For. Sci. 60(●):000 – 000
http://dx.doi.org/10.5849/forsci.13-097
Copyright © 2014 Society of American Foresters
FUNDAMENTAL RESEARCH
silviculture
Converting Even-Aged Plantations to Uneven-Aged
Stand Conditions: A Simulation Analysis of
Silvicultural Regimes with Slash Pine
(Pinus elliottii Engelm.)
Ajay Sharma, Kimberly Bohn, Shibu Jose, and Wendell P. Cropper
There has been increasing interest in managing forest stands as uneven-aged structures to promote sustainable harvests as well as maintain ecosystem services. This
study provides a framework for simulating conversion of mature even-aged stands to uneven-aged slash pine (Pinus elliottii Engelm.) stands using the USDA Forest
Vegetation Simulator (FVS) model. A total of 73 scenarios, representing combinations of two harvest methods (based on either “BDq” and/or “low thinning”), two
harvesting cycles (10 or 20 years), three harvest intensities (4.6 or 8.0 or 11.5 m2 ha⫺1 residual basal areas), and six levels of regeneration (0 –2,224 seedlings
ha⫺1) were evaluated for structural diversity, timber production, and carbon (C) stocks over a 100-year period. The BDq harvest approach, which applied selection cutting
based on diameter regulation from the first cutting cycle onwards, resulted in higher structural diversity. Scenarios based on low thinning in the first cutting cycle and
BDq method from the third cutting cycle onwards tended to result in higher total merchantable timber and C stocks over the entire simulation period, particularly at
higher residual basal areas and longer cutting cycles. None of the scenarios maximized all of the three variables simultaneously. Based on the desired objectives, land
managers can choose among scenarios presented. The study revealed that regeneration and establishment of as low as 247 seedlings ha⫺1 can lead to successful
conversion and multiple benefits from uneven-aged slash pine stands.
Keywords: stand conversion, forest vegetation simulator, structural diversity, volume production, flatwoods
U
neven-aged stands are structurally diverse with three or
more age classes and exhibit, in a balanced state, a reverse
J-shaped dbh distribution (Smith et al. 1997, Nyland
2002). Such stand conditions are often viewed as ecologically and
economically efficient systems for managing forestlands (O’Hara
and Nagel 2006, Tahvonen et al. 2010, Boncina 2011, Kuuluvainen
et al. 2012), and it is widely believed that uneven-aged silviculture
can improve forest resource use for multiple objectives in a sustainable manner (Brockway et al. 2005, O’Hara and Nagel 2006). Although from a timber and fiber production standpoint even-aged
systems have long been generally perceived to be more efficient and
profitable (Cafferata and Kemperer 2000, Henderson 2008,
Axelsson and Angelstam 2011), these products are not the only
value derived from forestland, and increasingly, they are not the
primary objective of management or even the most profitable product or service produced (Tarp et al. 2005, Pukkala et al. 2010,
Pukkala et al. 2011, Laiho et al. 2011). Biodiversity, wildlife habitat,
recreation, and environmental services such as carbon sequestration
are becoming increasingly important (Sedjo 2001, McConnell
2002, Food and Agriculture Organization [FAO] 2006, Gorte
2009) and may be incompatible with the forest structure associated
with even-aged stands (MacArthur and MacArthur 1961, Thomas
1979, McConnell 2002, Laiho et al. 2011, Marion et al. 2011).
Uneven-aged forest stands inherently may have higher resilience and
potential to adapt to changing climate (Guldin 2011) and, thus,
have been suggested to be an appropriate form of future risk management. Uneven-aged silviculture is also believed to mimic natural
disturbances in sustaining many forest ecosystems (Brockway et al.
Manuscript received June 29, 2013; accepted March 6, 2014; published online April 3, 2014.
Affiliations: Ajay Sharma ([email protected]), University of Florida, West Florida Research and Education Center, Milton, FL. Kimberly Bohn
([email protected]), University of Florida, Milton, FL. Shibu Jose ([email protected]), University of Missouri, Columbia, MO. Wendell P. Cropper
([email protected]), University of Florida, Gainesville, FL.
Acknowledgments: The study was supported by the cooperative for Conserved Forest Ecosystems–Outreach and Research (CFEOR) funding, McIntire Stennis, and the
University of Florida, School of Forest Resources and Conservation. The authors would like to express their sincere gratitude to Michael Morgan, Melissa Kreye, Justin
McKeithen, and others who helped in the fieldwork and the Florida Forest Service for the opportunity to use the state forestlands and for in-kind support in timber
marking and facilitating logging operations.
Forest Science • MONTH 2014
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2005, Sharma et al. 2012). Not only are the uneven-aged forest
stands valued for their ecosystem services (Axelsson and Angelstam
2011, Boncina 2011), but many comprehensive studies in a variety
of forest types have emphasized the equity or superiority of unevenaged stands in economic production as well (Haight 1987, O’Hara
and Nagel 2006, Tahvonen et al. 2010, Pukkala et al. 2011, Kuuluvainen et al. 2012). Changing public preferences and perceptions
have led to increasing disapproval of even-aged methods such as
clearcutting (Gobster 1996, Lindhagen 1996, Bliss 2000, Gundersen and Frivold 2008), and the stand conditions associated with
uneven-aged forest structures are being viewed as esthetically favorable (Silvennoinen et al. 2001, Bradley and Kearney 2007, Edwards
et al. 2012). Any form of forest management must consider all the
diverse benefits, including timber production and provision of ecosystem services, ranging from biodiversity to carbon storage to various social functions (FAO 2006, Diaci et al. 2011).
As a consequence, there has been a renewed and increasing interest in managing forest stands as uneven-aged structures (Nyland
2003, Pommerening and Murphy 2004, Loewenstein 2005, Mizunaga et al. 2010, Diaci et al. 2011). Many agencies and some private
landowners now have a mission that includes conversion of plantations into uneven-aged stands (Guldin and Farrar 2002, Loewenstein and Guldin 2004, Florida Division of Forestry 2007, Brockway and Outcalt 2010). However, our experience with such
conversion and uneven-aged silviculture is limited. Since most of
the research and practice in the past century has focused on evenaged silviculture, forest managers lack information to guide their
long-term stand conversion projects. Additionally, the implications
of stand conversion and application of uneven-aged silviculture in
managing forestlands for multiple benefits are uncertain. Managing
forests for balanced production of wood and providing ecosystem
services on a sustainable basis is critical for meeting the demands of
the 21st century (United Nations 2007, Diaci et al. 2011).
Several strategies have been suggested to convert and subsequently manage plantations as uneven-aged stands. These typically
involve partial cuttings either uniformly over the stand or in patches,
intensive enough to allow successful establishment of multiage cohorts and eventually allowing the application of uneven-aged silvicultural systems such as single tree selection or group selection system (O’Hara 2001, Guldin and Farrar 2002, Nyland 2003,
Loewenstein 2005, Kerr et al. 2010). One approach to partial cutting of even-aged stands involves heavily thinning the even-aged
stand in the beginning by removing the inferior or suppressed trees
to create a vigorous residual stand to which a new age class will be
added following natural regeneration (Nyland 2003, Loewenstein
2005). Another suggested approach is to apply the selection cut
based on diameter regulation, which aims at creating a reverse-J
shaped diameter distribution directly (Della-Bianca and Beck
1985). Field trials to evaluate these strategies generally span a period
of many decades before their relative effectiveness can be evaluated.
