)) ; NOR-X-328.
SD387.B48K55 1993
*
ii
634.9'2
C93-099428-0
This report has been printed on recycled paper.
In! Rep. NOR·X·328
Kimmins,
J.P. 1993. Scientific foundations for the simulation of ecosystem
. .
function and management in FORCYTE-11. For. Can., Northwest Reg.,
North. For. Cent., Edmonton, Alberta. Inf. Rep. NOR-X-328.
ABS TRACT
The FORCYTE-II (FORest nutrient Cycling and Yield Trend Evaluator) forest simu­
lation model combines the traditional historical bioassay mpdeling approach with process­
based simulation modeling. It provides a method of predicting future forest biomass yield
under a variety of management conditions. The hybrid simulation approach to modeling the
management and long-term productivity of forested ecosystems is reviewed, and the
representation of production ecology. succession, and various nutrient transfer pathways in
FORCYTE,II are described. The key driving function in the model (foliage nitrogen
efficiency), the concept of site quality, and the simulation of site quality change that is used
in the model are discussed. The importance of an accurate definition of the state ot the
simulated ecosystem at the start of a run is emphasized, and the use of the ECOSTATE
(STATE of the ECOsystem, as generated by FORCYTE-II ) file in this process is described.
Examples of how FORCYTE-l l can simulate secondary succession and the impacts of
forest management on ecosystem fann and function are given. There is a brief review of the
limitations of the FORCYTE-II modeling approach.
RESUME
Le modele de simulation forestiere FORCYTE-II (FORest nutrient Cycling and Yield
Trend Evaluator: evaluation des tendances du rendement et du cycle des elements nutritifs
forestiers) allie la modelisation traditionnelle fondee sur les antecedents connus au moyen
des dosages biologiques et la modelisation de simulation de processus. n permet de prectire
Ie rendement de forets soumises a divers regimes d'amenagement. L'auteur explique en quoi
consiste la simulation hybride de I'amenagement et de la productivite a long terme des
ecosystemes forestiers, et il decrit la representation, par Ie modele FORCYTE-l l , de
l'ecologie de Ia production forestiere, de Ia succession des communautes et des diverses
voies de cheminement des elements nutritifs. II traite de la fonction fondamentale qui anime
Ie modele (\'efficience de l'azote foliaire), de la notion de qualite stationnelle et de la
simulation deIa variation de ce dernier parametre parIe modele. II insiste sur l'importance
d'une definition precise de l'etat de l'ecosysteme modelise, des Ie deman-age de l'essai de
simulation, et il decrit l'emploi du fichier ECOSTATE (de l'etat de l'ecosysteme) produit
par Ie modele pour cette "tape. II donne des exemples de la simulation de la succession
secondaire par Ie modele et les consequences de l'amenagement forestier sur la forme
et la fonction de l'ecosysteme. Enfin, il decrit succinctement les limites du modele
FORCYTE-I l .
lnf Rep. NOR-X-328
iii
THE ENFOR PROGR A M
ENFOR (ENergy from the FORest) is a contract research and development (R &D)
program managed by Forestry Canada. It is aimed at generating sufficient knowledge and
technology to realize a marked increase in the contribution of- forest biomass to Canada's
energy supply. The program was initiated in 1978 as part of a federal interdepartmental
initiative to develop renewable energy sources.
The ENFOR program deals with biomass supply matters such as inventory. growth,
harvesting, processing, transportation, environmental impacts', and socioeconomic impacts
and constraints. A technical committee oversees the program, developing priorities, assess­
ing proposals, and making recommendations. Approved projects are generally carried out
under contract.
General information on the operation of the ENFOR program, including the preparation
and submission of R&D proposals, is available upon request from:
The ENFOR Secretariat
Forestry Canada
Place Vincent Massey
351 St. Joseph Blvd.
Hull, Quebec
KIA lG5
This report is based in part on ENFOR Project P-370, which was carried out under
contract by J.P. Kimmins (DSS File No. 03SG.OIK45-8-04l4).
This work was supported in part by the Federal Panel on Energy Research and
Development (PERD).
iv
Tn! Rep. NOR-X-328
FOREWOR D
The FORCYTE (FORest nutrient Cycling and Yield Trend Evaluator) series of forest­
ecosystem management-simulation models were developed by Dr. J.P. Kimmins and
K.A. Scoullar, under contract to Forestry Canada. This model development was funded
through a series of ENFOR (ENergy from the FORest) contracts. Model development was
an ongoing process; with successive phases under the supervision of different scientific
authorities. The FORCYTE project began in the late 1970s (Dr. J. Carlisle, Scientific
Authority), proceeded through the completion of the FORCYTE-lO model (Dr. L.
Chatarpaul, Scientific Authority) and culminated with the benchmark FORCYTE- l l model
in 1 990 (Dr. M. Apps, Scientific Authority).
In addition to model development, there have been three related activities: production
of regional calibration data sets, model evaluation (sensitivity analysis and model valida­
tion), and development of software to facilitate FORCYTE-l l in the microcomputer
environment (PROBE). These activities have involved scientists at five regional Forestry
Canada centres (Newfoundland, Maritimes, Northern, and Pacific Forestry Centre, and
Petawawa National Forestry Institute) as well as a number of contractors and university
personnel across Canada.
This long-term research and development program required close collaboration be­
tween Forestry Canada personnel and private contractors. During the development of
FORCYTE-l l, overall scientific coordination was achieved through a team approach that
involved the creation of an interdisciplinary FORCYTE-1 1 user's community. Information
was disseminated and model progress stimulated through five workshops, held between
1987 and 1 990. These consisted offormal presentations, model testing, technical evaluation
sessions, and Forestry Canada planning reviews. These workshops strengthened communi­
cation within the FORCYTE user group and with the model developers and allowed
coordination of regional activities.
Results of the evaluations and analyses provided critical insight for model development
and have been presented at national and international conferences and in Forestry Canada
publications. An extensive list of related publications. reports, and conference presentations
have been generated by the FORCYTE project. In addition to Forestry Canada activities,
there has been continuing international interest in the FORCYTE-l l model.
Major contributors in the FORCYTE-1 1 community included Forestry Canada person­
nel Dr. M. Apps, Dr. L. Chatarpaul, H. Grewal, Dr. O. Hendrikson, Dr. T. Mahendrappa,
Dr. B. Meades, and Dr. A. Trofymow and contractors Y.H. Chan, P. Clarke, Dr. J.P.
Kimmins, Dr. W. Kurz, D. Macisaac, Dr. E.B. Peterson, B. Pike, P. Rideout, D. Sachs, and
K. Scoullar.
Copies of FORCYTE-l l and the PROBE software package can be obtained from Dr.
M. Apps. Forestry Canada, 5 3 20 - 122 Street, Edmonton, Alberta T6H 3S5.
Dr. Mike Apps
Scientific Authority
FORCYTE Project
Forestry Canada, Edmonton
In! Rep. NOR-X-328
v
vi
In! Rep. NOR-X-328
CONTENTS
ECOSYSTEM-LEVEL, HYBRID SIMULATION GROWTH AND YIELD
MODELS
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Early Development of Methods of Growth and Yield Prediction
.
.
.
.
.. . .
Development of an Ecological Approach to Growth and Yield Prediction
Alternative Yield Prediction Methods
.
THE HYBRID SIMULATION APPROACH
.
.
Advantages and Limitations of the Traditional Historical Bioassay Approach
Major Assumptions of the FORCYTE-II Hybrid Simulation Modeling
Approach . .. . . . . . . . . . . . . . . .. . . . . .
Hybrid Simulation in Relation to Other Types of Models . . . . . .. .
Use of the Hybrid Simulation Approach in FORCYTE-11
.. . .. . .
Differences Between the FORCYTE Series and JABOWA Series of Hybrid
Simulation Models . . ... . . . . .. . . . . . . ... ... . . . . ..
BASICS OF PRODUCTION ECOLOGY AND THEIR REPRESENTATION
IN FORCYTE-ll
.... . .. .. . .... .. .. .. . ... ..
Fundamental Principles of Forest Production.Ecology . ... ... . . .
Leaf Area and its Relationship to Net Primary Production
. . .. .
Allocation of Net Primary Production Between Aboveground and
Belowground Biomass
. . .. .. . ... . . .. . ... . ..
Allocation of Leaf Area and Net Primary Production Between Overstory
and Understory
2
3
3
.
Advantages and Limitations of Process-based Simulation
I
. . . . . .... ... . . ... .. .. .
Effects of Between-tree Competition on Resource Allocation
Factors Contributing to Losses of Net Primary Production
Representation of Production Ecology in FORCYTE-II
Leaf Area Relationships . . ... . . . ... . . . . .
Resource Allocation Relationships .. . . . . . . .. .
3
4
5
7
7
9
10
10
10
12
14
14
14
15
15
15
Losses to Aboveground and Belowground Litter Fall .
16
Respiration .. . .. . . . . . .. . . . ... ... . .
16
Losses to Individual Plant Death .. .
Overstory-Understory Relationships . . . . . .. . ..
Losses of Net Primary Production to Symbionts and to the Rhizosphere .
Section Summary . . . .. . . . . . .. . . . . .. .. . . . . .. . .
16
16
17
17
FOLIAGE NITROGEN EFFICIENCY AS A MEASURE OF CANOPY
FUNCTION AND ITS REPRESENTA TlON AS THE BASIC DRIVING
FUNCTION IN FORCYTE 11
.......... .... .. ..... ..
The Foliage Nitrogen Efficiency Concept
. . .. . .. . . . . .. . .
Application of the Foliage Nitrogen Efficiency Concept in the Modeling of
Forest Growth in FORCYTE-ll . . . . .. .
Calculation of Total Net Primary Production
A.Ll.Biomass
. .. . .
B.Ephemeral litter fall
. . . . . . . ..
C.Mortality .. . . . . . .. . . . . . .
Calculation of Shade-corrected Foliage Nitrogen Efficiency
Perfonnance of the Foliage Nitrogen Efficiency Function of FORCYTE-II
18
18
19
19
20
20
22
Section Summary . . . .. . . . . . .. . . . .. . .. . .. . . . . . .
26
SIMULATING THE BIOGEOCHEMISTRY OF FOREST ECOSYSTEMS
26
Major Features of Forest Biogeochemistry
The Geochemical Cycle
In]: Rep. NOR-X-328
17
17
. . . . .. . . . . . . . . . . . . . . . ..
26
26
vii
27
The Biogeochemical Cycle.. . .
27
Uptake . . . . ... . . . ..
27
Distribution within the plant
27
Losses from the plant. . .. .
27
Decomposition: mineralization/immobilization
28
The role of understory in forest biogeochemistry .
The Biochemical or Internal Cycle
.... . .....
28
Overall Biogeochemistry of a Forest Ecosystem and its Representation
29
in FORCYTE-l l . . .
.
Geochemical Outputs.
29
34
Distribution of Nutrients within Plants.
38
Geochemical Inputs
Uptake by Plants . . .
37
38
Nutrient Loss from Plants .......
Internal Cycling
39
.. . .... .
...
.
39
Decomposition: Mineralization/lmmobilization
Section Summary
.
. . . . . .. . . . . . . . . . .
39
39
TEMPORAL CHANGE IN FOREST ECOSYSTEMS .
Successional Processes that Should be Incorporated into Hybrid Models
40
Representation o f Successional Processes in FORCYTE-II and Other
Hybrid Models . .. . . .. . . . .. . . .
41
42
Simulation of Succession using FORCYTE-II
51
Section Summary ... . . . .. . . . . .. . .
SITE QUALITY AND INITIAL ECOSYSTEM CONDITION IN FORCYTE-II
51
52
Representation of Ecosystem Response to Site Quality Change
52
Definition of Site Quality . . . . . .. .. . . , .. . .. . .
53
Site Quality for Plants Versus Site Quality for Soil Processes
54
Initial Ecosystem Condition: The Ecostate File
54
Preparation of the Ecostate File .
55
Section Summary . .. . . . . . . .
OTHER PROCESSES IN FORCYTE-II
55
56
Derivation of Coppice Resprouting or Root Suckering
56
Simulation of Tree Mortality .. . . . . ..
Simulation of the Biomass of Trees that Die . . . . ..
57
Simulation of Nutrient Cycling and Competition for Nutrients
58
Simulation of Light Competition Among Species . . .. . . .
59
Simulation of Height Growth . . . . . . . . . . . . . .
58
Nutrient Availability in the Soil. . . . . . . . . .
58
SIMULATION OF THE IMPACTS OF FOREST MANAGEMENT ON
59
ECOSYSTEM FORM AND FUNCTION IN FORCYTE-II
59
Simulation of Management Practices .
59
Clear-cuttiog . .. . . .. . . . ... .. . .. . . .
Alternative Harvesting Methods . .. . .. . . . ..
60
60
Response of Remaining Live Vegetation t o Harvesting .
Response of Decomposing Litter and Humus t o Exposure Following
Harvestiog .. . . . . .. . .
Site Preparation. . . .. . . . . .
Control of Competing Vegetation
Regeneration .. . . . . .
Early Stand Management.
Fertilization.
Pruning . .. . . . . . . .
viii
60
60
60
60
61
61
61
In! Rep. NOR·X-328
Herbivory ... ...... . ...
61
61
61
62
62
62
71
79
80
'
Intermediate Thinnings. ... ..
Stand Underbuming and Wildfire
General Comments ... .....
Graphical Output of Two Sample Runs .
The MANAGRAF Output from the First Scenario
The MANAGRAF Output from the Second Scenario .
General Discussion . . ..... .. ..... .
Verification and Validation of Model Performance ....
LIMITATIONS OF THE FORCYTE-ll APPROACH IN ADDRESSING
CURRENT AND FUTURE MANAGEMENT ISSUES
80
ACKNOWLEDGMENTS
82
REFERENCES ......
82
TA BLES
1 . Classification of growth and yield models by modeling approach and model
complexity ......... .. ... ..... .. . .. . .... . . ..
8
2. Examples of nutrient inputs to forest ecosystems via mineral weathering .
29
3. Examples of nitrogen fixation'in ecosystems.. . . . . . . .
30
4. Examples of precipitation inputs to terrestrial ecosystems .
31
5. Examples of published estimates of nutrient uptake by forest vegetation
32
6. Examples of tissue nutrient concentrations in Douglas-fir .. . . . . . .
35
7 . Scenarios demonstrating FORCYTE-l l 's ability to simulate secondary
succession . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
. 8. Maximum values of the three variables shown in Figures 1 6 to 22 for each of
the species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
50
F IGURES
Inf Rep. NOR-X-3Z8
1 . Levels of biological orgartization and levels of integration in ecosystems
5
2. Flow chart of files and programs that constitute FORCYTE-l1 .
9
3. Fundamentals of plant production ecology . . . . . . . . . . . .
10
ix
4. Relationship between incident solar radiation and economic biomass
production (yield), and of the role of the basic site resources in detennining
this relationship ...... .. ..... ....... ... .... .. ..
11
5. Individual contributions of water and nutrients to the determination of net
�rimary production, and its allocation between different biomass components
12
6. Variation in achieved leaf area for four different combinations of soil
moisture and soil nutrient availability .......... . .. ..
7.
13
Smoothing routine used to obtain biomass/age arrays [rum limited
biomass/age input data . ...... .. . .... ...... .. .
20
8. Biomass accumulation and net biomass producton in different tree biomass
components as predicted by TREEGROW .. .....'......... ..
23
9. Natural mortality patterns and ephemeral litter fall as predicted by
TREEGROW ............................
24
10. Tree height and stem biomass data, and miscellaneous canopy function and
other variables as predicted by TREEGROW .. ... .... .... ...
25
11. The concept of ecological rotation: the time to recover to the predisturbance
ecosystem condition
....... .......................
28
12. Nutrient uptake by forest crops
33
13. Tree nutrient content and its distribution in four forest types
34
14. Biochemistry of a pine stand in Finland ...........
36
15. Major compartments and transfer pathways represented in FORCYTE-l1
37
16. Stem biomass, foliage biomass, and top height values for Douglas-fir,
red alder, and bryophytes in Scenarios 1 and 2 .. ....... .. ..
17. Stem biomass, foliage biomass, and top height values for Douglas-fir,
red alder, fireweed, salmon berry, and bryophytes in Scenarios 3 and 4
43
44
18. Stem biomass, foliage biomass, and top height values for Douglas-fir,
red alder, fireweed, salmon berry, and bryophytes in Scenarios 5 and 6
45
19. Stem biomass, foliage biomass, and top height values for Douglas-fir,
red alder, fireweed, salmon berry, and bryophytes in Scenarios 7 and 8
46
20. Stem biomass, foliage biomass, and top height values for Douglas-fir,
red alder, fireweed, salmon berry, and bryophytes in Scenarios 9 and 10
47
21. Stem biomass, foliage biomass, and top height values for Douglas-fir,
red alder, fireweed, salmon berry, and bryophytes in Scenarios 11 and 12
48
22. Stem biomass, foliage biomass, and top height values for Douglas-fir,
red alder, and bryophytes for Scenarios 13 and 14 . ..........
23. Graphs A and B (biomass and production) from MANAGRAF (first scenario)
x
49
63
InfRep. NOR-X-328
24 . Graphs C and D (stand density and self-thinning; litler fall) from
MANAGRAF (first scenario) ..... ..............
.
64
25. Graphs E and F (tree sizes and canopy function) from MANAGRAF (first
scenario) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
26. Graphs G and H (internal cycling and biogeochemical cycling) for nitrogen
from MANAGRAF (first scenario) . . . . . ' .' . . . . . . . . . . . . . .
66
27. Graphs I and J (litler decomposition) from MANAGRAF (first scenario)
67
28. Graphs M and N (forest-floor mass and humus mass) from MANAGRAF
(first scenario) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
29. Graphs 0 and P (soil nutrient variables) for nitrogen from MANAGRAF
(first scenario) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
69
30. Graphs A and B (biomass and production) from MANAGRAF (second
scenario) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
72
31 . Graphs C and D (stand density and self-thinning; litter fall) from
MANAGRAF (second scenario) . . . . . . . . . . . . . . . . . .
73
32. Graphs E and F (tree sizes and canopy function) from MANAGRAF (second
scenario) . . . . . . . . . . . . . . . . . . . . . . . . . ', . . . . . . . . . .
74
33. Graphs G and H (internal cycling and biogeochemical cycling) for nitrogen
from MANAGRAF (second scenario) . . . . . . . . . . . . . . . . . . . .
75
34 . Graphs I and J (litler decomposition) from MANAGRAF (second scenario)
76
35. Graphs M and N (forest-floor mass and humus mass) from MANAGRAF
(second scenario) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
36. Graphs 0 and P (soil nutrient variables) for nitrogen from MANAGRAF
(second scenario) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
78
NOTE
The exclusion of certain manufactured products does not necessarily imply disapproval nor
does the mention of other products necessarily imply endorsement by Forestry Canc:da.
In! Rep. NOR-X-328
xi
xii
In! Rep. NOR-X-328
ECOSYSTEM-LEVEL, HYBRID SIM ULATIO N
GROWTH AND YIELD MODELS
With global population projected to continue grow­
function in the future are essential to the development of
area of unmanaged plant communities are likely to con­
ing and. management planning, and"·to the maintenance
ing, current global deforestation and reductions in the
reliable economic analyses, to long-term yield forecast­
tinue. The los� of forest is particularly severe in. the
oflong-term site productivity and environmental qualiry.
rp�aged for,timber or fiber_pro4uction are also occur:
ring in Canada and the United States. If demands for
The objective of this report is to review the current
understanding of the basics of forest ecosystem function
t rClpics, but s�gnificant reductions in the area of land
forest products and land for uses other than forest crop
production continue to match the population growth,
much greater production of timber will have to be
achieved on a much reduced area of forest land.
If current levels of forest harvesting and manage­
ment are maintained, and if the current trend towards
mechanized whole-tree harvesting and shorter -rotations
are continued, future forest yields will undoubtedly de­
cline in many parts of the world, even if there are no
reductions in forest ,area.Such ,yield reductions are not
inevitable, however.The biological potential exists to
increase the volume of timber harvested per hec�are as
previously unmanaged forests are converted to inten­
a n d h o w this understanding is reflected in the
FORCYTE-II (FORest nutrient Cycling and Yield Trend
Evaluator) computer simulation ecosystem management
model. The report does not attempt to provide an in-depth
literature review of every topic that relates· to
FORCYTE-ll.Rather, it examines the major features
that are represented in the model and focuses on those cone
sidered to be critically important in a model of this type.
Early Development of Methods of
Growth and Yield Prediction
Sustained-yield forest management requires esti­
sively managed plantations (Gordon 1 982; Gordon et al.
mates of existing timber volumes and accurate predic�
nontimber value will undoubtedly prevent the full bio­
yield tables were available for major tree species in parts
achieved (Boyd 1 982).
tional methods and scientific investigations of yield were
1982). Financial considerations and consideration of
logical growth potential of the world's forestsfrom being
Maximum sustainable forest production must be
achieved on all land committed to commercial timber
production if the future demand for both timber products
tions of future harvestable production.As early as 1795,
of Germany, where both the development of mensura­
well-advanced by the middle of the nineteenth century.
The extensive collection of forest biomass data that
resulted led to the widespread use of empirical yield
prediction tools in that country by the latter part of the
and for unlogged protection fore,!!ts, recreation, wilder­
century.
The mensurationists of the time, however, noted
satisfied.Maximum sustainable economic production
economic definitions of yield resulted in inflexible yield
sound understanding of the ecological processes that
growth phenomenon (Assmann 197 0). This led to the
ness, ecological reserves, an� other land uses are to be
. can only be achieved if forest management is based on a
that production studies based solely on technical and
predictors that gave little understanding of the whole
determine net primary production and its allocation be­
development of a branch of forest growth and yield
biomass components. Accurate prediction of future for­
factors that regulate production.
ecology of forest production.
Detailed studies of the processes of forest produc­
tion had their beginning in the latter half of the nineteenth
tween economically harvestable and nonharvestable
est growth must similarly be based on a knowledge of the
Accurate assessments� of current levels of forest
biomass, productivity, and economic yield are necessary
research that focused on organic production and the
century, in work by Ebermayer, Hartig, Boysen-Jensen,
and others.This early work was elitended by Moller
to sustain present forest management levels. But success­
(1 945) and associates (Moller et al.1954a, b) to form the
accurate predictions of productivity and yield under vari-
balance approach to production research, an approach
dictions require a much improved understanding of the
past two decades.It was also the impetus behind exten­
ful intensification of forest management also requires
0us alternative future management strategies. Such pre­
factors that regulate net primary production, and of the
dynamics of production over the entire life of a tree crop.
Accurate predictions about how forest ecosystems will
foundation of the photosynthesis-respiration carbon
that has preoccupied many production scientists over the
sive investigations of the relationship between foliage
biomass and forest growth in Japan (e.g.
, Fujimori and
Whitehead 1 986), and it became the basis for the
<\evelopment of p hysiologically-based growth
s im ulation m odels .
The history of fores t yield andp roductions tudies up
to the 1960s is thus c harac teriz ed by a str ong tradition of
emp irical, historic al-bioass ay I biom ass and yield re­
search. This traditional approac h has the advantage of
p roviding c redible predictions of grow!h and yield under
the environm ental and m anagement c onditions that
prevailed during the period of growth, but it is inflexible.
I t c annot· predic t y ield in s ignif ic antly different growth
.
environments for .which there is no such bioas say. P aralleling this traditional approac h from the mid-nineteenth
c entury onward,_ a research activity .was developed_ that
sought to understand the p rocess es that c ontrol f or e. s t
production. The m ethods that resulted generally h ave the
flexibility to deal with some of ! he eff ects of changed
growth environments on y ield, but many q f them h ave
llic ked the appropriate level of c omp lexity. Models pro­
duc ed by this approach tend to be ec op hys iologic al or
p op ulation m odels ra!her than ecos ystem -level m odels .
A third activity, which began in !he 1970s , incorporates
elements of the other . two approaches into a hybrid,
ec osystem app roach to yield p roduction forecas ting.
This utiliz es the s treng!hs of each of the two c omp onent
approaches to c omp ens ate for the shortc om ings of the
o!her (Kimmins 1985).
Development of an Ecological
Approach to Growth and
Yield Prediction
The traditional method of stand-level yield p redic ­
tion is to gather data on how forests have grown in the
p ast, to summarize !hese data in a graphical, tabullrr, or
m a! hematic al form , and to p roj ect thes e p ast grow!h
trends into the future. This m e!hod. is a historic al bioas say
of !he yield p otential of the s ite. It is bas ed on !he
assumption ! hat !he bestm easure ofhp w trees ands tands
will grow on a s ite in the future is how they have grown
on !hat site in the p ast. It is diff icult to think of a better
m ethod ofpredic ting s tand-level yield ifthe future grow­
ing c onditions and utiliz ati on level s arc expected to
rem ain the s am e as in the p ast, and if an acc urate rec ord
of p ast growth uml er thes e c onditions exists . The key
question is whether the future m anagement arid enviroTI-:­
m ental c onditions wil l be sufficiently different from
thos e of the p ast to invalidate the exis ting
. historic al
bioass ay.
I
2
T he single m os t imp ortant fac tor determ ining !he
future c onditions u nder which trees will grow is human
p op ulation growth. Some projections s uggest that !he
global human p op ulation will stabiliz e at about 10.2
billion by 2100 (Repetto 1987), approximately double
!he 1989 level. Others suggest, however, an up ward
revis ion of the p op ulation p rediction to 1 4 or 1 5 billion
by 2100. Such imm ense increases in human num bers will
red uce the area of the world' s fores ts' and the quality of
the environmental c onditions under which'they gro w.
During the 1 850- 1980 p eriod the g lobal forest area
dec reased by 15%, with a 60% loss in N orth A fric a ari d
the Middle E ast, a 39% los s in China, 4 3% in South As ia,
20% in tropicalA fric a, and 19% in L atin Americ a, while
!he global area of crop-land exp anded by 179% (Rep etto
1987; Rep etto and Gillis 1988). During this time, the
world p op ulation increased by about 350%. It was p re­
dicted in 1980 (C ouncil on E nvironmental Q uality 1980)
! hat !he global forest area would dec line by 1 7 % in the
1980-2000 p eriod, while !he p op ulation would ris e by
67%. Acc omp anying thes e p op ulation increases and for­
est area declines are p redictions !hat between 1 97 5 and
2000 there will have been an' 80% incre ase in harvest of
fores t fiber, a 4 4 % increas e in sawlog harvest, and a 39%
increase in world demand for indus trial wood and fuel
wood (Industrial Working P arty 1982).
The inc reas ed demand for res ources from a declin­
ing global fores t land bas e will almos t c ertainly result in
a signific ant change in the way forests are m anaged, as
has happened in agriculture. These changes will inc lude
shorter rotations , m ore c omp lete biomass utiliz ation, and
m ore, vigorous c ontrol of noncrop vegetation. A greater
use of fertiliz ation may be limited by ec onom ic s and the
foss il fuel energy-use imp lic ations of fertiliz ers, and as a
c onsequence therem ay be greater us e of nutritional nurse
c rop s, spec ies m ixtures, and alternating c rop rotations .
All of these c hanges have the p otentia l to alt er the soil
c onditions 'exp erienc ed by trees, with c onc om itant
effec ts on tree and stand growth.
U ntil rec ently, p redic tions about the effec ts on
forests of alterations in atmospheric c hemis try were s o
spec ulative that !hey were given little serious c onsidera­
tion. That has changed. I t is now known that ac id rai n
and air p ollution can have advers e short- and long-term
effec ts on fores t grow! h. It is believed that a s hort-term
beneficial eff ect of ac id rain in some areas (due to a
nitrogen [NJ fertiliz ation effect) may � ubsequen tly tum
into an adverse effect. E ven m ore s erious than acid rain
is the threat of a s ignific ant inc reas e in global
The term "historical bioassay" refers to the historical pattern of plant growth and biomass accumulation. This pattern provides a bioassay of the
plant growth potential of a site under the prevailing site conditions.
Inf Rep. NOR·X·328
temperatures due to the greenhouse effect. -A maj or shift
in the geograp hic loc ation of the world's majqr vegeta­
tion typ es has b een predic ted, along w ith signific ant
ch anges in stand growth and the inc idence O f damage
from diseases, insec ts, and fire.
suitab le historic al b ioassays, the process-based
simulation approach is the only c hoice available.
_
C onsidering the weight of evidence in favor of
signific antly altered growing c onditions w ithin the, next
half-century (less than one rotation for most C anadian
tree c rop s), it is diffic ult to avoid the c onc lusion that
traditional yield p redic tion methods, on their own, are
inadequate.
Alternative Yield Prediction
Methods
The inab ility of the yield tab le method to make
acc urate yif� ld predictions for c hanged growing c ondi­
tions c onvinced nineteenth-c entury German foresters
that yield should b e predicted on the b asis of the deter­
minants of forest growth . Th is is p articularly important
for situations in which there is no historic al rec ord of tree
growth (i.e., no h istorical b ioassay) or experience of the
effec t on 'growth of new, previously unknown growth
c onditions. A c entury later , h owever, forest sc ience has
still noiproduced a practic al alternative to the yield tab le
or mensurational model method b ased solely on our
understanding of ec ologic al proc ess.
Rec ently, very c omp lex, ec osystem-level, process­
b asedc omp uter simulatio n models haveb een developed,
but b ec ause of their data demands and c omp lexity, such
models are still more of a research tool than a manage­
ment p lanning tool. A detailed, process-based simulation
model would b e the ideal app roach to simulating forest
growth and yield if there were a c omplete knowledge of
all signific ant ec osystem proc esses and unlimited acc ess
to b oth c alibration data and extremely p owerful com­
p uters. In most c ases, such models and/ or data availabil­
ity are prob ably still many years away. The development
of somewhat simplerprocess-b ased models such as those
of B ossel and Scha fer ( 1989), Krieger et al. 1 990, and
Sc ha fer et al. (1 988) appears to offer a signific ant
advance in this type of modeling,_ and in the ab sence of
Both the historic al b ioassay and the proc ess-b ased
simulation approac hes have, c onsiderable merits, but
b oth have signific ant shortc omings as prac tic al yield
p redic tors for use b y fore sters over the next few dec ades.
B y c omb ining the two approaches into a "hyb rid simu­
lation" model, it is p ossib le to gain many of the advan­
tages of eac h c omp onent app roac h without th e
S hortcomings. The first ecosystem-level hyb rid simula­
tion model, JABOWA (JAnak, B Otkin, WAllac e), was
developed b y B otkin et al. (1972), to simulate forest
succession alori g an elevational transect in the N ew
E ngland mountains. The JAB OWA model has sub ­
sequently b een mo dified into FORT N IT E ( FOResT
N itr ogEn) (Ab er and Melillo 1982), FORET (FORests
of E astern Tennessee) (Shugart 1 984), L IN KAGES
( L inke d Forest P roduc tivity- Soil P rocess Model)
( P astor and P ost 1 985), and several other derivative
models. B oth FORT N ITE and LIN KAGE S have some
app lic ations as yield predictors and management simu­
la tors, but their major value is in simulating long-term
forest succession under c limate change (P astor and P ost
1988). The FORET model has b een m odified to b ec ome
FORCAT, a model to simulate stand development fol­
lowing c lear-cutting (Waldrop et al. 1986). Develop ed
spec ific ally as yield predictors and forest management
simulators rather than as models of ec ologic al succes­
sion, the FORC YTE series of models is another example
of this gem e of model.
The app roach taken in hyb rid simulation yield
modeling is to use the historical p attern of growth as the
b est estimate of future growth under unchanged c ondi­
tions, -and then to simulate changes in that historic al
p attern b y simulating the expec ted c hanges in growth ­
determining proc esses and the effec ts of these c hanges
on growth . The proc esses that are simulated are those that
are expected to b e signific antly altered in the future; for
examp le, if b iomass utilization is expec ted to increase
and rotations are exp ec ted to decrease, a simulation of
soil nutrient availab ility will b e necessary.
THE HYBR I D SIM ULATION APPROA C H
Advantages and Limitations of
Process-based Simulation
In an ideal w orld, process-b ased simulation models
would b e the obvious choice of modeling strategy with
Inf Rep_ NOR-X-328
which to forec ast forest growth and yield for a c hanging
and uncertain future; however, we do not live in an ideal
world. Although c omp uter c ap ab ilities are advancing
rap idly, c omp uters large and fast enough to represent the
full c omp lexH y of ec osystem proc esses are not yet
3
available to m os t fores t m anagers. Steady progress
is b ei ng m ade i n unders tandi ng ec os ys tems; how­
ever. m any of the determ inants of fores t growth and
development are s till poorly u nders tood. For the deter­
m inants that are unders tood, the hum an-res ource and
financial c os ts of gathering the necess ary data to c ali­
brate and verify proc ess-bas ed s im ulati ons of thes e de­
terminants are b eyond the reach ofm os t fores t res ources
m anagers .
P rocess -bas ed fores t m odels c an s imu late the effects
on growth of changed future environmental c onditions
bec ause they c an repres ent the c ons equ ences of a range
of values of growth-determ ining vari ables . A proc ess ­
b ased m odel that inc ludes acc urate repres entations of all
the process es that determ ine growth over a whole rota­
tion would be an excellent predictive growth and yi eld
m odel. Unfortu nately, few of the cu rrently exis ting
process -bas ed m odels are s uffici ent ly c om prehensive.
Mos t of the fores t growth s im ulation models of this type
produced in the p as t two dec ades include only a s ubset
of the important growth determ inants that are expec ted
to c hange i n the future. Suc h m odels only have the
flexib ility to predict the c ons equ ences for fu ture growth
of changes i n thos e determi nants that are explic itly
repres ented. They wi ll not give acc urate predictions for
futures in which important growth determinants that are
not explicitl y repres ented are changed.
P rocess -bas ed m odels of low c omplexity can be
extremely us eful for educ ation and res earch, and they
have value for prediction in thos e sys tems i n whic h the
omitted proc esses are not expec ted to change in the
fu ture. It is b elieved, however, that m any are limited
i n their predictive value for the type of future that will
occur. P roc ess -bas ed m odels are thus flexible, but they
have generally lac ked the c om plexity needed to predic t
fu ture ec os ys tem fu nctions. Mohren (1987) p rovi des one
'o f a few exc eptions to this-situ ation. His m odel is an
example of a very detailed process -basedm odel that does
successfully inc orporate a large num ber of important
detenn inants .
Great progress has been m ade in the developm ent of
proc ess -b ased m odels over the pas t dec ade, and u lti ­
m ately our goal s hould be to develop m odels of this type
that c an replace his torical bioassay growth and yield
predictors. Ten years ago, i t appeared that realis tic and
prac tical proc ess -b ased growth and yield m odels s till lay
far i n the future, bu t it riow s eems that us efu l yi eld
prediction tools of this type m ay be developed much
s ooner. W hi le we are awaiti ng this developm ent.
however, an alternati ve approach is requ ired.
4
Advantages and Limitations of
the Traditional Historical
Bioassay Approach
The enormous c om plexity of the proc esses that de­
termine tree growth, s tand development, and timber yield
over a tree c rop rotation that may b e as long as a c entury
ormore have m ade thebi oass ay approach to yield forecas t­
ing very attrac tive. With ou r i nc om plete knowledge of
the fac tors that affect a: tree crop on a partic ular s ite over
a whole ro tation, and of exactly ho w the known f' actors
affec t the trees , the his torical rec ord of pas t fores t growth
generally offers the bes t m ethod of predicting how the
fores t will grow u nder thos e c onditions in the future.
The m ajors trength of the his toric al bi oassaym ethod
. is that it is , b y definition, an ecos ys tem -level approach.
For at leas t two dec ades , ec ologists h ave been pointi ng
out that while an u nders tanding of a particular ec ologic al
or bi ological phenomenon requires knowledge of the
c om ponents and proc ess es at " leve1s -of-bi ological­
organiz ati on" below that of the phenomenon of i nteres t,
acc urate prediction of fu tu re s tates or occu rrences of the
phenom enon requires knowledge of the next true "level­
of-integration" above the phenomenon� I n the c as e of
fores t growth, this is the ec os ys tem level (Fi gure 1 ; see
Rowe 1961 ; O dum 197 1 ; Kimmins 19 87). His torical
bi oassay models are an appropriate m ethod of predicti ng
fu tu re fores t growth bec aus e they are ecos ys tem-level
m odels; however. bec ause their repres entation of ec os ys ­
tem proc esses is implic it rather than explicit. they are
only appropriate as predic tions for fu ture growth and
yield under fu ture ec os ys tem c onditions that are s imi lar
to thos e of the pas t. His torical bi oassay m odels are
therefore inflexible, and this is thei r m ajor s hortc or;J. ing.
Many fores t growth and yield m odelers have
rej ected process -bas ed growth and yi eld simulation
m odeling i n favor of the tr aditi onal historic al b ioassay
approach bec aus e of thes e advantages of the his torical
b ioass ay approac h, and the di fficulties with the process ­
b ased s im ulation approach. This respons e is logic al i f
future growing c onditions were to rem ain the s ame, and
if an acc urate and unders tandable b ioass ay exists . If,
however, fu ture gro win g con ditions are ex pected to be
si gnific antly diff erent from thos e in the pas t, if no suit­
ab le his toric al rec ord of s tand growth and development
exis ts , or if the his torical rec ord is uninterpretable (i.e.,
the pattern of ec ologic al events thatc om bined to produce
the his toric al pattern of growth is u nknown), the
acc urac y, effic ienc y, and even the applic abili ty of the
traditi onal approach on i ts own is very qu es tionable.
In s pite of the m any posi tive attribu tes of the tradi­
ti onal his toric al bioass ay approach to growth and yield
Inf. Rep. NOR·X·328
Ecosystem
Ecosystem
1.
Community
Population
Forest
growth
Individual
Individual
o,gan�
'
Tissue
and effic ienc y of the approach.
The m aj or ass ump tions are
d iscussed in the following text.
levels of integration
levels of organization
__�t
---
Cell
Cell
Note: The ability to uncjerstand a phenomenon at any particular level of
biological organization normally requires knowledge from the levels below.
This requirement has been the driving force of reductionist science, which
is seen as an absolutely necessary, but not a sufficient activity to ensure
that the long-term goal of science is achieved: to understand and be able
to predict the human system and its local, 'global, and universal
environment. Prediction of future states or occurrences of a phenomenon
at any particular level normally requires knowledge of the next true level of
integration above. For ecological phenomena such as individual tree
growth or stand growth and development, prediction must thus be made at
the ecosystem level. (After Kimmins 1987; based on Rowe 1961.)
