Ecological Modelling 156 (2002) 153 /166
www.elsevier.com/locate/ecolmodel
A simulation study of landscape scale forest succession in
northeastern China
Hong S. He a,b,*, Zhanqing Hao a, David R. Larsen b, Limin Dai a,
Yuanman Hu a, Yu Chang a
a
b
Institute of Applied Ecology, Chinese Academy of Sciences, 72 Wenhua Road, Shenyang 110015, People’s Republic of China
The School of Natural Resources, the University of Missouri-Columbia, 203 ABNR Building, Columbia, MO 65211-7270, USA
Received 28 August 2001; received in revised form 8 March 2002; accepted 24 April 2002
Abstract
Changbai Natural Reserve in northeastern China provides an excellent opportunity to explore how temperate and
boreal forest ecosystems in northeastern China will evolve and recover over large spatial and temporal scales. Such
studies are increasingly needed to design scientifically sound forest management and restoration plans in this region.
Long-term (300 years) successional trajectories of the dominant tree species are simulated on the heterogeneous,
undisturbed area (within the reserve) using a spatially explicit landscape model. We also examine the spatial and
temporal constrains of landscape recovery on the human disturbed areas (surrounding the reserve). Simulation results
suggest that an equilibrium in landscape structure and composition is approached on the large landtypes dominated by
shade tolerant species, but not on landtypes altered by humans. Such equilibrium can be observed in spruce-fir,
mountain birch, and larch forests and not in aspen-birch forests. Our results suggest that direct and indirect human
impact may produce long-term alterations to forest landscape patch structure that persist for decades to centuries. For
example, even with complete natural succession over 300 years, Korean pine only recovers on 1/3 of the areas in the
landtypes it can dominate. We estimate a full recovery would take another 200 /300 years without human disturbance.
Our results also indicate that landscape-scale recovery is often limited by the available seed sources and this is
particularly true for Korean pines in this region. Comparison of simulation results for the entire study area with land
types (two scales) reveals the greatest variations at the land type scale. This discrepancy indicates that the ‘space-fortime’ substitutions can be flawed as species composition and age class at a given site and time may represent only the
specific successional history of that site. This is particularly true for human disturbed forest landscapes where higher
variations are observed. # 2002 Elsevier Science B.V. All rights reserved.
Keywords: Forest landscape succession; Spatial pattern; LANDIS; Spatially explicit landscape models; Changbai nature reserve;
Northeastern China
1. Introduction
* Corresponding author. Tel.: /1-573-882-7717; fax: /1573-882-1977
E-mail address: [email protected] (H.S. He).
The temperate and boreal forest ecosystems in
northeastern China provide the major timber and
0304-3800/02/$ - see front matter # 2002 Elsevier Science B.V. All rights reserved.
PII: S 0 3 0 4 - 3 8 0 0 ( 0 2 ) 0 0 1 0 4 - 7
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H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
woody supplies for the country with the increasing
demands from the growing population. The spatial
pattern and ecological functions of these systems
have substantially diverged from the natural
successional trajectories due to decades of forest
cutting and other human land uses. Understanding
how these human-altered ecosystems will evolve
and recover over long time-periods is limited. Such
information is increasingly needed for designing
scientifically sound forest management and restoration plans especially for human altered ecosystems (Zhang et al., 2000). In this paper, we
intent to fill the gap by providing baseline
information of how relatively undisturbed forest
landscape can evolve given known ecological
principles and the spatial and temporal constrains
to the recovery of the human disturbed landscapes.
We will do this by applying a landscape simulation
model to a relatively undisturbed area in eastern
China.