Simulation models, however, can be used to predict stand dynamics
or to conduct sensitivity analyses on different silvicultural regimes in
lieu of evidence that will take several decades to develop with experimental field trials (Vanclay 1994, Weiskittel et al. 2011).
In this study, we used USDA Forest Service’s Forest Vegetation
Simulator (FVS) model (Crookston and Dixon 2005) to evaluate a
variety of silvicultural regimes to convert a mature plantation to an
uneven-aged stand. A few other studies have used species-specific
models to simulate uneven-aged stand growth or conversions such
as the SILVA model for Norway spruce (Picea abies L. Karst.)
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Forest Science • MONTH 2014
(Hanewinkel and Pretzsch 2000) and PINEA IRR for stone pine
(Pinus pinea L.) (Calama et al. 2008). The FVS model, unlike species-specific models, has the ability to simulate conversion of a variety of species and forest types. In the present study, slash pine (Pinus
elliottii Engelm.), one of the commercially most important species
in southeastern United States, was used as a case study of this approach. Slash pine’s total acreage—including mixed stands with
longleaf pine (Pinus palustris Mill)—in the southeastern United
States was 5.3 million ha in 2007 (Smith et al. 2009). About 69% of
slash pine stands exist as intensively managed plantations with rotation ages of 30 years or less (Barnett and Sheffield 2005). Owing to
slash pine’s strong shade-intolerant nature (Baker 1949, Lohrey and
Kossuth 1990), the transition to an uneven-aged stand would possibly require a low residual basal area to promote an adequate
amount of understory light for regeneration and seedling growth. A
basal area approaching as low as about 4 to 5 m2 ha⫺1, as would be
used with the irregular shelterwood method, has been suggested for
slash pine conversion and management, and such scenarios are currently being evaluated in field trials on some Florida state forestlands
(Brockway and Outcalt 2010, Sharma et al. 2012). This approach
would likely result in adequate natural regeneration and, in particular, would help maintain a species-rich understory and open pinesavanna conditions (Jose et al. 2006, Brockway and Outcalt 2010).
However, a residual basal area of up to 11.5 m2 ha⫺1 has also been
reported to have no adverse effect on the early seedling establishment or growth of slash pine (McMinn 1981, Brockway and Outcalt 2010) and may, thus, be one of the possible levels of appropriate
residual basal areas to maintain in uneven-aged stands for timber
production and ecosystem services. Intermediate levels of residual
basal area (e.g., 8 m2 ha⫺1) are also options for uneven-aged management of pine stands in Florida. Overall, 4.6 and 11.5 m2 ha⫺1
may be considered as two extremes of residual basal area levels
within which slash pine can be managed as uneven-aged stands for
multiple benefits. The lower level may be more conducive to primarily promoting open pine-savanna conditions with diverse
groundcover dominated by grasses and forbs while the higher basal
area would be more reflective of a stronger objective of timber production along with other ecosystem services.
The success of uneven-aged silvicultural methods also depends
on its ability to promote regeneration of desired species and to have
that regeneration develop into merchantable size classes (Shelton
and Cain 2000). However, our understanding about the minimum
level of regeneration that would be periodically required for successful application of uneven-aged silviculture still remains limited
(Guldin 2006). Since slash pine in the southeastern United States
has been mostly cultivated as intensively managed, high-density,
short-rotation plantations for the purpose of timber and fiber, scientific literature is critically deficient in studies on its natural regeneration. This is, perhaps, true for a majority of the species that have
long been artificially regenerated for high-intensity plantations.
Lacking long-term studies of natural regeneration, simulation modeling is a useful starting point for addressing this question. We
simulated stand conversion over a range of regeneration possibilities
to identify optimum levels of regeneration that would satisfy forest
management objectives. Because regeneration in forest ecosystems is
influenced by a number of factors related to management, including
residual basal area, site preparation, competition control, and other
species-specific disturbances such as prescribed fire and herbivory,
(Dickens et al. 2004, Jose et al. 2006), an appropriate level of regeneration, when known, can be targeted by manipulating management practices and/or supplementing natural regeneration with
planting.
Thus, the overall objective of the study was to evaluate different
silvicultural regimes, over a range of levels of regeneration, for their
feasibility to successfully convert plantations to sustainable unevenaged stands. The FVS model used in the study is a highly versatile
system that is capable of modeling a variety of forest types and stand
structures ranging from even-aged to uneven-aged by specifying any
combination of management activities during any cycle of the simulation period (Crookston and Dixon 2005). We evaluated two
harvest approaches for stand conversion at three harvest intensities
and two cutting cycles. The regimes were assessed in terms of their
long-term ecological and economic benefits. Specifically, we evaluated stand structural diversity, carbon (C) stocks, and merchantable
timber production over a 100-year period.
Materials and Methods
Simulation Input Data
We used data collected from a mature slash pine plantation at
Tate’s Hell State Forest, Florida, to initiate simulation of stand
conversion. Tate’s Hell State Forest (29.83N, 84.79W) is a poorly
drained, lowland, mesic-hydric flatwoods site between the Apalachicola and Ochlockonee Rivers in the panhandle of Florida. Largescale silvicultural operations and hydrologic manipulations in Tate’s
Hell State Forest during the 1960s through the 1980s converted
extensive areas of native habitats to slash pine plantation. Stands
were established following intensive mechanical site preparation,
bedding, and planting at high densities. Fertilizers, particularly nitrogen and phosphorus, were applied at midrotation. These largescale disturbances and manipulations to the habitats altered the
ecological communities and hydrology of the area (Gilpin and Vowell 2006). Currently, one of the main land management goals in
Tate’s Hell State Forest is to convert and maintain these plantations
as low-density uneven-aged stand structures that will maintain biological diversity, while integrating public use (Florida Division of
Forestry 2007).
The input data were collected from a mature unthinned slash
pine plantation scheduled for active conversion to an uneven-aged
stand. For this, we established five sample plots of 25 ⫻ 25 m in the
stand. Within each plot, we recorded dbh and species for all the trees
greater than 10 cm dbh. Within the 25 ⫻ 25 m plot, we also
established two 5 by 5 m plots at diagonally opposite corners in
which we measured all trees smaller than 10 cm dbh.
Model Description
We used the FVS model Version 4280 —Southern US
PROGNOSIS RV: 12/20/11, developed by USDA Forest Service.
FVS is a distance-independent, individual-tree forest growth model
that uses individual stands as the basic units of projection. The
applications of FVS span a wide variety of forest types and stand
structures ranging from even-aged to uneven-aged and single to
mixed species. FVS can model single stands or landscapes and large
regional assessments involving thousands of stands. The key variables of each tree are its species, diameter, height, crown ratio, diameter growth, and height growth; however, only species and diameter information is mandatorily required. The key variables for each
sample point, or plot, include slope, aspect, elevation, density, and a
measure of site potential. The model has a self-calibration feature
that adjusts the internal growth models to match the measured
growth rates of a particular location consistent with the source of the
input data. Thus, it is able to modify predictions for local conditions. The growth cycles in the simulator are set to 5- or 10-year time
steps with up to 40 cycles allowed per simulation (Dixon 2002,
Crookston and Dixon 2005).