The historical bioass ay provides
the bes t available es timate of the
growth p otential over the entire
rotation ofthe individuals and the
s tand of the genotyp e of the tree
spec ies being s im ulated .
T he his toric al bioassay is as ­
s umed to provide a betterm eas ure
of the integrated c ons eq uences of
all growth determ inants for c an­
op y func tion, alloc ation ofphoto­
s ynthate and s tand d ynamics
under the prevailing' growth c on­
d itions than analytic al meas ures .
"B etter" in this s ens e means a bet­
ter rep res entation of s om e bio­
logical process or the outc ome of
process es, or a m ore practic al and
eas ily available m eas ure thereof,
or both.
A p otential d ifficulty with this as ­
s ump tion is that the m odel us er
m ay not be able to identify the
Figure 1. Levels of biological organization and levels of integration in
. his tory ofc ond itions that affected
ecosy�tems.
the tree p op ulation being us ed as
a bioassay. W ithout this knowl­
edge, it is d iffic ult to d etermine
m od eling in fores try, the acc um ulating evidence that
how the growth c ond itions that are ass um ed in the
future growing c onditions will be signific antly d iff erent
historic al bioass ay will d iffer from anticip ated future
from thos e of the p ast provides a c omp elling argument
growth c onditions . For examp le, the us er m ay not
for an alternative or c omplem entary approac h. O bvi­
know how m uc h p as t d efoliation, d rought, or fros t
ous ly, proc ess-bas ed m od eling on its own does not yet
d am age has affec ted the observed growth p atterns,
appear to offer a practic al altem ative for fores t yield
m aking it difficult to predict the c ons eq uences of
forec as ting.
changes in thes e p atterns. P rocess -bas ed s im ulation
m ay offers om e advantage in this respect ifthe effects
The weakness of the his toric al bioassay m odel is the
ofsuch growth-determining agents can be acc urately
s trength of the process s im ulation m odel, and vic e vers a.
s im ulated; however, the empirical field or growth
B y c om bining the two, theshortc omings of both c ompo­
chamber meas urements us ed to c alibrate s uc h proc- '
nent approaches c an be overc om e to s om e extent. This is
ess-bas ed m od els 'may reflect an unknown premeas ­
t he rat io na le behind the hybrid s imulatio n approach to
urement his tory offactors affecting theplant's growth.
growth and yield m od eling in fores try.
Thus, process -bas ed s im ulation can als o s hare this
p roblem .
Major Assumptions of the
Modeling Approach
The his toric al bioassay input d ata repres ent the
growth p otential of eac h spec ies if it were occ upying
the s ite on its own.
In sp ite of the app arent advantage of the hybrid
s im ulation method, FORCYTE -ll incorp orates s everal
ass ump tions which, if not met, c an reduce the acc urac y
In the FORCYTE-]] application ofthe hybrid s imu­
lation approach, a historic al bioassay of the s ite's
FORCYTE-11 Hybrid Simulation
In! Rep. NOR-X-328
2.
5
growth potential must be provided for each species
A drawback of this approach is that if input data from
to be simulated.A difficulty with this assumption is
only one site quality are available, changes in plant
that sometimes the only available historical bioassay
growth strategy in response to environmental change
data may be from mixed species stands, or from
stands in which there-has been a variation over time
cannot be simulated. Most models do not simulate
such growth strategy changes.Thus, failure to pro­
in interspecific competition.In such cases, the user
vide the appropriate range of data simply reduces the
must assume that the. historical pattern of growth of
capability of FORCYTE-l l to the level of most other
the species of interest approximates how it would
models.
have grown on its own. Clearly, this would be an
untenable assumption if the other species have had a
significant impact on the species of intere�t_
There is an advantage in extracting from the histori­
cal bioassay data various measures of canopy func­
tion that are then used as the driving function for the
simulation because of the difficulty with this
assumption, rather than using the historical bioassay
data more directly as was done in FORCYTE-lO.In
that earlier model, the management simulator was
driven by a measure of increment derived from the
historical bioassay input data (Increment
=
change in
biomass + losses to ephemeral litter fall + losses to
plant mortality).In FORCYTE-l l , this driving func­
tion is replaced by a measure of canopy function
(shade-corrected foliage N efficiency: see Section 4)
derived from the bioassay input data.Inspection of
the temporal patterns of this derived index of canopy
function may identify periods with unexpected
deviations from the anticipated pattern.These devia­
tions may be identified with historical events that the
user does not want represented in the driving func­
tion.Appropriate smoothing of the input data would
pennit the impacts of such events on the driving
function to be removed.
Another difficulty with this approach is that the
env ironments from which data needed to simulate
growth strategy changes are obtained may vary in
more than just the variable of interest.In many cases,
nutrient-poor sites are dry sites, and nutrient-rich
sites are moist sites.Thus, data on growth allocation
from sites of varying nutrient status will often be
confounded by varying moisture status.It cannot be
assumed that allocation and biomass turnover data
from a range of sites can be used as the basis for
simulating responses in these- parameters to simu­
lated changes in nutrient availability alone because
lack of moisture may limit tree growth response to
nutrient additions. The FORCYTE- l l model does
not . explicitly simulate the effects of moisture on
growth or the consequences 9f competition for mois­
ture. Instead
it has a user-defined limitation on the
maximum foliage biomass that a site can carry, irre­
spective of nutrient additions.This reflects the ideas
of Grier and Running (1977), although recent evi­
dence from fertilizer and sewage sludge research
(Barclay and Brix 1985; Cole et al. 1986) suggests
that in some climatic regions, growth on dry sites
may be more limited by low soil fertility than by
growing season moisture deficits. Summer drought
may have a greater effect on tree growth through its
3.
The best method t o simulate the consequences of
environmental change over time for growth alloca­
tion and biomass turnover is to use data on patterns
of allocation and turnover from environments with
different values for the variables of interest.
This approach is used because our understanding of
6
effects on soil biology than through its effects on
canopy function.
4. The initial conditions of the plant community and
y defined.
soil can be accuratel
The initial condition of the plant community and soil
the biochemical, physiological, and environmental
at the time of a management treatment or at the start
controls over the allocation of net growth to different
of an ecosystem experiment can have as big an
biomass components, and_ of the turnover of ephem­
influence on the results of the experiment, as the
eral biomass components (e.g., fine root turnover,
treatment itself. One of the most important require­
evergreen leaf retention) is still very incomplete.An
ments of a hybrid simulation growth and yield model
example of this approach is the simulation of the
is that it is able to define accurately the ecosystem
effect of changing soil nutrient availability on re­
condition that is the logical result of a given history
source allocation. This is simulated on the basis of
of stand development, treatment, natural distur­
empirical data on variation in allocation over a range
of sites of different nutritional status.In this case, a
approach to the need for this capability is the
bance, etc. A major feature of the FORCYTE-l l
bioassay of the results pf a poorly understood or
ECOSTATE (STATE of the ECOsystem) file, which
poorly quantified process is used rather than trying
is'used to represent any desired-ecosysteIl1: condition
to simulate the process itself at a physiological level.
from which to initiate a simulation.Use of the model
In! Rep. NOR·X-328
involves the assu mption that the E COSTATE file
has been accu rately prepared by the u ser.
5.
The method u sed to estimate certain process rates
indirectly from inpu t data produ ces accu rate
estimates of the rates.
In FORCYTE - l l the rate of some processes or the
effects of some conditions on growth are der ived
from a combination of relatively simple field meas­
u re ments and a knowle dge of the proc esses or e f­
fects. The effect of the condition or the rate of the
process is inferred from observati<;ms of the ou tcome
of the process or the growth consequ ences of the
condition u nder a range of field conditions. This is a
simpler means of estimating values for some vari­
ables than, direct measu rement, bu t requ ires the
assu mptio n that this method of estimating process
.
rates is acceptably accu rate.
The FORCYTE - l l U ser's Manu al (Kimmins. Scou llar,
and Apps 1990) discu sses other specific assu mptions
made in the model.
Hybrid Simulation in Relation to
Other Types of Models
There exists a confu sing diversity of approaches to
the modeling of forest growth and yield. It is perhaps
easier to u nderstand the relationship among hybrid simu ­
lation models in general, and of FORCYTE - l l in par­
ticu lar, to other models by organiz ing them into an
overall classification (Table I).
W hile the traditional yield table is implicitly an
ecosystem level model; none of this complexity is recog­
nized explicitly. Stand growth models based on the peri­
odical remeasu rement of growth and yield plots provide
data from which to determine transition probabilities for
individu al trees of a given siz e and competitive statu s to
survive and grow to specific new dimensions. Su ch mod­
els constitu te historical bioassay models with an intenne­
diate level of explicit complexity. W here measu rements
on thc data plots are stratified by ec ologic al site type and
inclu de noncrop as well-as crop species, su ch historical
bioassay models may be qu ite complex and can provide
a realistic representation of a real ecosystem.
Process-based simu lation models of forest growth
and yield may be limited to a representation of some
aspects of canopy fu nction from which individu al tree or
stand growth can be inferred. More complex' process­
based models may inclu de detailed representations of all
tree components and some of the specific interactions in
In! Rep. NOR-X-328
the crop popu lation, The most complex examples of this
class of model ' have explicit, process"based repre­
sentations of a large nu mber of ecopbysiological, soils,
population and commu nity processes.
Hybrid simu lation . models. also exhibit a range of
complexity. Most are fairly complex, bu t L andsberg
(1986) might be considered to be an example of a low
complexity model. L andsberg su ggests that the ou tpu t of
a whole series of detailed process-based simu lation mod­
els be c ombined as a series of growth modifie rs by which
the historical pattern of growth wou ld be modified to
represent the effects of changing futu re conditions.
Mitchell's (1975) model TASS,(Tree And Stand Simu la­
tor) is basically a simu lation of branch competition for
light based on measu rements of branch growth, overlap,
competition, and mortality. This rec_ord of canopy com­
petitive interactions is combined with simu lated canopy
light profiles to simu late the growth and su rvival of
individual trees, and thu s the devdopment of the stand.
It could be argu ed that this is a canopy process model
(simulating branch competition for light), bu Un many
respects it is more like a hybrid simu lation model than
the process-based growth simu lators of compar able com­
plexity level. High-complexity hybrid simu lation models
inclu de commu nity development (gap phase su cces­
sional models) su ch as JABOWA (Botkin et al. 1972),
FORE T (Shu gart 1984), ZELIG (Smith and Urban
1988), and FORSKA (Leemans and Prentice 1989), and
ecosystem development models su ch as FORTN ITE
(Aber and Melillo 1982), LINKAGE S (Pastor and P ost
1985) and FORCYTE - I l .
Use of the Hybrid Simulation
Approach in FORCYTE-"
One of the most difficu lt aspects of the use of a
predictive ecosystem-level hybrid simu lation model is
the verification of the simu lations and validation of the
model's long-tenn predictions. W hen a mixed commu ­
nity of trees,. herbs, shrubs, and mosses is simu lated, it
may be difficu lt to obtain long-term historical data sets
against which to verify the moders performance and
validate its pre dic tions. The problem is made even morc
difficu lt by the lack of any adequ ate description of the
site condition in most published descriptions of
chronosequ ence research on species composition, pro­
du ction, and biomass accu mulation that might be u sed.as
a sou rce of validation data. Such descriptions are needed
to define the starting conditions for a simu lation if resu lts
are to be u sed for verification or validation purposes.
The FORCYTE "II model was specifically designed
to address these issu es (Fig. 2). Having an ecosystem
7
Table 1.
Classification of growth and yield models by modeling approach and model complexity.
(Source: Kimmins, Comeau, and Kurz 1990.)
Modeling aE]2roach
Model
complexity
Historical bioassay
Process-based simulation
Low
Yield table'
Physiological (FAST)b
Intermediate
High
Stand growthd
Stand development
(PROGNOSIS)g
Stand growth (PT)' .
Highly aggregated stand growth'
Stand development (TASS)'
Ecosystem development ·
(SHAWN)h
Community or ecosystem
development
(FORCYTE)i
(FORET)i
(LINKAGES)k
Increase in ability to predict growth under changed future growth conditions .'
a
McArdle et a1. 1961.
b
Lohammar et a1. 1980.
c
Landsberg 1986.
d
Curtis et a1. 1981.
e
f
�
Hybrid simulation
"
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.,
S·
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Eo
5'
�
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11
@
.,
�
S·
""
�
S·
"
g. "�
"
'"
S
@
.g�r
0
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.,
5'
""
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"
><
[
q"
e.
'"
0
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s
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Agren and Axelsson 1980.
Mitchell 1975.
g
Stage 1973.
h
Barclay and Hall 1986; Mahren 1987.
i
Kimmins, Scoullar, and Apps 1990.
k
Pastor and Post 1985.
j Shugart 1984.
setup stage in the modeling activity- pennits evaluation
of the accuracy of the simulation of individual species
growing on their own, and of certain soil processes, -before
these simulations are combined in the ecosystem simulation
module MANAFOR (MANAgement of the FORest). In
most other hybrid simulation models, the transition from
raw field/growth chamber data to indexes or coefficients
that defme growth and other ecosystem processes is con­
ducted externally to the model. These iodexes/coefficients
are then used in the model's input file. This does nut
provide the user with any check on the veracity of these
indexes or growth modifiers of individual species within
the modeling activity itself. In FORCYTE- I I , this evalu­
ation activity is formalized as a key part of the overall use
of the model, facilitating verification and input data
checking for this part of the simulation exercise.
The modeling strategy used in FORCYTE-II results
in data input files that at first appear to be more complex
and data-demanding than the alternative theoretical
8
approach used by Agren (1983a, b) or the growth­
modifier approach used in the FORET and LINKAGES
approaches. The difference lies mainly in where and
when the calibration calculations are performed. In the
theoretical, equation-based approach, all the calculations
required to obtain values for equation parameters are
performed externally to the model. Similarly, io the
approach used in the JABOWA-based models, equation
parameter values are calculated externally to the model.
In FORCYTE-l I , the basic empirical data are fed into
the setup programs, which perform all these calculations
within the setup simulation activity. The files passed
between the setup programs and MANAFOR programs
thus become the equivalent of the input file for the
alternative-approach models.
The production of abundant graphical and tabular
output, another feature of FORCYTE-l I , allows rejec­
tion of input data and/or model performance. The ability
to make such a rejection is critical to the acceptability of
In! Rep. NOR-X·328
PLNTDATA ","""1""...,.,
Bi shrubs I
s
Input
lUe
�
�
w
Output!
Input
files
MANADATA
Program
I
2
C
•
E
�
•
�
�
PROBE", m�llip'le
ru-:a ,manager
PREPARE., SEQUENCE
UPDATE OVERLAY I
'COMPRESSt Df,SPLAY
•.
Output!
Input
files
.J
_
_
Programs
,;
%
0
s
Output
file
Figure 2. Flow chart of files and programs that constitute FORCYTE-ll.
any growth and yield model. The PROBE software was
developed independently of FORCYTE-l 1 to allow sen­
sitivity analysis of the. model, to conduct multiple runs,
and to provide increased flexibility in the presentation of
graphical output (Apps et al. 1988, 1992; Kurz et al.
1988; MacIsaac et al. J989). The PROBE software can
also be used with other simulation models.
nutrients (generally N ), temperature, and moisture. The
models vary in whether or how they calculate nutrient
(N) availability, and the' result is applied as a growth
modifier, rather than simulating the degree to which
nutrient uptake demand is satisfied, and various path­
ways of nutrient cycling, which is the approach used in
FORCYTE models,
Differences Between the FORCYTE
results in a relatively simple model structure and input
There is merit to the gap model approach in that it
Series and JABOWA Series of
Hybrid Simulation Models
The first and major lineage of hybrid simulation
model (the JABOWA series; Botkin et al. 1972) takes a
somewhat different approach than the FORCYTE line­
age. In the JABOWA series of models, there is a basic
growth function for a tree species. This basic growth
function, which represents growth of the species under
ideal conditions, is then subjected to a series of growth
modifiers, the number of which varies between different
models in the series; the list includes modifiers for light,
In! Rep. NOR-X·328
data file in spite of the number of growth-limiting deter­
minants that are simulated, FORCYTE-l l , in contrast,
derives an index of canopy function from the empirical
growth data rather than from fitting a growth equation to
it. The model then simulates the availability of nutrients
and light, and interprets the consequences for tree growth
based on input data on light responses of the foliage, and
the simulated nutrient demand to support the growth
predicted by the index of canopy function. The
FORCYTE approach is more mechanistic and detailed
than the JABOWA approach, permitting more detail of
canopy function and biogeochemistry to be incorporated.
9
This results in much larger and more complex computer
programs, and apparently moree complex input data
requirements; however, the derivation of the growth
multipliers in the JABOWA series is based on the caleu'
lation of coefficients for the modifiers, which require
substantial data sets for theIr'derivation. The separation
of these two activities in the JABOWA series models
results in a much more compact model, but the actual
total work involved in preparing the two models for use
is more similar than it appears to be:.
The modeling strategy used in the JABOWAmodels
does have several advantages over that used in
FORCYTE-l l with respect to long-term succession in
some types of unmanaged forest._The current versions of
most of the JABOWA series, however, have several
limitations _ with respect to simulating the impacts of
management or of large-scale natural ecosystem distur­
bance. They simulate events in small canopy gaps (0.083
hal, all the foliage is simulated to be at the top of the tree,
there is no detailed representation of s�ils in most
FORET-related models, and there is little or no ability to
simulate fore$t management, or natural ecosystem distur. bance. LINKAGES (Pastor and Post 1 9 8 5 ) and
FORTNITE (Aber and Melillo 1982) have fewer limita­
tions with respect to simulating management impacts
than the FORET model (Shugart 1984), although recent
development of models such as ZELIG (Smith and
Urban 1988) and FORSKA (Leemans and Prentice 1989)
addresses some of the limitations of FORET.
. BA SIC S OF PRODUCTION ECOLOGY AND TH EIR
R EPR ES ENTATION IN FOR CYTE-1 1
Fundamental Principles of Forest
Production Ecology
Leaf Area and its Relationship to Net
Primary Production
There are many aspects af forest stand growth and
development that must be considered in order to make
accurate predictions about growth and development, but
the most fundamental consideration is the flux and
storage of energy (Fig. 3).
Respi ration
Photosynthesis
»
Herbivory
Net plant
production
....----�,"-.:
Photosynthesis ' depends upon:
•
Leaf, area
•
Photosynthetic efficiency
Both of these are determined by
the availability of light, moisture
and nutrients.
Detritus
and other
losses
Reproduction
Litter fall
Plant death
Root exudates
Mycorrhizal and
other symbionts
Foliar leaching
Figure 3.. Fundamentals of plant pr(lduction ecology.
10
Most energy required for the net primary production
offorests is solar radiant energy in the photosynthetically
active visible wavelengths. The capture and conversion
of solar energy into plant biomass requires the presence
of green plants, a climatic regime to which they are
adapted, a substrate that provides sufficient anchorage so
that foliage can intercept the solar radiation, and
sufficient access to moisture and nutrients.
Leaf area and its photosynthetic efficiency are the
fundamental determinants of the "engine" that drives the
ecosystem. They determine the flux of energy into the
system, and there is a strong relationship between the
amount of intercepted radiation and total biomass pro­
duction for many crops (Monteith 1977; Waring et al.
1978; Legg et al. 1979). The major factors limiting these
two parameters, and hence forest net production, are
water and nutrients (Linder 1987). Only on the most
fertile, moist sites does light act as the limiting factor on
leaf area; otherwise, water is probably the ultimate de­
terminant of the site's leaf-area carrying capacity (Grier
and Running 1977; Kozlowski 1976, 1982; Gholz 1982).
The proximal det'enninant of leaf area, however, is usu­
ally the availability of nutrients needed to produce leaf
biomass, and the effect of nutrient availability on re­
source allocation. For many sites that are dry during the
summer growing season, there are many months when
neither temperature nor soil moisture limits photosynthe­
sis. On such sites it is probably the effect of the summer
drought on soil biology and nutrient cycling processes
that limits growth. Summer moisture deficits may thus
act as much or more through nutrient availability than
directly through canopy function.
In! Rep. NOR-X-328
W hen one or m ore nutr ients are in s hort s upply, the
lea f area and its photos ynthetic eff iciency are reduced to
below the level determ ined by the wa ter supply (B rix
1971). The imp ortance of nutrient ava ila bility as the
pmximal determina nt of growth is s uggested by experiences
from fertiliz er trials. The major resp ons e ofm os t fores ts
to fertilizer a dditions is the development of increased lea f
area (B rix 1983; Linder 1985), even on fa irly dry s ites
(Brix and Mitchell 1983; C ole 1983). The increase in lea f
area may result from increased size and numbers of new
leaves , or reduced lea fm orta lity (Linder and Rook 1 984).
by the rela tive ma gnitude of different biomass c ompo­
nents tha t diff er in their respira tion ra tes per unit of
biomass . This rela tivema gnitude is influenc ed by ca rbon
a lloca tion, which, as a lrea dy noted, is determ ined by
res ourc e ava ilability. The net primary produc tion tha t
rema ins ' a fter respira tio? loss is dis tributed 'between
ephemera l and p erma nent biomass s tructure in respons e
to s evera l fac tors , inc luding s ite res ource ava ila bility.
This dis tribution determines the ra te of net biomass
accum ula tion. It als o determines the "ha rves t index"­
the proportion of the accumulate d biomass that is
ec onom ically ha rves table.
Res ults of s tudies s uc h as L inder ( 1 987) a nd
Inges ta d et a l. (1981) oftherole ofm ois ture and nutrients
in determining net prim ary production are s umma riz ed
in Figure 4. Within a given c lima tic regime, and with a
pa rticular genetic p opulation, the cap ture ofs ola r ra dia ­
tion and its c onvers ion into net photos yntha te is deter­
m ined by lea f a rea and its photos ynthetic eff iciency.
Thes e are in tum determ ined by the ava ila bility of light,
water, a nd n utrients , and by the a lloca tion of net primary
production to lea f area ; which is a ls o determ ined bybasic
res ourc e a va ila bility. Some of the net photos yntha te is
los t to ma intena nce respira tion, this loss being affec ted
Figure 4 demons tra tes the imp orta nce of res ourc e
a va ila bility to pla nts in determ ining their tota l cap ture of
inc ident s olar _ ra dia tion, the net accum ula tion of the
res ulting photos yntha te, and how much of this c ons ti'
tutes ec onomic yield. The rela tive roles of m ois ture a nd
nutr ients in this en'ergy proc ess ing s ys tem a re illus tra ted
in Figure 5, which repres ents a n ecosys tem in which lea f
a rea is limited primarily by s oil nutrient a va ila bility. In a
nutrient-ric h but m ois ture-lim ited s ys tem , there would
be s uff ic ient nutrients to produce m ore folia ge biomass
tha n c ould be s us ta ined by the m ois ture s ta tus of the s ite,
andsoil wa ter a va ila bility would act to limit lea f area and
produc tion. On a m ois t-and fertile s ite,
the p otentia l lea f a rea set by both
m ois ture' and nutrients would be s o
high tha t light would proba bly act to
determ ine thema ximum achieved lea f
area .
I
Leaf area and
photosynthetic efficiency
I
Water
, light _ ,
inutrients
.
. ...
I,
•
Carbon
allocation
L
.
L..._.......J
r
?
o
------+t: Respiration
Net primary
production
)"
-+-<
/
_
_
_
_
I
I
Net photosynthesis
.--J.... T""-----...
1
. nlte, fall
..
_
_
_
_
Net biomass
accumulation
I
Harvested biomass
-(economic production or yield)
I
I
I
Solar
radiation
Aboveground
Root death
Plant death
Figure 4. Relationship between incident solar radiation and economic
biomass production (yield), and of the role of the basic site
resourc�s In determining this reh.ltionship.
Inf Rep. NOR-X·3Z8
This difference am ong sites is
illus tra ted in Figure 6, which s hows
qua lita tively the achieved lea f area
rela tive to the p otentia l lea f a rea
ca rrying capac ity as set by the three
major s ite res ources on sites of differ­
ent s oil m ois ture and fertility s tatus .
The a nticipa ted cha nges in the rela tive
a lloca tion of net production between
a boveground a nd belowground
biomass res ulting from diff erent c om ­
bina tions ofs oil m ois ture and nutr ient
ava ila bility are a ls o shown. On dry,
nutrient-p oor s ites , lea f a rea is gener­
a lly limited by nutrient a va ila bility
rather tha n -m ois ture, bec a us e
a lthough themois ture-determined lea f
area p otentia l is low, the lac k 'ofm ois ­
ture s everely limits the process es of
nutr ient c yc ling. Dry s ites genera lly
ha ve reduc ed geochem ical inputs , a nd
may ha ve a lower s oil capac ity to
reta in nutr ients .
II
to fine roots in a 23-year-old s ubal­
p ine P acific s ilver fir (Abies amabilis
[Dougl.] Forbes ) s tand in c omp ari­
s on to 66% in a 1 80-year-old s tand.
C om eau and Kimmins (1 986) es ti�
m ate that 3 1-39% of net primary
production was alloc ated to the fin e
roots of lodgep ole p ine (Pinus con­
torta Dougl.) on a m edium-quality
s ite in the s outhern Canadian Rocky
Mountains, c ompared to 50---62% on
p oor, dry s ites .
Achieved leaf area
(in this example)
set by nutrient availability
Note: ' The diagram represents an' ecosystem in which nutrients are the
dominant limiting site resource. In a very moist and fertile site, leaf
area would be set by the light factor, according to the foliar light
adaptations of the plant species involved. In a nutrient-rich, dry site,
leaf area would be set by moisture limitations. To the extent that
management or environmental change alter the availability of site
resources, the factor that limits productivity through leaf area will
change.
Figure 5. Individual contributions of water and nutrients to the deter­
mination of net primary production, and its allocation between
different biomass components.
Allocation of Net Primary Production
Between Aboveground and
Belowground Biomass
Res earch on res ource allocation (espec ially to fine
roots ) c onducted over the p as t dec ade has res ulted in a
signific ant reassessment of theories on ecosystem func ­
tion, several· ec os ys tem process es , and the net primary
produc tion ofterres trial ec osys tems. For examp le, Keyes
and Grier (1981) rep ort a diff erenc e of only 13% in the
net primary production between the trees in a low and a
high productivity s tand of Douglas -fIr (Pseudotsuga
menziesii [Mirb.] Franc o), in c omp aris on with a 1 00%
difference in aboveground and c oars e root produc tion.
The diff erence was attributed to variations in the alloc a­
tion to ephemeral fine roots . Similarly, Grier et al. (1981)
rep ort that 36% of net p rimary production was alloc ated
12
There have been fews tudies of
the relative imp ortance of m ois ture
and n utrients in detenn ining alloca­
tion to fine roots . L inder (1 987),
L inder and Axelsson (1 982). and
Axels s on and Axels s on (1 986)
describe the eff ect of irrigation and
fertiliz ation, alone and in c om bina­
tion, on ·the alloc ation to fme roots in
the SWEC ON project. They c on­
c lude that improving the m ois ture
s tatus of a s ite has little effect on net
primary production or alloc ation to
fine roots , but that nutr ien t additions
increase production but reduce allo­
c ation to fme roots . Kurz (1989) and
Santantonio and Herm ann (1 985)
s tudied ,c arbon allocation between
aboveground and belowground
biomass c omp onents in a series of
Douglas -fIr s tands ranging from a
m ois t, fertile s ite to a p oor, dry s ite.
They c onfIrm the shift in alloc ation
p atterns rep orted by Keyes and Grier
(1981), but are not able to s ep arate
out the individual eff ec ts ofm ois ture
and nutrients .
I n sp ite of the app arent unanimity on the effects of
nutrient availability on c arbon alloc ation to fine roots ,
there is an altern ative viewpoint. Nadelhoffer et al. (1985)
contends that fme root production actually inc rease s with
greater N availability, although that s tudy is c on founded
by c hanges in spec ies c ompos ition over their gradient of
N availability. KlITZ and Kimmins (1987) explain why differ­
ences among the m ethodologies us ed by the prop onents
of the different viewpoints , and differences in temp ora! pat­
terns of fin e root p roduction and m ortality in different
environments,m ay hlive led to the differences ininterpretation.
With the increas ed s tress imp os ed on trees by acid
rain; air p ollution, and c limate change, there is growing
Inf Rep. NOR-X-318
Leaf area potential
as determiried by:
•
•
•
Light
Water
Dry
�
H. d
Leaf area achieved
%
Production to aboveground (AG)
%
Production to- belowground iBG)
Nutrients
�B
M_ _ _ _ _ • • • •__• • • • • • •______ _ _ _ _ _ _ ••_____• • • • • • • ______ _ _ _ _ _ ____• • • ______ _ _ _ _ _ _ _ _ _______
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Fresh
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(AG)
(8G)
(AG)
(8G)
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u
w
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Moist
C
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c
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3
iAG)
(BG)
(AG)
iBG)
..--.---- .-----�
Poor
Medium
Ecosystem nutrient status
Rich
Note: Sites can be moved from one location to another on the edaphic grid by fertilization and
irrigation, or by site nutrient depletion and reductions in site moisture status.
Figure 6. Variation in achieved leaf area for four different comhinations of soil moisture and soil nutrient
availability. T his indic ates the relative imp ortance oflight. m oisture, and nutrients in determ ining achieved
leaf area, and. the ratio of aboveground to belowgroQnd p roduction allocation.
Inj Rep. NOR·X·328
13
interest in the effects of these stresses on resource
Factors Contributing to Losses of Net
Primary Production
Allocation of Leaf Area and Net Primary·
Production Between Overstory and
Understory
tion
allocation.
Substantial quantities of net photosynthetic produc­
are
lost through respiration, defoliation, aboveground
litter fall, root death, and individual plant mortality, and
to symbiotic organisms such as mycorrhizal fungi,
rhizospheric organisms, or symbiotic N-fixing microbes.
Net production in most forests is divided between
overstory and understory species.Leaf litter fall can be
taken as an index of the annual production of new foliage,
Respiratory losses depend on the average respira­
tory rate of the various plant tissues and on temperature
total leaf litter fall was found to vary little over a wide
conditions. Fast-growing, light-demapding species tend
to have higher respiratory rates - than slower-growing,
tation competes with trees for light, soil water, and nutri­
a surface area effeCt, such that the same volume of stem
between tree ,and lesser vegetation canopies for their
faster by respiration than when distributed in fewer larger
and Bray and Gorham (1964) report studies in which
range of stand densities on a particular site.Lesser vege­
ents, and therefore in most - forests there is competition
share of the leaf-area carrying capacity of the site. Early
in the life of a stand, noncrop vegetation may fully
occupy the site and control the light-determined or soil
shade-tolerant species (Grime 1966). There may also be
biomass distributed in many small stems may lose energy
stems (Keane 1985).
Defoliation losses will depend on the physical and
water-determined site leaf-area carrying capacity. Crop
chemical availability of the foliage to herbivores, and on
control the carrying capacity before they can achieve
Herbivory losses depend upon the type of plants and their
physiological and nutritional status. In coniferous for­
trees must compete with this vegetation to capture and
their full potential leaf area and full potential net primary
production.
Where the moisture and nutrient status of a site is
good, light will probably set the leaf-area c arrying capac­
ity, and the competition between overstory and under­
the population and feeding behavior of the herbivores.
ests, annual herbivory losses from trees are generally
low, but may periodically increase to high levels during
major defoliator outbreaks (Mattson and Addy 1975;
Schowalter et al. 1986).
Aboveground litter fall iosse, depend upon the de­
story is largely determined by the relative heights of their
gree of evergreens present ("evergreeness").Deciduous
tation as soon as their leaf area is physically above that
elevation conifers may lose as little. as 3-5% of their
canopies. On such sites, trees will dominate lesser vege­
of the lesser vegetation.In contrast, on sites where leaf­
area carrying capacity is determined by soil nutrient or
water availability, belowground competition will con­
tinue as long - as there is sufficient light for the more
diminutive plant species to maintain their root systems.
Evidence suggests that this root competition can signifi­
cantly delay the development of overstory leaf area, and
that it plays an important role in the production ecology
of young forests.
trees lose their entire foliage every year, whereas high
foliage each year (Kimmins 1987):
Belowground biomass turnover depends on the
allocation of net primary production to fine roots and the
longevity of the roots. The annual production offine-root
biomass depends upon total plant production and the
proportion allocated to fine roots.Both of these parame­
ters are determined by site resource availability and stand
density. The longevity of fine roots appears to be related
to soil moisture and nutrient status, and to anything that
Effects of Between-tree Competition on
Resource Allocation
Little research has been conducted on the effects of
stand density on the allocation of net growth to different
tree biomass components.There has been considerable
discussion about the effects of stand density on tree
affects canopy function.Severe defoliation can result in
fine root death (Redmond 1959), and drought or other
climatic or atmospheric stress to foliage can similarly
resuJt in fine root mortality because of the restriction on
the carbohydrate supply to the roots.
Individual plant mortality occurs throughout the life
of the stand, long after net stand biomass accumul�tion
height growth, but there have been few investigations of
has slowed down or ceased. As the average tree size
What work has been done suggests that competition
outcompeted and die (tree mortality at this stage of stand
the details of the production ecology of this phenomenon.
alone can result in altered resource allocation (Keane
1985; Worrall et al. 1985).
14
increases following canopy closure, smaller trees are
development is primarily a function of light competi­
tion), and there appears to be a fundamental relationship
In! Rep. NOR·X·32S
�
between the mean stem size and the number of stems per
hectare (Westoby 1984; Weller 1987. This relationship
was not used, however, in the development of
FORCYTE-l l). Maximum biomass accumulation rate
thus occurs when the difference between net photosyn­
thesis and mortality is greatest, and not necessarily when
net production is at its maximum. .
Loss of net primary production to microbial sym­
bionts can account for a very significant but poorly
quantified proportion of production. Some forest floors
eontain a large mass -of fungal material, and in some
forest ecosystems, carbon allocation to mycorrhyzal
fungi may equal or even exceed the allocation' to fine
roots.. Symbiotic N fixation is an energy-requiring activ­
ity, and high rates ofN fixation must require considerable
amQunts of net primary production. In addition to the
energy costs of sustaining such intimately-related micro­
organisms, plants may secrete significant amounts of
photosynthate into the rhizosphere,
Representation of Production
Ecology in FORCYTE-1 1
If a hybrid simulation growth and yield model is to
have the needed flexibility, it must represent much of the
production ecology described above. The following is a
description of the extent to which FORCYTE- l l
achieves this objective. (It should be noted that
FORCYTE-I I does not represent the effects of tempera­
ture explicitly. These effects are implicit in the input data,
and the model cannot simulate the effects of changes in
temperature, other than by changing the input data.)
Leaf Area Relationships
The basic driving function of the tree, plant, and
bryophyte growth modules of FORCYTE - l l is a
measure of canopy function called foliage nutrient effi­
ciency. This is the net primary production per kilogram
of a defined foliage nutrient, corrected for the effects of
within-canopy shading. In most temperate coniferous
forest ecosystems, N is thought to be the limiting nutrient
that detennines photosynthesis, subject to' the availabil­
ity of other essential nutrients. Consequently, most users
of FORCYTE-l l will use "foliage N efficiency" as the
driving function for the model. Leaf area (actually,
FORCYTE-l l uses leaf biomass) is not used alone as the
driving function for two reasons: the photosynthetic
efficiency of a given area or mass of foliage can vary as
its nutrient content (especially nitrogen) varies (Brix
2
1981), and self-shading within a tree canopy can alter the
photosynthetic production of a unit area or mass of
foliage (Agren 1983a, b).
Numerical values for shade-corrected foliage N
efficiency (SCFNEj2 are calculated internally in the
model from input data on temporal patterns of biomasS
accumulation, on light intensities below canopies of
different known foliar biomass, and on the photosyn­
thetic light saturation curves of the species in question.
Shade-corrected foliage nutrient efficiency valu�s are
calculated for each age of each species on each site type.
There is no explicit representation of the effects of soil
moisture status on SCFNE, but if the data for different
sites represent sites of different soil moisture status,
FORCYTE-l l provides an implicit representation of the
effects of changing soil moisture availability on canopy
function. The effects of changing soil nutrient availabil­
ity on SCFNE can be simulated explicitly if data are
given for sites that vary in nutrient availability. The
FORCYTE-I I model also represents the photosynthetic
function of "sun foliage" and "shade foliage" separately,
if appropriate data are provided. The relationship be­
tween foliage nutrient content (usually N) and net pri­
mary production thus depends both on simulated light
levels and on the simulated allocation of the foliage
between these two types of foliage.
There is no representation of how SCFNE might
change as a result of direct climatic effects on plant
physiology, nor is there any simulation of increasing
atmospheric CO2 concentrations on photosynthetic rates
or water use efficiency. There is an inevitable error in the
simulation of growth responses to simulated change in
nutritional site quality because FORCYTE-l l does not
simulate the effects of moisture explicitly, and because
of the confounding effects of soil moisture and nutrients
in the input data that are used to calculate variation in
SCFNE values for sites of different nutritional site qual­
ity. Shade"corrected foliage nutrient efficiency values
calculated in the setup plant growth modules are used in
the MANAFOR program simply by multiplying the
simulated foliage nutrient content (usually N) of each
species, corrected for the effect of simulated canopy light
levels on the photosynthetic pcrfonnance of sun and
shade leaves, by the appropriate SCFNE value for the
current age of the species and the current site quality.
Resource Allocation Relationships
Net primary production in FORCYTE-l1 is divided
among the different biomass components in the same
SCFNE can also refer to shade�corrected foliage nutrient efficiency if a nutrient other than N is used as the limiting nutrient.
In! Rep. NOR-X-328
15
ratios as the input data on biomass accumulation. If data
heights of the smallest and largest live canopy trees at any
are given for sites that differ in nutrient status, the model
age), and the light below the canopy in stands with varying
will simulate changing resource allocation strategies as
amounts of foliage biomass. From these input data, the
the simulated nutritional site quality varies during a run
setup tree growth module of FORCYTE-ll establishes
of the model. Thus, empiricaUy-observed variations in
for each stand age the lowest relative light intensity at the
production allocation strategies on sites of different nu­
top of a tree at which the tree wiU remain alive.
tritional site quality are used to guide the simulation of
MANAFOR, trees will die at simulated light intensities
changing production allocation in response to simulated
below this.) The model simulates the relative height
changes in nutritional site quality.
