Trans. Tianjin Univ. 2009, 15: 178-186 DOI 10.1007/s12209-009-0032-3 © Tianjin University and Springer-Verlag 2009
Effects of Spatial Aggregation on Forest Landscape Model Simulation in Northeastern China∗ ZHOU Yufei(周宇飞)1, 2, HE Hongshi(贺红士)1, 3, BU Rencang(布仁仓)1， JIN Longru(金龙如)1, 2, LI Xiuzhen(李秀珍)1 (1. Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; 2. Graduate School of Chinese Academy of Sciences, Beijing 100049, China; 3. School of Natural Resources, University of Missouri-Columbia, MO 65211, USA) Abstract：Issues of scale and aggregation become important when large range of space and time scales is
considered in landscape models. However, identifying appropriate levels of aggregation to accurately represent the processes and components of ecological systems is challenging. A raster-based spatially explicit forest landscape model, LANDIS, was used to study the effects of spatial aggregation on simulated spatial pattern and ecological process in Youhao Forest Bureau of the Small Khingan Mountain in Northeastern China. The model was tested over 500 simulation years with systematically increased levels of spatial aggregation. The results show that spatial aggregation significantly influences the simulation of fire disturbance, species abundance, and spatial pattern. Simulated fire regime was relatively insensitive to grain size between 30.m and 270.m in the region. Spatial aggregation from 300.m to 480.m dramatically decreased fire return interval (FRI) and increased mean fire size. Generally, species abundance and its aggregation index (AI) remained higher level over simulation years at the fine-grained level of spatial aggregation than at coarser grains. In addition, the simulated forest dynamics was more realistic at finer grains. These results suggest that appropriate levels of spatial aggregation for the model should not be larger than 270.m. Keywords：scale; spatial aggregation; LANDIS; fire disturbance; succession; spatial pattern; Northeastern China
In the last two decades, forest landscape models have greatly evolved with the development of computer technology and conceptual scientific growth in forest and landscape ecology[1-3]. As ecologists seek to understand broad-scale forest changes (e.g. climate, succession, disturbance, and management), forest landscape models are increasingly becoming an effective tool to study the complicated interactions between forest succession and disturbances at large scales over long periods because controlled field experiments designed to investigate such broad-scale interactions are difficult or impossible. However, ecological patterns and processes are nearly always scale-dependent and are often hierarchically structured[4]. Therefore, determining appropriate levels of aggregation in applying these models is challenging because it is hard to accurately represent the components and processes of forest landscape by many ecological processes operated in a range of space and time scales. Most forest landscape models employ a raster data
format, and the forest landscape is conceptualized as a grid of equal-sized cells or sites. These models frequently involve the use of satellite imagery and the aggregation of data from fine resolutions to coarse resolutions due to computational loads. As a result, aggregation is particularly important when data management and processing limitations constrain the use of fine resolution data[5]. Coarsening the resolution of landscape data can alter landscape pattern in static maps, typically by reducing landscape heterogeneity, or number of different cover types[6]. Most studies have shown that data distortion or even data loss were associated with aggregation or scaling. In particular, dominant classes become more dominant in abundance or percentage cover, while minor classes become less common or even disappear through aggregation processes, so that a phenomenon may appear heterogeneous at finer-scale and homogeneous at coarserscale[4,7]. Also, spatial heterogeneity can influence the ecological processes such as the movement of organisms
Accepted date: 2008-01-11. ∗Supported by National Natural Science Foundation of China (No.30870441, 40331008) and the Project of Chinese Academy of Sciences (No. KSCX2SW-133) ZHOU Yufei, born in 1979, male, Dr, lecturer. Correspondence to ZHOU Yufei, E-mail:
[email protected].
