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Forest Ecology and Management 225 (2006) 82–93

Simulating the effects of reforestation on a large catastrophic fire

burned landscape in Northeastern China

Xugao Wang a,c,*, Hong S. He b, Xiuzhen Li a, Yu Chang a, Yuanman Hu a,Chonggang Xu a,c, Rencang Bu a, Fuju Xie a,c

a Institute of Applied Ecology, Chinese Academy of Science, P.O. Box 417, Shenyang 110016, Chinab School of Natural Resources, University of Missouri, Columbia

c Graduate School of Chinese Academy of Science, Beijing 100039, China

Received 20 May 2005; received in revised form 2 November 2005; accepted 16 December 2005

Abstract

We use the LANDIS model to study the effects of planting intensity and spatial pattern of plantation on the abundance of three main species

(larch (Larix gmelini), Mongolian Scotch pine (Pinus sylvestris var. Mongolica), and white birch (Betula platyphylla)) in the Tuqiang Forest

Bureau on the northern slopes of Great Hing’an Mountains after a catastrophic fire in 1987. Four levels of planting intensity (covering 10%, 30%,

50%, and 70% of the severely burned area) and two spatial patterns of plantation (dispersed planting and aggregated planting) were compared in a

4 � 2 factorial design over a 300-year period. The results showed that increasing planting intensity positively influenced larch and Mongolian

Scotch pine abundance, but negatively influenced white birch abundance. However, the increased degree of larch abundance with increasing

planting intensity was significantly different between intensities. The difference in larch abundance between the 10% planting intensity scenario

and the 30% planting intensity scenario was greater than that between the 50% planting intensity scenario and the 70% planting intensity scenario.

However, the difference between 30% and 50% planting intensity scenarios was significantly low. Hence, given considerable labor input and

economic costs, 30% planting intensity would be effective for forest recovery. In addition, dispersed planting showed more promising results on

forest recovery than aggregated planting. However, the difference of larch abundance between dispersed planting and aggregated planting under

intermediate planting intensity scenarios (30% and 50% planting intensity) was greater than that under a low planting intensity scenario and a high

planting intensity scenario. Therefore, it is necessary to incorporate spatial pattern of plantation into planting practice, especially under an

intermediate planting intensity scenario. These results have important implications for forest managers to design sound forest restoration projects

for landscapes affected by large infrequent disturbances. In particular, the results suggest that the current planting strategy (50% planting intensity

with aggregated planting) employed after the catastrophic fire in 1987 could not be optimum, and the dispersed planting strategy covering about

30% of the severely burned area would better stimulate forest recovery.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Great Hing’an Mountains; LANDIS; Planting intensity; Spatial pattern of plantation; Forest recovery

1. Introduction

The deciduous and coniferous forests of the Great Hing’an

Mountains in northeastern China provide the most timber of

any forested area in the country; simultaneously, this area

encompasses rather unique ecological and environmental

systems in the region (Zhou, 1991; Xu, 1998). Human

activities, particularly timber harvesting, have substantially

altered the spatial pattern and ecological functions of these

* Corresponding author. Tel.: +86 24 83970350; fax: +86 24 83970351.

E-mail addresses: wxg_7980@163.com, wangxugao@hotmail.com

(X. Wang).

0378-1127/$ – see front matter # 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.foreco.2005.12.029

systems. Decades of fire suppression have reduced fire size,

prolonged the fire return interval (i.e., the number of years

between two successive fire events for a specific area), and

indirectly influenced forest composition and dynamics (Shu

et al., 1996). The success of fire suppression, coupled with a

warmer, drier climate due to global warming (Xu, 1998), have

led to a fuel buildup and resulted in fires of greater intensity and

extent than those that occurred previously in the region.

Catastrophic fires can have disastrous effects on forest

composition and structure, ecosystem processes, and landscape

pattern (Romme, 1982; Turner et al., 1997, 1999). On May 6,

1987, a catastrophic fire occurred on the northern slopes of

Great Hing’an Mountains, burning a total area of 1.3 � 106 ha.

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–93 83

Fig. 1. The location of Tuqiang Forest Bureau and burn severities after the

catastrophic fire in 1987 (1, unburned area (MA 1); 2, severely burned area

where conifers were dominant before the fire (MA 2); 3, severely burned area

where conifers were not dominant before the fire (MA 3); 4, other burned area

(MA 4)).

This immense, high-intensity fire consumed vegetation cover

and precipitated the exposition of mineral soils – as well as the

subsequent erosion and runoff – during post-fire rain events

(Xiao et al., 1988; Shu et al., 1996). Forest recovery in such

vastly burned areas is challenging because the long-term

landscape-level vegetation dynamic in a forest landscape is

complicated by spatial and temporal interactions among

multiple ecological and anthropogenic processes.

In many cases, natural succession can eventually lead to

post-fire recovery. This is especially true for cases in which

there are sufficient residual forests remaining nearby to act as

seed sources (Turner et al., 1999; Borchert et al., 2003). In cases

such as the 1987 fire in the Great Hing’an Mountains, natural

recovery is difficult because the severely burned area is

extensive, the burn severities are high, and the seed sources are

far removed (Xiao et al., 1988). In these situations, the process

of vegetation recovery is slow, increasing the risk of soil erosion

and environmental degradation. Thus, ecological restoration

through human mediation is necessary. After the 1987 fire,

forest management in this region shifted from timber harvesting

to reforestation – particularly in the severely burned area – in

order to accelerate forest restoration.

Various approaches have been developed to restore forest

vegetations for degraded systems where natural recovery is

unlikely. Grass seeding provides quick, temporary vegetation

ground cover; these are typically annuals or short-lived

perennials that can hold soil (Beyers, 2004). However, such

a treatment does not facilitate long-term ecosystem restoration.

Long-term ecosystem restorations are accomplished either

through the planting of a small number of early successional

nursery trees or shrubs to create habitats for seed-dispersing

birds (e.g., Lamb, 1998), or through high-density plantation of

tree species once present in the disturbed areas (e.g., Smale

et al., 2001; Nagashima et al., 2002).