The model has the ability to simulate many types of harvest and
other silvicultural activities. The user has the options to specify how
many trees or the basal area from which size classes should be retained at the end of any simulation cycle along with other activities
such as shrub removal or artificial planting. Typical growth and
mortality rates are predicted as functions of postharvest conditions
(residual densities/basal area etc.). Broadly, the FVS model has four
primary components: diameter growth, height growth, mortality,
and crown change. Diameter growth, height growth, and crown
change models are further divided into submodels that pertain to
“small” trees (ⱕ 7.6 cm dbh) and those pertain to “large” trees
(⬎ 7.6 cm dbh). The small tree growth relationships are driven by
tree height from which diameter growth is then estimated. On the
other hand, the large tree growth relationships are driven by diameter first, and then height growth is estimated from diameter growth
and other tree and stand variables. The FVS-sn model is a widely
used model (Teck et al. 1996, Gilmore 2003, Johnson et al. 2007,
Sorensen 2011, Saunders and Arseneault 2013) and more details
about its contents, description, structure, and applications can be
found in McGaughey (1997), Van Dyck and Smith (2000), Donnelly et al. (2001), Dixon (2002), Crookston and Dixon (2005),
and Keyser (2008).
FVS-sn does not have a full regeneration model. The user must
schedule natural regeneration events by specifying input values for
density and size of expected new trees based on other published
literature and/or its understanding of species ecology. However, it
can simulate regeneration from sprouting species, the feature we
turned off in our simulations.
The inventory data collected from the slash pine plantation were
used to initialize the simulations in FVS model using specific control
variables (Table 1; Figure 1). Parameter inputs (e.g., location, ecological codes, etc.) were selected that best represented the site conditions at Tate’s Hell State Forest. The outputs generated from FVS
were used to calculate structural diversity, carbon stocks, and merchantable timber production as described below.
Silvicultural Regimes
We followed two approaches in developing silvicultural regimes
for stand conversion. One approach was the direct application of the
uneven-aged BDq method beginning with the first cut. The BDq
method is a common approach used in developing marking tallies
for single tree selection system where “B” refers to residual basal area,
“D” is the maximum diameter class to retain in stand, and “q” is
diminution quotient. Specifications of B, D, and q constitute a
unique reverse J-shaped distribution of diameter, which, theoretically, characterizes an ideal balanced uneven-aged stand. The diameter distribution of the stand to be cut is compared to that of the
ideal, balanced uneven-aged stand, and a marking tally is generated
that brings the existing stand closer to the balanced uneven-aged
structure (Farrar 1996, Smith et al. 1997, O’Hara and Gersonde
2004, Guldin 2006, Keyser 2008). In the FVS model, the BDq
method is implemented by supplying the specifications for B, D,
and q along with the diameter class width and a cutting cycle. In
cases where deficit diameter classes are found, excess basal area is
Forest Science • MONTH 2014
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Table 1. Control variables inputs specified to FVS-sn variant to
control the simulation for the study.
Parameter or attribute
Location Code
Ecological Unit codes
Slope (%)
Aspect
Elevation(m)
Site Species Codes (Alpha code)
Site Index(m) for slash pine
Maximum density (trees/ha)
Number of projection cycles
Projection cycle length
Volume equations
Volume Specifications
Minimum dbh/top
Diameter inside bark (cm)
Stump height (cm)
Sampling Design
Large trees (fixed-area plot)
Small trees (fixed-area plot)
Breakpoint dbh (cm)
Input setting
8,0501 (Apalachicola National
Forest in Florida, Latitude:
30.44 N, Longitude: 84.28W)
232Dd (Gulf coastal lowlands)
0
0
9.15
SA, PC, MG,BY
27.43
1075
21
5
National Volume Estimator
Library
Pulpwood
Sawtimber
10.2/10.2
25.4/17.8
30.5
30.5
⫺6.5
81
10.2
Figure 1. FVS processing as used in simulating different silvicultural regimes for converting even-aged plantations to uneven-aged
stands.
proportionally distributed to the other classes to maintain the target
residual basal area (Keyser 2008).
The other conversion approach we used involved thinning from
below during the first cutting cycle, followed by thinning across
diameter classes at the second cutting cycle, and then following with
BDq based cuts from third cutting cycle onwards (Table 2). “Thinning from below” or “low thinning,” involved cutting all the trees of
small size first followed by larger-sized trees till a desired residual
basal area was achieved. During the first cut this created a residual
stand consisting of trees with large diameters. At the second cutting
cycle, thinning across all the diameter classes resulted in removal of
some trees from both established age classes during this time period.
From the third cutting cycle onwards, a BDq cut similar to our other
harvest regime was implemented across the three established cohorts
following regeneration and growth modules of the simulator. Thus,
the two harvest approaches simulated in the study differed only at
the first two cutting cycles.
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Forest Science • MONTH 2014
A total of 73 scenarios were simulated, each with three replications. Each scenario represented a unique silvicultural regime consisting of a harvest type, residual basal area, cutting cycle, and regeneration density (Table 2). These scenarios varied from “no action” to
direct application of “BDq approach” beginning with the first cut
(hereafter called “BDq scenarios”), to “low thinning in the first cut,
thinning across diameter classes at the second cutting cycle, and
BDq-based cut from third cutting cycle onwards” (hereafter called
“low thin scenarios,” named after the first cut). For all the cuts based
on BDq in both BDq scenarios and the later cuts in the low thin
scenarios, we set the values of parameters such that D ⬎ 86.4 cm and
q ⫽ 1.5 for diameter-class width of 5 cm. The residual basal areas
following harvests were either 4.6, 8.0, or 11.5 m2 ha⫺1 (hereafter
called “low,” “intermediate,” or “high”) on a 10- or 20-year (hereafter called “short” or “long”) cutting cycles during the simulation
period. Regeneration scenarios varied from complete failure of natural regeneration (hereafter called “no regeneration scenarios”) to
abundant regeneration of as many as 2,224 seedlings ha⫺1 in increments of about 495 seedlings ha⫺1 during each cutting cycle. For
these simulations, we specified regeneration as the seedlings of slash
pine with average height of 60 cm surviving 3 years after each cutting cycle. Each simulation was run for a total of 100 years after the
first cut at simulation cycle lengths of 5 years. The naming scheme
for the scenarios consisted of a sequence of four variables in the form
“basal area-harvest type-cutting cycle-regeneration.” For example,
the scenario “11.5-thin-10 –247” represents a regime with residual
basal area of 11.5 m2 ha⫺1, treated with the low thinning harvest
method, a cutting cycle of 10 years, and 247 seedlings ha⫺1 of
regeneration following every harvest.
Model outputs included a tree list, a carbon report, a stand summary, and a cut list for each simulation (Dixon 2002). The individual trees in the tree list were then classified into diameter classes and
height classes. We used a total of 18 diameter classes with class
widths of 5.08 cm, ranging from size 0 (less than 2.54 cm) to 86.4 ⫹
cm (equal to or greater than 83.8 cm). For height, we used a total of
16 height classes with class widths of 3.05 m, ranging from 0 (less
than 1.52 m) to 45.72 ⫹ (equal to or greater than 44.2 m). In both
the cases, the classes represented the midpoints of the class widths.
Evaluation of Silvicultural Regimes
The scenarios were evaluated based on the resulting stand structural diversity, C stocks, and merchantable timber.
Stand Structural Diversity
At every cycle of the simulation period, we calculated the Shannon Diversity Index for both the diameter class and height class
distributions with respect to the basal area constituted by them. The
Shannon Index was calculated as equal to ⫺冱pi ln pi, where p is the
proportion of basal area constituted by a diameter class i (Magurran
1988). Stand structural diversity was then obtained as the average of
the Shannon Index values for both diameter and height classes.