Various modeling approaches have been developed to simulate vegetation dynamics in terrestrial
ecosystems (Jørgensen, 2000). Majority of them
are used for simulating forest vegetation dynamics
of forest stands or gaps (Botkin et al., 1972;
Shugart, 1984; Pastor and Post, 1985; Urban et
al., 1993; Fischlin et al., 1995; Bugmann, 1996)
and forest ecosystem processes (Aber and Federer,
1992; Aber et al., 1995; Running and Coughlan,
1988; Running and Gower, 1991; Parton et al.,
1987, 1988). Gap models are useful for examining
trends of forest succession and reveal quantitative
vegetation dynamics for a given plot that represents a small sub-set of the landscape. Ecosystem
process models divide study areas into plots or
pixels (up to 500/500 m), simulate vegetation
dynamics along with ecosystem processes for each
pixel, and then summarize simulation results for
the entire area. Since each plot or pixel is treated as
a spatially independent entity in gap and ecosystem process models, spatial processes such as seed
dispersal and disturbance occurring at spatial
extents larger than a plot cannot be simulated
realistically. In addition, ecosystem process models
usually lack of species-specific information. Markov chain models are also used in simulating plot
and landscape scale vegetation dynamic and have
the advantage of aggregating very complex infor-
mation in the transition matrix (e.g. Gardner et al.,
1999; Balzter, 2000). However, The applicability of
Markov chain models is often limited by lacking
spatially explicit predictions and sometimes species-specific information.
To study forest landscape change at regional
scales, spatially explicit landscape models can be
used to integrate broad-scale spatial and temporal
processes. Landscape models are effective tools to
formalize our understanding of complex landscapes in situations that are difficult or impossible
to conduct field experiments because of the large
spatial and temporal scales involved (Turner et al.,
2001). With the recent development of forest
landscape modeling approaches and techniques
(Turner et al., 1995; Mladenoff and Baker, 1999;
Mladenoff and He, 1999; He et al., 1999a), it is
possible to study landscape scale dynamics based
upon the existing ecosystem-level processes. In this
study, we used a spatially explicit forest landscape
model, LANDIS (Mladenoff et al., 1996; Mladenoff and He, 1999; He et al., 1999a,b; Gustafson et
al., 2000). LANDIS is a new generation forest
landscape model that simulates forest succession,
seed dispersal, wind and fire disturbances, harvesting and the interactions of all above over large
spatial and temporal domains. It has been extensively described and applied under various species
and environmental settings (Mladenoff et al.,
1996; Mladenoff and He, 1999; He et al.,
1999a,b; He and Mladenoff, 1999a,b; Gustafson
et al., 2000; Shifley et al., 1997, 2000; Franklin et
al., 2001). These studies have shown that the
model is capable of deriving spatially explicit
results and yet maintaining information at individual tree species level for a wide range of
ecosystems.
Changbai Nature Reserve in northeastern China
is examined as a case study using simulation
modeling to determine the likely long-term successional development. Changbai Nature Reserve is
the only remaining large forest reserves in northeastern China (Fig. 1), with forests relatively
undisturbed by human activities. The reserve is
the home of 1250 species of seed plants, 251 fungi,
148 lichens, 339 bryophytes, 87 pteridophytes, and
numerous bird, insects, and mammal species (Xu,
1992; Shao et al., 1994). In 1979, Chinese Acad-
H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
155
Fig. 1. Location of Changbai Nature Reserve and the major forest types. Labels are land unit Identifiers. Land units are delineated
based on existing land cover, elevation, and landform data under Arc/Info GIS (Fig. 1). These units were classified into landtypes based
upon the similarity of the above variables.
emy of Sciences founded Changbai Forest Ecosystem Research Station. Since then, numerous
research projects have been conducted to understand the structure, function and productivity (e.g.
Wang et al., 1980; Burger and Zhao, 1988;
Harmon and Chen, 1991), dynamics (e.g. Miles
et al., 1983; Barnes et al., 1993; Shao et al., 1994),
and nutrient cycling (e.g. Geng et al., 1993) of the
dominant forest ecosystems in the reserve. These
studies, however, are generally at ecosystem levels
and do not address issues at multi-ecosystems or
landscape scales.
The objectives of this study are to examine
landscape scale forest succession and the spatial
and temporal factors affecting natural restoration
of the human disturbed landscapes in eastern
China. Specifically, we will examine successional
trajectories of the dominant tree species as predicted by the LANDIS model and their distributions in a spatially explicit manner. Understanding
the likely dynamics of species composition and
spatial pattern in the reserve, we will not only gain
insights into landscape scale processes, but also
provide baseline information of a relatively undisturbed forest landscape in northeast China.
Such information is invaluable to the management
planning of northern hardwood and boreal forest
ecosystems throughout northeastern China.