(In
growth perfonnance of trees in different crown classes,
and the effects of increasing shade on height growth.
As previously noted, changes in competition can
emulate nutritional site quality change; however,
In addition to this density-dependent, light­
FORCYTE- I I . only simulates nutritional site quality in
competition-related mortality, FORCYTE- i l can simu­
tenns of the quantities of nutrients availableto the entire
late temporal patterns of mortality caused in other ways,
plant community. Thus, there is no simulation of the
such as postplanting seedling mortality, root rot, snow­
effects of either inter- or intraspecific competition on
break, and animal damage. These are referred to as
resource allocation. Competition for soil nutrients does
"density-independent" mortality. The different types of
reduce the growth of the competitors, but the model does
densitY'independent mortality cannot be simulated indi­
not simulate a change in the growth strategies of the
vidually; they are merely represented as a collective
competitors in response to this nutrient competition.
mortality agent in the input data.
Climate change effects can be expected to change
It is assumed in the model that competition for
resource availability to trees and other plants, thus induc­
nutrients will not kill trees directly, but only through its
ing changes in allocation. The FORCYTE- l l model
effect on a tree's -, ability to maintain - its foliage in
cannot simulate such changes within an individual run.
appropriate light environments. The effects of competi­
The lack of an explicit simulation of water prevents any
tion for water on tree mortality cannot be represented as
representation of the effects of variations in site moisture
density-dependent mortality in FORCYTE- l l .
status on resource allocation.
Death of individual plants can only be simulated for
Losses to Aboveground and Belowground
trees. Herbs, shrubs, and bryophytes are represented in
Litter Fall
FORCYTE- l l as populations or communities (in terms
Aboveground ephemeral litter faU is simulated
according to input data on the number of years of foliage
reteution, the simulation of canopy self-pruning, and data
on the number of years that dead bark is retained. Below­
gronnd litter faU is simulated on the basis of input data on
root turnover rates and the simulated biomass of the roots.
It is recog�ized that the simulation of leaf litter faU
of biomass per hectare), not as individual plants. Loss of
biomass for these plant types is simulated through litter
fall rates.
There is no simulation of how climate change might
alter mortality rates within a given run of the model.
Respiration
used in FORCYTE- l l has some error associated with it,
. In forest ecosystems, respiratory losses account for
especiaUy in the case of young evergreen trees that have
a significant proportion of gross photosynthetic produc­
long needle retention. A less-detailed and less-realistic
tion and for some of the net primary production. The
simulation approach was used in FORCYTE-l l than in
FORCYTE- i l model assumes that respiratory losses are
FORCYTE-IO, but the reduced complexity of the com­
represented implicitly in the input data on net biomass
putation in the FORCYTE- l l approach is believed to
accumulation, and respiration is not simulated explicitly.
justify the reduced accuracy of the simulation. The errOr
There is no simulation of how respiratory - losses may
will only be significant when evergreen trees or other
change as stand density or biomass component ratios
plants are smaU, when the amonnt of wrongly simulated
change. The lack of any explicit representation of respi­
litter faU will be triviaUy smaU.
ration precludes any simulation of how respiration losses
might change if climate changes.
Losses to Individual Plant Death
The FORCYTE- l l model simulates the process of
overstory-Understory Relationships
stand self-thinning according to input data on self-thinning
The division of site leaf-area carrying capacity be­
(stand density change with stand age), tree sizes (the
tween crop and noncrop species on the basis of moisture
16
In! Rep. NOR·X·328
competition is not simulated explicitly in FORCYTE-l l ,
but these two types of plants do compete for light andfor
nutrients within the simulation. The model accounts for
their relative occupancy of the soil (via fine roots), their
height growth, and their effect on light availability. The
limiting effect of site moisture on leaf-area carrying
capacity is simulated in terms of the maximum-leaf area
that could be carried by a particular species growing
alone on the site if nutrients were not limiting, but there
is no explicit simulation of interspecific competition for
the site's moisture-controlled leaf-area carrying capacity.
Consequently, there is no simulation of how climate
change might alter the competitive relationships between
overstory and understory species.
Losses of Net Primary Production to
Symbionts and to the Rhizosphere
There is no explicit representation of these losses in
FORCYTE- I 1 .
Section Summary
Flexibility in forest growth and yield predictors
requires an explicit representation of canopy function
and production allocation, because altered future growth
conditions can alter these two plant characteristics and
the leaf area, biomass and foliar nutrient content. Future
changes in manufacturing technology, markets, and
economics will change utilization levels. Consequently,
growth and yield predictors should simulate both total
production and its allocation between different trees (i.e.,
stand development dynamics) and different parts of a
given tree in response to various stand manage­
ment systems. The FORCYTE- l l model follows
Gillespie and Chaney's (1989) suggestion that the objec­
tive in yield modeling should be to develop "process­
oriented models para�eterize4 with empirical
measurements to avoid complexity in management use,
yet sufficiently mechanistic to predict responses to
fertilization and to define the upper limits of forest
production. "
FOLIA GE NITROGEN EFF ICIENCY AS A MEASURE OF
C ANOPY F UNCTION AND ITS REPRESENTATION A S
THE BA SIC DRIVING F UNC TION IN FOR CYTE-"
This section examines the use of the foliage nutrient
efficiency concept, referred to hereinafter as "foliage N
efficiency," as the driving function for FORCYTE-I 1 . It
is important to establish the scientific veracity of the
simulation strategy used in multiple rotation, ecosystem­
level hybrid simulation models such as FORCYTE-l l
because of the difficulty in verifying the model's
representations and validating its predictions.
The driving function of most hybrid-simulation
models is the historical pattern of biomass accumulation
described in a growth equation. The growth pattern
described by such equations is then modified by growth
multipliers that represent the action of one or more
growth-determining variables (the gap model approach),
or by simulating both the uptake demand and the avail­
ability of nutrients to satisfy this demand (by simulating
the three major biogeochemical cycling pathways) and
modifying the predicted growth according to whether or
not the uptake demand is satisfied (the FORCYTE-IO
approach). In FORCYTE- l l , this fixed-growth equation
approach to the driving function is replaced by a measure
of the photosynthetic function of the canopy derived
from simple historical bioassay and other input data. This
is done to give the model greater reality and flexibility in
In! Rep. NOR·X-328
its response to various simulated changes in stand
structure and growth conditions.
,
The Foliage Nitrogen
Efficiency Concept
Various terms have been proposed for the relation­
ship between foliage and net production. Comeau and
Kimmins (1986) suggest that the term "foliage effi­
ciency" (FE) be used in conjunction with a modifier to
make its use clear. They propose the following terms: FE
(ANPP)-aboveground net primary production per kilo­
gram of foliage; FE (TNPP)-total net primary produc­
tion per kilogram of foliage; FE (NSP)-net stem
production per kilogram of foliage; and FE(F)--net
foliage production per kilogram of foliage.
Considerable variation among FE values results
from variatiofis in photosynthetic efficiency due to
differences in light, air temperature, moisture stress, and
mineral nutrition, especially N. Variations in FE values
calculated from empirical field measurements, may also
be due to variations in the allocation of photosynthate
between different biomass components as the availability
17
of moisture and nutrients varies as stand -age varies, and
as stand density varies.
The considerable variation in FE values led to a
growing interest in a more physiologically and ecologi­
cally sensitive index of foliage efficiency. Comeau and
Kimmins ( 1986) propose the use of the tenn "foliage
nitrogen efficiency" (FNE), in conjunction with various
modifiers, -to def4Ie various production parameters per
kilogram of foliage N (cf. previous discus8ion of FE).
Agren (1983a) noteu that the N productivity of several
species ofconifer decreases as foliage biomass increases,
and that' this decrease is much greater for shade-intolerant
species than for shade-tolerant species. This decline was
attributed to the increasing self'shading of foliage within
the canopy' as foliage biomass increases.
Comeau and Kimmins (1986) investigated the re,
duction in FNE as foliage biomass increases for lodge­
pole pine, and reported that there appears to be a simple
linear relationship between FNE (TNPP) and foliage
biomass that is independent of site. The relationship
between foliage biomass and TNPP was much better than
that between foliage biomass and ANPP. The FNE
(ANPP)-,--foliage bibmass relationship exhibited consid­
erable variation with stand age and stand density, variables
that influence the allocation of growth between above­
ground and belowground biomass components. Site quality
acts to alter the amount of foliage biomass and the
allocation of net photosynthates between aboveground and
belowground biomass components (Comeau and Kimmins
1989). When the effects of site quality, age, and stand
density were accounted for, the relationship between
FNE (ANPP) and foliage biomass was much improved.
Application of the Foliage
Nitrogen Efficiency Concept
in the Modeling of Forest
Growth in FORCYTE-"
One approach to the use of the FNE concept is to
develop a theoretical framework from which equations
are derived that relate yield and production parameters
of interest to measures ofN productivity and the amount
dfN in the plant (Agren 1983a, b, 1985; McMuttrie and
Wolf 1983a, b; McMurtrie 1985). The calibration of such
models involves the calculation of values for the various
parameters that m<tke up these equations.
Such equationcbased models will require a consid­
erable amount of calibration data because most FNE
values relate to aboveground production only, and
because FNE (ANPP) may vary with stand age, site
quality, soil resource availability, and stand density. The
18
use of equations based on FNE (TNPP) reduces the.
problem, but use of FNE (TNPP) requires the simulation
of resource allocation and its variation with age, density
and site quality. Our incomplete understanding of the
relationships that must be simulated, and the difficulty of
obtaining the necessary calibration data, make difficult
the development and use of a purely process-based, FNE
(TNPP)-driven theoretical model.
Another approach to the use of the FNE concept in
yield simulation is to represent canopy function in a more
empirical, mechanistic manner such as is done in FOR­
CYTE-I l. In this approach, FNE values are calculated
internally within the model from a series of data sets
gathered from chronosequences or sites that vary in the
ecosystem attributes that are to be varied in the simulation,
and to which the plant variables of interest are responding.
The difference in these two approaches is largely a
question of where the ca�culations and estimates are
done. In the theoretical approach, the variation in plant
growth responses is embodied in the equations that make
up the model: the data that describe these variations are
manipulated externally to the model. In the FORCYTE-I I
approach, data on variation i n plant growth responses are
entered directly into the setup programs ofthe model that
extract the necessary relationships and pass them on to the
ecosystem simulation program. The two approaches do
differ, however, in that the theoretical approach assumes
an understanding of the regulation of the processes
embodied in the equations, whereas, the FORCYTE-I I
approach uses empirically-observed relationships
between the processes and site conditions without
necessarily understanding them at a theoretical level.
In FORCYTE, it is necessary to keep the input data
requirements for each species in the ecosystem simula­
tion program MANAFOR as simple as possible. The
modeling strategy that was adopted in FORCYTE-l l
involves the assembly of largely inventory-type data sets
in a series of setup plant growth modules and a soils
module (Fig. 2). The setup programs are used to calcu­
late, from the inventory infonnation and other input data
for a number of different sites, a series of indexes of
growth; resource allocation, soil 'processes, and their
variations as various site-related'parameters vary. One of
the indexes passed from each setup plant growth module
to the ecosystem management simulator MANAFOR is
FNE (TNPPj.
Calculation of Total Net Primary
Production
The FORCYTE-l l model requires the following
input data for the plant growth setup modules (trees,
understory plants, and hryophytes) covering the range of
In! Rep. NOR·X·328
ages that the user wishes to simulate: biomass accumu­
lation for ,various biomass components, canopy top and
bottom heights and the height of the smallest live canopy
tree, stand tree density in self-thinned stands, tree stem
size distributions, tissue nutrient concentrations, and
various other data. To simulate the changes in tissue
nutrient concentrations, biomass component ratios, alloca­
tion to and mortality of fine roots, and internal cycling
that may occur as nutritional site quality varies, complete
data sets must be provided for two or more sites that
cover the range of nutrient site qualities expected to
result from simulated management. The data should
come from single-species populations because the in­
dexes calculated are assumed to represent the growth
potential and resource allocation strategy of each species
if it alone were occupying- the site.
From this input data, the setup plant growth
programs calculate, for each plant species for which data
are provided, total net primary production for each
simulation time step (TNPP,):
TNPP,
�
I!.Biomass, + Ephemeral litter fall,
+ Mortality,
[1]
where:
I!.Biomass, the sum of the change in mass of all
the -biomass components of the species being
represented in time step t,
�
Litter fall, the sum of the mass of all ephemeral
tissues that are lost in time step t (e.g., leaf,
branch, bark and reproductive litter fall, and
root death),
�
Mortality, the mass of individual plants that die in
time step t.
�
A. 8Biomass
Change in biomass in each time step is obtained
from the 10 or fewer age-biomass data pairs in the input
file using the following interpolation (data-smoothing)
method (Fig. 7). For each biomass component:
1.
2.
The program establishes the rate of change in
bIomass of each biomass component between
successive biomass/age data pairs.
The slope of the lines joining the age midpoints of
these rate-lines are used to calculate the rate of
biomass change for ages between the input data ages.
3. Rates of change of biomass for each time step taken
from these linear interpolations between rate-line
centers are then used to reconstruct, a change-of-
In! Rep, NOR-X-328
biomass-over-age curve, which is forced through the
origin and through the maximum value given in the
input data file.
4.
The smoothed biomass/age curve then becomes the
basis for calculating the change in biomass of the
component (A Biomass) in each simulation time step.
The smoothing approach can handle all biomass/age
relationships equally well. Most mathematical equations
have , their own "character" that may not fit a particular
biomass/age curve well. While it may be appropriate to use
such equations for monotonically-increasing functions
such'as stem wood accumulation, the smoothing technique
described above accommodates variables such as foliage
biomass and fine-root biomass, which may have a variety
of characteristic temporal patterns, including a bimodal pat­
tern or a single early peak followed by decreasing values.
It is assumed that entered data provide a good esti­
mate of reality. The user is responsible for editing the
field data to exclude or adjust any data points that might
deviate from the "normal" biomass/age relationship for
that species on that site. The problem can be avoided by
using averages over many sites of a particular age or
output from a well-calibrated and reliable historical bio­
assay model based on a large data set. Where the latter
options are not available, the user must exercise judgment
concerning unexpected trends in biomass/age data. The
smoothing routine described above forces the smoothed
biomass/age curve through the origin and the maximum
value. It does not force it through the other data points.
When the pro gram has created a smoothed
biomass/age array, it selects 30 biomass/age pairs from
the array and saves these as a data base. The "ages-for­
saving" are defined by the user and should be concen­
trated over the sections of the biomass/age curve at which
there is rapid change in the accumulated biomass, with
fewer data pairs being assigned to other portions of the
curve that are changing more slowly. Reduced array sizes
are used to economize on memory. The program per­
forms linear interpolation between the saved data ages.
The same "ages-for-data-solving" array is used for all
bioffi£lss components of a species.
8,
Ephemera/ /itter fall
Input data define the proportion of roots of various
sizes and of bark that die and become litter fall in each
time step. The number of years of old-leaf and dead­
branch retention are also given, and the reciprocals of
these values give the proportions of the old foliage and
dead-branch biomass that fall as litter each year. These
rates are applied to the existing live biomass of the
19
A
�
Age
Data ages
B
Linear interpolation between the
midP
of Ihe rale lines
c--=::""C_�
j
Forced through
zero
i
Age
c
j
Original
data point
i
Original
a poinl
Estimated biomass
values for
intermediate ages
Age
Figure 7. Smoothing routine used to obtain biomass/age arrays from
limited biomass/age input data.
corresponding live biomass component at the end of each
time step, and the result is an estimate of the ephemeral
litter fall in that time step. Use of this method results in
an overestimate oflitterfall in the early time steps of the
simulation, and this may contribute to the higher values
of SCFNE early in the simulation: These early overesti­
mates of litter fall rates, however, are applied to a very
small biomass, so in terms of rotation-length simulations,
the error involved should be very small.
C.
Mortality
The input data on tree stand density as a function of
stand age are treated in much the same manner as the
20
biomass/age input data. The rate of
change between successive stand
density-age input data pairs is calcu­
lated and the slope of the lines join­
ing the median age of each of these
rate lines is used to produce a
smoothed density-age curve, which
is then forced through the origin and
the maximum value in the input data.
This smoothed density curve then
becomes the basis for the array of
mortality rate/age data pairs. Thirty
data pairs from this array are saved. .
As was the case for the biomass/age
data, it is the responsibility of the
user to ensure that the input data on
stand density represent the best
available estimate of the charac­
teristic temporal pattern of stand
self-thinning for a normally-stocked
stand for that site.
In addition to tree mortality in
each time step, the model also re­
quires data on the mass of the different
componeuts of the dying trees. For
the calculation of TNPP, TREE­
GROW (TREE GROWth setup
module) uses the tree stem-size dis­
tribution data given in the input file
as a basis to defme the stem wood mass
oftrees that die. Having obtained this
estimate of stem wood mass, the pro­
gram estimates the mass of the other
components of the dying trees by mul­
tiplying the mass of "mortality stem
wood" by the ratio of stem wood to
the other tree components obtained
from the input biomass data. This
may overestimate the foliage and
branch biomass of dying trees.
There is no individual plant mortality or stand self­
thinning component in the net primary production calcu­
lation for life forms other than trees, because understory
plants · are not represented as individuals. For herbs,
shrubs, and bryophytes; biomass losses, including both
litter fall and mortality, are represented entirely as
ephemeral litter fall.
Calculation of Shade-corrected Foliage
Nitrogen Efficiency
Once TNPP is determined, the setup plant growth
programs calculate the total amount of nitrogen in the
foliage (FN) as:
Inf. Rep. NOR·X-328
FN
=
Biomass of
foliage
X
Nitrogen concentration
in foliage
[2]
Foliage nitrogen efficiency will vary as a function
of foliage biomass, the light adaptations of the foliage,
and the degree to which a plant can alter its foliage
morphology and physiology as light intensity changes.
The latter two factors will be a fixed characteristics of a
species. The first factor requires either that the model be
provided with an equation describing how FNE varies as
a function offoliage biomass, or that the effect of shading
on photosynthetic activity is simulated in the model. The
major difference between these two approaches' is
whether the calculations are perfoniled externally to, or
internally in the model. The FORCYTE-1 1 model adopts
the latter approach because .it gives the IJ10del greater
flexibility in the simulation of events such as thinning,
pruning, and defoliation.
Thus, instead of calculating foliage nitrogen
efficiency as:
FNE (TNPP)
=
TNPP
FN
[3]
for different values of foliage biomass, FORCYTE-I l
calculates it as:
SCFNE (TNPP) = TNPP
SCFN
[4]
where: SC = "shade-corrected".
To simulate the effect of shading on photosynthesis,
input data are required thatdefine, for various stand ages,
the biomass offoliage, the top and bottom heights of the
live canopy, and the relative light intensity below the
canopy at these various stand ages. The photosynthetic
light saturation curves for sun and shade foliage are also
required. The model simulates the canopy as an "opaque
blanket", distributing the foliage uniformly over a simu­
lated hectare, and uniformly with height in the canopy;
the total foliage biomass is distributed - evenly between
successive quarter-metre height -increments in the live
canopy.
(
The input data on relative light intensity below
canopies of various different foliar biomass are used to
simulate the vertical pattern of light extinction within the
canopy, while the photosynthetic light saturation curves
are used to define the extent of the photosynthetic activ­
ity associated with the foliage N content of each quar­
ter-metre height increment in the canopy. The simulated
net photosynthetic activity of all height incr�ments is
then totalled to give TNPP. If this total were. to be divided
Inf.Rep. NOR·X·328
by the total foliage N, the resulting FNE value would
vary as a function of foliage biomass. Instead, the model
expresses the total foliage N as "shade-corrected foliage
nitrogen". This is the amount of fully illuminatedfoliage
N that would be required to produce the TNPP value that
has just been calculated for the entire canopy accounting
for the effects of shading.
SCFN =
i
n
L
=
where:
SCFN
FNj ;::::
=
(FNi
1
X
RPR,)
[5]
shade-corrected foliage nitrogen,
mass of foliage nitrogen in the ith quarter­
metre height increment in the live canopy,
RPR, = relative photosynthetic rate of foliage in the
ith quarter-metre height increment in the live
canopy,
n ;:::: number of quarter-metre height increments in
the live canopy.
The RPR, is the mean photosynthetic rate of the
foliage in canopy level i as a proportion of RPRm" (the
rate for sun foliage in full sunlight). The RPR, is calcu­
lated by combining simulated light intensities in canopy
level i with input data that define photosynthetic light
saturation curves for the foliage type in question. The
details of RPR calculation are as follows. Input data are
required that define the average relative intensity of
photosynthetically active radiation (PAR, as a percentage
of that above the canopy) beneath stands that cover a
range of canopy biomass values. These data can be
obtained in the same stands as the input data on canopy
biomass (but remember that FORCYTE-l l represents
the canopy as a horizontally-uniform "opaque blanket",
with no representation of canopy gaps). The model then
establishes a relationship between relative PAR and the
amount of foliage biomass, and uses this to simulate
canopy light profiles as follows. For each time step, the
foliage biomass in that time step is distributed uniformly
into quarter-metre- canopy height increments between the
top and bottom of the live canopy. The foliage biomass
above each quarter-metre canopy level is calCulated, and
a relative PAR for that canopy level is established.
Having established the canopy light intensity pro­
file, the photosynthetic rate at each canopy level is cal­
culated from input data on the relationship between
photosynthesis and PAR (photosynthetic light saturation
curves). This rate is then expressed as an RPR by com­
paring the calculated absolute rate with the maximum
absolute rate for fully illuminated sun foliage. The model
recognizes shade foliage and sun foliage. Foliage
21
produced at canopy levels that receive more than 50% of
full PAR is defined to be sun foliage; that below 50% full
PAR to be shade foliage. The model does not simulate
the acclimation of a given foliage type to changes in light
intensity, nor does it simulate changes in- photosynthetic
activity of foliage as a function of foliage age. Photosyn­
fhetic rates are expressed per kilograro offoliage N rafher
than per kilogram of foliage biomass.
The RPR value for each canopy level is then multi­
plied by the quantity of foliage N at the corresponding
level, and the products are summed for the entire canopy
to give a total that is equal to the amount of sun foliage
foliar N that would be required, under full illumination,
to produce fhe saroe TNPP as the existing sun and shade
foliage. This equivalent aroount of "fully illuminated sun
foliage foliar N" is what is referred to as shade-corrected
foliage N (SCFN).
Dividing TNPP by SCFN yields a shade-corrected
FNE or SCFNE (Equation 4), which is the driving func­
tion for plant growth · in MANAFOR. Wherever self­
shading within the canopy reduces photosynthesis,
SCFNE will have a higher value than FNE.
Calibration of FORCYTE- l l with several different
preliminary data sets has resulted in SCFNE values fhat
are highest in seedlings and decline with time to reach a
level that remains fairly constant over the rotation
(Kimmins et al. 1986). The consistency of the pattern
among species suggests that the decline represents, in
part, an increasing respiration tax as. the ratio of photosyn­
thetic biomass_ to respiring nonphotosynthetic, biomass
decreases as the stand ages. The pattern of initial decline
in SCFNE is not a precise mirror image of the increase
in nonphotosynthetic live biomass, however. Nonphoto­
synfhetic live biomass is dominated by sapwood and
"
large root biomass, which would have a lower respiration
rate than fine and medium roots (Szaniawski 1981;
Amthor 1984) and of branches. These two biomass com­
ponents show a pattern of increase in biomass that mirrors
fhe decline in SCNFE more closely fhan does fhe biomass of
stem sapwood and large roots. The higher early SCFNE
values could also reflect the known errors in simulation
of aboveground litter fall and fine_ root turnover.
As noted earlier, SCFNE values are calculated in the
setup modules and then used to simulate potential growth
(PG) in MANAFOR.
PG
�
SCFN X SCFNE
[6]
The achieved growth will depend upon whether the
nutrient uptake demand created by this potential growth
can be satisfied by the level of available nutrients in fhe
22
soil that can be accessed by the plants. Competition between
plants is a function of fhe relative heights of fheir canopies,
their photosynthetic light saturation curves, their nutrient
uptake demands, and their soil occupancy by fine roots.
Performance of the Foliage
Nitrogen Efficiency Function of ·
FORCYTE-11
Figure 8' presents graphs of biomass accumulation
and net biomass production for various biomass compo­
nents oftree species I on Data Site 1. The vertical axis
represents the simulation time step (years in this case).
The horizontal axis represents a 0-1 00% scale; the abso­
lute values of the 10 variables equal to fhe scale maxi­
mum are given in the table above the graph. Where two
variables have the same relative scale -value, and would
fherefore print on top of each ofher, an X is printed. A
relative horizontal scale is used rather than a single
absolute scale so fhat the temporal dynamics of small as
well as large biomass components can be ideritified.
Every variable is plotted as a percentage of its maximum
value. The biomass accumulation graph (Graph A)
represents the net biomass production (Graph B) minus
tree mortality and biomass losses to ephemeral litter fall
(Graphs C and D, respectively, Fig. 9). The net produc­
tion is simulated on the basis of SCFNE, starting with the
aroount of foliage N contained within the I-year-old
seedlings at fhe initial stocking density given in the setup
input data file. Total foliage N, uncorrected for shading,
is shown as Variable A in Graph F, Figure 10, as is fhe
shade-corrected foliage N (photosynthetic N, Variable
B), which is the amount of fully-illuminated foliage N
that would be required to result in the same net primary
production as. the actual foliage N (Varia!)le A). The
difference between variables A and B in Graph F, Figure
10, thus represents the effects on photosynthesis of
wifhin-canopy shading, as defined by input data on pho­
tosynthetic· light saturation curves for sun and shade
foliage. Variable I in Graph F gives the ratio of shade
foliage to sun foliage. It is zero until Time Step 17, when
the canopy has closed, and new foliage produced low in
the crown is shade foliage rafher fhan sun foliage. The
criterion by which the model switches from sun foliage
to shade foliage is a within-canopy-light-intensity of less
tJ:tan 50% full sunlight intensity.
The amount of shade-corrected N (Variable B,
Graph F, Fig. 10) is compared wifh net production (Vari­
able I, Graph B, Fig. 8), which is estimated from the input
data on biomass accumulation, litter fall, and stand self­
fhinning. Shade-corrected foliage N efficiency is fhen
calculated as kilograms of production per unit of SCFN
(Variable C, Graph F, Fig. 10). Note fhat in this exarople
In! Rep. NOR·X·328
DATA SITEHl
TREEHl
GRAPH A
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Figure 8. Biomass accumulation (Graph A) and net biomass production (Graph B) in different tree biomass
components as predicted by TREEGROW. The vertical (Y) axis is in annual time steps. The horizontal
(X) axis is a scale of 0 to 100% of the maximum biomass or production values given in the table above the
graph. Thus, each of the ten variablesrepresented is plotted on its own scale, with full scale value being
equal to the maximum value for that variable. Where more than one variable has the same print position an
X is printed.
In! Rep. NOR-X-328
23
DATA SITEI/l
TREEU
GRAPH 0
,, * VARIABLE PLOTTED *""******* UNITS **,,* MAXIMUM *""
DATA SITE#1
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LARGE ROOT LITTER FALL
MEDIUM ROOT LITTER FALL
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STEM DENSITY
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NUMBER OF TREES DYING
(STEMS/HA)
MORTALITY RATE
( . %/TIME)
PROPORTION BIOMASS DYING
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DENSITY INDEPENDENT- MORT
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DENSITY DEPENDENT MORT
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Figure 9. Natural mortality patterns (Graph C) and ephemeral litter fall (Graph D) as predicted by
TREEGROW. The vertical (Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 100%
of the maximum biomass or production values given in the table above the graph. Thus, each of the ten
variables represented is plotted on its own scale, with full scale value being equal to the maximum value for
that variable. Where more than one variable has the same print position an X is printed.
24
In! Rep. NOR-X-328
.
DATA SITE#!
TREE#!
GRAPH II'
** VARIABLE PLOTTED ********** UNITS * * * * MAXIMlJloI ***
OATA SlTE#1
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Figure 10. Tree height and stem biomass data (Graph E) and miscellaneous canopy function and other variables
(Graph F) as predicted by TREEGROW. The vertical (Y) axis is in annual time steps. The horizontal (X)
axis is a scale of 0 to 100% of the maximllm biomass or production values given in the table above the graph.
Thus. each of the ten variables represented is plotted on its own scale, with full scale value being equal to
the maximum value . for that variable. Where more than one variable has the same print position an. X is
printed.
In} Rep. NOR-X-328
25
no SCFNE values are given for the first two time steps,
and that the declining trend (attributed, in part, to increas­
ing respiration loss) is not as smooth as was suggested
by the discussion above. Very high early values, if
plotted, serve to lower all other values because of the
relative Y axis. This would make changes in the value of
C later in the stand life less obvious on the graphical
output, and therefore values for time steps I and 2 are not ·
plotted. The lack of a smooth change in the first 10 years
is attributed to the lack of reliable calibration data on
early stand growth in the run that produced Figure 10. As
noted above, the manner in which litter fall is calculated
may contribute to the early declining trend.
accumulation, net production, canopy function, mortal­
ity, litter fall, and height and individual stem biomass
graphs are all simulated values using various indexes of
canopy function, production allocation, mortality, etc.,
derived within the model from the input data. These
graphs can be compared with the comparable input data
to evaluate the accuracy of the simulation- in the setup
growth modules, and the adequacy of the growth
representations for use in MANAFOR.
Graph E, Figure 10, depicts a variety of height and
stem biomass variables. The height of the smallest live
canopy tree (Variable B , Graph E), together with the light
intensity at the top of the smallest live canopy tree
(Variable H, Graph C, Fig. 9) contribute to the simulation
of natural stand self-thinning and thus to the calculation
of net production. Light at the canopy bottom (Variable
J, Graph C, Fig. 9) is involved in the simulation of branch
death, which in turn contributes to the ephemeral litter
fall component of simulated net production.
Using the FNE concept as the driving function for
the management simulator of FORCYTE-ll (MANA­
FOR) provides a stable, ecophysiologically-sound basis
for simulation. The irregularity in the SCFNE variable in
the first few years of Graph F is attributed to an inaccu­
rate definition of the biomass accumulation in the first
few years of the stand in the input data, and possibly to
both the way early litter fall is simulated and to unac­
counted competition from noncrop vegetation experi­
enced by the crop trees in their first few years. Detailed
studies of community dynamics from the time of planting
up to crown closure are required before
this interpreta­
.
tion can be accepted as accurate.
Most of the graphs shown in Figures 8-10 are not
merely smoothed versions of the input data. The biomass
Section Summary
SIM ULATING THE BIOGEOC HEMIS TRY
OF FOREST ECOSYS TEM S
Major Features of Forest
Biogeochemistry
The term "forest biogeochemistry" refers to the
inputs and outputs of nutrients to and from an ecosystem
(the geochemical cycle), the circulation of nutrients
within an ecosystem (the biogeochemical cycle), and the
internal conservation of nutrients within an ecosystem
(internal cycling within plants). The availability of nutri­
ents that is a result of all these processes contributes to
the detennination of growth thruugh its effect on plant
nutrition.
The Geochemical Cycle
The geochemical cycle, which consists of nutrient
inputs to and losses from tbe ecosystem, is of great
importance to long-term forest productivity. The balance
between these inputs and outputs determines the quantity
ofnuirients in an ecosystem's biogeochemical and inter­
nal cycling pathways. A very small net positive balance
26
between small annual inputs and outputs can, over time,
result in a substantial inventory of nutrients and a signifi­
cant level of net primary production because of the
efficient nutrient conservation mechanisms of forests.
This can occur even on irtitially infertile and unproduc­
tive sites. Conversely, even an initially fertile and pro­
ductive ecosystem can eventually suffer reduced soil
fertility and productivity if the geochemical outputs are
consistently greater than geochemical inputs over a
sustained period.
Management-induced disturbance of forested
watersheds can result in periods of net negative geo­
chemical balance (Feller and Kimmins 1984; Hornbeck
et al. 1986). Removal of nutrients by leaching, fire, or in
harvested materials can cause major episodic net nutrient
loss, which can result in a depletion of the site nutrient
capital (Kimmins 1977). Given sufficient time, however,
small annual inputs from precipitation, mineral weather­
ing, or slope seepage can completely replace these losses.
The geochemical cycle thus plays a major role in
In! Rep. NOR-X-328
determining the length of the "ecological rotation" for a
site (Fig. I I ) (Kimmins 1974).
The importance of the geochemical cycle varies for
different nutrients at different times and in different
areas. Soil weathering inputs (Table 2) are particularly
important for mineral nutrients on sites with geologically
young soils. Biological fixation is a major input of N in
some ecosystems, especially early in succession follow­
ing ecosystem disturbance and loss of N (Table 3). Pre­
cipitation inputs can be an important contribution in
some areas, especially downwind of coastlines or in areas
affected by air pollution (Table 4).
The Biogeochemical Cycle
Uptake
Plants take up nutrients mostly from the soil via fine
roots and mycorrhizae, but for some plants (especially
mosses), uptake from precipitation and/or tbroughfall
may be the major source for at least some - nutrients
(Tamm 1953). The rate of uptake is determined by the
rate of new biomass creation, concentrations of nutrients
in the various new tissues, loss of nutrierits from plants,
availability of nutrients within the plant by internal
cycling, and nutrient availability in the soil. Uptake
varies among plant species, plant ages, nutrients, and
sites (Table 5 and Fig. 12). It is fairly modest early in the
life of a tree stand (when seedling demand for new
growth is low) and in a mature stand (when internal
cycling has become important and the rate of net new
biomass accumulation has declined), and is highest in the
young pole crop stage when the rate of accumulation of
biomass, especially foliage and fine roots, is particularly
high (Switzer and Nelson 1972; Miller 1984).
Distribution within the plant
Different plant tissues vary in their nutrient concen­
trations, and therefore in their nutrient content (Fig. 13).
Foliage is generally particularly rich in nutrients (Table
6). The relative magnitude of both concentrations and
content of different biomass components varies consid­
erably among plant species and on different sites where
growing conditions - may alter biomass ratios. These
ratios also change as plants age.
Losses from the plant
Plants can lose nutrients through several biogeo­
chemical pathways. Aboveground litter fall of leaves,
branches and bark, foliar leaching, and reproduction can
be a significant loss for some species on some sites. For
some forest plant species on some sites, however, the sum
of these loss pathways may be equalled or exceeded by
In! Rep. NOR·X·328
the loss associated with the annual production and death
of fine roots (Vogt et al. 1986). Defoliation by disease,
drought, wind damage, or herbivores normally results in
minor nutrient loss, but during episodic events, defolia­
tion may be a major nutrient transfer pathway within the
biogeochemical cycle. Heavy defoliation may cause
fine-root death and loss of mycorrhizae, which may have
an impact on nutrient recovery by the plants. Defoliation
can also increase the leaching of soluble nutrients such
as potassium (K). In addition to these losses from indi­
vidual plants, there arc losses of nutrient capital from the
live plant community by plant death or wind breakage of
plants.
Decomposition: mineralizatiolvimmobilization
Organic matter derived from aboveground litter fall,
plant mortality, or root death may accumulate on the
surface of the mineral soil as an ectorganic layer (the
forest floor), or may be incorporated into the mineral soil
by soil animal activity, root death, or by the leaching of
organic matter. Soil organic matter may undergo physical
or biological fragmentation prior to microbial decompo­
sition. This decomposition results in the release of carbon
(usually as carbon dioxide [C02]), and may result in
either release of nutrients to the available soil nutrient
pool (mineralization) or uptake from it (immobilization).
Each type of decomposing material will have a charac­
teristic temporal pattern of weight loss, immobilization,
and mineralization on a particular site. These charac­
teristics reflect the chemical and physical characteristics
of the litter type, the nutrient and moisture status oUhe
site, climate, composition of the microbial and soil faunal
communities, and the oxygen and pH status of the site.
It has been shown that the rate of decomposition can be
related to the ratio of actual to potential evapotranspira­
tion of the site (Meentmyer 1 978) and to the lignin­
carbon ratio of the litter (a measure of the accessibility
of high-energy carbon-based molecules to microbes)
(Melillo et al. 1982).
Litter fall chemistry reflects the nutritional status of
plants. Under conditions of high nutrient availability in
the soil, the plant community will include many nutrient­
demanding species that have highly decomposable litter,
but species that are less nutrient-demanding and can also
grow on poorer sites may -also grow on such rich sites.
When they do, their litter may be more rapidly decom­
posed than when they are growing on a poor site. It
appears that as plants experience increasing nutritional
stress, their foliage and other tissues have lower concen­
trations of nutrients, ,they recover more nutrients by
retranslocation at the time of tissue senescence, and their
litter contains higher levels of lignin, tannin, and other
organic materials that make the litter. more resistant to
27
A
of lower biomass accumulation, can
sometimes have appreciable levels
of net primary production. The result
is that understory vegetation often
accounts for a disproportionately
large role in the biogeochemical
cycle of the forest. Early seral vege­
tation can play a very important role
following disturbances such as fIre or
clear-cutting by taking up and
retaining on-site nutrients that would
otherwise be leached. Minor vegeta­
tion may become important again in
late successional forests. The impor­
tance of this mechanism will depend
upon the rate of net biomass accumu­
lation by this vegetation and its
nutrient concentrations.
Ecological rotation B
E
i
w
Disturbance C
Ecological rotation C
, Time
B
,
Longer than ecological rotation
Ecological rotation
.. ... .. ...
I
I
I
I
1____--...
I
A
,.' I
I
I
I
I
Shorter. than
ecological rotation
1
__
........
------ I
I
I
I
Time
Note: The ecological rotation depends upon the natural rate at which an ecosystem recovers from
disturbance and the degree to which the ecosystem has been disturbed. Ecosystems will be
dlilgraded if recovery is too slow, the rotation is too short, or the disturbance ' is too great
relative to the rotation length, degree of disturbance, and/or rate of recovery.
The Biochemical or Internal
Cycle
Plants can exhibit very efficient
internal conservation of nutrients
that are in short supply. Internal con­
servation is achieved by mobilizing
nutrients at the time of tissue senes­
cence, or simply by withdrawing
them from older tissues and re­
translocating them to locations in the
plant where new growth or other
physiological activity is limited by
their availability.