ZHOU Yufei et al: Effects of Spatial Aggregation on Forest Landscape Model Simulation in Northeastern China
and the spread of disturbance across a landscape[8]. Consequently, changes in initial spatial pattern are expected to affect the seed dispersal and the occurrence of disturbance across the forest landscape. Although there has been a considerable amount of work done on the effects of changing scales on landscape pattern, few systematic studies have been done as to how spatial aggregation affects the simulation results of forest landscape models[9,10]. Therefore, an urgent understanding is needed on how spatial aggregation affects the ecological process and landscape pattern simulated with forest landscape models. This paper is to examine the effects of spatial aggregation on ecological process and forest dynamics in Youhao Forest Bureau of Small Khingan Mountains, using a forest landscape model LANDIS[3]. Specifically, the objective of this research is to study: (1) how spatial aggregation affects ecological process, such as fire disturbance; (2) how species abundance responds to spatial aggregation; (3) how spatial aggregation affects forest spatial pattern. The results will provide insights for ecological researchers to identify appropriate levels of spatial aggregation when they use forest landscape models.
1
Study area
Youhao Forest Bureau (Fig. 1), comprising nearly 2.8×105 ha, is located in the middle part of Yichun area of Heilongjiang Province in Northeastern China (47°45′56″—48°33′25″N and 128°07′34″—128°59′ 53″ E). The area has a continental climate with briefly rainy summers and long cold winters. The average annual precipitation is 629.6,mm, with great inter-annual change. Most rainfall occurs between June and August. Topographic features are different: north slope is relatively flat and south slope is relatively steep. Elevation ranges from 227,m to 795,m; the mean elevation is 300,m. Brown forest soil is representative and comprises about 71% of the region. The plant species in the region belong to Changbai flora. Broad-leaved Korean pine forest is the zonal climax vegetation type dominated by Korean pine (Pinus koraiensis), but its area and stock have been dramatically decreased in the last 50 years caused by the over-cutting and poor regeneration[11]. A mountain ridge runs cross the region from the west to the east. On the south slope, the main forest types include Korean pine forests, larch (Larix gmelinii) forests, and mixed broad-leaved forests;
on the north slope, the main forest types include larch forests, white birch (Betula platyphylla) forests, and mixed conifer-broad forests. Besides these species mentioned above, the main tree species in the region include spruce (Picea koraiensis and Picea jezoensis), fir (Abies nephrolepis), elm (Ulmus japonica), basswood (Tilia amurensis), and maple (Acer mono).
Fig.1
2
The location of Youhao Forest Bureau and the forested and non-forested areas for LANDIS
Methods
2.1
Description of LANDIS LANDIS is a spatially explicit landscape model designed to simulate large forest landscape over long time span. The model operates on a raster landscape map that is divided into cells and cell size is user-specified depending on the research scale. LANDIS simulates natural and anthropogenic disturbances, including forest succession, seed dispersal, windthrow, fire, biological disturbance, harvesting, fuel accumulation and decomposition, and fuel management. Detailed descriptions of various LANDIS components can be found elsewhere[3, 12, 13]. For the ease of understanding the purpose of this study, we briefly described two basic spatial input maps in LANDIS. The first spatial input map required to run LANDIS is a land type map. LANDIS stratifies a heterogeneous landscape into relatively homogeneous land types, which can be derived from abiotic data layer such as climate, soil, geology, and topography. Within a land type, a somewhat uniform suite of ecological conditions that result in similar species establishment patterns and fire disturbance characteristics, including ignition frequency, mean fire return interval, and fuel decomposition rate are
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assumed. This assumption has been supported by numerous experimental and empirical studies[14,15]. In LANDIS 4.0, species establishment, the time since last windthrow disturbance (TSLWD), and the minimum age of cohort growth (MACG) are mainly considered when each land type is parameterized. Species establishment is simulated by using the species establishment coefficient (ranging from 0.0 to 1.0), which quantifies how different environmental conditions favor or inhibit the establishment of a particular species. Species with high establishment coefficients have higher probabilities of establishment. These coefficients, which are provided as input to LANDIS, can be derived either