1.1. Study area
Changbai Nature Reserve is in Antu County,
Jilin Province, northeastern China and has about
200 000 ha from 127842? to 128817?E and 41843? to
42826?N (Fig. 1). Changbai Nature Reserve includes highest part of Changbai Mountain, located
along the border of China and North Korea. The
mountain is the highest in northeastern China and
is the head of three major rivers (Songhua, Yalu,
and Tumen) in northeastern provinces. Elevation
increases from 740 m at the lowest part of the
reserve to 2691 m at the highest point of the
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H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
mountain. Topographic features are different on
the four slopes of the mountain with the northern
slope relatively flat (average slope B/3%) and
other slopes relatively steep (10%). The area has
a temperate, continental climate, with cold and
long winter and warm summer. Annual mean
temperature varies from 2.8 8C at the bottom of
the reserve to /7.3 8C near the volcanic crater
lake at the highest elevation in the reserve, and
annual mean precipitation varies from 750 to 1340
mm. Recent small-scale volcano eruptions occurred in 1597, 1668, 1702 and a very large-scale
eruption occurred during 1000/1410 (Zhao, 1981;
Liu et al., 1992).
Topographic and geological features along with
climate variations result in a vertical distribution
of major forest ecosystems especially distinct along
the northern slope. From 1100 m and below, is a
typical temperate forest with mixed Korean pine
(Pinus koraiensis ) and hardwood species. From
1100 to 1700 m, is evergreen coniferous forest
including spruce (Picea jezoensis ) and fir (Abies
nephrolepis ), with typical characteristics of boreal
forest in North America. From 1700 to 2000 m, is
the sub-alpine forest dominated by mountain birch
(Betula ermanii ) and larch (Larix olgensis ). From
above 2000 m, there are tundra, bare rock, and a
volcanic crater lake. Such a vertical structure
provides a condense picture of vastly distributed
temperate and boreal forests found across northeastern China. Other dominant forest types such
as larch and hardwood forests include aspen
(Poplus davidiana Dode), birch (Betula platyphylla
Sukachev), basswood (Tilia amuresis ), oak (Quercus mongolica ), maple (Acer mono ), and elm
(Ulmus propinqua ). Our study area extends about
8 km outside the nature reserve where human
activities have transformed the pine-hardwood
forests into hardwood forests.
2. Materials and methods
2.1. The LANDIS model
LANDIS is a spatially explicit succession and
disturbance model based on rasters that grid the
study area into cells. Each raster unit or cell is a
spatial object that tracks: (1) the presence or
absence of age cohorts of individual species in a
binary format, (2) fuel levels based on fuel
accumulation and decomposition characteristics,
(3) mean fire/wind return interval, (4) the time
since last fire/wind/harvest disturbance, and (5)
species establishment ability in particular environments. At a site scale, species birth, growth, death,
regeneration, random mortality, and vegetative
reproduction are simulated at 10 year time steps
for each cell. At a landscape scale, seed dispersal,
disturbances, and forest harvesting are simulated
each time-step as well. To simulate heterogeneous
landscapes, ecoregions derived from climate and
soil GIS data layers can be used to stratify the
landscape (He et al., 1998). At a given focal
resolution such as within each ecoregion, environmental variables such as climate and soils are
assumed homogeneous, as are some characteristics
such as mean fire return intervals, fuel decomposition rates, and species establishment (Mladenoff
and He, 1999; He et al., 1999b). The species/age
cohort information can be derived from classified
satellite imagery integrated with forest inventory
data (see description of input data).
Currently LANDIS simulates four spatial processes: fire, windthrow, seed dispersal, and harvesting (Mladenoff and He, 1999; He and
Mladenoff, 1999a; Gustafson et al., 2000). Fire
and windthrow disturbances are stochastic processes based on the probability distributions of
mean disturbance return intervals and mean disturbance sizes characterized for various ecoregions
(He and Mladenoff, 1999a). For example, fire in
certain ecoregions is more frequent than in others.
In the LANDIS fire module, small, young trees are
more susceptible to damage than large, older trees.
LANDIS simulates five levels of fire intensity from
ground fires to crown fires. The intensity is
determined by the time since the last fire on each
site, a surrogate for the amount of fuel accumulation. Correspondingly, tree species are grouped
into five fire-tolerance classes. Fire severity is the
interaction of susceptibility based on species age
classes, species fire tolerance, and fire intensity (He
and Mladenoff, 1999a).