Every simulation had 21 values of Shannon indices for diameter and
height distributions each, one for every cycle plus one at the beginning of the simulation. Values were plotted over time and the average of Shannon Index values at all cycles during a simulation was
calculated as average stand structural diversity under a given
scenario.
C Stocks
The outputs of the C reports obtained from the model were
grouped into aboveground stored C (consisting of aboveground live
Table 2. Overview of the scenarios used in simulating conversion of slash pine plantations to uneven-aged stands. Each of the harvest
types (BDq or low thin) was simulated over each combination of residual basal area (4.6 or 8.0 or 11.5 m2 haⴚ1), cutting cycle (10 or
20 yr), and level of regeneration (0 to 2224 seedlings haⴚ1) as described below. All the scenarios had maximum diameter (D) > 86.4
cm, and diminution quotient (q) ⴝ 1.5 for simulating BDq cuts in final stages.
Harvest
type
BDq
Low thin
No action
Description of silvicultural regime
BDq cut from first cutting cycle onwards
1. Thinning from below during first cutting cycle
2. Thinning across diameter classes during second cutting cycle
3. BDq cut from third cutting cycle onwards
No cut of any kind
Residual basal areas
(m2 ha⫺1)
Cutting cycles
(years)
Regeneration ranges (seedlings/ha)a
4.6, 8.0, 11.5
4.6, 8.0, 11.5
10, 20
10, 20
0, 247, 741, 1,236, 1,730, and 2,224
0, 247, 741, 1,236, 1,730, and 2,224
NA
No harvest and no regeneration were
simulated in this scenario.
NA
a
The regeneration is defined as the seedlings of slash pine with average height of about 60 cm surviving 3 yr after each cutting cycle.
The name of a scenario consisted of four parts representing respectively “basal area-harvest type-cutting cycle-regeneration.”
tree, standing dead trees, down deadwood, forest floor, and
understory), below ground stored C (consisting of below ground
live tree and below ground dead tree), and total C removed in
harvest at each year during the simulation period (Hoover and Rebain 2011). The total stand carbon at a given time was the sum of
above and below ground stored carbon at that time. The following
variables were calculated:
(a) Total stand C in the beginning of simulation (year 0) ⫽ Total
aboveground stored C in year 0 ⫹ Total below ground stored C in
year 0.
(b) Total stand C at the end of simulation (year 100) ⫽ Total
aboveground stored C in year 100 ⫹ Total below ground stored C
in year 100.
(c) Total additional C stored during simulation period ⫽ (Total
stand C at the end of simulation (year 100) ⫹ sum of C harvested
during different cycles in simulation period) ⫺ Total stand C in the
beginning of simulation (year 0).
Average annual C stock for each scenario was then obtained by
dividing the total additional C stored by 100.
Timber Production
From the FVS summary output generated in simulations, we
calculated values of the following variables:
(a) Total merchantable timber produced during simulation period ⫽ Total merchantable timber removed during simulation ⫹
Total merchantable timber left standing after year 100 ⫺ Total
merchantable timber in year 0.
(b) Total sawtimber produced during simulation period ⫽ Total
sawtimber removed during simulation ⫹ Total sawtimber left
standing after year 100 ⫺ Total sawtimber in year 0.
Average annual productions of these variables were then obtained by dividing the totals by 100.
Statistical Analyses
As recommended by Hamilton (1991) for the application of
FVS, we ran three simulations for each scenario, reseeding the random number generator, and thus, producing variation in the projection results (Hong and Mladenoff 1999, Haefner 2005). A low
and stable variance across the simulations was observed. Means were
then calculated for average annual values of structural diversity, C
stocks, merchantable timber, and sawtimber for each scenario. Analyses of variance (analysis of variance [ANOVA]) were carried out for
all the scenarios to test for overall main effects of harvest type (BDq
and low thin), residual basal area, cutting cycle, regeneration level,
and their two-way interactions. ANOVA were done separately for
the scenarios within the three levels of basal area, i.e., within the
groups of scenarios with 11.5 or 8.0 or 4.6 m2 ha-1 basal areas.
Tukey’s Honestly Significant Difference (HSD) test was performed
at ␣ ⫽ 0.05 to test for significant differences in all of the analyses.
For each level of residual basal area, we then selected the top five
scenarios based on the means that maximized stand structural diversity, C stocks, and merchantable timber. Additionally, the top five
scenarios leading to maximization of multiple benefits (i.e., collective provision of structural diversity, carbon storage, and merchantable timber production) were identified for each level of residual
basal area. For this purpose, we first scaled the values of all three
criteria variables as percent of their maximum among all scenarios
and summed them for each scenario. Absent a good metric to directly assess the multiple attributes with different units and different
economic and noneconomic values, we assumed that higher sums
corresponded to greater ability to provide multiple benefits.
Results
Initial Stand Conditions
The overstory was dense (1,136 trees ha⫺1) with an average basal
area and mean quadratic diameter of approximately 28.7 m2 ha⫺1
and 18 cm, respectively. The diameters at breast height of the initial
slash pine plantation stand exhibited typical bell-shaped distribution associated with even-aged stands (Figure 2A and B). Occasionally sweetbay (Magnolia virginiana L.) and pond cypress (Taxodium
ascendens Brongn.) were found in the overstory. Slash pine made
about 63% of tree density and 96% of stand basal area. The lower
diameter classes were dominated by sweetbay that had regenerated
under the dense canopy of slash pine. Natural regeneration of slash
pine was lacking.
Evaluation of Silvicultural Regimes
None of the scenarios maximized all criteria variables simultaneously. There were tradeoffs involved between structural diversity
and C stocks, as well as merchantable timber production in different
scenarios. The scenarios that led to higher structural diversity generally resulted in lower C stocks and merchantable timber production and vice versa (Tables 3–5; Figure 3A–D).
Not surprisingly, either harvesting approach followed by complete failure of regeneration (no-regeneration scenarios) was among
the worst in terms of structural diversity and C stocks, as well as
merchantable timber production. The no action scenario, wherein
no cutting and no regeneration occurred during the period of simulation, also led to low values of these variables. Values for stand
structural diversity, C stocks, and merchantable timber production
Forest Science • MONTH 2014
5
in a significant (P ⬍ 0.001) harvest type by cutting cycle interaction.
Within each harvest type, scenarios with higher residual basal or
with the shorter cutting cycle led to higher average structural
diversity.
The top scenarios that maximized average structural diversity at
the high basal area of 11.5 m2 ha⫺1 included BDq scenarios with a
10-year cutting cycle and regeneration varying between 247
through 2,224 seedlings ha⫺1 (Shannon Index ⫽ 2.03 to 2.06;
Table 3). At the intermediate residual basal area of 8.0 m2 ha⫺1,
BDq scenarios with 10-year cutting cycle and regeneration varying
between 247 through 1,730 seedlings ha⫺1 again resulted in the
highest average structural diversity (Shannon Index ⫽ 2.03 to 2.05).
At the low basal area of 4.6 m2 ha⫺1, BDq scenarios” with 10-year
cutting cycle and regeneration of either 247 through 1,236 seedlings
ha⫺1 led to the highest average structural diversity (Shannon Index ⫽ 1.97 to 1.99) (Table 4).
Although low thin scenarios led to overall low average stand
structural diversities over the simulation period, they had achieved
comparable levels by the end of simulation and sometimes by year
50 (Figure 4A). The low overall average structural diversity in low
thin scenarios was highly influenced by initial cutting cycles during
the simulation period.