The efficiency of internal
cycling is determined by many fac­
tors. On very nutrient-poor sites,
where much of the N in foliage may
Figure 11. The concept of ecological rotation: the time to recover to the
be structural or where nutrient defi­
predisturbance ecosystem condition.
ciency may impall: phloem function,
there may be little retranslocation as
the leaves senescence. On a very rich
decomposition. As a consequence, fungal-dominated
site, there may also be little or no retranslocation, or even
mor humus fonns are characteristic of nutrient-poor
negative retranslocation. Generally, it appears that with
sites, even in the tropics, whereas bacterial and meso­
the exception of these extremes, the percentage of nutri­
faunal-dominated mull humus forms are characteristic of
ent recovery from senescent foliage increases as soil
nutrient-rich sites, even in boreal climates. Although not
, nutrient availability decreases; however, there may still
widely documented, it therefore seems reasonable to
be a greater absolute translocation on rich sites even if
hypothesize that equivalent changes in pathways and
the percentage translocation is less. This may simply be
rates of litter decomposition will occur as a. given site is
the result of higher foliar contents on good sites, or it may
either improved or degraded by management.
also reflect the confounding of moisture and nutrients­
fertile·,sites are often moist sites. Moist soil conditions
permit a large leaf area to be carried, leading to high
The role of understory in forest biogeochemistry
growth potential and therefore high nutrient demand.
This can result in fairly high translocation values even
Forest understory frequently has much higher nutri­
on quite fertile but moist sites.
ent concentrations than the overstory trees" and in spite
28
In! Rep. NOR-X-328
Table 2.
Examples of nutrient inputs to forest ecosystems via mineral weathering (kg ha·1 yr.l). (Source:
Kimmins et al. 1985.)
Dominant
vegetation
Location
Rock type
Phosphorus Potassium Calcium Magnesium Sodium
Other
Reference
Plagioclase
K-Peldspar
-'
23.0
26.0
10.0
52.0
Maroondah,
Eucalyptus
Victoria, Australia
Dacite
2.7
20.8
18.6
8.4
16.9
New Hampshire,
USA
Mixed
Quartz
Plagioclase
Biotite
7.1
21.1
3.5
5.8
Aluminum 1.9 Likens et al.
1 8 . 1 1977
Silicon
Maryland, USA
Mixed
Schist
2.3
1.3
1.7
2.6
Silicon
Maryland, USA
Mixed
Serpentinite
Tr'
Tr
34.1
Tr
Silicon
55.8
California, USA
Pinus aristata
Dolomite
Adamellite
4.0
86.0
52.0
2.0
8.0
17.0
2.0
1.0
Silicon
32.0 Marchand 1971
15.2
17.4
4.7
120.0
7.2
1.6
47.0
1 1 .6
28.0
0.2
8.7
15.7
9.0
Verstraten 1977
1 1 .0
24.3
8.3
6.8
Woodwell and
Whittaker 1967
Idaho, USA
Conifers
Washington, USA Pseudotsuga
-'
Oregon, USA
Pseudotsuga
menziesii
Andesitic tuffs
Oregon, USA
Pseudotsuga
Tllffs/breccias
Luxembourg
Quercus, Fagus Metashale
New York, USA
Quercus, Pinus
0.2
Iron
1 1 .0 Clayton 1979
Feller 1981
1 1 9.0 Cleaves et. al.
1970
Cleaves et al.
1974
21.0
Cole et al. 1967
Sollins et al.
47.0
1980
'Silicon
213.0
Fredriksen 1972
Note:_ A wide range of values can be found according to the minerology of the bedrock from which the soil parent material was derived, or over
which the forest is growing.
a
Nurieot oat inch.ided in study.
b
Tr = trace amount found.
e
Rock type not defmed.
Overall Biogeochemistry of a
Forest Ecosystem and its
Representation in FORCYTE-11
Figure 14 presents a detailed summary of the bio·
geochemistry of a particular forest ecosystem to illustrate
the complexity of parameters required to characterize
this aspect of ecosystem function. Figure 15 presents a
flow chart of FORCYTE· l l showing the major nutrient
compartments and nutrient transfers represented within
the model to simulate this complexity. The major features
of this representation are as follows.
In! Rep. NOR·X·328
Geochemical Inputs
Input data are provided on rates of inputs of nutrients
in precipitation, mineral weathering, and slope seepage
for each of the nutrients to be simulated on each of the
sites on which growth is to be simulated. These rates do
not change within an individual run, even if site quality
is simulated to change, because these variables are deter­
mined by a site's location in the landscape and its surfi·
cial and bedrock geology, rather than by the site's
nutrient status change. The ionic forms of these nutrient
inputs (e.g., nitrate/ammonium ratios for N) are assumed
to be the same as the ionic forms defined in the soils input
29
Table 3.
Examples of nitrogen fixation in ecosystems. (Source: Kimmins et aI. 1985.)
Location
Dominant vegetation
Source
Age
Fixation rate
!
I
(kg ha' yr- )
Reference
Oregon, USA
Conifers
450
Arhoreal lichens
3-4
Denison 1979
New Zealand
Conaria
14-25
Biomass and soil
130-190
Silvester 1969
Australia
Eucalyptus, and Daviesia
Georgia, USA
Hardwood
Europe
Hippophae
Western Australia
Tennessee, USA
-'
Ecosystem
5-7
McColl and Edmonds 1983
Logs
0.89
Cornaby and Waide 1973
Ecosystem
2-15
Akkermans 1971
Macrozamia riedlei
Coralloid roots
18.7
Holliday and Pate 1976
Mixed deciduous
Twigs
0.27
Todd et aI. 1975
2-15
Branches
Logs
L horizon
H horizon
Soil 0-10 em
0.08, 0.09
0.30, 0.92
0.03
0.63
4.04
Soil 10--20 em
1.77
Soil 20-40 em
2.72
Total
10.85
Massachusetts. USA
Myrica gale
Ecosystem
37
Schwintzer 1983
Scotland
Myrica gale
Symbiotics
30
Sprent and Scott 1979
Oregon, USA
Pseudotsuga
Decaying logs
1.4
Silvester et at. 1982
New Zealand
Pinus
Symbiotics (Coriaria)
90
Harris and Morrison 1958
New Zealand
Pinus radiata
Lupinus
90
Gadgil 1976
Lupinus
160
Total system
48.2
Ovington 1951 (from Moore 1966)
Forest floor
0.15
West et al. 1981
450
4
0-4
England
Pinus sylvestris
Clemson, S. Carolina, USA Pinus taeda
40
Soil
Clear-cut
1.60
Forest floor
0.1 1-0.31
Soil
1.73-1 .79
Note: For additional data and sources, see Kimmins et al. 1985.
a
Ages not recorded.
30
Inf Rep. NOR-X·328
Table 4.
Examples of precipitation inputs to terrestrial ecosystems (kg ha-1 yr-I). (Source: Kimmins et al. 1985_)
Location
Total
precipitation
(cm/yr)
-'
Nitrogen
Phosphorus Potassium Calcium Magnesium Sodium
Other
Reference
4.95
0.09
8.09
I !.I8
0.84
Ontario, Canada
6.53
0.20
2. 1 1
9.50
8.76
10.48
Ontario, Canada
6.80
0.06
1.56
4.01
0.68
1 .82
N. Carolina, USA
8.80
0. 1 1
2.10
4.80
.swank 1979
Jensen 1962
Ontario, Canada
_b
Foster 1974
Iron
1.38 Kramer 1976
Sulfate 26.00
Chlorine 2.69
Iron
l . l 8 Swanson 1976
Sulfate 26.70
Chlorine 6.66
Denmark
60.0
6.90
3.10
6.50
3.00
Washington, USA
75.0
1.20
0.27
2.63
0.17
0.49
Tiedemann et al. 1978
Santa Ynez Mt.,
California, USA
77.0
0.50
1.90
1.00
6.10
Schlesinger and Hasey
Ammomium
0.10
1980
Nitrate
1.10
Ontario, Canada
99.3
10.10
0.41
10.80
0.28
New Yark, USA
1 16.0
Kyoto, Japan
174.0
6.42
Kiryu, Japan
193.0
5.39
197.0
181.0
0.56
1.59
6.76
1.02
5.78
4.02
0.81
3.93
7.30
9.80
19.10
141.50
2.80
10.35
2.15
2.64
1.14
0.62
2.59
0.57
10.72
7.34
9.80
0.90
5.20
197.0
5.00
5.42
10.82
1.90
4.70
!.I4
179.0
1.72
0.52
6.42
9.20
1.77
18.45
Hier, Japan
260.0
4.37
1.89
7.80
5.28
2.19
1 1 .80
141.0
4.77
8.72
6. 1 1
0.28
1.67
169.0
169.0
0.53
3.19
9.93
1.i3
2.99
0.24
4.52
10.57
1.11
94.0
8.06
0.85
3.57
1 1.50
171.0
2.03
Japan
9.35
0.70
6.98
2 1 .00
9.93
Ashiu, Japan
140.0
8.71
0.31
3.68
3.93
3.57
Kyoto University,
Tsutsumi 1978
5.32
Jaku, Japan
Kawigamo, Japan
Art et aI., cited in Gray
and Schlesinger 1981
Maruyama et al. 1965
1.91
3.95
Iron
1.47 Scheider et a1., 1979
Iron
0.85
Sulfate 29.60
Sulfate 30.80
Chlorine 3.14
Chlorine 2.29
Note: For additional data and sources, see Kimmins et al. 1985.
a
Precipitation amount not given.
b
Nutrient not included in study.
In! Rep. NOR,K-328
31
Table S.
Examples of published estimates of nutrient uptake by forest vegetation (kg-' ha- ' yr)
Dominant
vegetation
Location
Solling. Germany Fagus sylvatica
Sweden, Norway Hy/ocomium
Age
Nitrogen Phosphorus
Potassium
Calcium
Magnesium
125
71.9
4,89
45.9
33.3
3.20
-"
10.0
1.10
4.0
4.0
4.00
Other
Reference
Chlorine 27.50 Ulrich and Mayer
3.70 1972
Iron
Manganese 6.03
5.70
Sodium
35.80
Sulfur
_b
Tamm 1953
splendens
England
Larix
20
16-33
Ovington 1956
England
Larix decidua
45
<I I
Ovington 1956
Japan
Mixed broadleaf
108.2
- lower slope
- upper slope
41.8
2.64
86.7
4.20
Kyoto, Japan
Mixed broadleaf
Chamaecyparis
Wales
Mosses
New Zealand
Nothofagus
40.0
USSR
Picea
62.0
England
Picea abies
USSR
Picea excelsa
USSR
Picea excelsa
England
Picea omorika
England
Picea omorika
USSR
Pinus sylvestris
31.9
20
72
9.69
76.5
3 1 .2
226.7
28.90
83.5
10.70
Katagiri et a1. 1978
Iwatsubo 1976
95.5
26.30
21.1
61.7
9.30
14.0
4.2
3.90
3.30
34.0
84.0
12.00
Miller 1963
2.70
12.0
48.0
7.00
Soon 1960
1.30
98.6
Rieley et al. 1979
44-49
Ovington 1956
30.0
Remezov et al.
55
1 14.0
200
75.0
Marchenko and
Karlav 1961
47
1 1-18
Ovington 1956
49.0
120
1959
5.00
19.0
32.0
24.0
5.00
Sodium 1.80
Ovington 1961
P'yavchenko 1960
Note: For additional data and sources, see Kimmins et al. 1985.
a
Age not recorded.
b
Nutrient not included in study.
32
In! Rep. NOR-X-328
A
240
220
-------
-
200
------
.c
180
'"
'"
�
140
"=>
120
"iii
=>
<=
<=
«
.
Minor vegetation uptake -----Tree uptake
160
Q>
-'"
g
!II
100
80
60
40
20
0
N p.
f'.!:
K CaMg
, forest
70-75 yr
Mixed oak
P K CaMg
forest
Oak-ash ·
Wheat
Scots pine
plantation
45 yr
115-160 yr
Sugar beet
Potatoes
B
,-- 500
Q)
Q>
--------,.-
-- _ Ca
.):::: 40Q _ - - ·
�
N
K
-- p
#
300 ---�-----��
#�-
"'
§- 200
-:::;_""""-:
L---#
_
_
_
_
_
" 1 00
Q>
�
80
-------
�
60
-
�
E.::::I
�
E
«
20
40
60
80
Stand age
100
120
140
--------­
...
20
.
-
I
"
� ,
';7
·
(yr)
20
40
60
Stand age
80
50
40
,.. ...
'"
"- 20
=>
"
-'"
=>
<=
�.
o;...,...r-r
.,
-r-.,...,..,.
.,
..,.
T
0
�
S
"iii
I!
,�,;,.---
<=
«
30
...
10
100
20
(yr)
40
60
Stand age
Pine
Birch
Oak
(A)
�
/�
�-;;..:-
'-
40
0
.;
Q>
80
100
(yr)
Variation in the annual uptake of the major macronutrients by several different forest and
agriculture plant crops.
(B)
Variation in uptake with age for three tree species. Some of these
uptake estimates are based on studies of the aboveground organs alone (uptake
=
net increment
of nutrients in aboveground parts + replacement of losses from aboveground parts). Inclusion of
belowground biomass production and turnover could result in a considerable increase in the
estimates (Source: Kimmins
1 987) . . Used with
permission of Macmillan, N.Y.
Figure 12; Nutrient uptake by forest crops.
33
A
N P K Ca Mg
Mixed oak forest,
'
Belgium
?Q-75 yrs
B
N
P K Ca ' Mg
White spruce-subalpine
fir forest; Canada
1 1 O�350 yrs
N P
Douglas-fir
United
36
K
Ca
plantation,
States
yrs
N
P K Ca Mg
Lodgepole pine
forest, Can,ada
'
125 yrs
Foliage
Branches
Stemwood
Stembark
Roots
Scale: 0
50
100%
(A) The total content of five macronutrients in tree biomass (kg ha-1).
(B) Percentage distribution of
this content between the five major biomass components. (Source: Kimmins 1 987). Used with
permission of Macmillan, N. Y.
Figure 13. Tree nutrient content and its distribution in fonr forest types.
data for the site quality being simulated_ The user can
also specify rates of nutrient additions in the form of
fertilizer.
In addition to the mineral inputs, the user can give
rates ofN-fixation by simulated symbiotic N-fixing plant
species or free-living N-fixing microbes. The fonner is
determined by a user-provided rate that is a function of
the amount offoliage (kilograms ofN fixed per kilogram
of foliage carried by the N-fixing plant), derived from
published data on N-fixation rates for N-fixing plants of
known foliage biomass. Symbiotically-fixed N is entered
directly into the N-fixing plants, where it is used to satisfy
their N-demand for new growth prior to their uptake of
N to satisfy any remaining uptake demand. The fixed N
reaches the soil via litter fall, plant death, leaching, or
defoliation. Free-living N-fixation (bacteria or other soil
organisms living nonsymbiotically) is given as a constant
annual input rate directly to the available soil nutrients
compartment, where it is divided between nitrate and
34
ammonium fonns in the proportions given for the site in
the input file.
These geochemical inputs are either incorporated
directly into plants (symbiotic N-fixation or uptake by
foliage from precipitation) or into · the available soil
nutrient pool.
Geochemical Outputs
The FORCYTE-ll model simulates the geochemi­
cal losses of nutrients from the "soil available nutrient"
pool by leaching, immobilization (N), volatilization of
fertilizer N, fire (wildfire or management-controlled
slash bum), and removal of harvested materials. As well,
nutrients are removed from the soil pool by plant uptake.
Soil leaching losses are determined by rates of min­
eralization, rates of plant uptake, the ionic forms of the
nutrients, and simulated soil cation and anion exchange
In! Rep. NOR-X-328
Table 6.
Examples of tissue nutrient concentrations ( % by weight) in Douglas-fir
Location
Age
Colorado, USA
2
British Columbia,
Canada
Componenta
Nitrogen Phosphorus
Potassium
Calcium
Magnesium
Needles
1.20
0.23
0.72
0.10
0.08
13-50 Current needles
0.88-1.37
0.12�.22
0.38�.70
0.16-0.44
0.07�.18
1.04
0.20
0.95
0.09
0.07
0.301
0.054
0.242
0.371
0.057
0.291
0.191
0.040
0.071
0.031
0.122
California, USA
2
Needles
Washington, USA 28-52
Upper Pack
Live branches (U)
Live branches (F)
Dead branches (U)
Dead branches (F)
Bark (U)
Bark (F)
Wood(U)
Wood (F)
Needles (U)
Needles (F)
Darrington
Lower Pack
Matlock
Live branches (U)
Live branches (F)
Dead branches (U)
Dead branches (F)
Bark (U)
Bark (F)
Wood (U)
Wood (F)
Needles (U)
Needles (F)
Live branches (U)
Live branches (F)
pead branches (U)
Dead branches (F)
Bark (U)
B",k (F)
Wood (U)
Wood (F)
Needles (0)
Needles (F
Live branches (U)
Live branches (F)
Dead branches (U)
Dead branches (F)
Back (U)
Bark (F)
Wood (U)
Wood (F)
0.228
0.298
0.057
0.336
0.061
0.052
0.007
0.054
0.006
Reference
b
-
Boron
5-16
Beaton et
Chlorine
90-170 al. 1965a
Cobalt
0.10-0.45 and b
Copper
2.40-5.60
Iron
39-<;8
Manganese
450-1 150
Zinc
15-41
Aluminum
200-750
Silicon
1400-8700
Sulfur
1400-2500
Molybdenum 0.05--0.10
Heilman
and Gessel
1960
0.250
0.313
0.056
0.056
0.905
0.291
1.071
0.110
0.274
0.054
0.176
0.315
0.053
0.168
0.174
0.024
0.874
0.781
0.049
0.181
0.029
0.051
0.254
0.060
0.227
0.043
0.006
0.042
0.043
0.005
0.039
0.900
0.334
0.5 1 2
0.978
0.195
0.423
0.299
0.053
0.220
0.266
-'
Other
0.054
0.191
0.3 1 1
0.052
0.241
0.020
0.295
0.032
0.256
0.046
0.283
0.045
0.213
0.050
0.005
0.029
0.054
0.006
0.028
1.010
0.341
0.884
1.066
0.173
0.688
0.243
0.033
0.069
0.210
0.363
0.047
0.382
0.061
0.201
0.033
0.234
0.039
0.025
0.362
0.070
0.343
0.512
0.092
0.298
0.048
0.006
0.031
0.049
0.006
0.034
0.256
0.154
0.060
Note: For additional data and sources, see Kimmins et al. 1985.
a
U = unfertilized; F = fertilized.
In! Rep. NOR-X-328
b Source not given.
C
Nutrient not included in study.
35
w
'"
Nutrient cycling
(kg ha"' yr" )
Nutrients in understory
biomass (kg ha-i)
Nutrients in tree
biomass (kg hS'1)
95149
166
21
96
123
Total Biomass
N
p
K
C.
Total annual net
biomass production
8410
kg ha"
Total Biomass
N
p
K
C.
5 490
59
7
29
22
Foliage
Branches
Aboveground
biomass
1.11
Stembark
Stumps
large
Fine
roots
30
Total soil reserves to
a depth of
N
P
K
�
'"
'"
il
�
C.
em (kg ha"')
Humus
Minerai
soli
373
17
7
1282
2.
10
"
80
30,
_ 'Available'
in the soil
Input
• Uptake by
Soil leaching
the vegetation
•
Return from
the vegetation
111
.liI1I. �.L..-L
N
P
L
31
K
..
Uptake, return, and "available" soil reservoir (to
Figure 14. Biochemistry ofa pine stand in Finland. (Source: Kimmins 1987.)
".i.
30
Ca
em)
Soil weathering
and lateral water
flow (no data)
3320
capacities. The model assumes that
there is sufficient net downward
movement of soil water on the site
that any "available" nutrients not
held on an exchange site (cation
exchange capacity [CEC] or anion
exchange capacity [AEC])3 in the
forest floor or mineral soil, not
immobilized during the process of
organic matter decomposition, and
not taken up by plants, will be
leached out of the ecosystem.
Leaching is thus likely to occur with
various combinations of reduced
plant uptake (e.g., following dear­
cutting), reduced CEC or AEC (e.g.,
following intense slash burning), or
accelerated input of nutrients into
the available soil pool. Accelerated
nutrient inputs may occur following
slash burning or fertilization, and
during the "assart" period, which is
a period of accelerated mineraliza­
tion of the forest floor following
clear-cutting or other vegetation­
removing disturbance (Vitousek
1981, 1983).
Loss
To available
soil n trients
Loss
��
��
,
·
·
-- -
.
--
·
·
Driving
Junction
Loss
Input
Input
Input
Input
Leaching of nitrate-N is more
Figure 15. Major compartments and transfer pathways represented in
accurately defined as "disappear­
FORCYTE-ll.
ance" of nitrate-N because the
model does not explicitly simulate
the process of microbial denitrification. Nitrate leaching
data on the percent loss of each nutrient being simulated
loss is probably the combination of both leaching and
from material that is converted to ash.
denitrification. Most of the simulated leaching in
FORCYTE-II will be nitrate-N rather than ammonium­
Harvest removals are simulated by accounting for
N because the simulated soil will normally haye a higher
the nutrient content of each of the biomass components
CEC than AEC. The model will simulate Nf4-N leaching,
removed from the site during any intermediate or final
however, if the CEC is defined to be very low or if it is
harvest (e.g., a clear-cut).
reduced by simulated fire, and plant uptake is also low.
The failure to deal explicitly with denitrification, which
The model predicts the long-term site nutrient budget
can be a major source of postharvest N loss in some types
as the balance of the geochemical inputs and outputs, and
afforest, is a limitation of FORCYTE-l l 's representation
the simulated consequences of this balance on the circula­
of the geochemical cycle.
tion of nutrients and plant nutrition within the ecosystem.
Removals by fire include losses of fly ash and vola­
tilization during simulated burning. The model can simu­
late various types of fire (wildfire-crown, surface, or
ground; slash burning; or stand underburning), nutrient
loss being predicted on the basis of user-provided input
3
Uptake by Plants
Within FORCYTE-I l , plants can take up nutrients
from the precipitation inputs, from throughfall (under­
story plants), or from the available soil nutrient pool.
The representations of the cation exchange capacity and the anion exchange capacity are-very simplistic. They do not represent competition at the
exchange sites by different ioniC species, and there are several other shortcomings. These limitations"raise questions about the accuracy of simulated
leaching losses.
In! Rep. NOR-X·328
37
Uptake from precipitation or througbfall is defined
by input data on the difference in nutrient content of
precipitation or throughfall above and below the plant
canopy in question. Mosses are assumed to be able to
withdraw from throughfall as much nutrient as is
required to support their expected new growth. · In
FORCYTE- l l , mosses are assumed to be of the feather
moss type, which obtain their nutrients from throughfall
or precipitation (Tarnm 1953) and do not take up
nutrients from the available soil nutrient pool.
When the total site uptake demand by all species is
less than the total available supply, corrected for soil
occupancy, nutrient uptake by different plant species
occurs simply in. response to the -individual species up"'"
take demand, When the total supply is less than the total
demand, nutrient competition occurs. In this situation,
the available supply of nutrients is divided between
species in proportion to the ratio of the FRB/MFRB
ratios of the individual species.
Distribution of Nutrients within Plants
Uptake from the soil via fine roots is determined by
the uptake demand of the plants, the total quantity of
available soil nutrients, and the fraction of the soil occu- .
pied by fine roots. The uptake demand (VD) is simulated
on the basis of: predicted new biomass (PNB) multiplied
by expected concentration of nutrients in that new·
biomass (EC), minus the quantity of nutrients provided
to new biomass by internal recycling and/or uptake from
precipitation or throughfall (lC) plus the replacement of
nutrients leached out of the foliage (L).
Thus:
UD
=
(PNB X EC) - IC + L
[7]
The magnitude of the available soil nutrient pool is
simulated as the balance between the various inputs and
outputs. The available pool size at the start of a simula­
tion time step consists of nutrients held on exchange sites
at the end of the last simulation time step plus precipita­
tion, weathering, seepage, nonsymbiotic N-fIxation, and
fertilization inputs in that time step. Plant uptake from
this pool occurs in response to net plant uptake demand
and root occupancy of the soil. The quantity of nutrients
available to plants (NAP), as determined by root occu­
pancy, is equal to the total available nutrients (TAN)
multiplied by the proportion of the soil occupied by fine
roots. - This proportion is derived from the current
biomass of fine roots/site maximum biomass of fine roots
ratio (the FRB/MFRB ratio).
Thus: NAP
=
TAN (FRBIMFRB)
[8]
The site maximum fine-root biomass is the maxi­
mum that has been observed in a fully stocked stand on
the site, which is assumed to be the maximum achieved
by that species on that site. This value is given in the input
data file. Note that in the setup data files of FORCYTE1 1 , the smallest root class is referred to as "sman roots"
rather than fine roots. That is because roots were divided
into large, medium, and small for data entry. "Fine" roots
(normally defined as being less than 2 mm in diameter)
can be the same as "small roots" if small roots are defined
as roots less than 2 mm. The term "small root" is also
used in the graphical output.
38
Nutrients taken up by plants or internally cycled are
allocated to new growth of different biomass compo­
nents according to the relative magnitude of new biomass
multiplied by the expected concentration, calculated
separately for each biomass component. Where there are
insufficient nutrients from internal cycling and uptake,
new growth is limited to that which can be provided with
adequate nutrients to _ achieve the expected concentra­
tions. The ratios of the different biomass components in
the new growth, and thus the distribution of nutrients
among different new biomass components, are defined
by input data on biomass ratios for different site qualities
given in the setup data files.
The FORCYTE-l l model cannot simulate changes
in internal cycling or the internal distribution of nutrients
within plants as a function of changing competition
because site quality is determined by the total availability
of nutrients on the site, and there is no simulation of
change in site quality due to between-species competition
or change in stand density.
Nutrient Loss from Plants
Nutrient loss from litter fall is determined by litter
fall biomass and the concentration of nutrients in litter
fall. The former is determined by the simulated mass of
each live plant biomass component that produces ephem­
eral litter fall, and user-provided input data on the pro­
portion of each of these live biomass components that
becomes litter fall at each time step. With the exception
of small roots, litter fall mass in any time step cannot
exceed the mass of the biomass subcompactment from
which it came. For small roots, the user can simulate litter
fall within a single time step that exceeds the biomass of
that component at the start of that time step. For example,
the biomass of small roots that die in the time step might
be 150% of the quantity of small roots at the start of that
time step because of death, regrowth, and death of this
size class of roots within the time step.
Foliar leaching loss is simulated on the basis of the
difference in the nutrient content of precipitation and
throughfall above and below the foliage canopy in
InfRep. NOR-X-328
question. Input values for these nutrient contents are
given in the input setup plant growth modules, in con­
junction with estimates of the foliage biomass- associated
with the measured difference in content. This permits a
simulation of leaching per kilogram of foliage (this also
applies to the estimates of uptake from precipitation or
throughfall), and therefore a change in the amount of
nutrients transferred by foliar leaching as the biomass of
foliage changes (e.g., by a thinning).
Defoliation-related losses of nutrients are -simulated
on the basis of the user-defined removal of foliage
biomass by defoliation and the current concentration of
nutrients in that foliage biomass (i.e., there is no internal
cycling before the foliageis lost by defoliation).
Internal Cycling
It is impractical to simulate the biochemistry of
internal cycling in rotation-length modeling of entire
stands. Instead, FORCYTE-l l adopts the very empirical
approach of requiring data on the concentrations of
nutrients in plant tissues of various ages or condition, and
on weight change at the time of tissue senescence, for
sites that vary in their nutritional quality. As the model
simulates nutritional site quality change within a run, it
interpolates between the input data for various site qualf..
. ties to find the concentrations and weight loss appropri­
ate for the current site quality. It then calculates the
quantity of nutrients retranslocated at the time of senes­
cence, or during tissue aging, from the appropriate
concentration and weight change data.
Decomposition: Mineralizationl
Immobilization
The simulation of litter decomposition in
FORCYTE-l l is very straightforward and lacks the
more sophisticated approach used in LINKAGES (Pastor
and Post 1985). The FORCYTE simulation is simply
driven by empirical input data on: weight loss rates as a
function of the type of decomposing material and its age
(since litter fall); the concentrations of nutrients in fresh
litter and in humus; and the temporal pattern of change
in nutrient concentration between fresh litter and humus.
These data are combined to calculate the change in the
total content of nutrients in a cohort of decomposing
material in each time step from fresh litter to humus. An
increase in content is interpreted as immobilization; a
decrease as mineralization.
This simple approach is applied to annual cohorts of
all the diffcrcnl lypcs oflitler fall simulated by the model,
or the user can opt to combine different litter fall types
that have similar decomposition characteristics intq a
few generalized decomposition types. Decomposition
rates given in the input file may be modified by a set of
exposure factors provided by the user in the soils input
file. These control the simulation of the response of
humus and litter decomposition rates to the removal of
vegetation by harvest or other disturbance. Empirical
data on decomposition rates must be provided for each
climate region in which the model is to be used because
FORCYTE lacks any representation of the effects of
temperature and moisture on decomposition processes.
Section Summary
Many existing hybrid simulation models do not
simulate nutrient dynamics and its role in regulating
growth and yield. Those that include nutrient dynamics
often simulate the availability ofN, bUllhen use the result
as a growth multiplier. There is no representation of
internal cycling, of nutrient uptake and loss, and of
various geochemical inputs and outputs. If nutrient feed­
back is going to be represented in a hybrid simulation
growth and yield predictor, all three major cycles and all
major pathways within each cycle should be represented,
together with the major consequences of the nutrient
dynamics for plant growth and ecosystem function. The
FORCYTE- l l model includes most of the major
pathways.
TEM POR AL CHANGE I N FOR EST ECOSYSTEM S
This section is based on the review of major succes­
sional theories provided in Kimmins (1987). The long,
term patterns of natural change in the composition and
productivity of unmanaged vegetation, and their altera­
tion by environmental changes such as climate change
and acid rain, are of great scientific interest and public
concern. Such long'terrn change, called ecological
In! Rep. NOR-X-32B
succession, has been the subject of study and debate for
nearly a century.
The earliest theory of succession was the monocli­
max theory, developed in the U.S. and based on studies
of sand dune succession by Lake Michigan. This theory
claims that, irrespective of the initial soil and topographic
39
conditions, all the vegetation in a climatic region will
eventually be similar and will reflect the climate of that
region. The theory claims that this will result because of
the way autogenic succession moderates the extremes of
soil physical and chemical conditions, causing a conver­
conditions, and of the interspecific interactions of com­
petition, physical interference, and allelopathy. This
recognition of the need to build ,models of succession
based more on individual species' life histories, toler­
ances, requirements, and interspecific interactions led to
gence of soil conditions toward an average or medium
the development of the "vital attributes" approach to
medium conditions, a shade-tolerant plant community
multiple-pathway model.
condition (succes sional convergence). Under the
that can reproduce beneath its own canopy under the
prevailing regional climatic conditions will eventually
dominate.
The monoclimax theory was soon challenged by the
polyclimax theory. This theory agrees that there is a
tendency for successional convergence to occur, but
understanding and predicting succession, and to the
Successional Processes that Should
be Incorporated into Hybrid Models
Models of succession should be ecosystem'models
rather than community or population models. They
notes that on many sites the time required is extremely
should incorporate all relevant intra- and interspecific
that cause successional retrogression. The theory also
account for the interaction between these biological
long compared with the frequency of disturbance factors
states that climate may change within the successional
time frame, thus preventing the development of a stable,
interactions and autecological factors, but should also
determinants and the temporal patterns of change in
abiotic factors.
climatically determined climax. The result is a mosaic of
vegetation types and ecosystem'conditions, the determi­
nants, of which include:
the time since the last distur­
bance and the nature of that disturbance; the physical and
chemical characteristics of the underlying landscape;
and the degree , to which successional convergence has
occurred.
More recently, successional the�ries have focused
The first process in successional change is coloniza.,.
tion. This consists of the invasion of the site by reproduc­
tive propagules, their establishment and early survival.
The deteqninants of plant invasion include the natural
pr<?cesses of seed rain or the activation of on-site seed
banks, seedling banks, and/or bud banks. Artificial
regeneration by planting is merely another form of colo­
nization. The determinants of establishment and early
on the differences in succession between different physi­
survival include the autecological tolerances and
cession� Succession in physically or chemically extreme
chemical conditions of the microsites that have been
cal environments and on the actual mechanisms of suc­
environments, or those previously unaltered by auto­
requirements of the species, the prevailing physical and
colonized, and the prevailing biological mortality factors.
genic succession, is generally very predictable, and
requires a moderation of the physical or chemical envi­
ronment by earlier successional communities before later
communities can successfully invade and occupy the
area'In contrast, succession in physically or chemically
moderate or favorable environments, or in more extreme
The second major process in succession is environ­
mental alteration. Simply by occupying a site, a popula­
tion or community of plants alters the physical, chemical,
and biological conditions of the site. The resulting
change in site resources and biological factors will
environments that have already been modified by early
influence the colonization of the site by other species, as
and can follow a variety of developmental pathways.
community already established on the site.
autogenic succession, tends to be much less predictable,
Thus, there is the "relay floristics" model of primary
succession and succession in extreme environments, and
the "initial vegetative composition" model for moderate
environments or the later stages of succession. This
two-pathway model was subsequently modified into the
three-pathway model that identifies a facilitation path­
way (essentially relay floristics), a tolerance pathway,
and an inhibition pathway (these last two are variants of
"initial vegetative composition").
The development of the three-pathway model was
driven by the importance in succession of both the
complex of resource availability and physical/chemical
40
well as' the survival, growth, and reproduction of the
Species replacement is the third major mechanism
involved in autogenic successional change. Species
replacement can simply be a matter of species longevity
and failure to regenerate under the new physical and
chemical conditions of the microsites in which invasion
(in this case self replacement of a species) must occur.
Alternatively, replacement may be the result of competi­
tion from other species for light due to differential height
growth patterns, leaf-area development, and shade­
tolerance characteristics. In some cases, the replacement
may involve physical or chemical interference. Compe­
tition for moisture and nutrients probably does not cause
In! Rep. NOR·X·328
mortality directly, but may be very important because of
its effect on height and leaf-area growth, and thus on
competition for light.
Both the monoclimax theory and the polyclimax
theory imply a unidirectional progression through succes­
sion toward some climax, although in the case ofthe polycli­
max theory it is assumed that repeated disturbance or
some arresting factor prevents succession from proceed­
ing to a climatic cliinax on many sites. Even on sites
where successional convergence - does' ur.;cur, ·however,
s.uccession might not lead to a stable forest climax under
the control of climate. Accumulation Of large woody
debris with a high carbon/nitrogen (C/N) ratio (Pastor et
al. 1987) or the production of a very cold soil microclimate
(Heilman 1966, 1968) may result in the stagnation of
nutrient cycles, the breakup of the climax forest
community, and its replacement by some other plaut
assemblage. Succession in the long..:tenn absence of dis­
turbance may thus be very different from succession with
intermittent disturbauce of a frequency and intensity that
pennits the ecosystem to recover back to what seems to
be the climatic climax for the region between successive
disturbances.
Whether some or all of these successional mecha­
nisms should be represented in a hybrid simulation
model will depend on model objectives. Models intended
for use as predictors of long-tenn succession in unman­
aged ecosystems wiII clearly need different attributes
thau models of managed ecosystems. In the former, there
should be an emphasis o� species replacement in mid­
and late succession" and the accompanying changes in
resource availability and the functional processes of the
ecosystem. Models of mauaged ecosystems should be
able to simulate the details of early succession (either
primary or secondary), the response of au ecosystem to
repeated disturbance, aud the staud cycle of either early,
mid-, or late successional seral stages. This broader set
of capabilities is necessary to simulate a wide range of
management systems including short-rotation pioneer
tree crop species grown in monoculture, even-aged
stands with clear-cutting, and uneven-aged management
of monoculture or mixed species, late-successional, or
climax forests.
Representation of Successional
Processes in FORCYTE-11
and Other Hybrid Models
The FORCYTE- l l model is a deterministic model.
There is no simulation of stochastic events. Thus, simu­
lation of invasion by seed rain or regeneration from seed
banks requires definition in the MANADATA
In! Rep. NOR-X-328
(MANAgement DATA) input file of the timing and
extent of these regeneration events. The FORCYTE- l l
model also has an input data-controlled simulation of
coppicing and suckering. In models of the lABOWA
lmeage, by contrast, the user defines the fecundity (and
therefore the seed rain) of the species being simulated.
Whether this leads to successful colonization depends on
the simulated availability of light and soil water, the
temperature regime being simulated, and input data
that define the species' responses to these conditions.
There is no simulation of the details of establishment and
early survival, and, with the exception of the most recent
members of this family of models, only trees are
represented. These models input trees at a diameter at
breast height of 1 .43 m, and then simulate their
competition, survival, and growth. In- contrast,
FORCYTE-l l has the ability to represent the invasion,
establishment, growth, aud competition for light and
nutrients among herbs, shrubs, mosses, and trees from
when they are very small. All these plant life forms are
initiated from the size defined for the first time step in
the input file. There is no seasonality represented in
FORCYTE- l l ; all time steps are considered to be the
same climatically.
Once the plants are established in the simulation,
both lABOWA-derived models and FORCYTE- l l
simulate the competition for light; in some lABOWA­
derived models aud in FORCYTE-l l there is also a
simulation of competition for nutrients. According to the
outcome of this competition, species replacement occurs
and successional change proceeds. In models like ·
LINKAGES and FORCYTE- l l , the accumulation aid
dynamics of soil organic matter are simulated, and,
according to the input data on decomposition processes,
this can lead to large accumulations of high C/N ratio
woody material, a stagnation of nutrient cycling, and the
decline of nutrient demanding species (Pastor et al.
1987).
The major differences between the representations
of succession in FORCYTE-l l and LINKAGES are in
the simulation of early successional events and the rep­
resentation of the full spectrum of plant life forms in
FORCYTE-l l , aud the more stochastic representation of
species recruitment, replacement, and long-tenn succes­
sion in the tree layer in LINKAGES. These differences
reflect the different origins aud objectives of the two
models; FORCYTE- l l focuses on successional retro­
gression arid recovery from large-scale or intense distur­
bauces, while LINKAGES aud other lABOWA-related
models focus on small-scale disturbauces that are typical
of some types of forests.