Although these approaches have shown promising results for

development of forest structure, increase of species richness,

and recovery of natural successional processes, they are often

limited to relatively small areas (Lamb, 1998). In the Great

Hing’an Mountains, it is not sufficient to restore only a few

burned areas, and it is highly impractical to plant trees in the

vast severely burned area due to the considerable labor and

economic resources required. It is therefore necessary to assess

the planting intensity (the proportion of the burned area for

reforestation) and spatial patterns of plantation (the spatial

allocation of reforestation in the field) for forest recovery. In the

Great Hing’an Mountains, the planting strategy after the

catastrophic fire in 1987 was to plant coniferous seedlings in an

aggregated fashion over about 50% of the severely burned area

where coniferous forests used to grow. Others such as dispersed

planting, an alternative to the aggregated planting, have not

been evaluated and compared. A dispersed planting would

entail planting trees in a random fashion across the entire

severely burned area.

Descriptive studies and field experiments are often

inadequate sources of input for managers developing and

implementing reforestation plans. Comparing ecological

restoration strategies on a large-scale landscape is often

beyond the limits of traditional or experimental studies. Models

have therefore become an important tool for predicting the

effects of alternative management options. Landscape models

are particularly important in this study because other types of

models, such as gap models and ecosystem process models, are

often limited in spatial extents (He et al., 2002b; Mladenoff,

2004). While other anthropogenic disturbances – such as fire

suppression and forest harvesting – have been studied using

landscape models (Gustafson et al., 2000; Franklin et al., 2001;

Sturatevant et al., 2004; Wimberly, 2004), the effects of

reforestation on long-term forest dynamics has not been

explicitly simulated.

The purpose of this research is to study the effects of

different reforestation scenarios on vegetation dynamics in the

severely burned area of the Great Hing’an Mountains. We will

examine the effects of planting intensities and spatial patterns

of plantation (dispersed vs. aggregated) on forest landscape

recovery. We will apply a factorial design of planting intensity

and spatial pattern of plantation on the realistically para-

meterized forest composition maps of 1987 to identify the

combinations of planting intensity and method that could best

accelerate forest restoration. Understanding the probable post-

fire dynamics of the region under different reforestation

scenarios will not only provide insights into landscape scale

processes, but also will provide baseline information on forest

landscape restoration in northeast China after catastrophic fire

disturbances.

2. Study area

The Tuqiang Forest Bureau (Fig. 1), encompassing

approximately 4 � 105 ha on the northern slopes of Great

Hing’an Mountains, is in the Mohe County of Heilongjiang

province in northeast China (from 5281505500 to 5383304000N,

and 12281800500 to 12382900000E). It borders Russia to the north

(separated by Heilongjiang River), the Xilinji Forest Bureau to

the west, the Amur Forest Bureau to the east, and the Inner

Mongolia Autonomous Region to the south. The area has a

cold, continental climate, with an average annual temperature

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–9384

at �5 8C. Monthly mean temperature ranges from �47.2 8C in

January to 31.4 8C in July. The average annual precipitation is

432 mm, with great inter-annual variations. Seventy-five

percent of the rainfall occurs between June and August.

Uplands and small hills characterize this region, though it

possesses a relatively smooth topography. Slopes are generally

less than 158; the maximum slope is less than 458. Hills

undulate throughout this area, and the mountain ranges mostly

run in north and south directions. Elevations range from 270 m

to 1210 m; the mean elevation is 500 m. Brown coniferous

forest soil is representative in the Bureau. Vegetation is

dominated by larch (L. gmelini) forests. White birch (Betula

platyphylla) is the major broad-leaved species in the region. In

addition to larch and white birch, the tree species include

Mongolian Scotch pine (P. sylvestris var. mongolica), spruce

(Picea koraiensis), aspen-D (Populus davidiana), black birch

(Betula. davurica), aspen-S (Populus suaveolens), and willow

(Chosenia arbutifolia).

On May 6, 1987, a catastrophic forest fire of 1.33 � 106 ha

took place on the northern slopes of the Great Hing’an

Mountains. The burned area covered 2.31 � 105 ha in the

Bureau, and the severely burned area covered roughly

9 � 104 ha (Fig. 2). The conflagration caused incredible

damage to Tuqiang Forest Bureau, and this has led to great

difficulty in restoring the forest ecosystems.

Fig. 2. Landtype maps of Tuqiang Forest Bureau.

3. Methods

3.1. Description of LANDIS

LANDIS is a landscape disturbance and succession model

that facilitates the study of the effects of natural and

anthropogenic disturbances on forest landscapes, and has been

described extensively elsewhere (Mladenoff and He, 1999; He

and Mladenoff, 1999; He et al., 1999; Gustafson et al., 2000).

Here we provide a general description of the model. In

LANDIS, a landscape is organized as grid of cells, with

vegetation information stored as attributes for each cell. Cell

size can be varied from 10 m to 500 m depending on the

research scale. At each cell, the model tracks a matrix

containing a list of species by rows and the 10-year age cohorts

by columns. The model does not track individual trees. This

differs from most from stand simulation models than track

individual trees (Grimm, 1999). Additionally, computational

loads are greatly reduced, because actual species abundance,

biomass, or density is not calculated. A species presence/

absence approach allows LANDIS to simulate large landscapes

and avoids any false precision of predicting species abundance

measures with inadequate input data or parameter information.

LANDIS stratifies a heterogeneous landscape into land

types, which are generated from GIS layers of climate, soil, or

terrain attributes (slope, aspect, and landscape position). It is

assumed that a single land type contains a somewhat uniform

suite of ecological conditions, resulting in similar species

establishment patterns and fire disturbances characteristic,

including ignition frequency, mean fire return interval, and fuel

decomposition rated (He and Mladenoff, 1999). These

assumptions have been supported by many empirical and

experimental studies (e.g., Brown and See, 1981; Kauffman

et al., 1988). Furthermore, land types can be redefined by users

to partition the landscape into strata that are most relevant for a

particular application.