Figure 2. Input stand conditions inventoried in 2009. (A) Slash
pine (Pinus elliottii Engelm.). exhibited bell shaped diameter distribution typical of even-aged stands. However, due to dense stand
conditions and absence of any hardwood control measures, sweetbay (Magnolia virginiana L) had grown densely as an understory
and was fast becoming a part of midstory, (B) More than 95% of
basal area was constituted by slash pine. The stand basal area was
28.7 m2 haⴚ1.
under the no action scenario were overall similar or slightly superior
to the harvest scenarios with no regeneration.
Stand Structural Diversity
There were significant interaction effects of regeneration level
with harvest type, cutting cycle length, and residual basal area. Average structural diversity increased significantly (P ⬍ 0.001), from
0.75 to 1.76 when scenarios shifted from no regeneration to 247
seedlings ha⫺1, though the magnitude of the increase differed by
harvest types and cutting cycles (Figure 3A). Any further increases
from 741 to 2,224 seedlings ha⫺1 did not significantly change structural diversity except for a few scenarios, resulting in a slight but
statistically significant (P ⬍ 0.001) decrease in structural diversity
with increasing regeneration levels at the lower residual basal area
and longer cutting cycle.
Generally across all regeneration scenarios, harvest type had the
biggest influence on stand structural diversity value, which ranged
from 1.4 to 2.06 (Figure 3A). BDq scenarios resulted in higher
average structural diversity than the low thin scenarios within any
given residual basal area and cutting cycle (P ⬍ 0.001). However,
values for BDq scenarios at a 20-year cutting cycle were not different
from those of a low thin scenario at a 10-year cutting cycle, resulting
6
Forest Science • MONTH 2014
C Stocks
Annual average C stocks increased significantly (P ⬍ 0.001) and
substantially, by as much as three times, when shifting from no
regeneration to 247 seedlings ha⫺1 (Figure 3B). Thereafter, significant increases were observed with increasing regeneration in a few
scenarios, mostly with longer cutting cycles. The sensitivity of C stocks
to an increase in regeneration was evident when basal area was low and
cutting cycle was long. Also, in general, low thin scenarios were more
sensitive to regeneration than BDq scenarios. For example, low thin
scenarios with a 20-year cutting cycle and 4.6 or 8.0 m2 ha⫺1 residual
basal areas showed the highest sensitivity to increases in regeneration,
such that their C stocks became comparable to those of the scenarios
with 11.5 m2 ha⫺1 at high regeneration levels (Figure 3B).
Generally across all regeneration scenarios, we found significant
interactions between harvest type with cutting cycle (P ⬍ 0.001)
and with residual basal area (P ⬍ 0.001) on average C stocks. Generally, longer cutting cycles resulted in higher C stocks than shorter
cycles; however, C stock values derived from BDq scenarios were less
sensitive to changes in length of cutting cycle than low thin scenarios
(Figure 3B). This was more evident at the low residual basal than
intermediate or high residual basal areas. The main effect of harvest
type on C stocks was not significant.
Evaluation of the top scenarios at basal areas of 11.5 as well as 4.6
m2 ha⫺1 indicated that annual average C stocks were highest (2.23
and 1.92 metric ton ha⫺1 year⫺1 for 11.5 and 4.6 m2 ha⫺1, respectively) in low thin scenarios with a 20-year cutting cycle and high
levels of regeneration at 1,730 and 2,224 seedlings ha⫺1. The BDq
scenario with a 20-year cutting cycle and regeneration level of 2,224
seedlings ha⫺1 was also among the top scenarios (Table 3). At a basal
area of 8.0 m2 ha⫺1, the highest annual average C stocks (2.24
metric ton ha⫺1 year⫺1) were led by the low thin scenario at 20-year
cutting cycle and high regeneration of 2,224 seedlings ha⫺1 (Table
4), similar to the maximum realized at 11.5 m2 ha⫺1.
Over the simulation period, total stand carbon fluctuated as influenced by periodic harvests at different cutting cycles during the
simulation period (Figure 4B). At any given time, total stand carbon
was proportional to the basal area, and greater fluctuations in C
Table 3. Estimates (mean) of structural diversity, carbon stocks, and timber production in top scenarios at 11.5 m2 haⴚ1 basal area. Five
top scenarios for each of the variable that maximized their value and five scenarios that maximized multiple benefits were selected. Eight
scenarios overlapped resulting in a total of 12 scenarios.
Scenario
Average stand structural
diversity (Shannon
Index)
Annual average C stocks
(metric ton/ha/year)
Annual average merchantable
wood production
(m3/ha/year)
Annual average sawtimber
production
(m3/ha/year)
Rank (provision of
multiple benefits)
11.5-BDq-10–0247 (D)
11.5-BDq-10–0741 (D)
11.5-BDq-10–1236 (D,M)
11.5-BDq-10–1730 (D)
11.5-BDq-10–2224 (D)
11.5-BDq-20–1730 (C,M)
11.5-BDq-20–2224 (C,M)
11.5-Thin10–1730 (T)
11.5-Thin10–2224 (T)
11.5-Thin20–1236 (C,T)
11.5-Thin20–1730 (C,T,M)
11.5-Thin20–2224 (C,T,M)
2.05a
2.05a
2.05a
2.06a
2.03a
1.85b
1.83b
1.83b
1.82b
1.63c
1.63c,d
1.60d
1.77e
1.85d,e
1.89c,d
1.87d
1.87d
2.10b
2.17a,b
1.91c,d
1.97c
2.12b
2.18a,b
2.23a
5.77f
6.00d,e,f
6.11c,d,e
6.00d,e,f
5.88e,f
6.09d,e
6.22c,d
6.26b,c,d
6.39a,b,c
6.54a,b
6.58a
6.57a
5.02d,e,f
5.07b,c,d,e,f
5.04c,d,e,f
4.88e,f
4.78f
5.30a,b,c,d
5.39a,b,c
4.93e,f
4.88e,f
5.47a
5.40a,b
5.20a,b,c,d,e
15
9
3
8
12
5
1
13
10
11
4
2
The name of a scenario consisted of four parts representing respectively “basal area-harvest type-cutting cycle-regeneration.” D, C, and T represent one of the top scenarios
in structural diversity, carbon stocks, and merchantable timber production. M represents the top scenarios with multiple benefits.
The values with the same letter are not significantly different.
Table 4. Estimates (mean) of structural diversity, carbon stocks, and timber production in top scenarios at 8.0 m2 haⴚ1 basal area. Five
top scenarios for each of the variable that maximized their value and five scenarios that maximized multiple benefits were selected. Nine
scenarios overlapped resulting in a total of 11 scenarios.
Scenario
Annual average stand
structural diversity
(Shannon Index)
Annual average C
stocks (metric
ton/ha/year)
Annual average merchantable
wood production
(m3/ha/year)
Annual average sawtimber
production (m3/ha/year)
Rank (provision of
multiple benefits)
8.0-BDq-10–0247 (D)
8.0-BDq-10–0741 (D)
8.0-BDq-10–1236 (D)
8.0-BDq-10–1730 (D)
8.0-BDq-10–2224 (D)
8.0-BDq-20–1236 (T,M)
8.0-BDq-20–1730 (C,M)
8.0-BDq-20–2224 (C,T,M)
8.0-Thin20–1236 (C,T)
8.0-Thin20–1730 (C,T,M)
8.0-Thin20–2224 (C,T,M)
2.05a
2.04a
2.04a
2.03a,b
1.99b
1.82c
1.80c,d
1.76d
1.60e
1.56e,f
1.53f
1.48g
1.54f,g
1.62e,f
1.59e,f,g
1.67e
1.95d
1.97c,d
2.09b,c
2.02b,c,d
2.10b
2.24a
4.62e
4.79d,e
4.94c,d
4.90c,d
5.12c
5.77b
5.64b
5.75b
6.06a
6.21a
6.29a
3.99b
4.02b
4.05b
3.88b
4.00b
5.03a
4.87a
4.81a
5.00a
4.97a
4.91a
16
15
10
13
8
4
5
2
6
3
1
The name of a scenario consisted of four parts representing respectively “basal area-harvest type-cutting cycle-regeneration, D, C, and T represent one of the top scenarios
in structural diversity, carbon stocks, and merchantable timber production. M represents the top scenarios with multiple benefits.