41
Simulation of Succession using
FORCYTE-11
The scenarios simulated in the following seven fig­
ures are described in Table 7. Fignres 16-22 show stem
biomass, foliage biomass, and top height data for the
simulated species over a 40-year period. Each variable
on each graph is plotted to full scale at its maximum
value, the absolute value of which is beside the line for
each variable plotted. Table 8 summarizes the maximum
values for each of the previously mentioned variables for
the various species for the 14 scenarios.
In Figures 16-19 (Scenarios 1-8), 1 800 Douglas-fir
2--0 seedlings were planted in year 2 into various com­
munities, including a scattered bryophyte community (Fig.
16, Scenarios 1.and 2), a developing herbaceous community
(Fig. 17, Scenario 3), a developing shrub community
(Fig. 17, Scenario 4), and a developing mixed herb and
shrub community (Fig. 18, Scenario 5). Figure J8, Sce­
nario 6, represents planting into a bryophyte community,
with herbs and shrubs invading in year 5. In Figures
16-18 (Scenarios 1-6), Douglas-fir was spaced to 600
stem/ha in year 15 in Figures 16-18 (Scenarios 2-6), and
Table 7.
Scenario
number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
red aIder invaded the stand in year 16. Figure 19, Scec
nario 7, repeats Figure 17, Scenario 3, but with fireweed
(Epilobium angustifolium L.) invading in year 5 rather
than year 1 . Similarly, Figure 19, Scenario 8, repeats
Figure 17, Scenario 4, but with salmon berry (Rubus
parviflorus Nutt.) invading in year. 5 rather than year 1.
In Figure 16, Scenario 1 , Douglas-fIr achieved a
stem biomass of 293. t ha·l, a foliage biomass of 16.7 t
ha·l, and a top height of 35 m at 39 years, just prior to
harvest in year .40. The mean annual incrern,ent of 7.5 t
ha·1 yr·1 and the average height growth of nearly 90 cm
reflects the low stand density (600 stems hac I) after
spacing and the complete lack of competition. !t aIso
reflects an initial ecosystem condition with l¥gecrest!rves
of forest floor N (the result of logging a previously
urunanaged old-growth stand). The moss community
reached its maximum biomass following the spacing, but
was gradually reduced as the Douglas-fir canopy
increased in mass. This reflects the requction in light
available to the mosses; FORCYTE-I I does not simulate
the smothering of mosses by litter faIl, which in reality
would have reduced the moss biomass to a lower level
than shown in the figure.
Scenarios demonstrating FORCYTE-ll's ability to simnlate
secondary sllccession
Red alder
(year)
Fireweed
Salmon berry
Year
Year
_b
16
16
16
16
16
16
16
I
5
10
I
5
10
I
50
1
5
5
50
50
50
I
I
50
50
50
I
1
5
50
50
50
5
I
I
I
50
50
50
50
Note: All scenarios include a bryophyte community. 'Douglas-fir ( 1800 stemslha) were planted i n year
2, and spaced to 600-stems/ha in year 15. Whenever red alder is represented, 600 stems/ hectare
were simulated, and these were not removed in the spacing in year 15. Scenarios vary in the year
of invasion of red alder, fireweed, and salmon berry, and, for frreweed and salmon berry the
amount of plant cover established.
a
b
% plant cover given in the setup input files.
Species not included in simulation.
42
The addition of red alder
invasion in year 1 6 (Fig. 16,
Scenario 2) had a negligible
effect. The alder only reached a
height of 1.5 m before it was
shaded out by Ihe reclosing of
the Douglas-fIr canopy. Planting
into an established community
of fireweed (Fig. 17, Scenario
3), on the other hand, resulted in
a great delay in the development
of Douglas-fir, which only
achiev ed 100 t ha·1 of stem
biomass, about 10 t ha·1 of foli­
age, and a top height of 22 m.
Many of the Douglas-fIr 'were
killed by the shading caused by
the fIreweed, with only about
5% surviving un.til the time of
harvest in year 40; however, the
individual surviving trees were
larger than in Scenario 1 .
Planting Douglas-fIr into a
salmon berry �ommunity had an
even more dramatic effect on the
tree growth (Fig. 17, Scenario
4). Virtually all of the Douglas­
fir were shaded out; the few trees
that did survive achieving a
In! Rep. NOR·X·328
1 . Stem biomass
Scenario
100
•
,
_• _
•
. ..
�
•.
'
.
,.,.....'..
•
·"...
. .
.i'6.4
•*.".
. .
. . .
..
,...
..
...,
.
,,.... . ...
.
....
..... .
.
.
.
1 00
Scenario 1 . Foliage biomass
•
•
16.7
..�"
••••
•
•
•
•• • •
• •••
••••
10.5
.. .
-=··�
·
.
..
. .
... .
.
o�--�
1 00 Scenario 1 . Top height
.
. ...
..
__�__________
.
....
�
,,· _______
.
.
35.2
'"
Legend
"
�
E
Douglas-fir
E
"
"-'""""'""
.�
E
'0
'"
Scenario
'" 1 00
�'"
�
'"
a.
•
.
_�
2. Stem biomass
...
.
. .. .
.
.
.
.
�.• •
.. .
..
..
..
.
.....-
Red alder
Bryophytes
,'
Numbers indicate the
maximum value
attained by each
variable.
�_,,......
.
/�.7 \ ..............
26.3
.
..
.
.
\,.
.
.
Biomass units - Uha
Height units - m
·
�/�'..,�
::::::�:I��:: ' ��
0 ------�==�
------ ��
.
____________"T
Scenario
100
2. Foliage biomass
10.5
•
•
16.9
....••
•.
..,
.
.. .•
.••.
,--......
'
.....
...\..
"
......
.
,
o�-----===:.-- -----"-- �'!'"- ·���··:��
Scenario 2 . Top height
100
1 .��- - - - - - - - - - - - - - - - - .
"
____
.....
.
____
___
.
...
..
��______
______-.
I
I
,
I
,
o t-o
�
--
�
10
----
�
----
�
20
----
----
Stand age (yr)
--------�
30
---
--------�
40
Figure 16. Stem biomass, foliage biomass, and top height values for Douglas-fir, red alder, and bryophytes in
Scenarios 1 and 2. Note: in the bottom graph red alder top height remains at its maximum value even though
.
the trees are dying.
In! R,p. NOR-X-328
43
Scenario 3. Stem biomass
o
_
100 I_�.�
.
-...._
. ,
.
..
7.2
.. ..... ..
.. ... ,
.........�"
.....�
.........
...
......
100
-..._....
. ..
.
. ...
.
.•.
,
. ::........ .... ...
. .
.:,.::
�
�
�
.
.. ,
.
��
..
O +---------�--�--�------��----���
Scenario 3. Foliage biomass
100
!! -\\
6.3 "
.
.
.
�
9.8
0.6
......
.
•
-".
...
...
...
.
o�--------------��
��.=
�oa..----��--------..��-.------�..----�
.
Scenario 3. Top height
�.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-._._._,_._._
100
2.0
I
••,•••,.,.,.,.'
22.0
w
�
Legend
�
-
E
E
�
E
'0
.�
f
Scenario
w
2
�
Douglas�fir
- - - - Red alder
4. Stem biomass
r�
l
100
" ,>oi.:•..•.•• •
i; "
..
.
.....
.'
!.,
1/
__
. ... .
. •
.. .
...
. .
,
.. , .... .. .
. . . ... .,.. . ,
+O
---���---�---'--�-��4.
2
rV-
Scenario
100
:·: ,
I ' ,,
i:
_."
.
.
.."..
..
.
. . ... .
... .. .
.
.
Foliage biomass
.
i
." . " ' .
.
......
••••••
" .....,
Fireweed
Salmon berry
.
.:!2.0O� -- - -· - · ·· ·· -·---··-1 - --- 8. 0
.
•••••••.
•_•• • • ••
,
.
..""• "'"
Bryophytes
Numbers indicate the
maximum yalue
attained by each
variable.
Biomass units - tlha
Height units - m
1.0
�:�;.��------------.----­
:
...
•
·�
0 �--------------��:�. �--�---,----------�==��-------------4
Scenario 4. Top height
100 ,... ....................................................•..............
2.8
1.5
:
.
...
.
...
...
I
'
..
•
•
•
•
•
10
20
Stand age (yr)
40
30
Figure 17. Stem biomass, foliage biomass, and top height values for Douglas·fir, red alder, fireweed, salmon berry,
and bryophytes in Scenarios 3 and 4. Note: the top height graphs do not decline even if the plants they
represent have died (i.e., they represent "standing dead" plants).
/
44
In! Rep. NOR·X·328
�
Scenario
4.2
'
"
100
5. Stem biomass
�•
. . . . . . -. . . . . . .
.. . . . . . . . . . . . . . .
.
.. .
10.1
....
.................
.......
!
.
.,
O �""""""��"""�"""��--���"""�·
....
" .'......•..,•.
.......,
.....
..
.
.
1.5
100
_
--
..
..
o +-"""""""����----�
_
Scenario
100
5."-=::::::::::::::::2
Top,height
::
.0::::::========1.6
1.6
-
--
_ _ _
�
�0.9
__
_ _ _ _ _ _ _ _ _ _ _ _ _ _
-
J
,-------,
Legend
-
--"
Douglas-fir
- -,- - Red alder
Scenario
288
,. .., ... . . :
'...
.
.
'
. . . •
.......•...
. •...•,
.......
CoDA
Scenario
Salmon berry
Numbers indicate the
maximum value
attained by each
variable.
���
�����__���
�
�______�
__
100
Fireweed
.............. 8ryophytes
6. Stem biomass
o +-__L-
••• ,•••
•••••••
6. Foliage biomass
Biomass units - Vha
Height units - m
16.8
7.S r·....
.',
..
..
$�••••"..,
6. Top height
2.0rr----------�-------�/2.8
1.4
•
Scenario
100
o
10
20
Stand age (yr)
30
40
Figure 18. Stem biomass, foliage biomass, and top height values for Douglas-fir, red alder, fireweed, salmon berry,
and bryophytes in Scenarios 5 and 6. Note: the top height graphs do not decline even if the plants they
represent have died (i.e., they represent "standing dead" plants).
Inf Rep. NOR-X-32B
45
Scenario 7. Stem biomass
Scenario 7. Foliage biomass
7. Top height
,-·.·.·.·.·
...·.·.·.·;--.................
. .......
. ...............
2.0
1.6 1
34.3
.
Scenario
100
....
---
/
I
0 ����
�
�
--
���
--
-4
--
�
---
---
r-It
f ,I · .,,�:····, .. l· .
· f ······· .
,
'''
I'
.
..
I
.
.
�
.�
.. .
•.
.
,'
/
,
0.6
298
..
..
.
....
....
"
I
.. .. ..
..
..
... . .
.
... ... . ..... .
." . ........ .. . .. . ..
. . .. . ... ....... ....
...
. ..
.
.....
=- -.--------------�
��
o -�----�'"--------.------:%:===:::.--.�.:.::.�.�'..
8. Foliage biomass
"" ,/'""" 9
8.8 . "f�.. ',0.. .
Scenario
100
*"-
I
�
o
,
:
i
,I
�
,
•
•••••••
Fireweed
- • • Salmon berry
...........".. Bryophytes
Numbers indicate the
maximum value
attained by each
variable.
Biomass units - tlha
Height units - m
17
'.
'..
......� ..
'.,
�------'�..-=�-.------�������-----------.---------------\
Scenario
100
:
•
.
.
...
......
Douglas-fir
- - - - Red alder
•••••
8. Stem biomass
15.5 ,., . ,
6.0
. .
Scenario
100
Legend
8. Top height
�
2.8 ,
:
•
,
•••• - ••••
_ _
••
�.�.A.A.A.A•••A
•• _ ••
�_
•••••
1 .4
••••••••••••••••
34.9
•
•
,
,
,
O ���----��---.---�
o
10
20
40
30
Stand age (yr)
Figure 19. Stem biomass, foliage biomass, and top heightvalues for Douglas-fir, red alder, fireweed, salmon berry,
and bryophytes in Scenarios 7 and 8. Note: the top height graphs do not decline even if the plants they
represent have died (i.e., they represent "standing dead" plants).
46
In! Rep. NOR-X-328
100
Scenario 9. Stem biomass
1.9
."."....�;.�.. ..... ..
:
.
"
.
..
.�
..
....
....:
.. "
.
.
.
.
.
:......-...... 2�
..
..
....
......
..
......
.
......
. .••••,,� ..., ,.,,,
.
."'ft'!1't
.......
.... .
, ...'
:;.... ..
.
....;,:
,
"
r - - - - -5.8
---------
100
,
0.2
..'....
'.
",
a
",
100
Legend
---
o
.
Douglas-fir
-
- - - - Red alder
Scenario 10. Stem biomass
,.......4.2
.....
100
.... . ..
.
...
Scenario 1 O. Foliage biomass
100
,"
..
.
.. ....
... .. ..
... .
... ... .
.. ..
..
.. .. ..
��
-�
-
... ... _ _
..- .. - -
.
.
. .
.
.
.
.
.
.
Fireweed
••••••••
Salmon berry
"..,,"'...... Sryophytes
:
:
:.
..
.
. ..... ..
:
...
. .. . . .
.. .. ..... . .
............,...." .::
..
...
.
•...
.
.. . . . .
1�}.... ............
.. _ _ _ _ _ ••
•••,•••
.
Numbers indicate the
maximum value
attained by each
variable.
Biomass units .. Vha
Height units .. m
. . . . .. . . . . .... .. . .1.:? . . . . . . ...... ..
�
.
:,
.
o �----�����--�--Scenario 10. Top height
100
2.0
------------------------------
2.0
_ _ _ _ _ _ _ _ _
...;
-QA
_ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
,
0 �--�----�------��------__--------_1
10
30
20
40
o
Stand age (yr)
Figure 20. Stem biomass, foliage biomass, and top height values for Douglas-fir, red alder, fireweed, salmon berry,
and bryophytes in Scenarios 9 and 10. Note: the top height graphs do not decline even if the plants they
represent have died (Le., they represent "standing dead" plants).
In! Rep. NOR-X-328
47
Scenario 1 1 . Stem- biomass
100
,..,..�.2
. :, =� �.:.�
....
... . ...
.. - - ..
---
-
'.........
.
..
..
....
.•. .
.
10.1
---••••••••••••••••
,
:
�
�
'
......".
. .
...
. .....
..•. .•
•
..
.
":
0
:
....,.. ... ,.,:
.
.
o �--....,.I-----...;:;;:::..:::.--..:,.
Scenario 1 1 . Foliage biomass
o.
100
o
.....
.... ...... _ _ _ _
. ..
""""'-
.
...
__ ___
1·.§ _
•• _
. .. . ..
. .�
...
..
.
..
:
.
.
.
.
�L.________-",.__���____��__________________________�
Scenario 1 1 . Top height
100
-------�------
�..Q._-------------
=���'
1.8
- - - - - - - - - - - - - - - - � - - - - - - .. -
0.9
-
- -
Legend
,
------
o �-�-�----�----�
Scenario 12. Stem biomass
.r....••••• • 30.9
,
•
100
... ... - - --
.. . -. .�:.��::=:��.:.:�:.�.�. . . .
.
.
..
.
.
.
......
. ...
.
o �---��_---_----�--�
Scenario 12.'Foliage biomass
�-9.1
- --------------
100
"
,
-
Fireweed
..
.. .. . .. Salmon b�rry
............... Bryophytes
Numbers indicate the
maximum value
attained by each
variable.
,.
..
..
.0.0.0.
.
_-!! fl.-- - -
Douglas-fir
.. .. .. .. Red alder
Biomass units - tlha
Height units - m
.
41
o �-��-_---_-�--_----l
Scenario 12. Top height
100
____1__ �.g, - - -
_ _ _ _ _ _ _
�
__
21.9
/"
--
o ���--�------�-----------__--�
30
40
10
o
20
Stand age (yr)
Figure 21. Stem biomass, foliage biomass, and top height values for Douglas.fir, red alder, fireweed, salmon berry,
and bryophytes in Scenarios 11 and 12. Note: the top height graphs do not decline even if the plants they
represent have died (i.e., they represent "standing dead" plants).
48
In! Rep. NOR-X-328
Scenario 13. Stem biomass
100
'
.
"
•.
•...
•. .
.. .
..
,.
..
•.
...
..
323
25.7
. .. .
.
..........
.
. . .
..
•
Scenario 13. Foliage biomass
100
18.4
a
Scenario 1 3. Top height
9.8
100
ID
�
OJ
E
>
E
·x
'"
E
-0
ID
'"
�
�
ID
e
Legend
---
a
Douglas-fir
- - - - Red, alder
.......,....... 8ryophytes
Scenario 1 4 .
100
ID
0..
.
....�.6.1
.
.
..
"
.../
.'
. .....
.....
.. ..
" . .
.
.
....
,
.
...
300
.
. ..
....
.....
".......
Numbers indicate the
maximum value
attained by each
variable .
Biomass units - tlha
Height units - m
a
100
Scenario 14. Top height
100
0
1 .7
.----
7-
a
��
10
-�
�
20
----
�
30
----
----�
40
--
Stand age (yr)
Figure 22. Stem biomass, foliage biomass, and top height values for Douglas·fir, red alder, and bryophytes in
Scenarios 13 and 14. Note: the top height graphs do not decline even if the plants they represent have died
(i.e., they represent "standing dead" plants).
In! Rep. NOR-X-328
49
Table 8.
Maximum values of the three variables shown in Figures 16 to 22 for each of the species
Scenario
number
SBa
DouB:las-fir
b
FB
TH'
SB
16.7
35.2
_d
2
293.0
295.0
16.9
35.2
0.7
0.09
1.5
3
100.0
22.0
0.1
0.03
0.9
4
8.0
9.8
15.0
0.1
0.03
0.9
1.0
Red alder
5
0.4
0.1
1.6
0.1
6
288.0
34.2
286.0
0.6
7
8
16.8
16.5
298.0
17.0
9
34.3
PB
TH
SB
FB
26.4
10.5
26.3
10.5
7.2
2.0
10.0
1.5
2.8
14.2
6.3
1 1 .2
0.03
0.9
2.3
0.5
2.0
10.1
1.5
2.8
4.2
0.08
1.4
1.8
0.4
2.0
3.3
0.6
2.8
12.1
7.5
6.0
0.9
2.8
15.5
8.8
1.6
1 1 .2
1.4
49.0
5.80
4.8
1.9
0.4
2.0
9.0
1.5
2.8
5.9
0.1
0.9
2.3
0.5
2.0
10.1
1.5
2.8
4.8
0.30
4.2
0.9
2.3
0.5
2.0
10.1
1.5
2.8
4.2
3.5
0.30
1.2
11
0.4
1.8
0.1
12
0.1
39.0
4.1
21.9
96.0
1 6.8
36.5
35.3
9.10
12.2
7.6
0.90
0.8
0.10
2.2
3.5
0.08
0.1
18.4
SB
0.10
0.2
300.0
0.6
TH
0.6
0.4
323.0
3.0
FB
0.8
2.0
14
SB
B!X°Ehytes
Salmon be!!l
Fireweed
TH
34.9
10
13
PB
0.5
2.0
13.2
8.1
3.5
30.9
15.3
9.8
25.7
1.7
10.8
26.1
10.4
a 5B = stem biomass (t/ha). "Stems" for bryophytes means total bryophyte biomass.
b
C
d
FB = foliage biomass (tlha).
TH = top height (m).
Species not included in simulation.
foliage biomass per hectare of about I t, and a top height
of 15 m. This degree of competition is possibly a bit
excessive, but is not unrealistic. Established salmon
berry thickets are known to be capable of resisting inva"
sian even by trees more shade-tolerant than Douglas-fir
for many decades. This scenario emphasizes the need for
vegetation management where dense salmon berry be­
comes established on a site prior to planting. Salmon
berry is more capable than fireweed of inhibiting
Douglas-fir growth. This reflects its greater height
growth and leaf biomass, and its different canopy
architecture.
Figure 18, Scenario 5, shows that an established
community of salmon berry and fireweed can result in
the total failure of a planting of small Douglas-fir seed­
lings if no effort is made to control the competition. The
salmon berry was the more successful of the two competing
species. Its initial growth was somewhat suppressed by
the presence of the fireweed, but because it achieved a
greater total height, it was able to overtop and suppress
the fireweed by year 4 and eliminate it by year 9.
Figure 18, Scenario 6, shows the predicted conse­
quences of a delay .in the invasion offireweed and salmon
berry until year 5, by which time the planted Douglas-fir
had already achieved about 5% ( l .7 m) of their maximum
height growth (34 m). As a result, the plantation was
essentially "free to grow" and the shrub and herb com­
petition had a negligible overall effect on the plantation
50
performance. Additional runs of the model to test the
effect of the invasion of the competing vegetation in the
years between Scenarios 5 and 6 resulted in predicted
levels of Douglas-fir stem biomass between 105 and 276
t ha-I. This illustrates the critical nature of the timing of
invasion events on plantation success: from total failure
when planting into an established community to no nega­
tive effects when the trees have 5 years "head start" on
the competition, with an exponential increase in the
negative impact on the Douglas-fir growth as the length
of the head start decreases.
Figure 19, Scenarios 7 and 8, repeats Figure 17,
Scenarios 3 and 4, but with a 5-year delay in the invasion
of the herb (Scenario 7) and the shrub (Scenario 8). As
was the case with Scenario 6, the herb or shrub compe­
tition had no effect on the tree growth when there is a
5-year delay in invasion.
Figures 20--22 (Scenarios 9-14) explore the conse­
quences for Douglas-fir growth of variation in the timing
, of red alder invasion, with and without invasion by
salmon berry and fireweed in year l . Invasion ofred alder
in year I resulted in heavy Douglas-fir mortality, with
only a few individuals surviving to reach a maXim1,lffi
height of I I m. The alder achieved 49 t ha-I stem wood·
biomass, and was able to shade out and eliminate the herb
and the shrub. Elimination of the salmon berry released
the surviving Douglas-fir, which was negatively affected
by the shade of both the alder and the shrub; however,
Inf Rep. NOR-X·328
when the alder invasion was delayed
5 years (Fig. 20,
10) and 10 years (Fig. 21, Scenario 1 1), the
so the runs do not simulate any change in the growth
the herb and shrub community, and the simulation results
changes. The examples are therefore for demonstration
until year
various scenarios would have differed somewhat had
Scenario
Douglas-fir was unable to grow up through and suppress
were essentially the same as if the alder had not invaded
1 6 (Fig. 18, Scenario 5).
Figures
salmon berry and fireweed were for one site type only,
strategies of these - species - as simulated site quality
purposes only. It is anticipated that the outcomes of the
complete data sets for these three species been available.
21-22 (Scenarios 12 to 14) show a rather
different outcome. In the absence of the herb and shrub
competition, Douglas-fir was able to grow, though at
much reduced rates, under the alder, and eventually to
grow above it. Invasion of alder in year l' reduced
Section Summary
The FORCYTE- l l model has a more detailed
13% of what it
representation of early succession than other models; it
great reduction in the number of surviving Douglas-fir,
trees. It is therefore better able to simulate the succes­
m). Invasion of alder in year
large-scale natural disturbances. Although .it uses the
Douglas-fir stem accumulation to about
achieved in the absence of alder. This was because of a
but also because of a reduced height growth
(35 m vs 22
5 actually resulted in an
increase in Douglas-fir growth. This is attributed to the
increase in site quality due to the N fixation by the alder,
which more than compensated for the light competition
also has the ability to simulate herbs, shrubs, mosses, and
sional consequences of management disturbances _and
"opaque blanket" canopy representation, like the
FORSKA (Swedish word for forest) model it has a ver­
tical distribution to the foliage. It does have the capability
1 0 resulted in a
of simulating longer-tenn successional processes in
never able to catch up in height with the Douglas-fir and
multiaged forests, but FORCYTE- l l does not have as
caused by the alder. Invasion in year
negligible effect on Douglas-fir growth. The alder was
therefore did not cause any light competition, but
because it was shaded out so quickly, there was little N
unmanaged, relatively undisturbed multispecies and
convenient a modeling strategy as the JABOWA-derived
models for this purpose.
addition by alder.
If the reproduction of the fireweed and salmon berry
had been by rhizomes rather than seed, and if the alder
growth had been coppice growth rather than seedling
One of the major deficiencies of FORCYTE-II is
its inability to. represent competition fDr sDil mDisture and
the effect of such competition from herbs and shrubs on
the early growth oftrees. In some forest types (those that
growth (all of which can be simulated by FORCYTE-
are largely moisture-controlled), this deficiency may
Different initial tree densities would have affected the
JABOWA-derived models have a moisture growth modi­
11), the results of these runs would have been different.
outcome, as would differences in the ECOSTATE file
that defines the site's resources of mineralizable organic
matter and nutrients. This emphasizes the critical impor­
tance of both an accurate definition of the initial ecosys­
tem state, the timing of invasion events, the mechanism
of invasion, and the relative den'sities of the competition.
The data sets from which these runs were prepared
were incomplete. In particular, the data for red alder,
seriously impair the accuracy of the simulation. The
fie� that facilitates a simulation of the effect of changes
in site mDisture caused by climate change, but there is no.
change in site mDisture bver time within a run, and no.
explicit simulation of the competition for the available
water between different tree species. The lack of repre­
sentation' of herbs and shrubs in many JABOWA-type
models eliminates the possibility of representing compe­
tition fDr mDisture between the trees and the lesser
vegetatiDn species in thDse mDdels.
S ITE QUALITY AND I NI TIAL ECOSYSTEM
C ONDI TION IN FOR C YTE-1 1
Site quality evaluation is an important part of
quality to suggest the need to switch to. a new yield table
silviculture and fDrest management because silvicultural
class, site index curve, etc. when sites are degraded or
and management prescriptiDns must be site-specific. Tra­
improved because such yield predictors are not able to
ditional historical-bioassay yield predictors have always
predict site. quality improvement or deterioration in
been tied to some system of site quality evaluation.
response to management. PrDcess-simulation growth and
FDresters have relied Dn independent assessments Df site
yield modelers have generally not attempted to rectify
In! Rep. NOR·X-328
51
this deficiency by incorporating a representation of site
quality and site quality change.The JABOWA class
models. Mohren's ( 1987) model, and ecophysiological
canopy models do not simulate changing site quality
during a run.
The lack of site quality simulation in communit y '
succession and most ecosystem-level models is under­
standable.There is still disagreement on many of the
details of both the ecology of site quality change, and of
the physiulogical response of plants to it; however, it has
been demonstrated that change in crop yield in response
to changing resource availability is as much or more a
function of changing allocation of photosynthates as it is
of absolute changes iri net primary production. Tissue
chemistry, internal cycling, foliage leaching losses,
decomposition rates, nitrate-ammonium ratios, and a
variety of other important processes and site charac­
teristics also change as a function of nutritional site
quality change.It thus appears that some attempt should
be made to represent the change in these variables as
nuiritional site quality is either improved or degraded in
the flexible, ecosystem-level growth and yield predictors
needed for the future.This section explains how the
phenomena of site quality and site quality change are
addressed in FORCYTE- l l .
decomposition rates, nitrate-ammonium ratios of avail­
able N, and a number of other site quality-related
variables. These input data provide the basis for simulating
the system's respon�e to site quality change.
Definition of Site Quality
True to its empirical traditions, FORCYTE-l l
employs an "internal bioassay" to establish the prevail­
ing nutritional site quality during a "nutrient feedback
and site quality change" run of MANAFOR (a run in
which both site quality change and nutritional-limitation
of growth are simulated).These are options that can be
.
switched off.
In each of the plant growth setup programs, the
nutrient uptake that must have been associated with, the
historical pattern of growth and biomass accumulation
by the species in question is calculated for each site
quality (Equation 7). For each of the nutrients that are
defined in the data input files, the setup growth programs
estimate the total level of nutrients that must have been
available in the soil to support such a level of uptake,
accounting for the degree of soil occupancy by fine roots.
For example, if trees of species
"x",
whese growth is
nutrient limited, are calculated to have taken up 20 kg/ha
of nutrient I in time step Y, but to have only had 50% of
Representation of Ecosystem
Response to Site Quality Change
The FORCYTE-i l model adopts an empirical
approach to the representation of site quality change.Site
quality in FORCYTE- l l is defined in the input data files
by entering data for both plant growth and a number of
plant and "ecosystem processes for several sites that vary
the max.imum observed small-root biomass in that year,
the model assumes that the total amount of nutrient 1
available on the site in that time step must have been 40
kg/ha (Equation 8).
Using this approach, the setup programs calculate
the temporal pattern of nutrient availability that must
have occurred on each of the various site qualities to have
supported the observed pattern of growth on each of
in nutritional site quality. Each of these data-site data sets
those sites. For each site, the program then takes the
quality.This can be a conventional site index value
the availability of that nutrient for that site quality. If the
is given . a ilUmber on some user-chosen scale of site
(height at a given age), a yield table site quality class, an
arbitrary scale set by the user, or some other site quality
highest value in this temporal pattern as the definition of
availability of a particular nutrient is indeed limiting
plant growth, it is expected that the availability of that
evaluation system. The choice of scale does not matter,
nutrient will increase as site quality (and consequently
employ the same scale, and this is also used to define site
not confinn this expectation, the input data are in error,
or the assumption that the nutrient is limiting growth
as long as all the plant growth data and soil process data
quality in MANAFOR runs.These site quality "labels"
are used to reference the nutritional data: that define the
sitequality (discussed in the following text).
With data that define values for a number of plant
growth and soil variables across a range of nutritional'site
qualities, the model uses these defmitions and linear
interpolations between different site qualities to define
values for these variables in' response to simulated site
quality change. These variables include resource allocation,
tissue chemistry, internal cycling, litter fall chemistry,
52
tree growth) increases. Where setup program output does
must be wrong. In such a case, input data and the assump­
tions made about the role of the nutrient in regulating
growth must be reexamined.
If only one nutrient is being simulated as growth­
limiting in a MANAFOR run, then nutritional site quality
will be defined in that run by comparing simulated total
availability of that nutrient on the site with the defined
maximum value for nutrient availability for each of the
site qualities defined in the setup modules. If two or more
In! Rep. NOR·X-328
limiting nutrients are simulated in a single run, the model
can become rather jumpy because it is possible to have
defines site quality in terms of the nutrient that is most
some rather large changes in nutrient availability, and thus
limiting.
in site quality, between time steps. Consequently, there is a
damping function in the program that limits nutritional
In applying this approach to the internal calibration
of nutritional site quality, the model needs to define what
site quality change to some user-defined percentage
of the change indicated by the change in nutrient availability.
percentage of the available nutrients on the site are
actually taken up per
1000 kg of small roots. The model
does this by estimating the proportion of the available
nutrient that is actually taken up and then dividing this by
the biomass of fine roots_ This proportion is referred to as
root efficiency and is used in MANAFOR to ensure that
the pattern of nutrient uptake in MANAFOR for a given
site quality is exactly as defined in the setup modules for
the same temporal pattern of fine-root biomass_
This root efficiency sho]lid be constant if the fine
root biomass is defined by the input data to be at its
maximum value when simulated uptake is at its maxi­
mum value, and if there is a linear relationship between
uptake' and fine-root biomass between the maximum and
zero fine-root biomass. This will not always be the case,
however, and therefore a coefficient (root efficiency) is
For example, a value of
0. 10 for this damping
coefficient in the MANADATA input file indicates that
it would take
10 time steps for the tree-defmed site
quality to reach the new site quality level set by the
changed site nutrient availability. Expressed differently,
site quality for tree growth would change by
10% of that
indicated by the change in nutrient availability. This is
biologically reasonable be.cause all biological systems
have some degree of hysteresis and inertia. In the case of
a dramatic change in. site nutrient availability, however,
such as is caused by fertilization, the user has the option
of allowing tree site quality to change more rapidly for
the first time step after the fertilization than it is permitted
to change under other circumstances. Thus, the user has
the opportunity to define the speed of site quality change
as defined by trees and other plants in response to a
needed to ensure that the predicted access by a particular
variety of causes of change in site nutrient availability,
species to. available nutrients in MANAFOR is the same
time step after a fertilization_ If the user does not believe
as ill the corresponding setup module.
(It should be noted
that this coefficient of root efficiency is different from a
similar-named variable in the setup module input files
that is involved in the simulation of soil leaching loss. In
and to define the special case of site quality change in the
that such a difference exists, the site quality change for
tree or plant growth following fertilization can be set to
be the same as for other causes of site quality change.
the latter case, the coeff�.cient of root efficiency controls
the percentage of the available nutrients in the soil that
are taken up when the standing crop of fine roots is at its
maximum, and the total available soil nutrients is equal
to or .less than the uptake demand.)
In addition to site quality change for trees and other
plants, FORCYTE- l l has a site quality for soil
processes. Optimum nutrition trials, in which sites have
been repeatedly fertilized at short intervals, have shown
that a single fertilization fertilizes the trees, whereas
Site Quality for Plants Versus Site
Quality for Soil Processes
Two types of site quality are defined: nutritional site
repeated fertilizatioris fertilize the site. Forest soils have
a considerable "nutritional inertia'" that reflects the char­
acteristic large mass of high C/N ratio organic matter in
the forest floor, the prevailing soil fauna and flora, and
the prevailing understory vegetation and litter fall qual­
quality as perceived by individual plant species, and site
ity. Several years of sustained alterations of litter fall
quality for soil processes, which is an average of the site
quality and nutrient availability are required to cause the
quality set by the individual plant species. The user
fundamental changes in soil processes that result in sig­
defines the relative weightings of the individual species
nificant changes that reflect site qUality. Representation
to be used in calculating this average. Having access to
of this inertia is achieved by having the site lluality for
the FORCYTE- l l model's site quality, these definitions,
soil processes follow site quality for trees and plants, but
and various indexes of phint growth and soil processes
with a second user-defined damping function: the pro'
for sites of different nutritional quality, MANAFOR
portion of the change in site quality defined by the trees
simulates soil nutrient availability (accounting for soil
and other plants that can occur for soil site quality in each
occupancy by roots) for the simulation time step, and
time step. Individual tree and plant species can defme the
then identifies the nutritional site quality for each species
nutrient availability on a given site differently in terms
in the simulation in that time step. Where this site quality
of site quality; consequently, the user must define an
has changed from the previous time step, the model
average of the site qualities defined by the various simu­
selects a new set of indexes for growth and soil processes
lated trees and plants. It is this average that the soil site
that are appropriate for the new site quality. The simulation
quality follows.
1nf..Rep. NOR-X-328
53
Plant growth response to changing soil nutrient
availability will normally be much faster than change in
soil processes; consequently, the basis for the site quality
change damping function is different for plant growth
versus soil processes. The site quality change damping
function for plants is a percentage of the change possible
in one time step, baseQ on the plant's photosynthetic
growth potential (before nutrient limitation is accounted
for), whereas the site quality change damping function
for soils is a percentage of the actual achieved change in
plant-defined site quality in one time step based on the
nutrient-limited achieved growth. As noted, both per­
centages are defined by the user in the MANADATA
input file.
existing population of each of the tree and other plant
species to be represented in the simulation. It considers,
for example, whether one is starting with a clear-cut
devoid of living mosses, herbs, shrubs and trees, or if
there is an established community with both above­
ground and belowground live biomass, or belowground
live biomass only. In the latter case, whether this below­
ground biomass constitutes a budbank that will sprout or
sucker _at the start of the new rotation would be consid­
ered. Other such questions must be asked. In short, the
ECOSTATE file provides a statement of the ecosystem
condition in terms of organic matter and nutrient inven­
tories, and the forest plant community from which a
simulation is initiated.
An obvious shortcoming of the definition of site
quality used in FORCYTE-l l is that the setup inpufdata
used to represent sites of ,different nutrient status may
also reflect variations in - environmental variables not
explicitly represented in the model. For example, differ­
ences in nutrient site quality are frequently accompanied
by differences in moisture availability. When nutrient
site quality change on a given site is simulated in MANA_
FOR, it is based on input data from sites that may differ
in moisture conditions - as well as nutrient conditions.
This problem is partly rectified by not allowing certain
variables (e.g., maximum foliage biomass that can be
carried on the site) to change. The value of these variables
is fixed at the start of the run by the definition of starting
site quality and remain unaltered even if nutritional site
quality changes during a run. This is one of the few ways
in which FORCYTE-l l deals with the role of moisture
limitation. Clearly, the model is weak in this area.
Experience with FORCYTE-l l to date indicates
that the model's predictions are very sensitive to the
initial ecosystem state . as defined by ECOSTATE. Care­
ful preparation of an appropriate ECOSTATE ftle can be
just as important a modeling activity as getting accurate
calibration data. It is, in fact, one aspect of model cali­
bration and a major component of the setup activity of
FORCYTE-l l .
Initial Ecosystem .Condition: The
ECOSTATE File
Levels of organic matter resources and competing
vegetation at the start of and during a rotation are altered
in real ecosystems, and in FORCYTE-l l , by silvicultural
treatments or natural disturbance, and simulated treat­
ments or disturbance, respectively. In both reality and
FORCYTE-l l, however, the condition of the ecosystem
also reflects .past treatments, harvests, stand growth, and
stand development. The effects of past management and
natural disturbance events on ecosystem condition and
future productivity are dealt with in FORCYTE-l j by an
ECOSTATE file.
The function of the ECOSTATE file is to initialize
values for all of the types and ages of decomposing
organic matter in the forest floor and the levels of soil
humus at the start of the run: their mass, nutrient content
and stage of decomposition. The file also defines the
54
Preparation of the ECOSTATE File
A detailed statement of the ecosystem condition in
terms of all the variables represented in FORCYTE-l l
is extremely difficult, ifnot impossible, to prepare manu­
ally. Consequently, the model itself is used to prepare this
file. To start with, the setup soils module creates a blank
file INITSTAT (INITial STATe). This is used in place of
the ECOSTATE file at the start ·of a special run of
MANAFOR that will create an appropriate ECOSTATE
file: INITSTAT is copied to ECOSTATE to empty the
latter file (the user may wish to save the previous
ECOSTATE file before doing this).
With the empty ECOSTATE file, MANAFOR is run
with the simulation of nutrient feedback on growth and
site quality change turned off. In this mode, all the trees
and other plants in the simulation grow as they did
historically (i.e., as they did in the setup modules), sub­
ject only to competition from other species for light.