Four spatial processes and numerous non-spatial processes

are simulated by LANDIS. The spatial processes are fire,

windthrow, harvesting, and seed dispersal. Fire is stochastic

process based on the probability distributions of fire cycle and

mean fire sizes for various land types (He and Mladenoff,

1999). LANDIS simulates fire levels of fire intensity from

surface fire to crown fires. Fire intensity is determined by the

amount of fuel on a site. Tree species are also grouped into fire

fire-tolerance classes based on their fire-tolerance attributes and

five age-based fire susceptibility classes from young to old,

with young trees being more susceptible to damage than older

trees. Thus, fire severity is the interaction of susceptibility

based on species age classes, species fire tolerance and fire

intensity. A new LANDIS fire module (Yang et al., 2004)

employs hierarchical probability theory to allow even more

explicit simulation of different fire regimes across landscapes.

Windthrow is also stochastically simulated. In the LANDIS

wind module the probability of windthrow mortality increases

with tree age and size. Windthrow events interact with fire

disturbance such that windthrow increases the potential fire

intensity class at a site due to increased fuel load.

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–93 85

The LANDIS harvest module simulates forest harvesting

activities based on management area and stand boundaries

(Gustafson et al., 2000). These maps are predefined and are only

used by the LANDIS harvest and fuel modules. Harvest activities

are specified through rules relative to spatial, temporal, and

species age-cohort information tracked in LANDIS. The spatial

component determines where harvest activities occur and may be

used to enforce stand boundary and adjacency constraints. The

temporal component determines the timing (rotations) and

manner (single versus multiple-entry treatments) of harvest

activities. The species age-cohort component allows specifica-

tion of the species and age cohorts removed by the harvest

activities. For example, a clearcut removes all species and all

ages, whereas a selection harvest typically removes only a few

species and age cohorts. The ability to use a combination of

spatial, temporal, species, and age information to specify harvest

action independently allows a great variety of harvest prescrip-

tions to be simulated (Gustafson et al., 2000).

LANDIS simulates seed dispersal based upon species’

effective and maximum seeding distance (He and Mladenoff,

1999). Seed dispersal probability is modeled for each species

using an exponential distribution that defines the effective and

maximum seed dispersal distances.

Non-spatial processes of succession and seedling establish-

ment are simulated independently at each site. They also

interact with spatial processes such as seed dispersal, harvest-

ing, and disturbances. Succession is a competitive process

driven by species life history parameters in LANDIS. It is

comprised of a set of logical rules primarily using the

combination of shade tolerance, seeding ability, longevity,

vegetative reproduction capability, and the suitability of the

land type (Mladenoff and He, 1999). These rules are used to

simulate species birth, growth and death at 10-year intervals.

For example, shade intolerant species cannot establish on a site

where species with greater shade tolerance are present. On the

other hand, the most shade tolerant species are unable to occupy

an open site. Without disturbance, shade tolerant species will

dominate the landscape given that other attributes (e.g.,

dispersal distances) are not highly limiting and the environ-

mental conditions are otherwise suitable. Species establishment

is regulated by a species-specific establishment coefficient

(ranging from 0 to 1.00), which quantifies how different land

types favor or inhibit the establishment of a particular species

(Mladenoff and He, 1999). These coefficients, which are

Table 1

Species’ life attributes for Tuqiang Forest Bureau in northeastern China

Species LONG M

Larch (Larix gmelini) 300 20

Mongolian Scotch pine (Pinus sylvestris var. mongolica) 250 40

Spruce (Picea koraiensis) 300 30

White birch (Betula platyphylla) 150 15

Aspen-D (Populus davidiana) 180 20

Black birch (Betula davurica) 150 15

Aspen-S (Populus suaveolens) 150 25

Willow (Chosenia arbutifolia) 200 30

LONG: longevity (years); MTR: age of maturity (years); ST: shade tolerance (1–5

seeding distance (m); VP: vegetative reproduction probability; MVP: minimum ag

provided as input to LANDIS, are derived either from the

simulation results of a gap model (e.g., He et al., 1999) or from

estimates based on existing experimental or empirical studies

(Shifley et al., 2000).

Due to the stochastic nature of processes such as fire,

windthrow, and seedling dispersal simulated in the model,

LANDIS is not designed to predict the specific time or place that

individual disturbance events will occur. Rather, it is a cause–

response type of scenario model that simulates landscape

patterns over time in response to the combined and interactive

outcomes of succession and disturbance. It can provide managers

with guidance about management practices that can mitigate

current or anticipated problems on the forest landscape, and

provide a better understanding of long-term, cumulative effects

that may result from the combination of natural disturbances and

management practices. LANDIS has been applied and tested

with different species and environmental settings (Gustafson

et al., 2000; Shifley et al., 2000; Franklin et al., 2001; He et al.,

2002a,b; Mehta et al., 2004; Wimberly, 2004). In addition, the

current version, LANDIS 4.0, added new capabilities that

simulate explicit fuel dynamics, fuel-fire interactions, and

biological disturbances (He et al., 2004). Parameterization of

LANDIS 4.0 for the Tuqiang Forest Bureau involved several

aspects: species’ vital attributes, a forest composition map that

contains individual species presence/absence and age classes at

each cell, a land type map, establishment probabilities for each

landtype, fire disturbance regimes for each landtype, and forest

management scenarios.

3.1.1. Species attributes and forest composition map

A total of eight tree species were incorporated into LANDIS.