In the LANDIS wind module, the probability of
windthrow mortality increases with tree age and
H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
size. Windthrow events interact with fire disturbance by increasing the potential fire intensity
class at a site due to increased fuel load. In general,
windthrow becomes more important on mesic
landtypes with longer-lived species, and where
fire frequency is low (Mladenoff and He, 1999).
However, the probability of windthrow disturbance is often related to the local variables
including climate and landform.
LANDIS simulates seed dispersal in a spatially
explicit manner. Seed dispersal probability is
modeled using an exponential distribution in
which for each species the effective and maximum
dispersal distances control the shape of seedling
distribution (He and Mladenoff, 1999b). Species
establishment is simulated by using the species
establishment coefficient (0 /1), which quantifies
how different environmental conditions favor or
inhibit the establishment of a particular species
(He et al., 1999a). Species with high establishment
coefficients have higher probabilities of establishment. The establishment coefficient for a given
species may vary from one landtype to another.
These coefficients which are provided as input to
LANDIS are derived either from the simulation
results of a gap model (He et al., 1999a) such as
LINKAGES (Pastor and Post, 1985; Post and
Pastor, 1996), or from estimates based on existing
experimental or empirical studies (Shifley et al.,
2000).
Parameterizing LANDIS, which was initially
developed for the northern Lake States, for
Changbai Nature Reserve is a significant efforts,
involving spatial data processing using GIS and
remote sensing techniques, compilation of existing
data at the ecosystem levels, and consultation with
local experts (see the Section 2.2).
2.2. LANDIS input data for Changbai nature
reserve
2.2.1. Species attributes and forest composition
map
In LANDIS succession and dispersal are driven
by species vital attributes. We selected 12 of the
most common tree species and compiled the vital
attributes for each of them (Table 1) based upon
existing studies in the reserve (Wang et al., 1980;
157
Xu, 1992). Recently, Shao et al. (1996) used
remote sensing techniques and created a map
describing the distribution and spatial pattern of
the major forest cover types in the reserve and its
surrounding area. Using a data integration approach described in He et al. (1998), we combined
the dominant forest types data from remote
sensing with existing field data describing species/
age cohort distribution to derive a forest composition map that contains individual species/age class
distribution for the study area (e.g. Fig. 2). To
reduce computational loads during model simulations, the forest composition map was specified at
100/100 m resolution, which yielded 960 rows /
647 columns.
2.2.2. Landtype map and species establishment
coefficients
The reserve and its surrounding area was
delineated into small land units based on existing
land cover, elevation, and landform data under
Arc/Info GIS (Fig. 1). These units were classified
into landtypes based upon the similarity of the
above variables. On each landtype, biomass of
each species was simulated using a gap model,
LINKAGES (Pastor and Post, 1985). LINKAGES integrates environmental variables such
as climate (monthly temperature and precipitation) and soil (C, N and water) with ecological
processes such as competition, succession, and
water and nutrient cycling. The simulated biomass
from the LINKAGES model was used to quantify
the environment suitability for each species (Hao
et al., 2001). This result was used to derive
establishment coefficients using the method described in (He et al., 1999b).
2.2.3. Disturbance data
Fire and windthrow are two natural disturbances in the reserve. Mean fire return intervals
are substantially altered due to the extensive fire
suppression efforts. They only occur in small
patches (B/0.5 ha) with mean patch size /1.0 ha.
Mean return interval is estimated at 800 years in
the mixed Korean pine hardwood and hardwood
landtypes and 1000 years on the other landtypes
listed above. There was a recent windthrow event
in 1987 that damaged about 25 km2 of forest.
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H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
Table 1
Species life history parameters for Changbai nature reserve
Name
Abbreviation
Long
MTR
ST
FT
ED
MD
VP
MVP
Abies nephrolepis
Acer mono
Betula armanii
Betula platyphylla
Fraxinus mandshurica
Larix olgensis
Picea jezoensis
Pinus koraiensis
Populus davidiana
Quercus mongolica
Tilia amuresis
Ulmus propinqua
ABNE
ACMO
BEAR
BEPL
FRMA
LAO
PIJE
PIKO
PODA
QUMO
TIAM
ULPR
200
200
200
150
300
300
300
400
150
350
300
250
30
30
30
20
30
30
30
40
30
40
30
30
5
4
1
1
4
2
4
4
2
2
4
3
5
3
2
1
2
5
4
4
1
3
2
3
20
100
100
200
50
100
50
50
/1
20
50
300
100
200
300
4000
150
400
150
100
/1
200
100
1000
0
0.3
0.5
0.8
0.1
0
0
0
1
0.9
0.1
0.7
0
60
60
50
80
0
0
0
0
60
60
60
Long, longevity (year); MTR, age of maturity (year); ST, shade tolerance class; FT, fire tolerance class; ED, effective seeding
distance (m); MD, maximum seeding distance (m); VP, vegetative reproduction probability; MVP, minimum age of vegetative
reproduction (year).