There is no limitation on growth due to nutrient avail­
ability, nor is there any site quality change (because site
quality in FORCYTE-ll is defined in terms of nutrient
availability). Under these conditions, the simulated
plants grow, the stand develops, litter fall and tree mor­
tality occur, and a forest floor accumulates as a conse­
quence of the balance between litter and mortality inputs
and decomposition losses. The user creates a scenario for
this "no feedback, no site quality change" ron that recre­
ates the history desired as background for the subsequent
nutrient-feedback and site-quality-change simulations.
lnf. Rep. NOR·X·328
For example, ifthe desired starting state is an old-growth
forest that has just been clear-cut harvested with low
utilization standards, a no-feedback run of several hun­
file can reveal errors in these estimates.
If the decompo­
sition rates that are used are too high, it may be: impossi­
ble to build up realistic levels of soil humus in the
dred years without any management will be performed,
ENDSTATE file. If the rates used are too low, unrealistic
year.
ECOSTATE file provides a method of estimating humus
culminating in a low.,utilizatioo, clear-put in , tpe final
levels of humus will accumulate. Thus, preparation of the
decomposition rates when empirical data on these rates
At the end of a run, the model produces an
ENDSTATE file that describes the values for organic
are not available. Such estimates may be just as reason­
able, or more so, than estimates based on poor-quality
matter, nutrient, and plant biomass variables represented
field measurements. For further information on prepar­
copied by the user to ECOSTATE, mitrient-feedback and
Apps (1990).
in the model after the last time step. This file is then
ing the ECOSTATE file, consult Kimmins, Scoullar, and
site quality change are switched on, and MANAFOR is
ready to simulate various alternative rotalion-length
Section Summary
stand management strategies starting from a recently
clear-cut old-growth site. (Note:
ECOSTATE and
ENDSTATE are binary files that cannot be examined by
the user; however; output from the MANAFOR run used
to create the. ECOSTATE file can be examined to see
what the initial ecosystem conditions are.)
A particular version of the ECOSTATE file can be
used repeatedly because MANAFOR only changes the
ENDSTATE file, not the ECOSTATE file. A FORCYTE-
1 1 user will normally prepare a library of ECOSTATE
files covering all the site qualities for which simulations
may be required, and for all the various initial conditions
for these sites. It is then a simple job to load a specific
ECOSTATE file from the library into the current
ECOSTATE file for use with particular feedback and
site-quality-change runs of MANAFOR.
Preparation of forest-floor conditions using this
techrtique is fairly straightforward; however, the resil­
ience of a forest ecosystem in the face of disturbance is
also a function of soil humus. A site with a mull humus
form and a mineral soil contairting abundant soil organic
matter will tend to be much less sensitive to slash burn­
It is important to rememberthat FORCYTE- 1 1 can
be as sensitive to how the ECOSTATE file is prepared,
at least in the fIrst and possibly the second simulated
rotations, as it is to many of the silvicultural treatments
that the user can simulate, and to the accuracy of much
of the setup input data. The ECOSTATE file preparation
is therefore one ofthe more important setup activities that
must be completed before MANAFOR can be expected
to make realistic simulations.
The seIjsitivity of FORCYTE-11 to the irtitial state
of the ecosystem as defmed by ECOSTATE poses a
signifIcant dilemma for the user who wishes to verify or
validate aspects of the model. It takes a long time to
conduct the field experiments for either- verification
or validation; consequently, a user may wish to use
published results of either short- (for verification) or
long-term (for validation) studies. Unfortunately, very
few published studies have adequate descriptions of the
previous history of stand development and site treatment,
or of the ecosystem condition at the start of the experi�
ment or study. There is rarely a quantifIcation of the
inventory of organic matter, nutrients, competing plants,
ing, whole tree harvesting, and short rotations, at least
etc., that in most cases are significant determinants ofthe
mineral soil humus reserves. Consequently, it is impor­
fIeld trial. This makes it difficult to use most published
initially, than a site with a mor forest floor and little
tant to prepare an ECOSTATE file with realistic levels of
soil humus.
Empirical data that deflne mineral soil humus de­
composition rates are difficult to Obtain, and usually
estimates must be used. Preparation of the ECOSTATE
outcome of any experimental treatment or lon'g-term
studies for verifIcation or validation projects. In the
absence of these data, the user must estimate what the
site history has been, and use a no-feedback run of
MANAFOR to create an estimate ofthe initial ecosystem
state by creating an ECOSTATE file that reflects that
history.
OTHER PROC ESSES IN FOR CYTE-1 1
A detailed description of the algorithms used to
represent the various ecosystem compartments and
In! Rep. NOR-X-328
processes in FORCYTE-11 is beyond the scope of this
report, There are over
25 300 lines of source code
55
contained in the six programs that comprise the
FORCYTE-l l modeling framework. This section pro­
vides a description of the overall modeling approach in
terms of how the input data are used and, in general
terms, how the growth of plants is simulated. It supple­
ments information presented in previous chapters mi.-the
main processes represented in FORCYTE-l l , as well
as giving information- on processes not previously
discussed.
Derivation of Coppice Resprouting
or Root Suckering
Plant growth in FORCYTE-ll is driven by shade­
corrected foliage nitrogen efficiency (SCFNE) values,
simulated foliage N levels, and simulated light competi­
tion. This approach cannot be used, however, in plants
that resprout from bud banks on the stump or roots
following the removal of the stem and leaves. There is
no mass of foliage N to which SCFNE values can be
applied. The approach used to simulate the growth of
sprouts is to calculate the "grow power" of the stumps
and roots.
In order to do this, the user must provide data in the
appropriate setup growth module input file that define
the accumulation of biomass and the height growth of
vegetative sprouts in addition to similar data already
provided on seedling growth. For trees, the number of
stems and the stem biomass size-class distribution as a
function of age in a growing population of sprouts must
also be provided; in 9ther words, an historical bioassay
of growth from Sprouts. The program calculates the
proportion of this growth that can be explained by the
observed mass of foliage N, using the SCFNE values
calculated for seedling-grown trees. The difference
between observed growth and the growth that can be
explained by photosynthesis is the extra growth pro­
duced by stored carbohydrates in the stump and/or roots,
and this is attributed to the "grow power" of the below­
ground live biomass. The amount of grow power is then
related to the mass of live biomass that is producing it.
The user must enter data on the magnitude of this mass
that was associated with the,jnput data on sprout growth,
so that the model can calculate the grow power per
kilogram of live stump/root mass. Additional data are
required that define any loss of stump vigor at successive
coppice cuttings, and the number of sprouts as a function
of the size .of individual stumps. This data set enables the
model to simulate coppice sprouting or root suckering.
Resprouting of herbs and shrubs can also be simu­
lated, as well as growth from seeds. In general, the
simulation of the growth of these two life forms is
56
somewhat less detailed than the simulation for trees, but
the overall approach is the same.
Simulation of Tree Mortality
The user is asked to provide two types of data on
natural mortality: change in stand density as a function of
stand age, and the proportion of the observed mortality
at each data age that is not attributable to competition for
light. Shade-related mortality, which is density-dependent,
is simulated on the basis of simulated canopy light profile
data as described below. Density-independent IIlortality, .
the _mortality that is not attributable to competition for
light, is simulated as follows.
From the input data on stand density and on the
proportion of mortality that is density-independent, the
model calculates a rate of density-independent mortality
for each input data age. This is done by applying the
proportion of density-independent mortality to the num­
ber of live trees to give the number of trees dying in each
time step due to density-independent causes, and divid­
ing this number by the total number of live trees to give
a rate. A smoothed curve is fitted through these rate data
using the smoothing routine previously described in, the
section entitled "Foliage nitrogen efficiency as a measure
of canopy function and its representation as the basic
driving function in FORCYTE-l l ". This smoothed
density-independent mortality rate curve is applied in the
ecosystem simulation to define site-specific mortality
factors such as drought, animal damage, snowbreak, or
windthrow that are not related to light competition. There
is no change in these rates if stand density or species
composition is clmnged in the ecosystem simulation. Nor
do they change as simulated site quality changes; the
rates set at the start by the user when the initial site quality
is defined are used throughout the run irrespective of site
quality change.
Simulation of shade-related mortality involves input
data on the top height of the smallest live canopy tree
(trees whose apex is below the base of the live canopy
are assumed to be dead) and the simulated canopy light
profile. Where trees are dying due to light competition
the relative light intensity at the top of the smallest live
tree is assumed to be the minimum light regime at which
individuals of that species can survive. The model inter­
polates the height of the shortest live canopy tree
between the input data points (using the smoothing
routine) and this, in conjunction with a simulation of the
relative light intensity at various heights in the canopy in
each step, gives the criteria for shade-related mortality in
ihe ecosystem simulation.
In! Rep. NOR-X-328
The FORCYTE- l l model represents the canopy as
relative to growth rate of the medium tree, Thus, all the
an opaque blanket, ra)her than as a series of individual
trees in the largest stem size class would have an RGR
tree canopies. Consequently, even' a minor thinning of
the stand will raise )he average light levels through the
value of 1.35 iftheir stems were growing
35% faster than
the stem of the medium tree. Similarly, trees in the
simul�ted canopy of the entire stand. Thus, elimination
smallest size class would have RGR values of 0.10 if they
of only a few trees. per hectare will delay the death of
were only growing
some trees in the model, whereas in reality the death of
most of these trees would not have been prevented by
such a light thinning. To adjust for this shortcoming, the
user can define the minimum level of canopy-reduction
by thinning required to stop all shade-related mortality.
Between this defined level of canopy thinning and no
removal · there is a linear decrease in the reduction of
shade-related mortality.
10% as fast as the medium tree.
This allocation method assumes that the trees that
are largest at the start of a new input data age maintain
their growth advantage and continue to be the largest
trees. There is also an initial assumption that the smallest
trees are the least competitive and are the ones that die.
Thus, in calculatingthe RGR values for the smallest trees
in the stem-size distributions, the comparison is from the
smallest tree in one age-class distribution that remains
Simulation of the Biomass
of Trees that Die
The FORCYTE- l l model, which is basically a
stand-level model, uses infonnation on the size distribu­
tion of individual trees that die. The biomass of trees that
die is used in the calculation of net primary production
and SCFNE, and to defme the amount of organic matter
and nutrients added to the forest floor when trees die.
Individual-tree size infonnation is also needed for calcu­
lations of the economics of harvesting and the value of
the stem wood that is harvested.
Instead of an individual-tree growth approach,
FORCYTE- I I estimates stem wood growth per hectare,
then allocates this growth among the individual trees in
400 trees.
2000 trees in the simulation,
a tree list, which consists of a maximum of
Where there are exactly
each tree in the list represents five trees in the simulation.
950 trees in the simulation, there will be
31'6 trees in the tree list, each representing three trees in
Where ihere are
the simulation, plus two additional trees. These. addi­
alive into the next distribution, to the smallest tree in that
next distribution.
Some ofthe age-class members of a chronosequence
data set, which are based on data combined from several
stands, may not provide the sarne data as would a single
site followed over time. As a result, the smallest trees in
one age-class distribution that would be alive in the next
age-class distribution will not necessarily be smaller than
the smallest trees in that next distribution. Even where
the data set is from a true chronosequence (from a single
site), uneven spatial distributions of tree stems may result
in some smaller trees surviving under canopy gaps while
some larger trees, growing in a clump, may die from light
competition. In both cases, the empirical input data may
imply that trees have "shrunk" (i.e., the smallest trees in
one age-class data set may be smaller than the trees from
which they are assumed to have grown in the previous
age-class data set). This biological impossibility is
avoided in FORCYTE- I I by redistributing some of the
mortality to the next-largest stem-size class in cases
where the data imply tree shrinkage.
tional trees are kept in a separate, temporary list until they
can rejoin the main list, which will occur when the
Once the model has calculated RGRvalues for all n
number of the trees in the simulation can be divided
trees in the list, allocation of stand-level growth to the ith
exactly by the smallest integer· such that the product of
tree in any time step is calculated as:
the division is still less than 400.
The allocation of stand stem growth among individ­
ual trees is based on the relative rate of growth of trees
of different size as defined in the. input stand table data.
=
Stem wood increment treej
Stem wood incrementlha
X
n
RGR, I L RGR,
i=1
[9]
At each input data age, live trees are allocated to 10 tree
size classes (based on stem wood biomass), the size
The smallest trees in the tree list will be given a
classes being adjusted to contain one-tenth of the live
somewhat elevated RGR, because the model uses only
trees i n each class. The rate of biomass increase of the
10 stem-size classes in this calculation. Similarly, the
mean tree from one input data age to the next is calculated
stem-growth of the largesttrees in the list may be under­
and set to a relative growth rate (RGR) of 1.0. The mean
estimated. As a result, the stem size distributions siQ1u­
rate of biomass increase between data ages is then calcu­
lated by the model may tend to have fewer ofthe smallest
lated for each of the 10 stem size classes, and made
and the largest trees than were present in the input data.
In! Rep. NOR·X·328
57
In the input data file, the total stand biomass is given
or implied in two places: the input data on stem biomass
accumulation- as a function of age, and the stand table
data that defines, in conjunction with the stand
densitylage data, the number of trees in each of·1 0 stem
biomass' classes; In theory; these two estimates of stem
wood biomass accumulation over age should be the same
or very similar. If the two data,sets were _not obtained
from the same stand, however, the two estimates may be
different. Where this is the case, the FORCYTE- l l
method of simulating stem-size distributions will p.ot be
able to duplicate the historical stand table data set. Where
the two data sets are coordinated, the method appears to
work reasonably well, although its ability to simulate
tree-size response 'to various thinning regimes has not yet
been ,aq.equately te�ted._ If the biomass accumulation
input data set defines less biomass than is implied by the
stand table input data, the simulated tree-size distribu­
'
tions will fail to reach the sizes indicated in the stand
table, and vice versa;
' In order to calculate the biomass of other com­
ponents of trees that die, the model uses the biomass
ratios that exist" in the simulated stand in the time step in
which the trees die.' This will generally result in an
overestimate of the biomass of -branches, foliage, and
fine roots of dying trees, which will normally have much
less biomass of these components at the time 'of death
than would be suggested by the stand-average ratios for
these components.
Simulation of Height Growth
Input data define the height growth of the tallest and
shortest live canopy tree at each input data age. The
FORCYTE- ll model interpolates tree heights among
these input data &ges using the smoothing routine. The
only height simulation perfOlTIled is the variation in
height between these upper and lower bounds. It is
assumed that the height distribution between the tallest
and the shortest live trees is the same as the distri­
bution of stem biomass between the smallest and largest
trees.
An additional control of height growth is simulated
for the whole stand. Many species maintain height
growth rather than diameter growth when they are being
shaded or when nutrient availability is declining. Thus,
height growth is often a poor index of changes in site
quality and stand productivity, at least in the short term;
however, this height-'-diameter allocation shift varies be­
tween' species, so the user-must provide input data that
control it. This is done by an input variable that defines
how much the stem wood production of the stand must
decline before height growth is reduced. For example, an
58
input value ofO.25 for this variable means that the historical
pattern of height growth for the prevailing site quality
will be maintained until stand stem growth for that spe­
cies is reduced to less than 75% of expected growth at
that site quality. Once stem production,has dropped to
below 75%> of expected, there is a linear reduction in
height increment of all the trees in the stand (all trees
suffer the same proportional reduction of height incre­
ment), down to zero height increment when there is zero
stem biomass increment. Reduction of stem growth to
below the normal expected for the prevailing site quality
will mainly be the result of shading by the canopy of
another species, but could conceivably result from nutri­
ent competition by another species that reduces growth
below that expected for the total site nutrient availability.
Simulation of Nutrient Cycling and
Competition for Nutrients
Once the nutrient demand for new growth has been
calculated, FORCYTE-l l calculates the extent to which
this demand can be satisfied internally by retransloca­
tion, with the remaining unsatisfied demand becoming
the uptake demand. New growth that is actually achieved
is then limited to the amount of new biomass that can be
produced at the expected nutrient concentrations given
the availability of nutrients from internal recycling
and uptake. Uptake will mainly be from the roots, but
may be from precipitation or throughfall if the input data
define such an uptake. In the case of feather mosses,
all the nutrient uptake will be from throughfall or
precipitation.
The amount of soil nutrients available to a plant
species is a function �f the total £l!llount available in the
soil, the degree to which the fine roots of the species
occupy the 'soil (fme root biomass: maximum fine root
biomass [FRB/MFRB]), and the competition for soil
nutrients among the various species on the' site. Where
the total uptake demand of all species exceeds the total
available in the soil, competition will occur. The alloca­
tion between the species in such competitive situations
will be in the ratio of their uptake demands, modified by
their FRB/MFRB ratios.
Nutrient Availabil ity in the Soil
Nutrient availability in the soil is a function of all
the simulated additions to the soil available pool, minus
all the simulated losses and withdrawals. Simulated
additions are from mineralization of dead organic matter,
precipitation, fertilizer, nonsymbiotic N-fixation, mineral
weathering, slope seepage, and the solubilization of ash.
Simulated loss is limited to simulated soil leaching and
In! Rep. NOR-X·328
nutrients removed in harvested materials. Simulated
withdrawals are the simulated uptake by live plants and
microbial immobilization in decomposing organic matter.
The proportion of the available nutrients that are
available for uptake by a species is determined by its
FRB/MFRB ratio. The efficiency with which roots can
access available nutrients in a soil that is "fully occupied"
by fine roots (Le., FRB/MFRB 1 .0) can be varied in the
setup growth module input files. The default value is set
at 1.0, implying that a species with maximum fine-root
biomass will be able to access all the available nutrients
on the site; subject to competition with other species. This
measure of efficiency should not be confused with the
coefficient of root efficiency used to ensure'that nutrient
uptake in MANAFOR matches that defined in the setup
growth modules (see the section entitled "Site quality
and initial ecosystem condition in FORCYTE- I l ").
=
the foliage biomass in their canopies, and- the percent
reduction in light perunit offoliage biomass. The canopy
representation is as an "opaque blanket"; thus,. 30%
occupancy of a real Douglas-fir forest canopy by red
alder where the trees are all the same height would not
reduce the light received by most of the Douglas-fir,
whereas in the simulated canopy every Douglas-fir will
have its light supply reduced to some extent by the
simulated red alder "opaque blanket" canopy. Shrub and
herb species growing in gaps in a real forest created by
thinning will receive almost full sunlight for at least part
of the day, whereas in the model they will experience
only a thinning ofthe opaque blanket. This simulation of
the canopy as ail opaque blanket limits the model's
simulation of the response of minor vegetation to' stand
thinning as well as simulation of light competition be­
tween canopy species. Total light energy received by
Simulation of Light .Competition
Among Species
Light competition among species is a function of the
vertical distribution ofthe foliage ofthe different species.
plants in a canopy gap is less than full sunlight, however,
and the presence of red alder foliage in a Douglas-fir
canopy certainly does reduce the total light received by
Douglas-fir foliage per hectare, so the simulation is not
without merit.
SI M ULATION OF THE I M PA CTS OF FOREST
M ANA GEMENT ON ECOSYSTEM FOR M
AND F UNC TION IN FOR CYTE-1 1
Hybrid simulation models are predictive in purpose;
they modify the historical record of stand growth and
development by simulating the action of .some process
that is expected to change in the future. Ofthe two major
lineages of hybrid simulation models, the JABOWA se­
ries consists of ecological models designed primarily to
predict ecological succession over tune scales as long as
many centuries. Although these models have been used
to explore certain aspects of forest management, they
have commonly been used to examine long-tenn succes­
sional development in a variety of forest types and to
investigate the possible long-term implications of global
climate change. In contrast, the FORCYTE series was
developed from the outset to examine the long-term
implications for forest ecosystem fonn and function of a
variety of stand management practices and strategies.
This section describes FORCYTE-ll's management
simulation capabilities. Sample graphical output from
MANAFOR illustrates the model's performance in
response to simulated management options.
In! Rep. NOR-X·328
Simulation of Management
Practices
Clear-cutting
Clear-cutting is simulated according to the .user's
instructions in the MANADATA input data file. These
instructions define the simulation time step in which
c1ear-clltting is to occur, the utilization level, and the, fate,
of the harvested material. The utilization level defines
the proportion of stem wood; stem bark; branches; foli­
age; reproductive organs; and large, medium, and small
roots of the cut trees that are to be removed from the site
and marketed. The fate of umnarketed material (either
removed from the site or lefton the site as slash) is also
defined. The model accounts for the nutrients that are in
the biomass removed from the site or transferred from
live biomass to logging slash.
59
Alternative Harvesting Methods
The FORCYTE-l ! model .can be used to simulate
uneven-aged management; although its capabilities in
this respect are limited. The FORCYTE-l l model does
not permit the establishment of cohorts of individuals of
a species when there are live members of that species
already present in the simulated ecosystem. Thus, regen­
eration of tree species "x" cannot be simulated when
there are living individuals of tree species "x" already in
the tree li�t. This restriction can be overcome by naming
additional age cohorts of a particular tree or "plant"
species as a new species (e.g., three age cohorts of
Douglas-fir would b� called T l , T2, and T3). These
cohorts will .compete as though they were separate spe­
cies, but with identical adaptations and requirements.
Shelterwood, seed tree, wildlife leave-tree, and two- or
three-storied forest ecosystems can be simulated- in this
way. The model is not suitable for siinulation of an
old-growth climax forest or an uneven-aged, single tree
selection.
Response of Remaining Live Vegetation
to Harvesting
Unharvested trees and understory plants remain
alive on the site following a simulated harvest unless the
user defines in the MANADATA file that they are killed
by the harvesting operation; however, their growth may
change. Increased available light may affect their
growth, according to the response of their sun- and
shade-adapted foliage as defined by the photosynthetic
light saturation curves given in the setup input files. Their
growth may also change according to simulated changes
in soil nutrient availability.
Following harvesting, the remaining stumps and
roots of the harvested trees or cut herbs and shrubs may
or may not be dead; this is determined by MANADATA
input file instructions. The MANADATAfile also defines
if and when coppicing or suckering is to occur in the
simulation. The FORCYTE-l l model does not automat­
ically simulate coppicing or root suckering, even if a
species is declared to have this ability and is defined not
to have been killed by harvesting. The user must instruct
the modd to coppice or sucker after a harvest thinning
or clear-cutting. If no resprouting occurs-, nutrient uptake
by the cut trees is terminated, thereby reducing competi­
tion for soil nutrients and increasing nutrient availability
to the remaining live plants and leaching of nutrients out
.
of the system .
Response of Decomposing Litter and
Humus to Exposure Following Harvesting
The model simulates an exposure effect on rates of
decomposition oflitter (and logging slash ifit is present)
60
and humus caused by removal of vegetation by
harvesting or site preparation. This exposure effect may
alter the balance of immobilization and. mineralization,
and may result in an "assart flush" of increased nutrient
availability (Vitousek 198 1 , 1983). If increased nutrient
availability, occurs, it may cause an increase ,in site
quality, which in tum alters all plant and site pararneters
for which different values are given for sites of different
nutritional site quality in the setup input files. The
exposure effect may not begin until several time steps
after harvesting. This delay is controlled by the user, and
different exposure effects can be representedfor different
types and ages of decomposing material. The
FORCYTE-l l model does not explicitly simulate com­
petition for nutrients between soil microbes ' and plant
roots, nor the sponge- effect by which microbes can
immobilize a lotof nutrients, especially N, if there is an
abundance of high C[N ratio decomposing material after
a harvest. This immobilization phenomenon can be simu­
lated implicitly, however, via the input data that control
the rates of weight' loss and nutrient concentration
changes during decomposition. The extent of the expo­
sure effect is a function of the degree to which fine-root
biomass is reduced by the harvest.
Site Preparation
The FORCYTE-l l model provides the option of
defining the intensity of broadcast slash burning in the
MANADATA file. The proportions of forest-floor
bioniass burned, live plant biomass killed, and killed
biomass converted to ash are defined in the burning code
data in the final section of the file.
Control of Competing Vegetation
Control of simulated noncrop vegetation can be
represented by harvesting or thinning the noncrop
species. The proportion removed of each biomass com:"
ponent of each species being siinulated can be defined,
as can its resprouting, if this ability is defined in the setup
files for the species in question. The fate of the killed
material can also be defined (left on site or removed).
This control of noncrop vegetation 'can be conducted in
any time step of the- simulation: as a site preparation
technique following a final harvest, as early stand
maintenance, or as stand cleaning and species control.
Regeneration
The user has the option to simulate planting, or
natural-- regeneration by seeding or vegetative means.
Regeneration may be defined to occur in any time step,
with the constraint that a species cannot be regenerated
if there is already a live population of that speciesin the
simulation. The FORCYTE-l l model thus appears to be
In! Rep. NOR-X-328
limited to even-age populations of any particular species.
As noted above,. this restriction can be overcome to some
extent. -If multiple age .classes of a given species are
required in a simulation, the user must provide setup data
inputs for each age cohort of this species under a number
of pseudonames. For example. if one wishes to represent
three different-aged cohorts of Species 1, one must pro"
vide identical data sets for this species in TREEDATA
under the names "Species 1," "Species 2," and "Species
3." The model will then simulate the intraspecific com­
petition for light and nutrients among these three cohorts,
as well as coiuparable interspecific interactions among
the three age classes of this species -and any other species
represented in the simulation.
If the user wishes -to simulate planting, there is a
choice of planting trees (or other plant life forms) of any
desired age, size and shoot/root ratio, arid with any
user-defined level of fine-root mortality as a result of
planting. The variation in shoot/root ratio is to simulate
the effects of different nursery treatments that cause
variations in shoot/root ratios, tissue nutrient concentra­
tions, and other factors that mimic seedling growth on
sites of different nutritional quality. In FORCYTE-l l ,
planted trees and other plants are required to achieve the
shoot/root ratio appropriate for their site's quality before
achieving any new shoot growth. This can result in
retarded initial height growth where the planted trees
have a shoot/root ratio that is too large due to factors such
as over-fertilization in the nursery, and ' root mortality at
time of planting.
Trees and other plant species may be regenerated i�
any desired time step, and not only right after clear­
cutting. The simulation of mUltiple-age stands, with
younger age classes growing in gaps in the older canopy,
cannot be accurately simulated in FORCYTE- l l
because of itsuse of the "opaque blanket" representation
of the canopy, a problem shared with most of the "gap"
.
models.
Early Stand Management
The user can simulate weed (shrub/herb) control,
conifer release from deCiduous tree competition, and
crop stand density control at any time in the development
of the· stand. Complete or partial control of the noncrop
vegetation is possible.
Pruning
The dead and lower live branches of trees can be
pruned in any time step, or, if desired, removal of the
upper crown can be simulated. This latter management
strategy might be used to simulate the cutting ofthe upper
crown of an over-topping, N-fixing, noncrop tree, which
would increase light levels for the crop trees; The lower
live branches of the noncrop tree are retained ih�rder to
get the continued benefit of N fixation until the noricrop
species is shaded out by its now taller competitors.
Herbivory
The FORCYTE- l l model is not able to simulate
herbivory on the basis of a separate line of input data in
MANADATA, but by using the pruning and thinning
options, a limited ' representation of defoliation and
browsing damage can be simulated. With this approach,
however, there is no simulation of insect frass and anima�
feces,- which may have a much different rate of
decomposition than normal litter fall ofleaves and twigs.
Intermediate Thinnings
Commercial harvest of a portion of crop trees may
be simulated in any time step. The user can define the
intensity of the thinning, the utilization level, and the fate
of the biomass of cut trees that is not marketed. Due to
an error in the benchmark model, definition of large tree,
small tree, or random thinning regimes is inaccurate.
Commercial thinning, 'stand density control, weed
control, pruning, and herbivory will all change canopy
light levels and may affect soil occupancy by fine roots.
This will alter the growth of remaining live plants and
competition for nutrients, and may have ',a temporary
exposure effect on soil processes, the size and duration
of which will be proportional to the extent of fine-root
biomass change. The FORCYTE-ll model does not
simulate the dieback of fine roots and mycorrhizae that
may occur following a significant reduction in foliage of
evergreens.
Although FORCYTE-ll was developed primarily
as a tool for even-aged stand management, shelterwood
systems or mixed-age systems can be simulated by use
of the thinning simulation in conjunction with the simu­
lation of regeneration of a species under its own canopy.
Fertilization
Addition of one or more nutrients as 'fertilizer is
possible in any given time step, independently of other
silvicultural treatments. The user may define the imme­
diate loss of a percentage of added N fertilizer to simulate
volatilization loss (e.g., from urea fertilizer).
In! Rep. NOR·X-328
Stand Underburning arid Wildfire
The burning option in FORCYTE- l l is not limited
to postclear-cutting broadcast slash burning. Stand
underburning and wildfire scenarios of various timings
and intensities can be simulated.
61
General Comments
In all simulations, the model keeps track of canopy
architecture, biomass and light conditions, foliage light
adaptations, plant live biomass and nutrient content by
biomass component, litter fall, tree mortality, forest-floor
mass and nutrient content, various soil , processes, the
three major nutrient cycles, the soil nutrient inventory,
and the.l'ite,quality. A record can be printed out of a large
number of plant and soil variables, the mass and nutrient
content of harvested products, ecosystem net primary
production and nutrient budgets, the economics of man­
agement, and the energy benefit/cost implications of
management.
The FORCYTE-l l model has an apparent discrep­
ancy in the budgeting of nutrients. The model is unable
to account for the simulated change inecosystem nutrient
inventories from the start to the finish - of a run in tenns
of all the simulated nutrient inputs and outputs. This
accounting is not expected for nutrients represented in
the model in a "no-feedback" mode, but is expected for
nutrients for which nutrient feedback is switched on. In
most simulations, however, this error does not appear to
be great enough to invalidate the qualitative predictions
of the run.
Graphical Output of Two
Sample Runs
As an example of some of the simulation options,
two sets of MANAFOR output graphs are presented.
In the first scenario, - a medium-quality site -was
planted with 1200 stems/ha of Douglas-fir following
harvesting of an old-growth stand: there was a large
initial inventory of forest floor and slash materials. The
stand was spaced to 800 stem/ha in year 13 (a precom­
mercial thinning [PCT]), the lower half ofthe live canopy
and all dead branches were pruned in year 20 (removing
the lower 45% of the foliage), and the stand was thiuned
(commercial thiuning [CT]) from below in year 30 to
leave 400 stems/ha. Both thinnings started with the
smallest trees so that the larger ones were left. The run
was made with N as the single limiting nutrient.
In the second scenario, ,a medium-quality site was
planted with 1200 stems/ha as before, but the stand was
managed on a l5-year rotation with no thinning. Follow­
ing the second clear-cut, tl,le site was subject to a severe
slash bum.
The scenarios-shown in these graphs are not ecologi­
cally realistic because no mosses, herbs, shrubs; or other
tree species are included in the simulation. The graphs
62
merely illustrate certain management options. The
FORCYTE-l l model can represent all ofthese plant life
forms, but for simplicity and purposes of demonstration,
the simulation was limited to a single tree species­
Douglas-fir.
The MANAGRAF Output From the First
Scenario
Figures 23-29 present a selection of graphs from the
MANAGRAF output file for Scenario 1 . The format is
the same as previously discussed for Figures 8-10. Graph
A in Figure 23 shows the accumulation of tree biomass
over 45 simulation time steps. The reduction in biomass
in year 13 due to precommercial thinning and in year 30
due to commercial thiuning can be seen. Both t.he annual
production . and the total foliage biomass per hectare
recover within 2 years ofthe spacing (because only small
trees were removed, thus removing little foliage), and are
still increasing at year 20, when 45% of the foliage was
removed from the lower part of the canopy. Within 7
years of the pruning, �aximum foliage :t?iomass is
achieved, and foliage biomass is stable until the heavy
thiuning in ye"" 30. Foliage recovers by year 38 and then
starts a slow decline (a common phenomenon in
Douglas-fir stands).
Graph B in Figure 23 indicates the annual produc­
tion that, in conjunction with stand self-thinning and
litter fall, r.esulted in the biomass accumulation patterns
shown in Graph A. Values in Graph A are iocreased (or
decreased) each time step by the following amount:
Value in Graph B minus stand self-thinning minus litter
fall minus management removals.
Figure 24 (Graph C) contains a variety of stand
density and self-thiuning variables, and the relative light
at the top of the . smallest live canopy tree. Some density­
independent mortality occurs between years 1 and 13
(Variable F). The management-induced drop in stand
density in years 1 3 (PCT) and 30 (CT) can be seen. Some
density-dependent, shade-related, staud. self-thiuning
occurred between years 22 and 29 (Variables B, C, E, and
G), but this was terminated by the thinning at age 30.
Variable H shows the light at the top of the shortest live
canopy tree. This variable declines as the stand develops
from year 1 to year 1 3 when it is increased by the
precommercial thiuning. It declines as the canopy closes
again following this thinning, and the foliage biomass
increases to its maximum value, when the light value
stabilizes and then increases as the smallest trees in the
canopy begin to die from shading. This increase occurs
because the smallest canopy tree left alive after the
self-thinning is taller, has its leader higher in the. canopy,
and therefore experiences higher light at its top than
the trees just killed. The light value (H) increases
In! Rep. NOR-X-328
TREE#l
A
(T/IlA)
C
BRANCH BIOMASS
(T/IlA)
0
FOLIAGE BIOMASS
(T/HA)
E
LARGE ROOT BIOMASS
(T/HAl
F
MEDIUM ROOT BIOMASS
(T/HAl
G
H
SMALL ROOT BIOMASS
(T/HA)
OEAD BRANCHES BIOMASS
(T/HAl
,
TURNOVER ROOT BIOMASS
(T/HA)
J
FRUIT BIOMASS
·
X
,
X X, G
•
5
6
a
9
10
11
12
13
14
15
16
"
18
19
20
21
22
23
,.
25
26
"
"
28
30
31
32
33
BARK PRODUCTION
C
0
C
BRANCH PRODUCTION
(T/IlA/TlME)
0
FOLIAGE PRODUCTION
(T/IlA/TlMEl
E
E
LARGE ROOT PRODqCTION
(T/IlA/TlME)
F
F
MEDIUM ROOT PRODUCTION
(T/IlA/TIME-)
G
H
G
B
SMALL ROOT PRODUCTION
(T/IlA/TlME)
FRUIT PRODUCTION
(T/HA/TlME)
,
,
TOTAL TREE PRODUCTION
(T/HA/TIME)
J
J
TOTAL TREE BIOMASS
H X
XA
H
,
,
,
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35
36
IH XB XXG
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FCX
A
XB
,.
G J
(T/HA)
5"
3D
31
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J
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**
14.0145
2 . 3123
2 . 5757
3 . 3662
2 . 9631
.7490
5114
.4419
3 1 . 5674
411.7:721
(T/IlA/TlME)
18
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25
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3B
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35
36
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9
10
11
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15
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SAPWOOD PRODUCTION
0'
STEP
GRAPB :a
MAXIMUM
(T/IlA/TlME)
A
X
XOX
XXJC
*********************
B
X
XC FOI G
XX
FO ,
VARIABLE PLOTTED
A
100'lo
50'
1
2
3
**
B
, -- -- -- -- -- , -- -- -- -- -- ,
0'
STEP
259.4397
3 3 . 8862
24.8439
1 6 . 4855
55.2025
8 .-7559
4 . 8312
6 . 3985
3 . 7219
.4420
. (T/HA)
TREE#l
**
MAXIMUM
(T/IlA)·
STEMWooD BIOMASS
BARK BIOMASS
B
GRAPH A
*********************
VARIABLE PLOTTED
X
X
CF
AE
F
B' X
X,
X
BX
OH
B I X X
AE
B ,
F
AE
B
,
F
AE
B
,
F
F
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A E
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A E
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C
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OHX
,
,
,
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DJHG
0 H GJ
,
B
B
XH
0 H
o H
o H
JG
X
X
Figure 23. Graphs A and B (hiomass and production) from MANAGRAF (first scenario). The vertical (Y) axis is
in annual time steps. The horizontal (X) axis is a scale of 0 to 100% of the maximum biomass or production
values given in the table above the graph. Thus, each of the ten variables represented is plotted on its own
scale, with full scale value being equal to the maximum value for that variable. Where more than one variable
has the same print position an X is printed.
Inf Rep. NOR·X-328
63
GRAPH D
TREE.1
VARIABLE PLOTTED ********************* MAXIMUM **
. 5854 A
BARK LITTER FALL
(T/HA)
.5783 B
LIVE BRANCH DEATH
(T/HA)
.0001
C
DEAD BRANCH LITTER FALL
(T/HA)
2 8456 D
FOLIAGE LITTER FALL
(T/HA)
.0348 E
LARGE ROOT LITTER FALL
(T/HA)
.1053 F
MEDIUM ROOT LITTER FALL
(T/HA)
4 . 0334
G
SMALL ROOT LITTER FALL
(T/HA)
3 . 0466 H
TURNOVER ROOT BIOMASS
(T/HA)
,
. 4419
FRUIT LITTER FALL
(T/HA)
10 .-7584
TOTAL LITTER FALL
J
(T/HA)
TREE#l
GRAPH C
VARIABLE PLOTTED ********************* MAXIMUM * *
1198 5160 A
STEM DENSITY
(STEMS/HA)
NUMBER OF STEMS DYING
12 4580 B
(STEMS/HA)
MORTALITY RATE
.0166 C
( . 'II/TIME)
PROPORTION BIOMASS DYING
D
( . 'II/TIME) 324819 1000
TOTAL BIOMASS DYING
, :0111 E
(T/HA)
BASE MORTALITY RATE
'II/TIME)
;0024 F
SHADE MORTALITY RATE
. 'II /TIME)
.0165 G
LIGHT AT TOP OF SMALLEST
.9986 H
( . 'II )
, NOT USED
,
. 0000
J
J NOT USED
.0000
A
B
C
D
E
F
G
H
' -- ' -- ' --'-- ' -- ' -- ' -- ' -- ' -- ' -- '
X
C B
X
F
X
X
C B
F
C
X
X
B
F
C
X
HX
B
X
F
C
H A
B
X
F
C · B
B A
B A
X
F
C B
X CB
F
H
A
F
H
A
XC B
XCB
F
H
A
XCB
F
H
A
XCB
F
H
A
XCB
F
A H
F
H
X
A
F
X
A
H
X F
A H
X F
HA
XF
H A
XF
H
A
XF
A
H
XF
H
A
DFE X
H
A
DF
XB
H
A
E
DF
H E
A
X B
DF
H
A
E
X B
DF
H
A
E
X B
DF
A
X
E
H
DF
H
A
E X
DF
H A
BXE
DF
A
H BX E
X
A
H
H
X
A
H
X
A
H
X
A
X
H
A
X
H
A
X
A
H
X
A
H
B
X
A
X
A
H
X
A
H
X
A
H
H
X
A
H
X
A
H
X
A
"
STEP
1
2
,
3
5
6
7
a
9
1D
11
12
13
14
15
16
17
18
19
20
21
22
23
"
25
26
27
2B
29
30
31
32
"
33
35
36
37
"
3B
39
"
"
"
41
45
5"
A
B
C
D
E
F
G
H
,
J
100%
' --'-- ' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- '
X
D'
STEP
1
2
,
3
5
6
7
a
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
"
25
26
27
2B
29
3D
31
32
33
"
3S
36
37
3B
39
"
"
"
"
41
45
5D'
100%
X
X
X
XJX
XC JX
X D
JHG
X
D
JH G
XF'
D
X
G
X
XBFl
D
G
G
X BF
,
D
X
G
EA
X
,
D
H J
B
J
EA
F
B
,
D
G
,
G
FB
,
X
J
'X
F
J
X
B
xa
G
,
F
G
B H ID J
, X
H
BIDJ
G
X
F
,
H
G
X
'JD B
F
,
X
F
H
X
D
G B
,
X
FH
X
D G
B
,
G
X
D
HF
,
B
J
,
X G
HB
DJ
X
,
DJ F 'G
H
B
X
,
B
JDF
X
H
G'
,
,
X
H
X FD G
,
,
X H
J XGD
,
X BD
XE
,
J
,
X
H X
J GF
,
,
AE
F
J G
H
DB'
,
GJ
H
X
F
X
,
FD
'x BX I
H
,
XHB
XF
G
,
,
X X
G ,
XJ
,
xx
G ,
X
,
XH
FJDGI
,
B
X
X
EX
,
B
FJX
EX
,
B
X
XDG
,
B
X
XXG
,
B
]{A
IXG
,
B
XXDFJG
,
EXXFJ G
B
,
IHOXXG
B
,
, D xx
B
,
D
B
X
,
.