Species’ vital attributes (Table 1) were estimated based on

existing studies of the region (Ai et al., 1985; Duan, 1991; Hu

et al., 1991; Xu, 1998; He et al., 2002a; Xu et al., 2004) as well

as consultation with local experts. A forest composition map

was derived from an extant forest stand map of 1987, a stand

attribute database, and one scene of Landsat TM imagery taken

in 1987. The forest stand map recorded boundaries of stands

and compartments. (A compartment is a unit of forest inventory,

generally containing 10–100 stands.) The stand attribute

database provided information on the relative percentage of

canopy species, the average age of dominant canopy species,

timber production, and crown density. To reduce computational

loads during model simulations, the forest composition map

TR ST FT ED MD VP MVP

3 3 150 300 0 0

1 1 100 200 0 0

4 2 10 150 0 0

1 3 200 4000 0.8 40

1 3 �1 �1 1 40

1 4 200 1000 0.8 40

1 4 �1 �1 0.9 40

2 2 �1 �1 0.9 30

); FT: fire tolerance (1–5); ED: effective seeding distance (m); MD: maximum

e of vegetative reproduction (years). �1 represents unlimited seeding range.

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–9386

Fig. 3. Spatial distribution of part landscape occupied by larch at simulation year 200. (I1, I2, I3, and I4 represent simulation scenarios where the planting intensity

covers 10%, 30%, 50%, and 70% of the severely burned area, respectively. M1 and M2 represent dispersal planting and aggregated planting, respectively.)

Table 2

Attributes for each land type of Tuqiang Forest Bureau in China

Land type MFRI TSLF EC1 EC2 EC3 EC4 EC5 EC6 EC7 EC8

SS 300 150 0.3 0.25 0.03 0.3 0.01 0.1 0 0

NS 350 180 0.25 0.2 0.05 0.25 0.01 0.05 0 0

T 1000 500 0.002 0 0 0.004 0.001 0 0.01 0.05

BSS 300 10 0.3 0.25 0.03 0.3 0.01 0.1 0 0

BNS 350 10 0.25 0.2 0.05 0.25 0.01 0.05 0 0

BT 1000 10 0.002 0 0 0.004 0.001 0 0.01 0.05

MFRI: mean fire return intervals in years; TSLF: time since last fire disturbance;

EC1, EC2, EC3, EC4, EC5, EC6, EC7, EC8 are the establishment coefficient

for larch, Mongolian Scotch pine, spruce, white birch, aspen-D, black birch,

aspen-S, and willow. SS: southern slope; NS: northern slope; T: terrace; BSS:

burned southern slope; BNS: burned northern slope; BT: burned terrace.

was processed at 90 m � 90 m resolution, which yielded 1604

rows � 873 columns. Each cell contains the presence/absence

and age cohorts of all eight tree species.

For each cell in a stand, we used a stand-based assignation

(SBA) approach (Xu et al., 2004) to stochastically assign

species age cohorts to each cell based on forest inventory data.

Xu et al. (2004) used uncertainty analysis to evaluate the

approach, and their results demonstrated that uncertainty was

relatively low at the cell level during the beginning of the

simulation. The uncertainty increased with simulation years,

but the uncertainty finally reached an equilibrium state in which

input errors in original species age cohorts had little effect on

the simulation outcomes. At the landscape level, species

abundance and spatial patterns were not substantially affected

by the uncertainties in species age structure at the cell level.

Since the typical application of LANDIS is to predict long-term

landscape pattern changes, SBA can be used to parameterize

species age cohorts for individual cells.

3.1.2. Landtype map

LANDIS stratifies the heterogeneous landscape into relatively

homogeneous units (landtypes or ecoregions). Within each

landtype, similar environments for species establishment are

assumed (Mladenoff and He, 1999). In the study, we derived

eight landtypes, primarily based on terrain attributes, TM images

taken in 1987, and the catastrophic fire of 1987 (Fig. 3). These

landtypes include water, residential land, terrace, southern slope

(SS), northern slope (NS), burned terrace (BT), burned southern

slope (BSS), and burned northern slope (BNS). Because large

burned areas came into existence after the 1987 fire, fire/fuel

characteristics – such as fuel accumulation and the time elapsed

since the previous fire – were different for unburned areas.

Therefore, we differentiated BT, BSS, and BNS from unburned

areas. All landtypes were interpreted from the previous forest

inventory and the TM images taken in 1987. Non-active

landtypes (water and residential land) account for 0.90% of the

total area, while active landtypes (including terrace, SS, NS, BT,

BSS, and BNS) account for 0.94%, 23.57%, 17.39%, 3.39%,

31.43%, and 22.38%, respectively.

The species establishment coefficient is a critical feature of

each landtype. It is an estimate of the probability that a species

will successfully establish on a landtype, given the environ-

mental conditions encapsulated by that landtype. The species

establishment coefficients (Table 2) were derived from

available literature as well as the existing LANDIS para-

meterizations on northeastern China (Li et al., 1987; Zhao et al.,

1997; Liu et al., 1999; He et al., 2002a; Hu et al., 2004).

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–93 87

3.1.3. Fire regimes

The current fire regime for our simulations was parameter-

ized based on a database of 10-year fire dating from 1990 to

2000 (Hu et al., 2004). Current fire return intervals were

estimated by calculating the reciprocal of the annual proportion

of forest land burned within each landtype in the Bureau. Only

those fires greater than the resolution of the simulation recorded

in the database (0.81 ha) were used to parameterize the fire

regime (Table 2). Within the study area, the common planting

strategy consisted of planting coniferous trees over about 50%

of the total severely burned area (about 41300 ha). Two

endemic coniferous species of larch and Mongolian Scotch pine

(the ratio is about 7:3) were the main tree species planted.