Windthrow, however, is also not frequent with a
mean return interval of 1000 years and its effects
are secondary to fire in terms of altering spatial
pattern and species compositions. Therefore, it is
not simulated in this study.
Mladenoff, 1999a). Results from the simulation
were summarized as either % cover of the study
area or land units.
3. Results
2.3. Simulation scenario
We started with the realistically parameterized
forest composition and landtype maps with species/age classes that represent the initial status of
1990s. Then the entire study area was simulated
for 300 years (up to year 2290) and we examined
species composition, age structure, and spatial
distributions of all 12 species. Forest harvesting
was not simulated because our objective was to
examine the natural successional trajectories of the
main dominant species. In addition, harvesting is
not allowed in the reserve. In this simulation
scenario, model replications were not used because
the major stochastic component, fire, is infrequent
and is not the dominant factor altering species
composition and shaping landscape patterns in the
study area. Environmental constrains, and the
non-stochastic factors, plays greater roles than
the stochastic factors of natural disturbance.
Starting with the same forest composition map
and all other model parameters, replication runs
will not have noticeable variations (He and
3.1. Simulated species distribution and abundance
in the study area
Mountain birch is the dominant species in alpine
forest, it occurs between 1700 and 2000 m,
surrounding the bare rock and volcanic lake on
the top of the mountain and the other mountain
peak in the southwestern area of the reserve (Fig.
2). Starting in 1990s, mountain birch has three
major age groups, 190 (40%), 160 (7%), and 110
(50%). All of them reach their mean longevity of
200 years before 2090. Regeneration does not
appear to be a problem for mountain birch
reserve-wide. Abundance data across 300 years
suggests that mountain birch maintain a relatively
stable 7/8% of the study area (Fig. 3b). By 2090,
second generation of mountain birch reaches 90,
60, and 10 years old and this cycle continues. At
year 2290, age classes of mountain birch are
diversified, in the ranges of 10 /60 (35%), 100 /
130 (20%), and 160 /190 (30%) years. Simulation
H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
Fig. 2. Snapshot of simulated distributions and age classes of mountain birch, spruce, fir, Korean pine, aspen, and maple at years 1990, 2090, 2290. Other species are not
mapped due to the limitation of the color plate. However, the distribution of birch forest is similar to aspen and most other broad-leaf species are similar to maple. Refer
to Table 1 for species name abbreviations.
159
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H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
Fig. 3. Simulated 12 major tree species abundance in percent
cover over of the study area over 300 years (1990s /2290s).
Refer to Table 1 for species name abbreviations.
results indicate that its distribution remains relatively unchanged over the next 300 years.
Spruce and fir are the dominant species from
1100 to 1700 m. Spruce has a wider distribution
than fir, overlapping some of the area of larch
forest primarily on the North Korea side of the
reserve and some mountain birch forest surrounding the southwestern peak of the reserve (Fig. 2).
Trajectory of percent cover for spruce suggests
that its abundance increase significantly, from 12
to 26% of the study area. Such increases, however,
largely occur to its current distribution areas. In
year 1990s, majority of spruce is around 140 years
old with some older stands around 190 and
younger stands between 30 and 50 (Fig. 2). In
2090s, spruce of 240 years old dominates in the
typical spruce-fir forest. Spruce from North Korea
side of the reserve is gaining in abundance over
larch as we observe a simultaneous decrease of
larch reserve-wide from 2070 to 2110 (Fig. 3a).