Figure 24. Graphs C and D (stand density and self-thinning; litter fall) from MANAGRAF (first scenario). The
vertical (Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 100% of the maximum
biomass or production values given in the table above the graph. Thus, each of the teri variables represented
is.plotted on its own scale, with full scale value being equal to the maximum value for that variable. Where
more than one variable has the same print position an X is printed.
64
In! Rep. NOR-X-328
GRAPH E
TREE#l
VARIABLE PLOTTED **"'''' * *************.*** MAXIMUM **
TREEU
A
A
A
B
B
GRAPH F
***** MAXIMUM **
VARIABLE PLOTTED ******** ********
2 2 7 . 3001 A
FOLIAGE NITROGEN
(KG/SA)
2 2 7 . 3001
B
PHOTOSYNTHETIC NITROGEN
(KG/SA)
C
C
BIOMASS GROWTH FROM PHOTO
(KG/SA)
D
D
E
BIOMASS GROWTH FROM SPROUT
TOTAL LIVE BIOMASS
(KG/SA)
B
CANOPy TOP HEIGHT
(M)
SMALLEST TREE HEIGHT
(M)
C
CANOPy BOTTOM HEIGHT
(M)
D·
CANOPy DEPTH
(M)
Ee
F
DEAD BRANCHES BOTTOM HEIGHT
(M)
LARGEST TREE
(KG STEMWOOD)
AVERAGE TREE
(KG STEMWOOD)
,
G
H
J
SMALLEST TREE
(KG STEMWOOD)
NOT USED
NOT USED
1
2
3
,
,
5
6
8
9
10
11
12
13
"
"
15
16
18
19
20
21
22
23
"
E
F
F
G
G
B
NOT. USED
I
I
NOT USED
J
NOT USED
X
X X
�DA
X
,
X
X B
5
6
X
B
X
B
X
X
DA
B
X
DA
B
XXF
8
9
10
11
12
13
DA
B
XX
D A
B
EXX
A
BD
A
E HGX
X
E H GX
DB
DB
E
H G CF
D B
0
DG
15
16
A
H GCF
H G CF
A
A
B
F
X
H DG F
H OG F
28
29
30
31
32
33
A
EC
"
"
35
36
"
"
"
"
..
"
38
39
X
B
A
B
A
B
O
o
o
B
A
B
G C
o E
OE
OE
OE
X
X
X
EO
EO
E O
B
H
A
B
F
F
CG
A
B
F
C G
A
B
A
F
C
G
H
B
A
FB
C
G
H
A
BF
A
•
C
G
B
F
A
•
G
C
B
F
A
•
C
G
H
C
H C
HC
G
G
F
XC
X C
GX
C
GEX
G EX
F
F
C
EX
G
XA
XA
G
G
F
C
F A
B
FA
X
F
C
B
G
C
EX
B
G
B
G
X
E A
E
G
F
A
C
A
E
F
A
F
C
E
B
AE C
B G
B
XX
G
B
B
B
B
B
E
G A
G
A
G
G A
E CF
XC
FCE
X F CE
B
A F X
B
AF C
B
B
C F
CF
A
G
F
C
E
A
A
E
G
F
C
X
B
B
F AX
G
A
B
B
B
F
XF
G
B
F
XC F
G
B
B
F
E C
A E C
A
G B
B
F
F
EC
A
G
F
C
A
CE
B
G
F
C
A
E
A
B
G
F
F
C
X
B
G
F
C
E
B
B
G
F
C
B
G
F
C
C
EX
B
G
F
' c
EA
B
G
,
,
,
,
,
,
,
,
,
,
,
,
,
F
C
B X
G
,
F
F
C
38
39
B
G B
"
"
"
"
..
"
I
J
F
X
35
36
A
B
H
F
"
A
B
F
E
G
100'
50%
X
"
A
B
F
X
H
28
29
30
31
32
33
A
B A
GC
H
o E
A
B
FC
EH
25
26
A
B
F C
o E H
"
A
X G
FC
HE
G
CF
X
G
C F
o
"
A
B
EC
GE
20
21
22
23
A
F E C
FE
C
HO G
X C
H O G
HD
G
E F C
HD
DR
19
A
B
H OG
"
18
XB
B DG
25
26
"
A
E
E
"
A
D
BD
EHGX
X
1
2
3
,
XX
�
STEP
D
F
'--'--'--'--'--'--'--'--'--'-- ,
0%
100'
50'
SHADE FOLIAGE RATIO
H
C
32415.2500
. 0000
171 8798
73 5463
.4908
. 0000
.0000
.0000
(T/SA)
LIMITING NUTRIENT SITE QUALITY
J
'-- -- -- -- --'-- -- -- -- -- '
0'
STEP
39.1035
39.1035
39.1035
39 . 1035
3 9 . 1035
8 0 8 . 5374
8 0 8 . 5374
8 0 8 . 5374
.0000
.0000
X C
F A C
F A C
F A
F A
E
G E
X
EG
E
C
E
C
E
G
G
G
Figure 25. Graphs E and F (tree sizes ,and canopy function) from MANAGRAF (first scenario). The vertical (Y)
axis is in annual time steps, The horizontal (X) axis is a scale of 0 to 100% of the maximum biomass or
production values given in the tableabove the graph, Thus. each of the ten variables represented is plotted
on its own scale, with full scale value being equal to the maximum value for that variable, Where more than
one variable has the same print position an X is printed,
In! Rep. NOR-X-328
65
NITROGEN
TREE#1
GRAPH G
** VARIABLE PLOTTED ********** *********** MAXIMUM w*
1 0 . 9629 A
STEMWOOD INTERNAL CYCLING
(KG/SA)
3 . 1282
B
BARK INTERNAL CYCLING
(KG/SA)
B
C
2 . 0082
BRANCH INTERNAL CYCLING
C
(KG/HA)
o FOLIAGE INTERNAL CYCLING
15. 5877
0
(KG/HA)
E
2 . 1279 E
LARGE ROOT INTER CYCLING
(KG/HAl
(KG/HAl
F
. 7837
F
MEDIUM ROOT INTER CYCLING
(KG/HAl
15.7126
G
G
SMALL ROOT INTER CYCLING
(KG/HAl
47 6362
H
H
TOTAL INTERNAL CYCLING
I
NOT USED
.0000
I
.0000
J NOT USED
J
A
STEP
I
100%
50%
0%
, -- -- -- -- -- , -- -- -- -- -- ,
TREE#1
STEP
1
X
1
2
3
4
5
,
7
,
9
10
11
12
13
14
15
"
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
"
35
36
"
38
39
"
41
42
"
"
"
X
2
3
4
5
,
7
,
9
10
11
12
13
U
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
"
35
36
"
38
39
"
41
42
"
"
"
X
X
XBX
XXH FG
XXDH
FG
EXBD H
H
I EXBD
I E XCD
I
I
I
FG
FG
X
H
EBA X
X A DC
X
A D
I
BE A D
I
B
I
GF
H
B
EA
H
G F
C
H
CH
F
G
X
D
E A D
S
E A
F
G
D
F
G
X
D
B
HC
G
E A D
a
XA
0
H C
G
o
C
F
G
XG EA
X
F
F
G
HC
E A D
B
F
G
HC
EA
S
C
F
G
F
F
F
F
F
B H XA
o
C
B HXG
o
CB
XG
D
F
X XH
D
F
BGXX
GBCX
D
.X
F
SXX
FX
G
FXX
G
FX XB H G
FX
AX
FX
A
X
CE
CE
X
C E
C
E
A
ax
XF
A
X F
A
X
X
X
HG
X X
F
F
SGX
XX
A
A
F
A
F
A
F
A
F
A
F
F
A
A
XHD
X D
BXD
X D
)tOG
axo
X BG
DB B G
GRAPH B
NITROGEN
** VARIABLE PLOTTED ********* ********* *** MAXIMUM " *
107.0061
(KG/HA)
A
TOTAL OEMAND
(KG/HA)
104,. 6352
TOTAL UPTAKE
B
(KG/HAl
47 . 6362
TOTAL INTERNAL CYCLING
C
(KG/HA)
4 . 3916
o
LOSS TO FOLIAR LEACHING
53,,2370
(KG/HA)
E
LOSS TO LITTER FALL
(KG/HA)
LOSS TO TREE MORTALITY
9 . 1306
F
G
TOTAL FIXATION
. 0000
(KG/HA)
. 0000
(KG/HAl
H
FOLIAGE AGING UPTAKE
"73 5463
I
SITE QUALITY
. 0000
J NOT USED
A
B
C
0
E
F
G
R
I
J
50%
0%
100%
' --'--' --' _
. - ' -- ' --' -- ' -- ' -- ' -- '
,
X F
,
X
XX
X
X
AB
FDX
F OX
ox
F
x
o X
o X
F
F
F
,
,
,
AB
o CE
OCE
,
,
,
X
XE
AB
COE
F
F
o
F
,
AB
C DE
C
X
F
,
,
AB
C DE
F
,
AB
AB
,
EC X
X
F
,
AB
C
XC
F
F
,
A S
xo
F
'
AB
CED
F
CED
,
AB I
ABI
FCE DAX
CFXX
S
F
F
F
F
F
F
F
F
F
F
F
F
F
,
AB
AB
F
F
,
X
F
F
,
AB
OX
F
'
,
,
,
,
,
,
,
,
AB
O X
F
I
,
,
x
OX
F
,
X
XC
o
o
o
o
o
o
o
o
o
F
o
o
o
o
o
o
E
E
B E
S
AE
S
A
A
A
A
,
X
S
,
A
,
SA
,
SA
S A
,
I
I
A C I
EA
X
S
S
CA
'
E
S
S
XX
IEAC
B D
S
A
X
A
C
S
0
,
"'
C
C
X
C
'"
C
, E
C
E
C
C
E
E
E
E
C
C
C
Figl\re 26. Graphs G and H (internal cycling and biogeochemical cycling) for nitrogen fromMANAGRAF (first
scenario). The vertical (Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 100% of
the maximum biomass or production values given in the table above the graph. Thus, each of the ten variables
represented is plotted on its own scale, with full scale value being equal to the maximum value for that
variable. Where more than one variable has the same print position an X is printed.
66
Inf Rep. NOR-X-328
DECOMPOSING LITTER
GRAPH I
VARIABLE PLOTTED ******** (T/HA) ******* MAXIMUM **
#01 TREE#l HEARTWOOD
1 1 7 . 3500 A
#02 TREE#2 HEARTWOOD
.0000 B
#03 TREE#l BARK
39 3899 0
#04 TREE#2 BARK
.0000
D
12 4040
E
#05 TREE#l SAPWOOD
F
.0000
#06 TREE#2 SAPWOOD
119 3592
G
1t07 TREEItl LARGE ROOTS
H
.0000
#08 TREElt2 LARGE ROOTS
31 4486
,
#09 TREElt1 BRANCHES
#10 TREElt2 BRANCHES
J
.0000
A
B
0
D
E
F
G
H
,
J
STEP
1
2
3
,
5
6
7
8
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
"
28
29
30
31
"
33
"
35
36
37
38
39
40
41
42
43
44
45
5"
100%
"
, -- -- -- -- -- , -- -- -- -- -- ,
E
X
,
E
ICGA
,
E
, X A
E
,
X
A
E
,
G 0
A
E
,
G 0
A
E
G
0
A
,
E
0
A
G
E
0
A
G
E
A
G
0
E
,
G
A
C
E
G
A
0
E ,
G
0
A
, G
A E
0
0
A E
, G
, G
0
A E
, G
0
AE
, G
0
AE
X
, G
0
0
E A
, G
0
G
X A
0
G
X A
0
G
E A
0
G
,
X
0
G
A E
0
,
G
A
E
E
0'
G
A
X
E
A
, G
,
E
G
0
A
,
,
0
E
'G
A
G
'0
A
E
,
G , 0
A
E
,
G ,
0
E
A
,
0
G'
A
E
,
X
0
E
A
,
X
0
E
A
,
C
A
E
X
,
0
A
'G
E
,
C
A
E
, >G
C A
E
, 'G
C A
E
,'G
OA
E
'G
,
OA
E
, 'G
E
X
'G
E
AO
>G
GRAPH J
DECOMPOSING LITTER
VARIABLE PLOTTED ********** (T/HA). ****� MAXIMUM **
.0000 A
#11 MOSS BROWN
B
#12 TREE#l MEDIUM ROOTS
11 5564
0
. 0000
#13 TREE#2 MEDIUM ROOTS
D
#14 TREE#l FOLIAGE
10 5657
E
.0000
#15 TREE#2 FOLIAGE
F
.0000
#16 PLANT#l SHOOTS
1t17 PLANT#2 SHOOTS
G
.0000
#18 MOSS GREEN
H
.0000
#19 ASH
,
.0000
#20 TREE#l SMALL ROOTS
22 1445
J
A
B
0
D
E
F
G
H
,
J
"
STEP
1
2
3
,
5
6
7
8
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
"
28
29
30
31
32
33
"
35
36
37
3.
39
40
41
42
43
44
45
5"
100%
' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- '
J X
,
B
X
,
DJ
B
,
X
B
,
B
DJ
,
B
DJ
,
B
D
J
,
B
D
J
,
D
J
B
,
D
J B
,
D
BJ
,
D B
J
,
BD
J
,
B
J
D
,
B
J
D
,
B
D
J
,
B
D
J
,
D
B
J
,
B
D
J
,
D
B
J
,
D
J
B
,
•
J
D
,
•
X
,
•
D J
,
•
D
J
,
•
D
J
,
•
D
J
,
D
J
B
,
D J
B
,
DJ
B
,
JD
B
,
D J
B
,
D
J
•
,
D
J
•
,
D
•
J
,
D
J
•
,
D
•
J
,
D
•
J
,
D
B
J
,
D
•
J
,
0
J
•
,
•
D
J
,
•
D
J
,
B
D
J
,
B
D
J
,
Figure 27. Graphs I and J (litter decomposition) from MANAGRAF (first scenario). The vertical. (Y) axis is in
annual time steps. The horizontal (Xl axis is a scale of 0 to 100% of the maximum biomass or production
values given in the table above the graph. Thus. each of the ten variables represented is. plotted on its own
scale, with full scale value being equal to the maximum value for that variable. Where more than one variable
has the same print position an X is printed.
In! Rep. NOR-X-328.
67
SOIL PARAMETERS
* * VARIABLE PLOTTED
• TO'l'AL HUMUS MASS
B NOT USED
C NOT USED
0 NOT USED
E NOT USED
NOT USED
F
G NOT USED
H NOT USED
,
NOT USED
J NOT USED
SOIL PARAMETERS
GRAPH M
* * VARIABLE PLOTTED *********************
MAXIMUM * *
TOTAL DECOMPOSING LITTER
(T/HA)
355.,795 4 A
B LIMITING NUTRIENT SITE QUALITY
60'� 0701 B
C
NOT USED
.0000 C
0 NOT USED
.0000 0
E
NOT USED
.0000 E
F
NOT USED
.-0000 F
G NOT USED
.0000 G
H NOT USED
.0000 H
, NOT USED
,
. 0000
J NOT USED
.0000 J
A
0>
STEP
,
2
3
,
,
6
,
8
9
"
U
"
"
"
"
"
"
"
"
20
"
22
23
"
25
26
"
28
29
30
"
32
33
"
35
36
"
38
39
"
"
"
"
"
"
100%
,0>
' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- '
B
•
,
B
•
,
B
.
,
AB
,.
,
,
,
,
,
,
,
,
,
,
, .
,
,
,
,
,
,
,
•
A
•
A
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
A
A
A
A
A
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
0>
STEP
,
2
3
,
,
6
,
8
9
"
U
12
"
"
"
"
"
"
"
20
"
22
23
"
2S
26
"
28
29
30
"
32
33
"
35
36
"
38
39
"
"
"
"
"
"
*********************
GRAPH N
MAXIMUM * *
39.6416
.0000
.0000
. 0000
.0000
.0000
.0000
.0000
.0000
.0000
(T/HA)
•
B
C
0
E
F
G
H
,
J
100%
,0>
' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- ' -- '
•
,
•
,
A
,
•
,
•
,
•
,
•
,
•
,
•
,
•
•
•
•
•
•
•
•
•
A
•
A
•
A
•
•
•
•
•
•
•
•
•
A
•
•
•
•
•
A
A
A
•
•
•
•
Figure 28. Graphs M and N (forest-floor mass and humus mass) from MANAGRAF (first scenario). The vertical
(Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 100% of the maximum biomass or
production values given in the table above the graph. Thus, each of the ten variables represented is plotted
on its own scale, with full scale value being equal to the maximum value for that variable. Where more than
one variable has the same print position an X is printed.
68
In! Rep. NOR-X-328
.
..
SOIL NUTRIENTS
NITROGEN
VARIABLE PLOTTED ********** ********** *
A TOTAL LITTER NITROGEN
(KG/HA)
B TOTAL RELEASE FROM LITTER
(KG/HA)
C TOTAL AVAILABLE IN SOIL
(KG/HA)
(KG/HA)
D TOTAL IN ANION FORM
E TOTAL IN CATION FORM
(KG/HA)
F TOTAL UPTAKE
(KG/HA)
G PROPORTION UPTAKE
(KG/HA)
H LEACHING LOSS
(KG/HA)
I NOT USED
J NOT USED
STEP
.
1
2
3
,
5
6
,
8
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
"
35
36
37
38
39
40
41
42
43
..
45
GRAPH 0
MAXIMUM **
956.3142 A
6 6 . 1281
B
6 5 1 . 4288
C
3 9 . 6010 D
612 . 7 6 9 9 E
1 0 4 . 6352
F
1 . 0000 · G
27.1185
H
.0000
I
.0000
J
0%
100,"
50.,"
' -- -- -- -- '-- '-- ' -- -- -- -- '
X
X
GF
H
B D
H
X
0
H
A
X
D
B
X
A
A B
� CE
G F
" D
F
B A
X
CE
0
F
A
0
B
DH CE
0" X
F
A
,0
B
F
B
OX
A
,0
FB
XX
A
' 0
B
F
X"O
A
G
I
B
X
X 0
G
I
B
F
0
A
XC
o
,
B
" X
o
A
o
,
o
B
AS
X F
o
,
X
o
" X X
,
"
X
o
F
o
,
o
BD
F
H
ECA
,
X
F
EC
A X
,
"
o
X
X
B
F
,
"
xc
o A
B
F
,
"
o EC
o
B
A F
,
"
ox
F
o
B
A
,
"
X
o
FB
0
A
,
"
EC
GO
B F
A
"
X
o
o A
F
B
o
"
X
F
B
A 0
"
"
"
"
"
"
"
"
"
"
H
"
"
"
"
"
"
"
"
EC
o
EC
o
o
o
o
EC o
X o
X 0
X 0
X
X
X
X
ECD
X 0
X 0
xc
XO
XO
X
X
X
o
A
A
A
A
A
A
F
A
F
A
F
a
A
a
A F
A F B
AF
B
X B
FAB
B
AF
F BA
F BA
Fa A
Fa A
B
B
F
B
B
F
F
B
a
F
a
a
o
F
O F
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
SOIL NUTRIENTS
GRAPH P
NITROGEN
VARIABLE PLOTTED ********************* MAXIMUM **
A
5 4 8 . 0637
TOTAL HUMUS NITROGEN
(KG/HA)
1 4 . 4091
B
TOTAL RELEASE FROM HUMUS
(KG/HA)
5 9 0 . 1288
C
(KG/HA)
NUTRIENTS HELD ON CEC
NUTRIENTS HELD ON AEC
1 0 . 0000
0
(KG/HA)
60.0701
E
DECOMP SITE QUALITY
2027 1850.
F
CATION EXCHANGE CAPACITY
(KG/HA)
ANION EXCHANGE CAPACITY
10 0000
G
(KG/HA)
60 0000
H
(KG/HA)
SOIL AEC + CEC
I FOREST FLOOR AEC + CEC
1778 9770
I
(KG/HA)
198 2079
J
J HUMUS AEC + CEC
(KG/HA)
A
B
C
o
E
F
G
H
STEP
1
2
. 3
,
5
6
,
8
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
"
35
36
37
38
39
40
41
42
43
..
45
50'0
0,"
100'0
'-- -- -- -- -- '-- '-- '-- -- --'
C
E
X
B
BE
X X
C E IX
XX
IX C
BXX
IF E
CXBX
IF
E
XCX
E
IF
X X
I F
E X X·
E X X X
1 F
EXB X
1 F
X
F
1
C X
X
B XE
C
1 F
B
X E
X
1
F
C
C
B
X
E
X
1 F
1
F C
B
AJ
E· X
1 CF
B
X F
H
F
B
X
E X
C 1
.I F
B
X
E X
C
1
F
B
X
E X
C
,
1 F
B
X
E X
C
,
1
F
B
C
,
1
F
B
C
,
F
H
X
EX
.1
C
,
F
1
XD
EX
B
C
,
X
c
F
1
X
B 0
,
X
X F
X
, C
B
X
co
1
F
X
B
AJ
X
1
F
X
B
AJ
X
1
F
X
B
X
X
1 F
X
B
X
X
1 F
X
B
X
X
1
F
X
B
X
X
EX
B
1
F
X
X
EX
B
1
F
X
a
X
EX
1
F
X
a
X
E X
F
1
X
a
X
E X
1 F
X
a
X
E X
F
1
X
1
F .
a
X
E X
X
F
a
X
E
X
1
X
1
F
a
X
E
X
X
1
F
a
X
E
X
X
1
F
a
X
E
X
X
1
F
a
AJ
E
X
C
/
�
� � :::
��
.
..
Figure 29. Graphs 0 and P (soil nutrient variables) for nitrogen from MANAGRAF (first scenario). The vertical
(Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 100% of the maximum biomass or
production values given in the table above the graph. Thus, each of the ten variables represented is plotted
on its own scale, with full scale value being equal to the maximum value for .that variable. Where more than
one variable has the same print position an X is printed.
In! Rep. NOR-X-328
69
dramatically following the heavy thinning, but declines
as the canopy recovers, The trends in both shade-induced
mortality and the light at the top of the smallest live
canopy tree reflect the use of the "opaque blanket"
representiltion of canopy architecture in FORCYTF I I .
The full range of light changes accompanying thinning
in real Stands is underestimated.
Figure 25 shows the height growth and stem size
variables ofthe simulated stand (Graph E), and a series
.of canopy function variables (Graph F). Top height (Vari­
able A" Graph E) is unaffected by the PCT and CT, but
the height of the smallest live tree shows an increase: as
a result of both thinnings. Both the height of the canopy
bottom (Variable C) and the live canopy depth (Variable
D) reflect the pruning in year 20. The height of the lowest
dead branch (Variable E) is also raised by the pruning.
The pruning actually removed all dead branches, but
branch death (Variable B, Graph D, Fig. 22) continues,
giving a value for dead branches at the new height. The
height of the lowest dead branch on the stem remains
constant thereafter as a n�;sult of the input data on the
duration of dead branches.
The stem biomass of the largest tree (Variable F)
responds to the heavy thinning in year 30 by increasing
at a more rapid rate, reflecting the release of the large
canopy trees from both root and lower canopy co�peti­
tion. The average and smallest trees show a sudden
increase at year 30, re:Qecting the removal of smaller
trees. There is an error in the graphical routine that
produced output for Variables F, G, and H in Graph E, in
the year of pruning (year 20) and Variable H in the year
of thinning (year 30). Note that unlike most of the other
graphs, several of the variables in Graph E (e.g., tree
heights) share the same horizontal scale. This aIlows
comparisons am-cng variables. These variables are" ap­
parent on the graph because they share the same maxi­
mum value, and they do not reach 100% on ihe horizontal
scale.
Variable A, Graph F, shows the effect of PCT, prun­
ing, and CT on total foliage N. Variable B, the equivalent
amount of fuIly illuminated foliage N, shows a propor­
tionately smaller response to these treatments, reflecting
the phenomena of self-shading and shade-adapted foli­
age. Thinning reduces shade-corrected foliage N values
less than total foliage N, while pruning of 45% ofthe total
foliage from the lower, deeply shaded part of the canopy
reduced the shade-corrected foliage N by only about
20%.
Variable F, Graph F, shows the limiting site quality
for species I . This is the site quality as set by the nutrient
that is in most limiting abundance (N in this case, since
70
it was the only nutrient simulated to be limiting growth).
It increases up to year 24, reflecting the dynamics oftotal
available site N (Variable C, Graph 0, Fig. 29). It
declines after year 28 until year 44, when it levels off at
slightly below the starting value. The temporal pattern of
variation in site quality is influenced by th¢ site-quality
change damping coefficient and by the speed of response
of tree growth to changing soil nutrient availability. This
response is determined by the user via a response
damping coefficient in MANADATA.
The final variable of interest in Graph F, Figure 25,
is the ratio of shade foliage to sun foliage (Variable G).
This is very low until the canopy approaches closure in
the 16- to 20-year period. It declines when much of the
shade foliage is pruned off in year 20, increases as the_
canopy continues to develop, and declines again as the
light intensity low in the canopy increases following the
heavy thinning in year 30. Thinning leads to the tempo­
rary production of more sun foliage than shade foliage
lower in the canopy, the extent of the change in type of
foliage produced being determined by the degree of
change in light intensity in the lower canopy.
Figure 26 presents several nutrient cycling vari.
abIes. Graph G shows internal cycling variables for N.
'
There is a general increase in the importance Of internal
cycling as the stand ages, the totals per-hectare reflecting
the effects of the PCT, pruning, and CT on the amount of
biomass in which internal cycling can occur. Variables
A and B, Graph H, show the total N required to support
the predicted new growth (total demand), and the actual
uptake, respectively. Note that these two variables have
similar values up to the age of 27. The drop in year 27
coincides with the drop to near-zero values in the supply
of cationic N available on the CEC. The failure ofthe site
to provide the level of uptake required to support the
expected level of growth for the current site quality is
associated with the onset of the decline in site qUality.
Note also the change in relative importance of
uptake (B) and internal cycling (C) as the stand ages. The
decline in loss to foliar leaching (D) after year 26
parallels the decline in site quality (I). As site quality
declines, the concentration of foliage nutrients declines
and (in real life) so does foliar leaching. In the model,
this decline is the result of the input data that define lower
foliar leaching rates on more nutrient-poor sites.
Figure 27 (Graphs I and J) shows the mass of the
various components of the forest floor. Maximum values
of heartwood (A) occur at the start of the run due to the
heavy slash accumulation from the logging of an old­
growth stand as defined in the ECOSTATE setup run.
Little heartwood is added over the run; there is almost no
In! Rep. NOR�X-328
heartwood in the stems cut in the PCT, and most of the
cut stemwood in the CT is removed. There is a small
addition due to the stand self-thinning in years 23-30.
Sapwood and branch values (E and I) reflect the same
pattern of events, with most of the PCT material and the
slash from the CT being sapwood and branches. The
effect of the heavy pruning on the biomass of branches
on the forest floor is obvious. Difference's in the slope of
the lines of different categories of decomposing material
reflect different rates of weight leiss due to decomposi­
tion, different rates of addition due to ephemeral litter
fall, tree mortality, and thinning slash, and the fact that
the different variables have different X-axis scale values.
Figure 28 shows the total mass of decomposing litter
(Variable A, Graph M) and the total humus mass (Vari­
able A, Graph N). Clearly, the mass of forest floor
declines markedly over the 4S-year rotation due to the
loss of much of the large woody material present after
the logging of the old-growth stand, and the lack of inputs
of heartwood by litter fall due to the PCT and CT. The
rather rapid decline in forest-floor mass may reflect
unrealistically high input data on log decomposition
rates. Average heartwood decomposition rates are used
in the setup input file, irrespective of log size, whereas
in reality rates for larger logs are probably lower than for
smaller logs. The loss of humus is much less rapid than
the loss of forest floor, emphasizing the importance .of
the humus in the forest floor and mineral soil in
maintaining long-term site productivity.
generally possible to discover from the graphs which
variables are overlain.
Soil leaching of N (Variable H,. Graph 0) shows the
same pattern of change as does anionic N (Variable D,
Graph 0) until about year 8, when increasing uptake (F)
and increasing proportion of annual mineralized N that
is taken up (0) reduce the amount of N that is either not
taken up or is not held on exchange sites. Note that total.
litter N (Variable A, Graph 0) does not show as marked
a decline as total forest-floor mass (Variable A, Graph M,
Fig. 28). This reflects the change from an old-growth
forest floor dominated by high C/N ratio of woody
material to a second-growth forest floor dominated by
decomposing foliage, branches, and small roots­
materials that have higher concentrations of N than stem
heartwood and large roots.
Variables F and I in Graph P show the decline in site
cation exchange capacity as the mass of the forest floor
declines. The representation Of the site's exchange capac­
ity is probably more accuratdor a poor site (which would
normally have a clearly separate ectorganic layer) than
for a rich site (which would normally have only a thin
ectorganic layer, and a thicker Ah horizon) because
FORCYTE-II does not represent the deposition of
organic matter in the mineral soil by root death, nor the
mixing of surface litter down into the mineral soil. The
decline in quantity of N held on the CEC and ABC
reflects both the decline in CEC and the competition for
mineralized N between tree roots and the exchange sites.
Leaching of nitrate N (Variable H, Graph 0) ceases in
about thc same year that nitrate-N held on the AEC
(Variable D, Graph P) begins to drop (year 17 or \8),
indicating that all available nitrate-N is either being taken
up or is held on the AEC.
Variable B, Graph M, shows the limiting nutrient site
quality for soil processes. The site-quality variables
already discussed (F in Graph F, Fig. 25, I in Graph H,
Fig. 26) describe the site quality for tree growth. The site
quality shown in Graph M is the site quality for soil
processes. It is assumed in FORCYTE-l 1 that tree
growth and tree reSource allocation strategies respond
fairly quickly to variations in soil nutrient availability to
the tree, but that soil processes respond more slowly to
such changes. Changes in decomposition rates, for
example, must await changes in substrate quality and
quantity, and perhaps changes in soil flora and fauna.
Variable B in Graph M is calculated from the tree growth
nutritional site quality, but with a lag to reflect the greater
inertia of soil processes to change. A comparison of
Variable B, Graph M, with Variable F, Graph F, will show
a similar pattern of change, but with a smaller change in
Graph M. The assumption about the inertia of soil site
quality change can easily be altered by a change in the
appropriate coefficient in the input file.
tion only-it is doubtful that such a third-rotation severe
bum would ever be carried out as a management treat­
ment. Figures 30-36 present a selection ofMANAGRAF
output for Scenario 2. The discussion is not as detailed
in Scenario 1 ; it is restricted to pointing out a few key
interpretations of the slash burning effects.
Figure 29 presents a variety of soil variables. Several
of these have the same temporal patterns of change and
are therefore printed out in the X line; however, it is
Graph A, Figure 30, shows that there was an increase
in stem growth from Rotation I to Rotation 2. This
reflects a higher rate of release ofN from the decomposing
Inf. Rep.NOR-X-328
The MANAGRAF Output .. From the Second
Scenario
The objective of the second scenario is to demon­
strate the effect of a simulated slash bum. The scenario
involves three IS-year rotations, without thinnings.
There is no slash burning at the start of the first and
second rotations, but a very severe burn at the start of the
third rotation. This scenario is for purposes of demonstra­
71
TREE#l
VARIABLE PLOTTED
A
*********************
STEMWOOD BIOMASS
BARK BIOMASS
B
D
E
F
'·LARGE
(T/llA)
FOLIAGE BIOW\.SS
ROOT BIOMAS S :
(T/flA)
(T/flA)
MEDIUM ROOT. BIOMASS
G : ' SMALL 'RooT ' BIOMASS
H
DEAD BRANCHES BIOMASS
(T/flA)
,
TURNOVER ROOT BIOMASS
(T/flA)
J
FRUIT BIOMASS
(T/flA)
STEP
1
2
3
,
5
6
,
B
9
10
11
12
13
"
15
16
"
18
19
20
21
22
23
24
25
26
"
28
29
30
31
32
33
"
35
36
"
38
39
4'0'
"
"
"
..
"
"
'-- '
X
--
64.2936
, 0557
1 6 0127
12 2125
13 9252
6 2218
3 7437
.8891
.7110
.2465
(T/llA)
C .. BRANCH BIOMASS
'--'-- '
(T/flA)
(T/flA)
**
A
B
A
B
C
C
D
E
XCA _ x
,
G
FOLIAGE PRODUCTION
(T/flA/TlME)
LARGE ROOT PRODUCTION
(T/flA/TlME)
F
F
MEDIUM ROOT PRODUCTION
(T/flA/TlME)
G
G
(T/flA/TlME)
H
H
SMALL RooT PRODUCTION
FRUIT PRODUCTION
(T/flA/TlME)
,
,
TOTAL TREE PRODUCTION
(T/flA/TlME)
100%
50'
. - '-- ' -- ' -- '
-'-- ' _
-
XD
H
SFD
X X
B X
G .
XCA
H
,
G
G
X AC B FD
XACB
'H
,
,
,
,
,
H
,
G
FD
G
EAX C FD
H
H
,
G
E XJ XD
B
,
G
XAXDC
G
X
X
XB
XXB
XXXX
XEX XX
X X
HJ XA
H
J X
BX IG
JX
H
'H
,
,
,
,
,
IG
BDF
I G
BDF
X
H
B
XX
H
, G
X
XJ
B
H
H
I
G
I
XXB X
X
G
XCX X
B
G
,
X
X
X
XB
XXB
XXXB
BJX XX
H JXA
H
XX
JXA
H
JCX
BX I G
BDF I
XX
'H
,
,
,
,
,
,
XIG
H
H
X
J
X
J
G
B X
, G
X
GXD F
G
IXCOB
HG
J
J
F
DX CB
G
D
GD
F
XXB
F
XX I F
H
J
STEP
1
2
3
,
5
6
,
B
9
10
;11
12
13
"
15
16
"
18
19
20
21
22
23
24
25
26
"
2B
29
30
31
32
33
"
35
36
"
"
39
"
U
42
43
44
45
. TOTAL TREE BIOMASS
**
1 2 . 3430
1 . 4322
3 . 0157
3 . 1735
2 . 6541
.8189
5 5150
.2776
28 8397
125 8686
(T/llA/TlME)
E
I
HJXX
BARK PRODUCTION
D
J
GRAPH B
MAXIMUM
(T/llA/TlME)
(T/flA/TlME)
X
.G
*********************
SAPWOOD PRODUCTION
BRANCH PRODUCTION
XX,
XFXG I
XX X
TREEjj1
VARIABLE PLOTTED
GRAPH A
MAXIMUM
(T/llA)
(T/flA)
A
B
C
D
E
F
G
H
,
J
5"
"
100%
-- -- -- -- , -- -- -- -- -- ,
,
-X B
XXX F
XJXIXD
F
HEXAI XG
F
H EXA IB 0 G
J X
,H
,
,
,
,
,
,
,
H
F
X D
J
H
F
G
X XD
J
BX
H
B
J
F
G
X
F
G
G
AE XC
JB
H
H
F
AE 10 C
G
BJ
AE X
H B J
F
X
AEX
BH
F
CG F
X£OI XG
FCBXEX
J
G
XX B
XX B
XXX B F
HJ XX X
H
,
F.
F
JXX' X
F
JCX IG BD
'H
H
,
,
,
,
,
,
,
F
J XE IBGD
J
AX I X
H
H
F
SAXI DG
AEICOG
J
B
J
H
H
JB
F
F
F
AE' XG
,
H B J
AEIDG C
X
F
J AEXGC
BH
F C
J
F
XXX
X
XX B
XX B
XEX B F
BJGCX DB
H
,
,
,
,
,
,
,
,
,
F
JXXI DB
'H
J XEX
H
F
BD
F
J X EX B D
J
H
XX GD
H
XXI X
J
J
H
H
H B
HB
B
F
F
F
F
AEICOG
X lOX G
F
B
J
X DI
XF
X
X
J
CFG
HB AE
X
HBX
FlX
X
G
J
G
J
Figure 30. Graphs A and B (biomass and production) from MANAGRAF (second scenario). The vertical (Y) axis
is in annual time steps. The horizontal (X) axis is a scale of 0 to 100% of the maximum biomass or production
values given in the table above the graph. Thus, each of the ten variables represented is plotted on its own
scale, with full scale value being equal to the maximum value for that variable. Where more than one variable
has the same print position an X is printed.