The values with the same letter are not significantly different.
Table 5. Estimates (mean) of structural diversity, carbon stocks, and timber production in top scenarios at 4.6 m2 haⴚ1 basal area. Five
top scenarios for each of the variable that maximized their value and five scenarios that maximized multiple benefits were selected. Nine
scenarios overlapped resulting in a total of 11 scenarios.
Scenario
Annual average stand
structural diversity
(Shannon Index)
Annual average C
stocks (metric
ton/ha/year)
Annual average merchantable
wood production (m3/ha/
year)
Annual average sawtimber
production (m3/ha/year)
Rank (provision of
multiple benefits)
4.6-BDq-10–0247 (D)
4.6-BDq-10–0741 (D)
4.6-BDq-10–1236 (D)
4.6-BDq-10–1730 (D)
4.6-BDq-10–2224 (D)
4.6-BDq-20–1236 (M)
4.6-BDq-20–1730 (C,T,M)
4.6-BDq-20–2224 (C,T,M)
4.6-Thin20–1236 (C,T)
4.6-Thin20–1730 (C,T,M)
4.6-Thin20–2224 (C,T,M)
1.99a
1.97a,b
1.97a
1.93b
1.93b
1.70c
1.62d
1.58d
1.49e
1.45f
1.40g
1.13e
1.16e
1.21d,e
1.26d
1.29d
1.70c
1.79b
1.84a,b
1.75b,c
1.89a
1.92a
3.23e
3.29d,e
3.45d,e
3.56d
3.59d
4.86c
5.03b,c
4.95c
5.11a,b,c
5.40a
5.34a,b
2.79b
2.68b
2.75b
2.86b
2.79b
4.05a
4.04a
3.82a
4.14a
4.19a
3.99a
17
16
13
11
9
5
3
4
6
1
2
The name of a scenario consisted of four parts representing respectively “basal area-harvest type-cutting cycle-regeneration,” D, C, and T represent one of the top scenarios
in structural diversity, carbon stocks, and merchantable timber production. M represents the top scenarios with multiple benefits.
The values with the same letter are not significantly different.
stocks were observed in scenarios with longer cutting cycles. For
example, after the first cut, total stand carbon for low thin scenarios
at 11.5 m2 ha⫺1 residual basal area with moderate regeneration level
of 1,236 seedlings ha⫺1 varied between 55.51 to 71.44 or 58.46 to
106.14 metric ton ha⫺1 at cutting cycles of 10- and 20-year cutting
cycles, respectively. At low residual basal area, the low thin scenarios
with moderate regeneration level of 1,236 seedlings ha⫺1 had total
stand carbon fluctuating between 31.96 to 48.00 and 34.31 to
Forest Science • MONTH 2014
7
Figure 3. (A) Structural diversity (Shannon Index), (B) carbon stocks, (C) total merchantable timber, and (D) sawtimber production under
different scenarios of harvest type (BDq or low thin, distinguished by solid and dashed lines), residual basal area (4.6 or 8.0 or 11.5 m2
haⴚ1, distinguished by dark and faded lines), and cutting cycle (10 or 20 years, distinguished by separate markers) as affected by level
of regeneration in slash pine simulated over 100 years.
74.25 metric ton ha⫺1 at cutting cycles of 10 and 20 years,
respectively.
Merchantable Timber Production
As with other response variables, the greatest increase in annual
average merchantable timber production (as much as 4.2 m3 ha⫺1
year ⫺1) was observed when the scenarios shifted from no regeneration to 247 seedlings ha⫺1 (P ⬍ 0.001). Further, slight but significant increases were observed in many scenarios with increasing
levels of regeneration up to 1,236 seedlings ha⫺1 or higher, though
in some scenarios (mostly BDq scenarios at high and intermediate
residual basal areas) increases in regeneration over 1,236 seedlings
ha⫺1 led to decreases in total merchantable timber production. Similar to carbon stocks, a greater sensitivity to increases in regeneration
was observed in the low thin scenario with low residual basal area
and a long cutting cycle (Figure 3C).
8
Forest Science • MONTH 2014
Annual average merchantable timber production during the simulation period followed somewhat similar patterns as annual average
C stocks, with significant interactions of residual basal area and
harvest type (P ⬍ 0.001) and with cutting cycle (P ⬍ 0.001). However, there were no significant interactions between harvest type and
cutting cycle. The scenarios with higher residual basal area had
significantly higher total merchantable timber production than the
scenarios with low basal area (P ⬍ 0.001); however, longer cutting
cycles did compensate for lower residual basal area, resulting in
comparable amounts of merchantable timber production as those of
higher basal areas. The main effect of harvest type on total merchantable timber was not significant.
Interestingly, sawtimber production revealed very clearly the effect of residual basal area and cutting cycle (Figure 3D). The scenarios with longer cutting cycle had significantly (P ⬍ 0.001) and
substantially higher annual average sawtimber production than the
Figure 4. Changes in (A) stand structural diversity (Shannon index), (B) total stand carbon, and removal (yield) of (C) total merchantable
timber and (D) sawtimber at multiple cutting cycles during the simulation period of 100 years in slash pine for scenarios with moderate
level of regeneration (1,236 seedlings/ha) at residual basal areas of 11.5, 8.0, and 4.6 m2 haⴚ1. Notably, low thin scenarios (dashed
lines) approached to structural diversity values comparable to BDq scenarios at around year 50 as simulation progressed.
Forest Science • MONTH 2014
9
corresponding scenarios with shorter cutting cycle; the effect being
more pronounced at lower basal areas. Sawtimber production also
increased significantly (as much as 3.4 m3 ha⫺1 year ⫺1) when the
scenarios shifted from no regeneration to 247 seedlings ha⫺1 for all
scenarios (P ⬍ 0.001).
The top scenarios resulting in maximum average annual merchantable timber at the high residual basal area included low thin
scenarios with a short cutting cycle and high regeneration of 2,224
seedlings ha⫺1 and scenarios with a long cutting cycle but moderate
to high regeneration of 1,236 –2,224 seedlings ha⫺1 resulted in
maximum average annual merchantable timber production of 6.39
to 6.58 m3 ha⫺1 year⫺1 (Table 3). At low and intermediate residual
basal areas, the highest average annual merchantable timber
production (5.11–5.40 and 6.06 – 6.29 m3 ha⫺1 year⫺1, respectively) were achieved in the low thin scenarios at 20-year cutting
cycle with regeneration between 1,236 to 2,224 seedlings ha⫺1 (Tables 4 and 5).