3.2. Simulation scenarios

We began with the realistically parameterized forest

composition and landtype maps including species/age classes

that represent the initial status of 1987. Four levels of planting

intensity (10%, 30%, 50%, and 70% of the severely burned

area) and two spatial patterns of plantation (dispersed planting

and current/aggregated planting) were simulated in a 4 � 2

factorial design over a 300-year period (Table 3). Two

coniferous tree species (larch and Mongolian Scotch pine)

were planted in severely burned areas, and the proportion was

7:3. Five replicates of each scenario were simulated. The

current planting intensity of planting coniferous species over

about 50% of the severely burned area was simulated;

additionally, three other planting intensities (including 10%,

30%, and 70% of the severely burned area) were selected to

compare with the current planting intensity. The planting

strategy, dispersed planting, consists of planting coniferous

seedlings randomly throughout a severely burned area, whereas

aggregated planting consists of planting coniferous seedlings in

selected locations of severely burned areas where coniferous

forests were dominant prior to a fire; both strategies were

applied in our simulations. A ‘‘no reforestation’’ scenario was

also simulated. All scenarios were simulated up to 300 years to

examine the long-term effects of planting intensity and spatial

pattern of plantation on forest succession.

The study area was divided into four management areas

(MA) indicating the planting options practiced there: (1)

unburned area (MA 1), (2) severely burned area where conifers

were dominant before the 1987 fire (MA 2), (3) severely burned

Table 3

The scenarios simulated by LANDIS

Simulation scenarios Descriptions

I1M1 Planting coniferous trees, covering 10% of the severely bu

I2M1 Planting coniferous trees, covering 30% of the severely bu

I3M1 Planting coniferous trees, covering 50% of the severely bu

I4M1 Planting coniferous trees, covering 70% of the severely bu

I1M2 Planting coniferous trees, covering 10% of the severely bu

I2M2 Planting coniferous trees, covering 30% of the severely bu

I3M2 Planting coniferous trees, covering 50% of the severely bu

I4M2 Planting coniferous trees, covering 70% of the severely bu

area where conifers were not dominant before the fire (MA 3),

and (4) other burned area (MA 4) (Fig. 1). The goal of the

current planting strategy was to increase the proportion of

coniferous trees in the Bureau. Dispersed planting was applied

in MA 2 and MA 3, while current planting was restricted in MA

2. Within MA 2, planting activity was applied to 50% of the

severely burned area in 10 years to mimic the current planting

strategy designed to promote coniferous species. In MA 1 and

MA 4, natural regeneration was not simulated because there

were sufficient residual coniferous trees to provide seeds.

Table 3 gives an overview of different combinations of planting

intensities and spatial patterns of plantation.

For each of the three species (larch, Mongolian Scotch pine,

and white birch), the proportion of area (PA) was calculated for

the mapped output of species for each 10-year step in the 300

simulation years to qualitatively evaluate temporal trends in the

data. We divided the 300 simulation years into three stages: 10–

30 year as short-term stage, 40–100 year as mid-term stage, and

>100 years as long-term stage. The average PA in each of the

three stages was calculated for all three species. However, the

average PA for the three species is dependent on each other at

different simulation stages. Therefore, we analyzed the three

response variables (average area of the three species) together

in a multivariate analysis of variance (MANOVA) with planting

intensity (n = 4) and spatial pattern of plantation (n = 2) in

SPSS (SPSS 10.0) to test the global hypothesis that planting

intensity and spatial pattern of plantation affect the area of the

three species at different simulation stages. We used the Pillai’s

Trace statistic to test our hypotheses because it is the least

sensitive of the four multivariate tests provided by SPSS to the

heterogeneity of variance assumption of MANOVA (Zar,

1999).

In addition, to evaluate the effect of reforestation scenarios

on the spatial pattern of species distributions, we calculated an

aggregation index (AI) for the three species using the mapped

output of each species throughout the 300-year simulation. The

AI has a range between 0 and 1, with higher values indicating a

higher level of aggregation in the measured spatial pattern (He

et al., 2000). It assumes that a class with the highest level of

aggregation (AI = 1) is comprised of pixels sharing the most

possible edges. A class whose pixels share no edges

(completely disaggregated) has the lowest level of aggregation

(AI = 0). If ei,j represents total edges of class i adjacent to class

j, for class i of area Ai, aggregation index measures ei,i, the total

rned area (70% larch and 30% Mongolian Scotch pine); dispersed planting

rned area (70% larch and 30% Mongolian Scotch pine); dispersed planting

rned area (70% larch and 30% Mongolian Scotch pine); dispersed planting

rned area (70% larch and 30% Mongolian Scotch pine); dispersed planting

rned area (70% larch and 30% Mongolian Scotch pine); aggregated planting

rned area (70% larch and 30% Mongolian Scotch pine); aggregated planting

rned area (70% larch and 30% Mongolian Scotch pine); aggregated planting

rned area (70% larch and 30% Mongolian Scotch pine); aggregated planting

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–9388

edges shared by class i itself. The shared edges are counted only

once in AI, and currently only four neighbors are considered.

The level of aggregation of class i is calculated as:

AIi ¼ei;i

max ei;i:

Given a class is of area Ai, the maximum aggregation level is

reached when A clumps into one patch that has the largest ei,i

(e.g., it does not have to be a square). If n is the side of largest

integer square smaller than Ai, and m = Ai � n2, then the largest

number of shared edges for class i, max_ei,i, will take one of the

three forms:

max ei;i ¼ 2nðn� 1Þ; when m ¼ 0; or

max ei;i ¼ 2nðn� 1Þ þ 2m� 1; when m< n; or

max ei;i ¼ 2nðn� 1Þ þ 2m� 2; when m � n:

The two indices (PA and AI) were calculated in APACK

(Mladenoff and DeZonia, 1997). This analysis was conducted

for the entire study area.