This decrease is due to the existing age structure of
larch, which climbs back gradually to its 1990s
level in about 100 years (Fig. 3a). At year 2290, the
first generation of spruce in the spruce-fir landtype
have reached longevity and died, and the new
generation once again reached 140 years. The age
structure of spruce in the reserve is much more
diversified than in 1990s (Fig. 2). Fir, on the other
hand, experiences a slight and gradual decline
throughout the next 300 years. As the most shade
tolerant species with relatively poor seeding viability, fir in this area begins with about 12% in
percent cover in 1990s and slides to about 9% in
2290s (Fig. 3a).
Korean pine is widely distributed below 1100 m
and mixed with hardwood species such as maple,
elm, and ash. With an average longevity of 400
year and relatively high shade tolerance, Korean
pine dominated the region from 500 to 1200 m and
was the primary timber sources throughout northeastern China. It is now largely removed from
landtypes of the lower elevations (e.g. landtype 14)
(Figs. 1 and 2). In 1990s, Korean pine occurs on
only about 7% of the study area surrounding
spruce-fir forests. From 1990s to 2290s simulation
result shows a dramatic increase of Korean pines
in abundance (Fig. 3a). Such increases, however,
largely occur to the areas where Korean pine
already exists and the seed sources are abundant.
In year 1990s, the dominant age classes are 80 and
150/200 years. By year 2090s, these Korean pines
turns 180, 250/300 years old, while younger age
classes between 10 and 100 years increase. At year
2090, Korean pine is also seen spreading towards
the hardwood landtypes where they dominated
before the forest harvesting. The establishment
process is slow and shown primarily along the
edges of established Korean pine patches (Fig. 2).
By year 2290, Korean pine is simulated to occur on
about 1/3 of the area of the landtypes where it can
dominate. Its percent cover grows to about 28% of
the study area (Fig. 3a).
Hardwood forests occur mostly in low elevations (e.g. B/700 m). Typically after the mixed
Korean pine forests are removed, aspen-birch
forest regenerate. This is most common on the
western and northwestern landtypes where 30 /40
H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
years old aspen-birch forests are abundant. These
early successional species increase in abundance
quickly (Fig. 3c) from about 5% of the study area
in 1990s to 16% (for birch) and 22% (for aspen) of
the study area in 2050s. However, their abundance
decrease as mid and late successional species such
as basswood, ash, maple and Korean pine reestablish (Fig. 3a/c). Aspen has the highest percent
cover around year 2090 with majority of pixels
reaching an age of 140 years old. In 2290s, the
overall age of aspen decrease and its age classes
also diversified because of mortality related to
longevity (Fig. 2). Similar to Korean pine, the
abundance of maple also increases substantially,
from 3 to 14% of the study area largely within its
current area and is seen expanding to the lower
adjacent landtypes succeeding aspen and birchdominated areas (Fig. 2).
Unlike mountain birch and the coniferous
species that respond to environmental gradients,
hardwood species such as birch and aspen respond
to both fire disturbance and forest cutting
strongly. From age class distributions of all the
studied species except mountain birch, the effects
of natural disturbance can be observed. These can
be seen as small and young patches created by fire
nested within old patches (Fig. 2). Due to the fire
suppression and long mean return interval for
both fire and windthrow, these natural disturbances are not significant in altering species
composition and spatial pattern in the study area.
3.2. Variations within forest types along the
northern slope
Results on four land units along northern slope
show greater differences in detail and variation of
species composition than within the other major
forest types. These four land units are the typical
of sub-alpine (land unit 3.1), spruce-fir (land unit
4.3), mixed Korean pine hardwood (land unit 5.1),
and hardwood forests (land unit 14).
In land unit 3.1, mountain birch, instead of
maintaining a stable abundance reserve-wide (Fig.
3b), shows a dramatic decline after the majority of
mountain birch on the land unit reaches its longevity around year 2100, it will likely be replaced by
a steady increase of larch (Fig. 4a). In land unit
161
Fig. 4. Simulated major tree species abundance in percent
cover of four land units. Refer to Table 1 for species name
abbreviations.
4.3, we see a substantial increase of spruce in the
first four decades and relatively stable abundance
for the remaining 250 years. Such a trend is
different from what is seen for spruce on the entire
study area where a steady increase is predicted
only until year 2250. The trajectories of fir and
larch in this land unit are similar to that of the
study area (Figs. 3 and 4). In general, species
composition in land unit 4.3 remains relatively
stable at a quasi-equilibrium status. This result is
consistent with studies on this forest type (Zhao,
1980; Li and Deng, 1981). Results derived in the
mixed Korean pine-hardwood land unit and hardwood land unit also reflect the trends found at the
entire study area, but with greater variations (Figs.