As with total carbon stocks, total merchantable as well as sawtimber production across the simulation period fluctuated between
cutting and growth periods (Figure 4C and D). At a moderate level
of regeneration of 1,236 seedlings ha⫺1, the total merchantable
timber yield varied from 28.5 to 74.5 m3 ha⫺1 and 79 to 156.2 m3
ha⫺1—for all levels of residual basal areas and harvest types— on
cutting cycles of 10 and 20 years, respectively. Not surprisingly, the
lowest yields per cutting cycle were obtained at the lowest basal area
of 4.6 m2 ha⫺1 at short cutting cycles. Values varied from 28.5 to
37.9 and 30.7 to 41.3 m3 ha⫺1 for BDq and low thin scenarios. For
the most part, total merchantable and sawtimber yields begin to
stabilize by the latter half of the simulation period. This is more
evident for the shorter cutting cycle, perhaps because there have
been more cuts to regulate structure than with the 20-year cutting
cycle within the 100-year time frame of the simulation.
Best Scenarios for Multiple Benefits
Most of the scenarios that maximized multiple objectives were
similar across all residual basal area levels. For example, both BDq
and low thin scenarios with a 20-year cutting cycle and regeneration
varying from 1,730 to 2,224 seedlings ha⫺1maximized multiple
benefits across all residual basal areas (Tables 3–5). Additionally, the
BDq scenario with 20-year cutting cycle at 1,236 seedlings ha⫺1 also
maximized multiple benefits at 4.6 and 8.0 m2 ha⫺1. At the high
residual basal area, a BDq scenario with 10-year cutting cycle also
was among the top scenarios at moderate level of regeneration of
1,236 seedlings ha⫺1 (Table 3). It may be noted that higher ranking
of BDq scenarios was due to relatively greater contribution of structural diversity. In other words, BDq scenarios were more effective at
creating structural diversity as compared to timber production or C
storage. For example, at the high basal area, average structural diversity ranged from 1.83 to 2.05 in top Bdq scenarios while it was 1.60
to 1.63 in the top low thin scenarios.
Discussion
Comparison of Silvicultural Regimes
The scenarios that led to highest average structural diversity over
the simulation period at all levels of residual basal areas invariably
involved BDq cut from the beginning. This is because the BDq
approach specifically aims at creating a residual stand closer to the
balanced uneven-aged structure with a typical reverse J-shaped diameter distribution (Smith et al. 1997, Guldin 2006, Johnson et al.
10
Forest Science • MONTH 2014
2009), and high structural diversity was achieved quickly in those
scenarios. In applying BDq to a mature even-aged stand in the
initiation of conversion, however, this method also tends to cut a
large proportion of mid-sized and several large-sized trees in the
stand, leaving a residual stand of mostly inferior, suppressed trees
and a scattering of large trees (Kelty et al. 2003, Loewenstein 2005).
Notably, although the low thin scenarios resulted in low average
structural diversity, these approached similar structural diversity as
the BDq scenarios by about year 50 as the simulation progressed.
The low structural diversity in low thin scenarios in the beginning
was due to the fact that lower diameter classes were selectively removed during that cutting cycle leading to concentration of large
sized trees, which reduced overall average structural diversity. However, this reduction was restricted to the first cutting cycle only and
thereafter structural diversity increased rapidly to become comparable to BDq scenarios.
Although uneven-aged stands with higher structural diversity
than the even-aged stands are recommended for improving wildlife
habitat in pine forests of southeastern United States (James et al.
2001, McConnell 2002, Marion et al. 2011), there is no reported
level of structural diversity that would be optimum for all relevant
species. It is possible that the optimum structural diversity required
to maintain habitat as well as aesthetics is not necessarily the maximum possible that could be achieved in uneven-aged stands. For
example, red-cockaded woodpecker (Picoides borealis), a US federally listed endangered species, requires habitat characterized by open
stand conditions consisting of large old pines (for cavity) and low
density of small pines with little or no midstory and abundant native
bunchgrass and forb groundcover (US Fish and Wildlife Service
2003). Also, structural diversity as defined in our study is essentially
a measure of diversity in tree diameters and heights in a stand.
Structural diversity, in its most comprehensive sense, will also include snags, litter accumulation, coarse woody debris, and the spatial distribution of individual trees or age classes across the landscape
which all together creates a complex structure (McElhinny et al.
2005). These additional attributes should be examined in future
studies.
The scenarios that led to high C stocks, in general, also resulted
in high merchantable and sawtimber production. While other factors such as stocking density and forest productivity determine the
amount of C stored in the forest biomass, the length of the rotation
determines how long the C remains in the forest system (Kaipainen
et al. 2004). This is consistent with our model simulations as low
thinning with a long cutting cycle maximized C stocks by retaining
larger trees, which added more C than the respective BDq scenarios
with similar cutting cycles. For a landowner who is more interested
in direct economic benefits by selling timber and earning C credits,
these scenarios are highly desirable. Although long-term empirical
data for C storage and timber production in slash pine managed as
uneven-aged stands is not available, other studies on uneven-aged
loblolly- shortleaf pines in the southern United States have extensively reported on timber volume productions (Reynolds 1969,
Reynolds et al. 1984, Farrar et al. 1989, Murphy et al. 1991, Guldin
2002). In uneven-aged mixed loblolly-shortleaf pine stands in the
Upper West Gulf Coastal Plain west of the Mississippi River,
Guldin (2002) reported that after 60 years, long-term annual volume production was 6 –7.4 m3 ha⫺1 total merchantable volume and
5.0 –5.5 m3 ha⫺1 sawtimber when the residual basal area was 11–13
m3 ha⫺1. Our simulation outputs at 11.5 and 8.0 m2 ha⫺1 residual
basal areas appear to be in agreement with these reported values.
However, at the lower basal area of 4.6 m2 ha⫺1 (which was approximately 35– 42% of basal area in treatment reported in the same
study by Guldin 2002), our simulation outputs are approximately
50% of total merchantable timber and 60% of sawtimber production of the values reported by Guldin (2002).
Although the yields of total merchantable and sawtimber were
relatively stable during the simulation period, particularly for
shorter cutting cycles, the operability of the harvests in some of the
scenarios at the lowest residual basal area may be an economic concern for some forest managers. A harvest of more than 25 m3 ha⫺1
sawtimber (Guldin 2002) or more than 31.5 to 52 m3 ha⫺1
pulpwood/chip and sawtimber (Huang et al. 2005, Eric Howell,
Florida Forest Service, pers. comm., Dec. 6, 2013) is generally considered an operable harvest, and thus, most of the scenarios would
lead to this. Only with a few cutting cycles in scenarios at the lowest
basal area and short cutting cycles would be less than the operable
harvests will be obtained. Thus, from economic operability point of
view, the low basal area management should aim for longer cutting
cycles.
Most public agencies are interested in managing forests for larger
societal, environmental, and economic benefits. This requires strategies that optimize the simultaneous provision of multiple benefits.
Our optimization approach scales the values of all three criteria
variables quantified in the study (structural diversity, C stocks, and
timber production) as a percentage of their maximum among all
scenarios and then ranks the scenarios for maximizing the provision
of these three benefits. The use of scaling for this purpose does
assume that values of a variable are distributed uniformly or linearly
within its range and that each of the three criteria has equal value to
a landowner, however this provided a basic means for provision of all
three criteria among the 73 scenarios that we evaluated.