4. Results

MANOVA results indicate that both planting intensity and

spatial pattern of plantation also significantly affect the PA of

the three species (larch, Mongolian Scotch pine and white

birch) across the study landscape at short-, mid-, and long-term

stages (Tables 4–6), and their interaction of both treatments has

also a significant influence. Planting intensity always has a

significant influence on the successional dynamic of the three

species for all simulation years. However, the effects include

both positive and negative effects: greater planting intensity

Table 4

MANOVA and individual ANOVA results for the average area of the three species (la

planting intensity and spatial pattern of plantation

Effect df (h, e)

MANOVA global test of hypotheses

Intensity (I) 9,96

Spatial pattern of plantation (P) 3,30

I � P 9,96

Individual ANOVA tests of hypotheses

Average area of larch at short-term stage–Model R2 = 0.96

Intensity (I) 3

Spatial pattern of plantation (P) 1

I � P 3

Error (e) 32

Average area of Mongolian Scotch pine at short-term stage–Model R2 = 0.998

Intensity (I) 3

Spatial pattern of plantation (P) 1

I � P 3

Error (e) 32

Average area of white birch at short-term stage–Model R2 = 0.988

Intensity (I) 3

Spatial pattern of plantation (P) 1

I � P 3

Error (e) 32

positively influences larch and Mongolian Scotch pine

abundance, but negatively influences white birch abundance.

Larch abundance is greater under high planting intensity

scenarios than under low planting intensity scenarios. For

example, under the 10% planting intensity scenario, it takes

roughly 200 years for larch to reach 70% of the landscape, but it

takes only 60 years to reach 70% of the landscape under the 70%

planting intensity scenario. The PA of Mongolian Scotch pine

responds to planting intensity in a similar way, with a greater

abundance under high planting intensity scenarios than that

under low planting intensity scenarios. In addition, the PA for

larch increases with simulation years in all simulation scenarios,

whereas the PA for Mongolian Scotch pine decreases with

simulation years after roughly 10–20 years (Fig. 4). Mongolian

Scotch pine has a relatively low abundance – less than 10% of the

study area during most simulation years – because it is a fire-

intolerant and shade-intolerant species and is less competitive

than either larch or white birch. Contrary to larch, white birch has

a greater abundance under low planting intensity scenarios than

that under high planting intensity scenarios. For example, under

the 10% planting intensity scenario, the PA for white birch

reaches 30% after only 10 years, and exceeds 40% after 40 years.

However, under the 70% planting intensity scenario, it requires

between 30 and 40 years attaining 30% abundance, and never

reaches 40% in 300 simulation years.

Individual ANOVA results also show that spatial pattern of

plantation has a significant influence on the short-, mid-, and

long-term PA for larch, and has a significant influence on the

long-term PA for both Mongolian Scotch pine and white birch

(Tables 4–6). In other words, spatial pattern of plantation has a

significant influence on the abundance of larch during all

simulation years, but has no short- or mid-term influence on

rch, Mongolian Scotch pine and white birch) at short-term stage as a function of

Pillai’s trace/type III SS F P

1.33 8.53 <0.001

0.92 112.69 <0.001

0.72 3.39 0.001

441.49 253.86 <0.001

3.32 5.73 0.023

0.39 0.23 0.88

18.55

73.49 6643.83 <0.001

0.0006 0.16 0.69

0.003 0.29 0.834

0.118

309.30 880.75 <0.001

0.034 0.289 0.594

0.014 0.041 0.989

3.75

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–93 89

Table 5

MANOVA and individual ANOVA results for the average area of the three species (larch, Mongolian Scotch pine and white birch) at mid-term stage as a function of

planting intensity and spatial pattern of plantation

Effect df (h, e) Pillai’s trace/type III SS F P

MANOVA global test of hypotheses

Intensity (I) 9,96 1.65 13.00 <0.001

Spatial pattern of plantation (P) 3,30 0.96 237.22 <0.001

I � P 9,96 0.87 4.37 <0.001

Individual ANOVA tests of hypotheses

Average area of larch at mid-term stage–Model R2 = 0.994

Intensity (I) 3 521.9 1519.73 <0.001

Spatial pattern of plantation (P) 1 52.16 455.65 <0.001

I � P 3 6.12 17.83 <0.001

Error (e) 32 3.66

Average area of Mongolian Scotch pine at mid-term stage–Model R2 = 0.996

Intensity (I) 3 81.37 2610.86 <0.001

Spatial pattern of plantation (P) 1 0.027 2.59 0.118

I � P 3 0.06 2.03 0.13

Error (e) 32 0.33

Average area of white birch at mid-term stage–Model R2 = 0.975

Intensity (I) 3 631.96 423.24 <0.001

Spatial pattern of plantation (P) 1 0.16 0.32 0.574

I � P 3 0.75 0.50 0.684

Error (e) 32 15.93

Table 6

MANOVA and individual ANOVA results for the average area of the three species (larch, Mongolian Scotch pine and white birch) at long-term stage as a function of

planting intensity and spatial pattern of plantation

Effect df (h, e) Pillai’s trace/type III SS F P

MANOVA global test of hypotheses

Intensity (I) 9,96 1.64 12.8 <0.001

Spatial pattern of plantation (P) 3,30 0.99 796.98 <0.001

I � P 9,96 0.965 5.06 <0.001

Individual ANOVA tests of hypotheses

Average area of larch at long-term stage–Model R2 = 0.993

Intensity (I) 3 179.2 886.72 <0.001

Spatial pattern of plantation (P) 1 132.73 1970.36 <0.001

I � P 3 11.11 54.97 <0.001

Error (e) 32 2.16

Average area of Mongolian Scotch pine at long-term stage–Model R2 = 0.989

Intensity (I) 3 38.63 2610.86 <0.001

Spatial pattern of plantation (P) 1 0.20 15.30 <0.001

I � P 3 0.05 1.15 0.346

Error (e) 32 0.42

Average area of white birch at long-term stage–Model R2 = 0.969

Intensity (I) 3 287.37 310.41 <0.001

Spatial pattern of plantation (P) 1 16.88 54.69 <0.001

I � P 3 3.38 3.65 0.023

Error (e) 32 9.88

either Mongolian Scotch pine or white birch. The interaction of

treatment intensity and treatment method has no effect on the

dynamics of Mongolian Scotch pine throughout the simulation

years, and only affects the long-term stage of white birch

abundance. The PA for larch under the dispersed planting

scenario is higher than that under the aggregated planting

scenario, very likely because dispersed planting increases the

cover range of seed dispersal and benefits the regeneration of

larch. However, Mongolian Scotch pine does not respond to

spatial pattern of plantation in a similar way. Spatial pattern of

plantation has no influence on the abundance of Mongolian

Scotch pine at the early simulation stage because the pine has a

low proportion in the landscape and only relatively short

dispersal distances. At the long-term simulation stage, spatial

pattern of plantation shows an influence on Mongolian Scotch

pine because such a long time frame captures its slow and

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–9390

Fig. 4. Proportion of sites in the study landscape occupied by larch, Mongolian Scotch pine, and birch during each decade for all simulation scenarios. I1, I2, I3, and I4

represent simulation scenarios where the planting intensity covers 10%, 30%, 50%, and 70% of the severely burned area, respectively.