3 and 4). This is because both hardwood species
and Korean pine predominately occur on these
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H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
two land units. The results indicate a steady
increase in Korean pine, maple, and elm, and a
decline in oak. Early successional species such as
aspen and birch show more periodic dynamic with
their abundance decreasing after year 2150 as the
abundance of mid to late succession species
increase.
period (Fig. 3). The P /A ratio of Korean pine
shows a consistent increase throughout 300 years
indicating a less aggregated distribution. This is
because Korean pines are found expanding to
regions of lower elevation. The interspersion of
Korean pine with hardwood species results in
abundant edges and complicated shapes (Fig. 2).
3.3. Spatial pattern of dominant tree species
4. Discussions
To quantify the spatial pattern of dominant tree
species, we measured perimeter/area ratio (P/A )
using a landscape statistical software, APACK
(Mladenoff and Dezonia, 1997). Higher P /A
values usually indicate complicated perimeters
surrounding small areas, and, therefore, lower
degrees of aggregation. Relatively small variations
in P /A measurements are observed with P /A
values falling between 0.10 and 0.13 (Fig. 5).
This is because these dominant tree species are
distributed largely following the environmental
gradients such as elevation in Changbai area. Fir
has the lowest variation in P /A ratio, comparable
to its stable percent cover. The distribution and
abundance of fir remain fairly consistent throughout the simulation. P /A values for spruce have
relatively high variation and this also corresponds
to its’ percent cover and distribution. The decrease
in P /A (as low as near 0.1) from 1990 to 2090
indicates a more aggregated distribution resulting
from an increase in its percent cover during this
4.1. Result validations
Fig. 5. P /A ratios for dominant tree species. Higher P /A ratios
usually indicate complicated (long) perimeters surrounding
small areas, and, therefore, lower degrees of aggregation. Refer
to Table 1 for species name abbreviations.
A significant challenge facing spatially explicit
forest landscape models is how to validate the
simulation results. Validation in the traditional
sense involves using independent data at a given
time and space to check against model predictions
for that time and space. However, landscape
models such as the one presented in this study
derive results for a large landscape, over a long
period of time and since landscapes are not
spatially replicable and the temporal dimensions
of the model simulation often go beyond the
experimental and field observations collected
(Turner et al., 2001). Model validation in the
traditional sense (Rykiel, 1996) is not applicable
for spatially explicit landscape models. Furthermore, a goodness-of-fit simulation results to one
particular realization of data for the real system
may not indicate the same fits to other possible
realizations of the same real system (Loehle, 1997).
Loehle (1997) proposed a hypothesis-testing framework for evaluating ecosystem models. This
framework emphasizes biological and ecological
realism in addition to the typical model evaluating
techniques.
LANDIS has been subjected to typical model
evaluation procedures (e.g. O’Neill et al., 1980;
Gardner et al., 1981; Warwick and Cale, 1988;
Turner et al., 1994; Loehle, 1997) including
sensitivity analysis, uncertainty analysis, and
structural analysis (Mladenoff and He, 1999; He
and Mladenoff, 1999a). It has been widely validated against many different settings of species
and environments. The model provided scientifically acceptable explanations of the cause /effect
relationships among included parameters in many
H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
applications (He and Mladenoff, 1999a; Shifley et
al., 1997, 2000; Franklin et al., 2001). While typical
model evaluation techniques such as sensitivity
and uncertainty analysis provide insights on model
parameterization and result variations, they do not
necessarily prove the validity of the simulation
results. Thus, validation efforts in this study are
focused on the concept of ecological realism of
LANDIS simulation results.
4.1.1. Realism of species distribution
In relatively undisturbed natural area, environmental factors such as climate, soil, and landform
dictate landtype boundaries that usually closely
observed by vegetation types. Therefore, change of
vegetation boundaries is unlikely when these
environmental variables are held constant in this
simulation. Our results indicate that the distribution of each species conforms to the ecological
realism in this region. For example, mountain
birch is predominately found in areas between
1700 and 2000 m, spruce-fir, in general, stay within
their landtype boundaries, Korean pine and some
hardwood species such as maple expand and
establish towards the landtypes from which they
were previously removed. These results are not
predetermined in the model and the model did not
have any barrier for these species to seed up or
down slope. Rather, competition, succession, dispersal, and establishment processes simulated by
the LANDIS model realistically simulated species
distributions that are consistent with our understanding of how these species function in this
landscape (Wang et al., 1980; Li and Deng, 1981;
Xu, 1992).