The simulation analysis revealed that the combination of multiple benefits of structural diversity, C stocks, and timber production
at different residual high basal areas were maximally provided by
either BDq scenarios with moderate to high levels of regeneration
(1,236 –2,224 seedlings ha⫺1) or low thin scenarios with high levels
of regeneration (1,730 –2,224 seedlings ha⫺1). However, some concerns may remain regarding the feasibility of some of these top
scenarios. For example, the BDq scenarios may not be able to adequately provide a vigorous source of seed trees to produce even a
moderate level of regeneration during the initial harvests. The BDq
cut applied to a mature stand in the beginning essentially leads to
high grading by removing a large proportion of large, phenotypically superior trees and creating a residual stand consisting of suppressed inferior trees (Smith et al. 1997, Kelty et al. 2003, Nyland et
al. 2003). Such a stand with poorly developed crown structure is
likely to have reduced growth rates and may not result in production
of adequate seed crop following the cut. Owing to this reason, the
BDq approach has been considered inappropriate for converting
stand structures (Kelty et al. 2003, Loewenstein 2005). Supplemental regeneration following the first two cuts may be needed to ensure
success of these scenarios. However, once an uneven-aged structure
has been achieved, the BDq approach can be successfully applied for
maintaining uneven-aged structure (Kelty et al. 2003, Loewenstein
2005, Guldin 2006, Brockway and Outcalt 2010). Alternatively, in
the low thin scenarios, the residual stand would consist of vigorous
superior trees. However, a high level of regeneration in a stand with
high basal area may also be difficult to achieve as the high light
requirements of the species may not be met in dense stand
conditions.
At low and intermediate residual basal areas, either BDq or low
thin scenario with a longer cutting cycle and moderate to high
regeneration will maximize the provision of multiple benefits. The
top scenarios involving low thinning in the beginning will retain the
best grown trees (dominants and codominants) in the residual stand
(Smith et al. 1997, Nyland 2003) and may be advantageous. Such a
low-thinned stand illuminates the understory by removing low
shade (Nyland 2003). The vigorous residual stand with a bright
understory seems more likely to result in successful natural regeneration and groundcover diversity. In that sense, low thin scenarios
have greater ecological feasibility than the BDq scenarios.
The levels of residual basal area simulated in the study may be
considered to represent alternative densities for uneven-aged management in slash pine for multiple benefits. As the study has shown,
at these three levels (4.6, 8.0, and 11.5 m2 ha⫺1) of basal area, the
broad patterns of structural diversity, carbon storage, and timber
production are similar, with only the magnitude of the responses
differing. This suggests that our observations regarding the effect of
cutting methods on optimizing multiple benefits may be applicable
at many residual basal area levels though more empirical evidence
may be needed to support the actual quantitative projections of
simulations, especially at the lower basal areas.
Assumptions and Limitations of the Study
Although the simulation analysis provided a broad evaluation of
the conversion process and of the tradeoffs involved in different
conversion scenarios, there were some limitations that should be
addressed. The FVS model is not spatially explicit and, hence, could
not appropriately be used to simulate stand conversion using group
selection or patch harvest cuts, which are considered among some of
the best means to convert stands of intolerant species (Kelty et al.
2003, Nyland et al. 2003). Arseneault and Saunders (2012), and
Saunders and Arseneault (2013) addressed this limitation by using a
resampling approach to simulate group selection, shelterwood, and
single tree selection systems in FVS. The lack of a spatial model also
precluded us from evaluating the spatial complexity of age and size
distributions of trees across the forest, which may be inherent in
natural southern pine ecosystems (Jose et al. 2006.)
Although the FVS simulation outputs are derived from methods
and computations of C accounting consistent with the Intergovernmental Panel on Climate Change (IPCC) Good Practice Guidance
and US voluntary C accounting rules and guidelines (Hoover and
Rebain 2011), the carbon reporting in our study did not include C
fluxes such as management-related emissions (e.g., emissions related
to decomposing residues, harvest equipment use, timber transportation, etc.) A complete C footprint analysis would include life-cycle
analysis of all aspects of forest management, including emissions
from site preparation activities, prescribed fires, and emissions related to production, transportation, and application of fertilizers
and other chemicals, if used (Hoover and Rebain 2011). The changing climate will likely have significant effect on growth and yield of
the stand and was not accounted for in the study.
One of the most important limitations to this long-term simulation was the lack of a natural regeneration module for slash pine in
FVS. We, therefore, conducted a sensitivity analysis to evaluate a
range of plausible regeneration scenarios. Natural regeneration and
growth in southern pines are determined by numerous factors that
include both intrinsic (i.e., silvics) and external influences (Shelton
and Cain 2000). In slash pine, fire and competition from understory
Forest Science • MONTH 2014
11
shrub and hardwood vegetation are important influences on seedling germination and recruitment. These factors and their interactions were not specifically simulated in our study, however, we did
indirectly account for these factors by assessing a range of regeneration scenarios from complete failure of regeneration to abundant
regeneration that would span the likely range of regeneration densities with and without these additional control measures. This
helped us identify the levels of regeneration that could meet the
management objectives. Although most of the best scenarios were
found to require moderate to high levels of regeneration, the regeneration level of as few as 247 seedlings ha⫺1 also led to reasonable
benefits (especially in stand structural diversity and sawtimber production at all basal areas) with either decrease or only marginal gains
with increasing regeneration densities for many scenarios.
Land managers and practitioners can then target to achieve those
levels of regeneration by using information about the species and
manipulating management activities (Shelton and Cain 2000). For
example, seedbed preparation and removal of competition by removing shrub and saw-palmetto (Serenoa repens W. Bartram) significantly improves slash pine seedling establishment (Langdon and
Bennett 1976) and can be achieved using regular prescribed burning
(Lohrey and Kossuth 1990, Landers 1991). Though young slash
pine is susceptible to mortality following fire, mature trees are quite
fire-resistant (Brown and Davis 1973). A young cohort of slash pine
regeneration should not be burned for the first 5 years of age or until
when they are at least 4 –5 m high (McCulley 1950, Langdon and
Bennett 1976). The seedlings grow fast and in 10 –12 years slash
pine is resistant to fire that does not crown (Wright and Bailey
1982). Additionally, cattle grazing and chemical or mechanical
treatments can be used to reduce the buildup of the fine fuel and
hardwood competition until the new regeneration is resistant to
light fire.
Slash pine and other species in the southern United States are
difficult to assess using the southern variant of FVS, which lacks a
full regeneration model. For many other species and forest types
such as those in eastern Montana; central and northern Idaho; Inland Empire; Kootenai, Kaniksu, and Tally Lake; and coastal Alaska
and British Columbia, however, the FVS model has full regeneration establishment extensions that may significantly minimize the
uncertainties associated with assumptions regarding natural regeneration (Dixon 2002, David Lance, USDA Forest Management
Service Center, pers. comm., June 26, 2013).
Conclusions
Overall, our simulation analysis revealed that no single stand
conversion scenario led to maximization of all products and services
simultaneously. There were always tradeoffs involved. The scenarios
that resulted in higher structural diversity generally led to lower C
stocks and merchantable timber production. Given the need to
achieve multiple objectives, the scenarios under the assumptions of
our simulation that would best achieve that mostly require longer
cutting cycles and moderate to high regeneration.
An important observation to note, however, is that the greatest
gains for all of the variables in all of the scenarios were made when as
few as 247 seedlings ha⫺1 were regenerated. Further gains in most of
the scenarios were minor. If as few as 247 seedlings ha⫺1 can be
regenerated in slash pine stands following a harvest, reasonable benefits related to stand structure, C storage, and timber production still
can be achieved under most of the scenarios. In the worst case
scenario of complete failure of regeneration, planting of 247 seed12
Forest Science • MONTH 2014
lings ha⫺1 during a cutting cycle may not be economically prohibitive to a landowner.
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