cumulative dispersal behavior. However, the abundance of

Mongolian Scotch pine is only slightly higher under the

dispersed planting scenario than that under the aggregated

planting scenario (Fig. 4). Due to its strong dispersal and

colonization ability, white birch can invade open spaces created

by fire. This results in no significant influence of the two spatial

patterns of plantation on white birch abundance at the early

simulation stage. However, in the later simulation stages, the

abundance of white birch under the dispersed planting scenario

is lower than that under the aggregated planting scenario due to

strong competition with larch. The environment becomes more

unfavorable for white birch in a dispersed planting as the

canopy closure of larch forests increases.

Compared with the no planting scenario, the abundance of

larch in these planting scenarios was greater, which suggests

that planting activity is effective for increasing the abundance

of larch throughout these simulation years. Given its vital

attributes and low abundance, the decreasing abundance of

Mongolian Scotch pine with greater simulation years in all

simulation scenarios is not surprising. The difference between

these planting simulation scenarios and the no planting scenario

– especially in the later simulation stage – is discernable, with

the abundance of white birch under the no planting scenario

higher than that in the planting simulation scenarios due to its

diminished competition with larch.

The simulated results indicate that planting intensity and

spatial pattern of plantation not only significantly influence

the abundance of the three species, but also influence the

spatial pattern of these species (Fig. 5). However, the degree

of aggregation of species distribution differs by different

planting scenarios. The aggregation levels for larch under all

planting scenarios are similar, probably because larch has

relatively high proportions under all simulation scenarios;

high proportions tend to have aggregated distributions in the

study landscape. The aggregation level of larch is consider-

able high, with an AI larger than 0.7 at the start of the

simulation, and stabilizing at 0.8 after only a few simulation

decades. The distribution of Mongolian Scotch pine is more

aggregated under the high planting intensity scenarios than

that under the low planting intensity scenarios, whereas the

aggregation level of white birch is negatively correlated with

greater planting intensity. Also, the distribution of pine under

the dispersed planting scenarios has a lower aggregation level

than that under the aggregated planting scenarios. However,

the difference in the level of aggregation between the two

spatial patterns of plantation increases with the planting

intensity. There is a sudden decrease in the AI for pine after

about 200 simulation years; this is because the large number

of pines planted at the beginning of the simulation began to

die when they approached their 250-year longevity. The

aggregation level of white birch responds to spatial pattern of

plantation in a way similar to that for larch, with its AI lower

under the dispersed planting scenarios than under the

aggregated planting scenarios.

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–93 91

Fig. 5. Aggregation index in the study landscape occupied by larch, Mongolian Scotch pine, and birch during each decade for all simulation scenarios. I1, I2, I3, and I4

represent simulation scenarios where the planting intensity covers 10%, 30%, 50%, and 70% of the severely burned area, respectively.

5. Discussion

Both planting intensity and spatial pattern of plantation play

an important role in influencing tree species abundance.

However, planting intensity exerts the dominant effect on the

abundance of the three tree species examined. Greater planting

intensities simulated in the study increase the abundance of

larch and Mongolian Scotch pine, but decrease the abundance

of white birch. Although larch abundance increases with

increasing planting intensity, the degrees of the increase among

the planting intensity scenarios are different. Species abun-

dance has a greater, more sensitive response when planting

intensity is low. For example, the difference in larch abundance

under the 10% planting intensity scenario and the 30% planting

intensity scenario is obviously higher than that under 50%

planting intensity scenario and 70% planting intensity scenario

(Fig. 4). Additionally, the aggregation levels for larch among

the simulation scenarios are not significantly different. These

results suggest that intermediate planting intensity, such as 30%

or 50%, can achieve results for abundance and aggregation

levels of larch comparable to those simulated under high

intensity planting (e.g., 70%, 90%). For Mongolian Scotch

pine, intermediate planting intensity scenarios should also be

considered, since Mongolian Scotch pine cannot effectively

compete with birch and larch. The relatively high abundance of

Mongolian Scotch pine under a high planting intensity, which

would involve both great labor and economic costs, will

eventually decrease over the mid- and long-term periods.

With respect to spatial patterns of plantation, larch abundance

under a dispersed planting scenario is greater than that under an

aggregated planting scenario. However, the interaction between

spatial patterns of plantation and planting intensities can be

intriguing. For example, the difference in larch abundance

between dispersed planting and aggregated planting under

intermediate planting intensity scenarios (30% and 50% planting

intensity) is greater than that under both the low planting

intensity scenario (10% planting intensity) and the high planting

intensity scenario (70% planting intensity) (Fig. 4). In a low

planting intensity scenario, the space created by the 1987 fire

remained unplanted, which resulted in small differences between

dispersed planting and aggregated planting. However, in a high

planting intensity scenario, little space created by the 1987 fire

remained unplanted, which also resulted in the small differences

under dispersed planting and aggregated planting. These results

indicate that it is necessary to incorporate information about

spatial patterns of plantation into planting strategies under

intermediate planting intensity scenarios. Therefore, under the

current planting intensity (about 50%), dispersed planting would

be better for forest recovery than aggregated planting.