4.1.2. Realism of species composition
For each of the major forest types, our results
show species compositions consistent with our
expectations. For examples, sub-alpine forest
type has relatively low species diversity (Qian
and Zhang, 1980; Liu et al., 1992; Chen and
Feng, 1985) and our simulation resulted in the
species compositions of mountain birch and larch
with no other species presence. On spruce-fir forest
type, our results indicated that highest average
abundance for spruce (58%) followed by fir (38%)
and larch (28%). Korean pine and mountain birch
163
cover only 0.7 and 1.7% of this land unit. These
results agree with several ecosystem-level studies
(Wang et al., 1980; Li and Deng, 1981; Li et al.,
1994). For mixed Korean pine hardwood type, the
species with the highest abundance is Korean pine
(55%). Hardwood species includes basswood
(41%), maple (32%), and ash (31%), oak (14%),
aspen (9%). This result is also supported by
ecosystem-level studies (Wang et al., 1980; Shao
et al., 1994). Results of age class dynamics
presented for each species also falls in the correct
ranges reflecting the expected successional cycles
of each species.
4.2. Result implications
Forest landscape change driven by the natural
factors is a gradual process. Direct and indirect
human impact may produce long-term alterations
to forest landscape patch structure that persist for
decades to centuries. Our results show that even
with complete natural succession over 300 years,
Korean pine recovers only on 1/3 of the area on
the landtypes it used to dominate. A full recovery
would take another 200/300 years. The results
again show that humans can alter a forest landscape in a few decades creating effects that can
take hundreds of years from which to recover.
Landscape-scale recovery is often limited by the
available seed sources and this is particularly true
for Korean pines in this region also shown by a
separate study (Shao et al., 1994). Seed dispersal
generally occurs through time and within the radii
defined by species effective and maximum seeding
distances (He and Mladenoff, 1999b). If the seed
source is absent in the area, it will need to wait
until the seedlings arrive, establish, and mature.
The establishment process can be slow as shown
for Korean pines, with the establishing patches
occurring around the mature patches and encroaching into areas where Korean pine can
establish (Fig. 2). A forest harvesting plan which
preserves seed trees such as residual or shelterwood cutting (Gustafson et al., 2000) or other
landscape scale restoration efforts such as seedling
planting may shorten the natural recovery process
as discussed elsewhere (Radeloff et al., 2000).
164
H.S. He et al. / Ecological Modelling 156 (2002) 153 /166
Landscape equilibrium (Turner et al., 1993;
Baker, 1989) is dependent on landscape heterogeneity, landscape extent, and time scales. In this
heterogeneous system, equilibrium in landscape
structure and composition is approached on the
larger landtypes dominated by shade tolerant
species, but not on landtypes altered by human,
at the scales simulated. Such equilibrium is seen
for the spruce-fir, mountain birch, and larch
forests and not seen for the aspen-birch forests
that show much greater variation through time.
Results for the long-term simulation suggest that
temporal variations cannot be captured by shortterm experiments. The trajectories of species
succession at a given time are related to the
successional stage that operates at temporal scales
of decades even to hundreds of years. In addition,
results derived for a given land unit often have
greater variations and may not capture the trends
of the entire study area. Thus, the fluctuations of
species trajectories indicate that the space-for-time
substitutions can be flawed (Levin, 1992) as species
composition and age class on a given site at a given
time may represent a particular successional history for the site. This is particularly true for
human disturbed forest landscapes where higher
variations are observed. Therefore, large spatial
scale and long-term simulations are necessary to
understand the overall forest landscape dynamics.
Acknowledgements
The research is partly funded by Chinese
Academy of Sciences (CAS) and the University
of Missouri-Columbia GIS Mission Enhancement.
We thank Gufan Shao for providing several GIS
data layers including the classified TM satellite
image of the study area. We also thank the
Institute of Applied Ecology of CAS and ChangBai Open Laboratory of Forest Research of CAS
for providing data and necessary research facility.
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