72
Inf Rep. NOR-X-328
GRAPH D
TREE#l
VARIABLE PLOTTED *******1"************* MAXIMUM **
.0661 A
BARK LITTERFALL
(T/HA)
.3191 B
(T/HA)
B LIVE BRANCH DEATH
C DEAD BRANCH LITTER FALL
.0000 C
(T/HA)
1 8607 D
D FOLIAGE LITTER FALL
(T/HA)
E " LARGE ROOT LITTER FALL
.0035 E
(T/HA)
F MEDIUM ROOT LITTER FALL
.0369 F
(T/HA)
3 . 1642 G
G SMALL ROOT LITTER FALL
(T/HA)
1. 4543
H
(T/BA)
H TURNOVER ROOT BIOMASS
(T/HA)
.2465
I
I FRUIT LITTER FALL
(T/HA)
7127
J
J TOTAL LITTER FALL
GRAPH C
TREE#l
VARIABLE PLOTTED *********** ********** MAXIMUM **
STEM DENSITY
(STEMS/HA)
1198 5160 A
B NUMBER OF STEMS DYING
(STEMS/HA)
48 7659 B
C MORTALITY RATE
.0424 C
( . %/TIME)
D PROPORTION BIOMASS DYING
( . %/TIME) 324819 1000 D
(T/HA)
E TOTAL BIOMASS DYING
.1112 E
F BASE MORTALITY RATE
%/TIME)
.0024 F
%/TIME)
G SHADE MORTALITY RATE
.0000 G
H LIGHT AT TOP OF SMALLEST
(.%)
. 9988 H
I NOT USED
.0000
I
J NOT USED
.0000 J
A
0%
STEP
1
2
3
•
5
6
7
,
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
50%
X
X
F
F
F
F
F
F
F
E
F
F
F
"
X
F E
"
"
=
F
H A
B A
" A
•
A
B
A
"
A
•
A
A
A
A
H
X
X
F
F
E
F
eB
E
F
X
HA
B A
F
F
F
F
F
E
"
"
0
0
35
36
37
38
39
40
"
"
"
"
"
DX
DXE
X E
F
X E
F
X E
F
X E
E
F
X
E
F
X
E
F
X
E F
X
EF
X
"
B
"
"
F
F
E
AH
AX
X
F
F
E F
FE
F
E
0%
100%
-- -- -- --�-- -- -- -- --�
6B
XB
XCB
X X
XX
OX
XE
XE
X E
X E
X
E
E
X
X
X
X
CB
XB
0
0
DX
DXE
X E
X E
X E
X E
E
X
X
X
X
X
CB
XB
A
"
"
F
E
F
F
"
A
A
A
A
A
A
A
X
X
CAR"
A FHX
A "
AH
X
F
"
"
"
"
" A
"
A
A
"
"
A
A
A
A
A
STEP
1
2
3
•
5
6
7
,
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
"
35
36
37
38
39
40
"
"
"
"
"
50%
'
100%
'
�-- -- -- -- -- -- -- -- -- -X
X
=
X B
XAD
JG H
XFA D
JG
XIF A D
IX X A D
XA
I BE
X
"
J G
"
J G
"
"
J G
o
X
J G
"
,
XA
o
J G
o
B
EB
,
Xl
AF
1 0
J G B
,
E B
E
BAF
10
J G "
,
E A F B X
JG
,
X
X
X
XX
XXJX.
X X JX.
XIF AD JX.
IBEIF A D
JHG
J "G
I S E X A o
J" G
I
B E
XA o
XA
o
J" G
I
B
E
X G
E
FA!
o
B
,
B E
X 1 o
,
HJ G
,
X X H JG
B E
"
,
X
X
X
X
"
X
XXX
XIX
SEX
IBE
I B
I
,
,
,
,
,
XG
AD XG
X AD JBG
E IF A D
JH G
X G
B
E
X A
XA o
E
JH G
B
FXX o
1 B E
G HXE
1
X
J "
G
1
D EB AE
J
G
0"
1
O
E XF
Figure 31. Graphs C and D (stand density and self-thinning; litter fall) from MANAGRAF (second scenario).
The vertical (Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 1 00% of the maximum
biomass or production values given in the table above the graph. Thus, each of the ten variables represented
is plotted on its own scale, with full scale value being equal to the maximum value for that variable. Where
more than one variable has the same print position an X is printed.
In! Rep. NOR-X-328
73
GRAPH E
TREE#l
** VARIABLE PLOTTED ********** * ��******** MAXIMUM **
14.0726 A
A CANOPy TOP HEIGHT
(M)
1 4 . 0726 B
B
SMALLEST TREE HEIGHT
(M)
1 4 . 0726 C
(M)
C CANOPY BOTTOM HEIGHT
D
CANOPY DEPTH
1 4 . 0726 D
(M)
1
4 . 0726
E
E
DEAD BRANCHES BOTTOM HEIGHT
(M)
8 3 . 7574
F
(KG STEMWOOD)
F LARGEST TREE
83.
7574
G
(KG
STEMWOOD)
G AVERAGE TREE
83.7574
H
H SMALLEST TREE
(KG STEMWOOD)
. 0000
I
I
NOT USED
.0000
J
J NOT USED
,
"'"
O'lo
STEP
1
2
3
4
5
6
,
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
"
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
-- --
--
50'lo
,
-- --
X B X
X B
X
X
B
x
x
B
XF
B
x
XGF
X G F
B
X
DA
XH G
F
B
D A
F
B
ECH G
G
E CH
D
F
B
G
F B
E
CR
X
E
C8
G
E
G
B
C
H
G
E
C
H
X
X BX
X B X
X B
X
X
XF B
X
XGF B
X G F
B
DA
B
DA
XH G
F
F B
DA
X H
G
D A
G
EC H
F B
G
BF
D
E C H
F
H
B
E
C
G
E
GB
H
C
E
B G
C
H
B
C
E
H
X
X BX
X B X
X
B X
XF B
X
X
XGF B
B
X G F
DA
DA
XH
G F
B
DA
X H
G
F B
D A
EC H
G
X
G
H
B F
D
E C
E
C
F
H
B
G
E
H
G
B
F
C
B
E
G
C H
G
C H
E
B
E
G
X
--
--
--
A
D
A
D
A
F
D
B
D F
F
A
D
100'lo
,
--
A
A
A
X
A
D
GD
A
D
X
A
F
F
A
A
D
F D
B
FD
D
F
A
A
A
GRAPH F
TREE#l
** VARIABLE PLOTTED ******** ******** ***** MAXIMUM **
A FOLIAGE NITROGEN
169.7081
A
(KG/HA)
169.7081
B
B PHOTOSYNTHETIC NITROGEN
(KG/HA)
C
28839. 6700
C
(KG/HA)
BIOMASS GROWTH FROM PHOTO
D
o BIOMASS GROWTH FROM SPROUT
.0000
(KG/HA)
E
E TOTAL LIVE BIOMASS
109 4639
(T/HA)
F
F LIMITING NUTRIENT SITE QUALITY
7 4 . 5894
G
.1217
G
SHADE FOLIAGE RATIO
H
.0000
H NOT USED
I
. 0000
I
NOT USED
J
. 0000
J
NOT USED
�--
'
50%
0%
STEP
1
2
3
--
�
-- -- -- -- -- --
--
'
100'lo
--
F
X
F
XC
• "" C
F
F
5
GXX C
F
G X
C
6
,
F
G
C
XA
F
C
BX
8 . G
F
xx
C
9. G
C
F
G
B AE
1D
F
G
B A E
C
11
A E
C
F
G
B
12
B
F
G
C
13
A E
E
B
A
C F
G
14
,
B
A
E
C
F
G
15
,
F
X
16
F
X
"
F
18
X C
F
19
XE C
F
20
G XE C
F
21
G
C
X
F
C
X
22
G
F
XA
G
C
23
F
C
BX
G
24
F
B EA
C
G
25
F
EA
C
26
G
B
F
G
B
EA
c
"
F
X
C
B
G
28
,
X
X
G B
29
,
X
B
30
,
F
X
31
F
32
XC
F
X C
33
F
34
GX
C
F
G XX C
35
F
G
36
X
C
F
G
C
37
X
F
G
38
XA
C
F
C
G
BEA
39
F
B EA
G
C
4D
F
C
A
BE
G
41
F
C
G
BE
42
A
X
B
E A
43
G
F C
44
G
B
X
F C
G
B
45
A E
Figure 32. Graphs E and F (tree sizes and canopy function) from MANAGRAF (second scenario). The vertical
(Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 1 00% of the maximum biomass or
production values given in the table above the graph. Thus, each of the ten variaples represented is plotted
on its own scale, with full scale value being equal to the maximum value for that variable, Where more than
one variable has the same print position an X is printed.
74
In! Rep. NOR-X-328
TREE#l
GRAPH G
NITROGEN
VARIABLE PLOTTED ********** ********** * MAXIMlJM * *
STEMWooD INTERNAL CYCLING
(KG/HA)
5 . 4533 A
(KG/HA)
1 . 1460 S
BARK INTERNAL CYCLING
BRANca INTERNAL CYCLING
(KG/HA)
1 . 7600 C
(KG/HA)
FOLIAGE INTERNAL CYCLING
9 . 3404 D
(KG/HA)
1 . 1407 E
LARGE ROOT INTER CYCLING
(KG/HA)
MEDIUM ROOT INTER CYCLING
.9432 F
SMALL ROOT INTER CYCLING
(KG/HA)
12 8029 G
(KG/HA)
TOTAL INTERNAL CYCLING
3 2 . 5865 H
I
NOT USED
.0000
NOT USED
.0000 J
A
B
C
D
E
F
G
H
I
J
0%
STEP
1
2
3
•
5
,
,
8
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
"
35
36
"
38
39
"
"
42
<3
..
45
100%
50%
'
'
'
'
'
'
'
'
'
'
'
X-- -- -- -- -- -- -- -- -- -X
X
xx
XXXG
XADXB G
G
X A DX
A DBX
G
IX
G
X A DS X
I
EC
A
DB
X
G
I
X
A DS X
G
I
G
EC
ADB
X
I
EC
FH
AX
,
X
x
EC
,
EC
,
X
X
XS
XGS
EXHXB
I XXHGX
XAD H X
I
XA DH B X
I
CX D
X F G
I
CEA D BH F
G
I
CX D B H F
G
I
CX DB H F
I
X X
,
G
XAFH
G
HF
,
,
x
x
XB
XX B
'XXFB
I EXHGFB
xc HGBF
I
CX DH BGF
I·
cx o HB x
,
XE 0
X
,
XE
,
,
,
,
,
FG
G
X H F
X a F
xc
X AC B H F
EG X B llF
E G
X B X
o
G
G
G
XXDHF G
o
o
o
x
GRAPH H
NITROGEN
TREE#!
** VARIABLE PLOTTED * * * * * * * * * ***_********* MAXIMUM **
103. 1735
A
TOTAL DEMAND
(KG/SA)
103.1735
(KG/HA)
B
TOTAL UPTAKE
32.5865
C TOTAL INTERNAL CYCLING
(KG/HA)
3 . 3729
(KG/HA)
o LOSS TO FOLIAR LEACHING
3 8 . 1220
(KG/HA)
E LOSS TO- LITTERFALL
.5831
F LOSS TO TREE MORTALITY
(KG/HA)
.0000
(KG/HA)
G TOTAL FIXATION
-.0000
(KG/HA)
a FOLIAGE AGING UPTAKE
74 5894
I
SITE QUALITY
.0000
J NOT USED
STEP
1
2
3
•
5
,
,
8
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
J
100'0
50%
0%
A
B
C
D
E
F
G
H
I
�-- ' -- ' -- ' -- -- -- --i-- F---'
X
xx
x x
XEC X
F ox
x
F 0 X
x
F
x
o EC
o
EC
'F
o
EC
'F
o
, F
F
,
F
,
F
,
F
,
x
xx
x X F
X
X
F
FEX
x
IF EX
x
IF
EX
x
IF
EX
IF
XC
I F
F
I
F
I
F
I
F
I
F
r
xx
xx
X X
F
xc
X
FEX
x
x
I F EX
EX
x
IF
IF
ECO
EX
IF
I F
F
o
I
o
I
F
8
F
o B
I
D
B
F
I
D
B
I F
'
'
'
'
'
,
,
x
EC
o
,
,
,
x
EC
o
x
XC
,
x
DEC
x
EC
o
x
DEC
x
X
o
x
x
x
ox
,
,
,
x
,
x
,
1F
,
,
,
,
,
x
X
F
x
EX
B
x
x
A
E C
A
C
E
E
C
Al
C
x
E
C A ,
E
,
,
,
,
,
,
,
,
x ,
1X
X
X
,
,
,
,
,
,
,
,
,
,
Figure 33. Graphs G and H (internal cycling and biogeochemical cycling) for nitrogen from MANAGRAF
(second scenario). The vertical (Y) axis is in annual time steps. The horizontal (Xl. axis is a scale of 0 to
100% of the maximum biomass or production values given in the table above the graph. Thus, each of the
ten variables represented is plotted on its own scale, with full scale.value being equal to the maximum value
for that variable. Where more than one variable has the same print position an X is printed.
In! Rep. NOR·X·328
75
GRAPH I
DECOMPOSING LITTER
VARIABLE PLOTTED ********{T/RA) ******* MAXIMUM **
1 1 7 . 3500 A
#01 TREE#1 HEARTWOOD
B
. 0000
#02 TREE#2 HEARTWOOD
39 3899 C
#03 TREE#1·8ARK
.0000 D
#04 TREE#2 8ARK
E
15 1247
#05 TREE#1 SAPWOOD
F
.0000
#06 TREE#2 SAPWOOD
119
G
3592
#07 TREE#1 LARGE ROOTS
H
.0000
#08 TREE#2 LARGE ROOTS
I
31
4486
#09 TREE#1 BRANCHES
J
. 0000
#10 TREE#2 BRANCHES
A
B
C
D
E
F
G
H
I
J
STEP
1
2
3
4
5
6
,
,
8
10
11
12
13
14
15
16
"
18
19
20
21
22
23
24
25
26
"
28
29
30
31
32
33
"
35
36
"
38
39
"
"
"
"
..
.,
"
:
'
__ __
' __�__' __ ' __ ' __ ' __ ' __ ' __�
5"
100%
ICGA
I X
A
I
X
A
I
G C
A
G C
I
A
E
G
C
I
A
E
I
C
G
A
E
A
G
C
E
A
G
E
C
A
E
I
G
C
A
C
E
G
A
C
E
I
G
A
C
E I
G
X
A
C
G
E
C
A
G
E
A
C
G
E
I
A
C
G
A
E
C
G
E
G
A
C I
E
G
A
CI
E
A
G
IC
E
G
I C
A
A
E
G
C
I
A
E
G
C
I
A E
G
C
A E
C
G I
AE
C
GI
EA
GI
C
EA
C
IGI
XC X
XC X
XCX
XCX
XCX
XCX
>O<A
XEA
I
I
I
I
I
XX
XX
XX
XX
XA
XA
XA
E
E
l
GRAPH J
DECOMPOSING LITTER
VARIABLE PLOTTED ********** (T/RA) ***** MAXIMUM **
. 0000
A
#11 MOSS 8ROWN
1 1 . 5564
B
#12 TREE#1 MEDIUM ROOTS
.0000
C
#13 TREE#2 MEDIUM ROOTS
1 0 . 6502
D
#14 TREE#1 FOLIAGE
.0000
E
#15 TREE#2 FOLIAGE
.0000
F
#16 PLANT#1 SHOOTS
.0000
G
#17 PLANT#2 SHOOTS
.0000
H
U8 MOSS GREEN
. 3035
I
#19 ASH
2 1 4307
J
#20 TREE#l SMALL ROOTS
A
B
C
D
E
F
G
H
I
J
STEP
1
2
3
4
5
6
,
,
8
10
11
12
13
14
15
16
"
18
19
20
21
22
23
24
25
26
"
28
29
30
31
32
33
"
35
36
"
38
39
"
"
..
42
43
45
5"
100%
"
1 __ 1 __ 1 __ 1 __ 1 __ 1 __ 1 __ 1 __ 1 __ 1 __ 1
X
I
B
D J
I
B
D J
I
B
I
D J
B
D J
I
B
D J
I
B
D
J
I
J
B
I
D
J
B
I
D
J B
I
D
B J
I
D
I
D B
J
J
I
X
D
J
I
B
J
.D
I
B
B
D
I
J
D
I
JB
D
B
I
J
J
I
D B
D
I
J
B
I
J D
B
B
I
X
I
DJ
B
I
D
X
DB
J
I
BD
I
J
I
B
D
J
I
B
D
J
I
B
D
J
J
I B
D
BX
B
X
X ,
X
XJ
XCJ
XC
J
X D
J
X
D
J
J
X
D
X
D
J
X
D
J
D
X
J
D
J
X
Figure 34. Graphs I and J (litter decomposition) from MANAGRAF (second scenario). The vertical (Y) axis is in
annual time steps. The horizontal (X) axis is a scale of 0 to 100% of the maximum biomass or production
values given in the table above the graph. Thus. each of the ten variables represented is plotted on its own
scale, with full scale value being equal to the maximum value for that variable. Where more than one variable
has the sarne print position an X is printed.
76
In! Rep. NOR·X·328
SOIL PARAMETERS
**
VARIABLE PLOTTED
*********************
A
B
TOTAL DECOMPOSING LITTER
C
NOT USED
D
NOT USED
E
NOT USED
F
NOT USED
G
NOT USED
H
NOT USED
I
NOT USED
J
NOT USED
1
2
3
•
5
6
7
8
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
"
35
36
37
38
39
"
"
"
"
..
"
:--'--'--'--'--'--'--'--�--'--l
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,A
,A
,A
,A
,A
,A
,A
,A
,A
,A
,A
,A
,A
'A
'A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
SOIL -PARAMETERS
**
A
B
B A
X
A B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
VARIABLE PLOTTED
A
B
NOT USED
C
NOT USED
D
NOT USED
E
NOT USED
F
NOT USED
G
NOT USED
B
NOT USED
I
NOT USEO
J
STEP
1
2
3
•
5
6
7
8
9
10
11
12
13
"
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
"
"
"
"
..
"
*********************
(T/HA)
TOTAL HUMUS MASS
NOT USED
GRAPH N
MAXIMUM * *
3 9 . 6416 A
.0000
B
.0000
C
.0000
D
.0000
E
.0000
F
.0000
G
.0000
B
.0000
I
.0000
J
'--'--' -- '--'--' --'--'-- ' --'--'
0%
100%
50%
0%
STEP
(T/HA)
LIMITING NUTRIENT SITE QUALITY
GRAPH M
MAXIMUM **
355.7954
A
B
6 1 . 5197
.0000
C
.0000 D
.0000 E
.0000 F
.0000
G
.0000
H
.0000
I
.0000
J
50%
100%
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Figure 35. Graphs M and N (forest-floor mass and humus mass) from MANAGRAF (second scenario). The
vertical (Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 100% of the maximum
biomass or production values given in the table above the graph. Thus, each of the ten variables represented
is plotted on its own scale, with full scale value being equal to the maximum value for that variable. Where
more than one variable has the same print position an X is printed.
In! Rep; NOR·X-328
77
NITROGEN
SOIL NUTRIENTS
VARIABLE PLOTTED *********************
TOTAL LITTER NITROGEN
(KG/HA)
TOTAL RELEASE FROM LITTER
(KG/HA)
TOTAL AVAILABLE IN SOIL
(KG/HA)
TOTAL IN ANION FORM
(KG/HA)
TOTAL IN CATION FORM
(KG/HA)
TOTAL UPTAKE
(KG/HA')
PROPORTION UPTAKE
(KG/HA)
LEACHING LOSS
(KG/HA)
NOT USED
NOT USED
A
B
C
D
E
F
G
H
I
J
STEP
1
,
3
•
5
6
7
8
9
10
11
12
13
"
15
16
17
18
19
20
21
"
23
"
25
"
27
28
"
30
31
32
33
"
35
36
37
38
39
"
"
"
"
..
"
0%
�
I
50%
GRAPH 0
MAXIMUM **
956.3142 A
7 8 . 2571 B
651. 4288
C
D
50.6114
612.7699 E
103:1735 F
1 . 0000
G
3 8 . 3662
H
.0000
I
.0000
J
100%
I_-J.
I __ I __ I __ I __ ' __
I_- I
Bll
H
X
H
o
X
XB
A
H
o
GF
BX
A
H
G
F
o
B
A CE
F
G
HB 0
A
CE
F
B
G
H
X
CE
F
B
A H o
X
IG
F
B
A
H o
X
IG
B F
A
H
o
X
I G
B
F
G
A
H
o
X
I
G
B
AF H
o
X
I
G
B
A H
F 0
EC
I
G
B
H A
o
XF
I
G
B
H
F
A
0 EC
I
G
H
B
F
A DEC
I
X
H
AB
OX
GF
B
B OXA
A
G F
X
B
F
G
A
B ECX
A
F
G
B
EC X
F
A
B
EC X
IG
F
AB
EC X
I G
BFA
EC H D
I G
B
A
F
G
EX
0
I
B
A
G
o
F
XC
I
B
A
G
H
EC F
D
I
G B
A
H
EC
o
F
I
BX
A
EC
o
F
I
H
B GAX
o
F
I
H
ECBDA
G
F
X 0
GX X
X 0
GF AX
X 0
! GFX
H
! BX FH
X
0
!EA G HF
X
0
B A HG
F X
0
B
X
X X
G
X A
XG 0
F
X A
X
o
G
F
X
A ECD
F G
X
XDA
F
G
Hxes A
F
G
HXD
X
F
G
Hxe
AS
F
G
HX
BA
F
G
__ __
SOIL NUTRIENTS
GRAPH P
NITROGEN
-VARIABLE PLOTTED ************** ******* MAXIMUM **
'(KG/HA)
5 4 8 . 0637 A
TOTAL HUMUS -NITROGEN
TOTAL RELEASE FROM HUMUS
14.4091
B
(KG/HA)
(KG/HA)
C
590.1288
NUTRIENTS BELD ON CEC
(KG/HA)
NUTRIENTS BELD ON AEC
1 0 . 0000
D
DECOMP SITE QUALITY
6 1 . 5197
E
CATION EXCHANGE CAPACITY
(KG/HA)
2027 .1850
F
G
ANION EXCHANGE CAPACITY
(KG/HA)
1 0 . 0000
SOIL AEC + CEC
H
(KG/HA)
60. 0000
FOREST FLOOR AEC + CEC
1778.9770
r
(KG/HA)
HUMUS AEC + CEC
198 2079
J
(KG/HA)
A
B
C
D
E
F
G
H
I
J
STEP
1
,
3
•
5
6
7
8
9
10
11
12
13
"
15
16
17
18
19
20
21
"
23
"
25
"
27
28
"
30
31
32
33
"
35
36
37
38
39
"
"
"
"
..
"
0%
100%
I _- I _E
B
!
C
X
X
X
CE
IX
XX
!
_
D C
I
IF E
CXBX
IF
E
XCX
IF
E
X
X
, F
E
X
X
, F
E
X X X
, F
E XB X
X
C EX
,
F
X
C
, F
B X
X
,
F
B
X E
C
X
,
F
B
X
E
C
AJ E
X
,
B
FC
E
X
, X
B
X
E
X
XB
C
, F
E
X CB
X
, F
X
BC E
X
,
F
B
CE
X
AJ
,
F
X
B
, F
CE X
X
B
C E X
,
F
AJ
B C
E X
,
F
,
X
X
E X
F
,
AX B
E X
F
,
XB
E X
C
F
,
BX
EX
C
F
,
F
S AJ
EX
C
'C F
B
X
EX
XX
C ,
F
B
X
EX
F C
BAJ
I'
EX
F
C
XB
I'
EX
F
C
X
B
I'
F
B
X
C
AJ
I'
F
B
X
C
X
I'
F
X
B
X
C
I'
F
B
X
C
X
I'
B
X
F
AJ
C
I'
!I
FC
X
B
o
X
o
F
X
B
X
IX
CID F
X
AJB
Xl
F
X
X
Xl
F
EX
BAJ
Xl
F
B X
EX
Xl
F
B X
E X
:
50%
I __
-- -- -- -- __
�
__
--�
Figure 36. Graphs 0 and P (soil nutrient variables) for nitrogen from .MANAGRAF (second scenario). The
vertical (Y) axis is in annual time steps. The horizontal (X) axis is a scale of 0 to 100% of the maximum
biomass or production values given in the table above the graph. Thus, each of the ten variables represented
is plotted on its own scale, with full scale value being equal to the maximum value for that variable. Where
more than one variable has the same print position an X is printed.
78.
In[ Rep. NOR·X·328
litterinRotation2 (Variable B, Graph 0, Fig, 36)thatresults
from the change in the proportion of high C/N ratio
heartwood in the forest floor between Rotations. I and 2.
Growth over the entire third rotation is drastically
reduced because of the effec! of the slash bum on the
forest-floor N reserves (Fig. 35) and levels of available
N (Fig. 36). In spite of the dramatic loss of N caused by
the bum, however, the early growth of trees (years 1-8)
in this rotation is not dramatically different from that of
the first and second rotations. Young trees do not demand
much uptake (Fig. 33), so that even if the site has been
seriously depleted of forest-floor N, the mineralization
of the residual material, plus the release from the un­
burned soil humus, may satisfy the uptake demand. Only
when the increasing uptake demand of the developing
plantation exceeds the declining supply of available soil
N does the full impact ofthe bum reveal itself. It has been
demonstrated that early tree growth may be quite good
even on a site that has 'been severely depleted of nutrients
(Lundmark 1977), however, later growth will be severely
restricted: This reflects the" phenomena of the assart
effect and, in the case of fire, also the ash bed effect.
One consequence of the reduced growth of trees
following the bum is reduced foliage biomass (Fig. 30),
increasing the light passing through the canopy (Variable
H, Graph C, Fig. 3 1). If the understory had been simu­
lated, this increased light would have resulted in greater
understory growth, which might have resulted in greater
competition for light and N. If this were the case, the tree
growth might have been further reduced by the compe­
tition. The FORCYTE-l l model is not able to simulate
the increase in competition for soil moisture that might
accompany such an increase in understory vegetation.
Another consequence of the reduced foliage
biomass following the bum is the reduced self-shading
within the canopy. Thus, the total photosynthetic produc­
tion in the canopy (see Variable C, Graph F, Fig. 32) is
reduced less than the total N content of the foliage
(Variable A, Graph F, Fig. 32).
In spite of the dramatic reduction in total site N due
to the slash bum, site quality fur tree growth (Variable F,
Graph F, Fig. 32) does not decline until 8 years after
burning. This is because, in spite of reductions iri - total
and available N, there was still sufficient available N to
support the growth of the young Douglas-fir that was
expected for a site of the quality indicated at the end of
the second rotation.
Graph E (Fig. 32) suggests that in spite of the drop
in tree biomass accumulation (Fig. 30), height growth
was not affected overthe first 1 5 years after the bum. The
In! Rep. NOR-X-328
siroulation ofloss ofheight growth is controlled by changes
in site quality. The failure to reduce height growth in spite
of declining site quality reflects the input data that show
relatively little variation in height growth among different
site qualities early in the rotation in comparison to greater
height growth differences over longer time periods.
The second scenario resulted in a more rapid decline
in humus reserves (Fig. 35) than did the first scenario.
This reflects the three periods of clear-cut exposure effect
on humus decomposition rate, in comparison to only one
in the first scenario, and the two additional clear-cut
harvests, resulting in reduced inputs into the humus pool
from the forest floor.
The site quality for soil processes (Variable B, Graph
M, Fig. 35) behaves much as in the first scenario. This is
not what might have been expected given the severity of
the bum. It reflects the behavior of the site quality for
tree growth parameter and the damping factor. The lack
of a more immediate response in soil site quality to such
a severe bum might be considered a significant short­
coming of the simulation. In some-cases, however. min­
eralization of residual material after a hot bum can be
quite rapid, possibly because of the elevated pH and
temperatures. More empirical field data are required. to
detennine the extent to which this site quality change
simulation is in error. The lack of rapid response also
reflects the damping factors that regulate the speed of site
quality change. The slow response in this case suggests
that the action of these damping factors should probably
have been reduced.
General Discussion
Many additional interpretations of these graphs
could be made. In particular, comparing the patterns and
absolute values of the' different variables in various
graphs can lead to a greatly improved understanding of
how the model is operating and why particular variables
are behaving the way they are. The objective of such
investigations of the graphs is to establish a level of
confidence in the yield, biogeochemical budget, eco­
nomic, and energy predictions that the model presents in
tabular fonn. Almost inevitably, such inspections will
reveal behaviors of some parameters that may not con­
fonn to conventional knowledge, understanding, or con­
ceptual models of the ecosystem. In many cases this
probably reflects inadequate model perfonnance or inac­
curate input data. Alternatively, it may suggest an error
in the understanding of the ecosystem. Two cases of the
latter explanation occurred in the use of FORCYTE-IO;
model behavior that did not confonn to the user's current
paradigm of ecosystem function led to the identification
. of errors in that paradigm (Kimmins, Comeau, and Kurz
1990).
79
Verification and Validation of
Model Performance
Verification and validation4 ofthe simulated ecosys­
tem response to these two management scenarios is
difficult.
In the majority of cases, model users will not
have access to empirical data describing such responses
adequate description ()f the initial state of the ecosystem
at the start of the experiment, data from long-term field
trials might orntight not constitute an adequate verification
or validation test
of a model like FORCYTE- I I . Although
a mooel will always be an imperfect representation of
reality, a well-designed and calibrated ecosystem level
computer simulation mooel may be a better overall repre­
over time periods as long as one or two rotations. Even
sentation of reality than the descriptive word models
where substantial data sets do exist from long-term field
derived from the experiment or long-term field trial.
plots, there is a lack of an adequate description of the
initial state of the ecosystem (as defined in FORCYTE11 by the ECOSTATE file) at the start of the monitoring
From the current understanding of the behavior of
Douglas'fir ecosystems in the area from which the cali­
ofthese plots. In the absence of such a definition of initial
bration data used in the above scenarios were collected
ecosystem condition, it is difficult to tell whether differ­
(south-central Vancouver Island, low elevation sites), the
ences between model output and empirical field data
model 's performance appears to be qualitatively reason,
reflect a failure of the model to mimic reality, or a failure
able, given the unreality of simulating a single tree
species in the absence of the· other vegetative oompo­
of the empirical data t() define the reality.
nents of the B.C. coastal ecosystems. A full verification
The FORCYTE- 1 1 model's predictions, at least in
the first few rotations, can be as sensitive to the invento­
ries of organic matter and nutrients represented in the
ECOSTATE file as they are to the quality of the input
data that regulate the process simulations. Even some of
the simulated management treatments can have less
effect on rotation-length predictions than major vari­
ations in the content of the ECOSTATE fIle. Without an
would require the simulation of as realistic a combination
of plant life forms as possible, and a COmparison of the
results with data from chronosequence investigations (in
the absence of long-term field plot data), or from long­
term field trials that should be established to provide such
verification data. A key feature of such studies should be
a careful documentation of the state of the ecosystem at
the start of the experiment.
LlMITATlONS OFTHE FOR C YTE-1 1 APPROA C H
IN ADDRES SING CUR RENT AND F UTURE
M ANA GEMENT ISSUES
Recent advances, in timber conversion technology,
for growth and yield models to be sensitive to issues of
the use of tree species previously considered as weeds,
soil fertility will increase. Mechanization is also likely to
and the increasing competition from short-rotation tropi­
increase as log piece 'size gets smaller and as plantations
cal plantations support the contention that the trend
produce logs that are much more unifonn-in piece size '
toward more complete utilization of trees and shorter
than logs fromthe unmanaged forests that the plantations
crop-rotation times is likely to continue in temperate
replace. This will require that yield predictors address the
forestry. The current trend toward legislatively required
issues of soil compaction, the area in skid trails, and
newspaper recycling will undoubtedly have an impact on
various types of soil erosion.
the industry, and may cause a short-term decline in fiber
demand until global population growth once again
increases global demand. Such a short-tenn decline in
fiber demand, if it occurs, will only intensify the need to
Past and current concerns about the impacts of
intensified stand management have largcly focused on
the previously discussed soil issues. There is growing
reduce costs of production, which should favor intensive
evidence, however, that interference with crop trees by
biomass production on the most productive sites close to
noncrop vegetation can result in considerable loss ofcrop
mills and markets. If this prediction is correct, the need
production (e.g., Walsted and Koch
4
1987). Major shifts
Verification is used here to refer to the comparison between the model's representation of individual processes and shorHerm responses to simulated
management or other disturbance events, and our understanding of these processes or events and short-term data sets that describe them. Validation
refers to the comparison between the model's overall, long-tenn predictions and empirical data for comparable time: scales.
80
In! Rep. NOR-X-328
in the geographical range of species, and changes in
A limitation of all hybrid simulation models is that
growth potential, insect pest and disease relationships,
they require some historical bioassay input data. Where
and in the occurrence and severity of forest fires are
such a- bioassay does not exist, such models cannot be
expected to accompany anticipated climate change, As
used. In this respect, purely process-based simulation
climate changes and hnman population grows, growth and
models may be the only usable approach; but even
yield predictors will be needed for increasingly
process-based models require thai measurements of
heterogeneous agroforestry cropping systems,
process rates be made on existing examples of the
phenomenon being studied, so they are not immune to
One of the major environmental concerns of the
the need for an existing. stand of trees. They may, how­
public is the aesthetics of clear-cutting, Public pressure,
ever, be less directly tied to historical growth data on a
whether scientifically supported or not, will probably
particular site than are hybrid simulation models.
place significant , restrictions on clear-cutting in some
areas, and future yield predictors will need to be able to
address questions of species mixtures, aiternating crop
species, small group cuttings, shelterwood systems, and
various other more aesthetically acceptable silvicultural
systems for which we do not yet have the appropriate
field experience to develop purely historical bioassay
yield predictors,
Acid rain, air pollution, and atmospheric enrichment
of CO2 constitute other factors that may have to be
addressed in the calculation of future annual allowable
cuts, and our yield predictors should be able to address
these issues where they prove to be significant
Another limitation of FORCYTE-ll is that it is an
entirely deterministic model. Although deterministic
models have many advantages for management "gam­
ing," it would be useful for some applications to have a
better simulation of the successional processes of colo­
nization, with the option of some r�presentation of
stochastic events.
There is no representation of seasonality in
FORCYTE-I L Each simulation time step is the same as
all others. The model may be used for a time step of less
than a year, but if this is the case the model does not
represent seasonal differences. This places a limitation
on the use of time steps of less than I year in climatic
The FORCYTE-l l model is believed to have a
regions that have seasonally vitriable climatic conditious
greater ability to simulate the long-term impacts of man­
(which is most areas). The model does account for winter
agement and certain types of natural disturbance on site
photosynthesis by evergreens while deciduous competi­
productivity than other comparable models, The model
tors are leafless, and it can simulate mortality and
has several .significant limitations, however, some of
regrowth of fine roots on time steps shorter than one year.
which are shared by other similar models,
There is, however, no detailed representation of the vari­
ation of the other ecosystem processes between seasons.
Perhaps the single most serious limitation of the
model is its inability to simulate the effects of plausible
For some model applications. this may constitute a
significant deficiency.
cliJ11ate change scenarios within a single run. Correction
of this limitation would require the addition to the
The hybrid simulation modeling approach uses the
model of explicit simulations of the effects of moisture
historical bioassay as a starting point for simulation. For
and temperature, The JABOWA-type models already
this reason it shares with historical bioassay models the
have temperature and moisture modifiers of growth, and
problem that we may not know the combination of eco­
have been used to make predictions about the effects
logical factors and events that produced the historical
of climate change on forests, The growth modifier
pattern of stand growth and development. This is less of
approach used in these models does not, however,
a problem in FORCYTE-l l than in FORCYTE- IO or in
cover the full range of effects of changes in moisture
historical bioassay models, because FORCYTE- I l uses
and temperature.
an index of canopy production efficiency derived from
the historical bioassay rather than the historical bioassay
Many of the representations of soil processes are
very simplistic, The lack of representation of soil layers,
soil mixing, and root distribution limits the ability of
FORCYTE-l l to address issues of soil compaction and
soil erosion, The representation of the tree canopy in
FORCYTE- l l as an "opaque blanket" also poses some
directly. This one-step-removal ofthe simulation driving
function from the raw historical data reduces the effect
of unknown episodic events in the past, but
it must be
recognized that some of the problems with the historical
bioassay approach are carried over to some extent into
all hybrid simulation models.
difficulties for the simulation of individual tree growth,
thinning resppnse, agroforestry, sheiterwood, and
mixed-age stand management.
In! Rep. NOR-X-32B
There are a number of hypothesized mechanisms by
which acid rain and air pollution may affect forest growth
81
and yield that are not represented in FORCYTE-I I . The
structure of FORCYTE-l l lends itself to the addition of
such representations because it simulates fiile-root
mortality. canopy photosynthesis, and tree nutrition, all of
which are believed to be affected by acid rain and air
pollution.
Experience to date does suggest that, within the
limits of model design, FORCYTE- l l performs
predictably and with few known errors, and that it is able
to address most of its objectives,
The following works summarize evaluation to' date
ofFORCYTE-l1: Chan and Peterson (1987), Apps et al..
(1988), Peterson et al. (1988), Apps and MacIsaac (1989),
Grewal et al. (1989); Peterson and Apps (1989), Pike and
Meades ( 1989), Sachs et al. (1989), Grewal et al. (1990),
Sachs and Trofymow (1991), and Trofymow and Sachs (1991).
A C KNOW LEDGM ENTS
Without the support and enthusiasm of numerous
Forestry Canada personnel, the FORCYTE project
would not have received the decade of financial support
(under the ENFOR Program) that made model develop­
ment possible. Drs. J. Carlisle, L. Chatarpaul, and M. Apps
were the scientific authorities on the project. Although the
earliest work on the project predates the inyolvement of
Forestry Canada, neither FORCYTE-IO nor FORCYTE11 would have been developed without this support.
Appreciation is extended to K. Scoullar, who has
dedicated a decade of his life, often undertrying circum­
stances, to modeling and programming the FORCYTE
series of models. Numerous other people have made
great contributions to the project. Many graduate stu­
dents and associates have tested the model and provided
invaluable feedback on .errors and shortcomings; these
individuals and the support staff, P. Quay, B. Buchanan,
and M. Tsze, deserve much of the credit for bringing the
FORCYTE project to completion, FORCYTE-l 1 being
the final generation of this model.
Grati�ude is expressed for the very careful reviewing
of the report by a group of FORCYTE-l 1 users. Most of
their recommendations have been incorporated into the
report. The technical editing by D. MacIsaac deserves
partiCular mention. Responsibility for r.emaining
deficiencies lie with the author.
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