In Great Hing’an Mountains, recovery of the site and the

reestablishment of mature ecological systems (coniferous

forests) will probably require from 100 to 200 years to restore

the life forms (e.g., Zhou, 1991; Xu, 1998). Our simulations

show that coniferous seedlings planting have significantly long-

term influence on forest recovery in the catastrophic fire burned

landscape, which are consistent with some studies on

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–9392

reforestation (e.g., Miyawaki and Golley, 1993; Chen et al.,

2003), for coniferous seedlings planting will eventually

replicate the mature ecosystems of the site without going

through the lengthy process of succession. Residual trees play

an important role in forest restoration (e.g., Keeton and

Franklin, 2005). However, only few residual trees remained in

the severely burned area after the 1987 fire, which resulted in a

lower abundance of coniferous trees under the no planting

scenario than that under the planting scenarios. Therefore, large

area clear-cutting should not be applied in forest management

for this region. Coniferous trees planted in the severely burned

area serve as residual trees, which further disperse seeds for

forest recovery—that is, more residual trees are left with

increasing planting intensity. In addition, the simulation results

demonstrate that dispersed planting yields greater abundance of

larch forests than aggregated planting, suggesting that some

dispersed coniferous trees or seedlings should be left as seed

sources for forest recovery when timber harvesting is applied in

a forest landscape.

Two key assumptions likely affect our conclusions. The first is

that the species establishment coefficients do not vary at the scale

of landtypes. Fine scale heterogeneity, such as soil moisture and

temperature availability, are important variables to affect forest

dynamics because they would affect plant establishment and

growth, species diversity, nutrient cycling, rates of soil formation

and other biotic activity (e.g., Xu, 1998). In reality, drought sites

are often colonized by Mongolian Scotch pine rather than larch,

for the pine has a lower shade tolerance than larch. Hence, the

abundance of Mongolian Scotch pine may be underestimated in

these scenarios. However, given its vital attributes discussed

above, we believe that Mongolian Scotch pine could not have a

high proportion of the study landscape. In addition, species

establishment coefficients may vary with fire severity. In low-

severity burned areas, burning prepares seedbeds by removing

duff and competing vegetation, and by altering the thermal

regime of the soil, which favors germination. But in severely

burned areas, organic layers of soil were almost completely

consumed—an unfavorable condition for the regeneration of

larch and Mongolian Scotch pine (Xiao et al., 1988; Xu, 1998).

Using finer-scale land units to parameterize our simulations

could alleviate these problems. However, no available data

currently exists to estimate species establishments at a finer

spatial scale.

A second assumption is that once established, trees die only

because of disturbance-caused mortality, approaching their

longevity, and a small chance of random mortalities simulated in

the model. In other words, other types of mortality such as stress

related in the early stage of their life span are not considered in the

LANDIS model. This assumption is appropriate, given that

human interference is not considered. In this study, intensive

planting is employed in the burned area, but the success rates of

species establishment are also affected by the quality of the

seedlings and aftermath care after planting (e.g., Xiao et al.,

1988; Zhao, 1988; Tan, 1994). These factors are not addressed in

LANDIS modeling. In addition, LANDIS did not simulate the

effects of shrubs and herbs on forest recovery, which could have

significant influence on coniferous seedlings. For example, some

studies (Turner et al., 1997, 1999) have shown that shrub and herb

recruitment following forest fires in burned areas where conifer

seedlings were scarce may lengthen the time required for forest

development, or even preclude them entirely, for shrub and herb

may compete with trees for resources (e.g., light, nutrients).

Previous studies in the Great Hing’an Mountains showed that

shrubs and herbs would soon invade these open spaces created by

the catastrophic fire in 1987. In some cases, if vegetation

becomes too dense, light levels beneath shrubs and grasses may

be insufficient for tree seedlings to maintain a net positive carbon

balance, and thus inadequate radiation may cause low coniferous

seedling survivorship. Therefore, coniferous seedlings could not

regenerate in some places because of competition from the well-

developed shrub and herb community (e.g., Zhou et al., 1989;

Yang et al., 1998; Wang et al., 2003). If shrubs and herbs were not

cleared before coniferous seedling plantation, some seedlings

planted could not survival, which would cause lower abundance

of coniferous tree species (larch and Mongolian Scotch pine).

However, this may not be a significant factor: in our study areas,

shrubs and herbs must be cleared prior to the coniferous seedling

plantation (Shao, 1988; Wang et al., 2003). When coniferous

seedlings grow older, they will out-compete shrubs and herbs.

Thus, the simulated results could effectively reflect the long-term

forest dynamics in the landscape based on this assumption.

6. Conclusion

Landscape models are an efficient tool for evaluating the

effects of different planting strategies on landscape-scale forest

recovery. They provide insights into forest dynamics under

natural disturbances and reforestation. In summary, we found

that planting intensity can modify the abundance of the three

species in the study (larch, Mongolian Scotch pine, and white

birch) by encouraging larch and pine establishment that will

eventually decrease white birch abundance. Although larch

abundance increases with increasing planting intensity, the

degree of increased abundance among these planting intensity

scenarios is different. Given considerable labor inputs and

economic costs, intermediate planting intensity (30% or 50%)

would better facilitate forest recovery. In addition, spatial

pattern of plantation also influences the three species, with

higher larch and pine abundance and lower white birch

abundance under dispersed planting than that under aggregated

planting. This indicates that it is necessary to incorporate

information about spatial patterns of plantation into planting

strategies. These results have important implications for forest

managers to design sound forest restoration projects for

landscapes affected by large infrequent disturbances.

Acknowledgements

This paper is sponsored by the Chinese Academy of

Sciences, National Natural Science Foundation of China

(40331008, 30270225), and the Project of Chinese Academy

of Sciences (No. KSCX2-SW-133) and Sino-Russian Joint

Research Center on Natural Resources and Eco-Environmental

Sciences.

X. Wang et al. / Forest Ecology and Management 225 (2006) 82–93 93

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