from the simulation results of a gap model[3] or from estimates based on existing experimental or empirical studies[16]. The probability of windthrow mortality increases with tree age and size. Also, the probability of windthrow disturbance is often related to the local variables including climate and landform. Windthrow events may increase the potential fire intensity class at a site due to increased fuel load. MACG is required before enough shade is created so that a shade tolerance 5 (most tolerance) can seed into the site on this land type. For instance, a shade tolerance class 5 species such as balsam fir to seed into outwash site, at least one other species must be present in age class older than 40[17]. The second spatial input map is a species composition map which consists of species and species age classes. In many cases the species age map is not available for the study region. Interpolation of age information from other sources, such as the Forest Inventory Analysis (FIA) database, can be used[18]. Although original development region for LANDIS is a region of mixed deciduous and coniferous forests in northern Wisconsin (USA), the model has been successfully applied in many other locations as long as the species’ attributes can be parameterized. Unlike the land type map that remains stable over time, the species composition map is changed with simulation time because of species succession and competition and disturbances. The model does not track individual trees. This differs from most stand simulation models that track individual trees[19]. Each map cell contains information about the presence or absence of each species in 10-year age cohorts. An estimate of species is required to initialize LANDIS. Each species in the model is associated with life attribute parameters including longevity, maturity age, effective seed dispersal distance, shade tolerance, and fire tolerance (the last two are ordi—180—
nal variables ranging 1—5). Multiple pathways of succession may result from species’ life - history attributes[20,21]. Therefore, species composition is determined by seed resource, species’ response to shade and fire, and their interactions. 2.1.1 Species attributes and forest composition map A total of 17 species were incorporated into LANDIS, and species’ vital attributes (Tab.1) were derived based on previous studies in the region[22-26], as well as consultation with local experts. We used the 2001 forest stand map and the following stand attribute database to generate the forest composition map. The forest stand map contains boundaries of stands and compartments. The stand attribute database provides a dominant tree species and its age cohort. It also provides subdominant and accompanying tree species without age information. Although subdominant and accompanying tree species have no age information in forest stand map, all species in the stand are assigned with the same age cohort of the dominant tree species because almost even-aged forest is present in the stand due to plantations after long-term clear cutting. The resolution of the species composition map is 30 m × 30,m. According to the field studies in this area[25], the quadrate size (30,m × 30,m) is large enough to contain all types of tree species of the stand. So we endowed each pixel of the stand with all associated tree species of that stand. In order to isolate aggregation effects from the effects of variation in dispersal distance, the dispersal distance of each species was assigned to 500,m. Although the distance was unrealistically large for most of the species, it ensured that each species could move out the cells with increased levels of aggregation. 2.1.2 Land type map and species establishment coefficients In LANDIS, a land type is an area that has a homogeneous disturbance regime, species establishment coefficient, and unique characteristics such as climate, soil, geology, and topography[3]. In the study, we divided our study area into 63 land types by using a digital elevation model (DEM) and the forest stand map, primarily based on slopes, elevation, aspect, topographic position index (TPI), water, and residential land. Inactive land types (water and residential land) account for 15.41% of the total area, while active land types account for 84.59%. The species establishment coefficient (0—1) is a critical feature of each land type. It is an estimate of the probability that a species will successfully establish from
ZHOU Yufei et al: Effects of Spatial Aggregation on Forest Landscape Model Simulation in Northeastern China
Tab.1
Species’ life attributes for Youhao Forest Bureau in the Small Khingan Mountains
Species name
Long/a
MTR/a
ST
FT
VP
MVP/a
Korean pine (Pinus koraiensis)
400
80
4
3
0
0
Spruce (Picea koraiensis and Picea jezoensis)
300
30
4
2
0
0
Fir (Abies nephrolepis)
200
30
4
2
0
0
Larch (Larix gmelinii)
300
20
2
4
0
0
Pine-M (Pinus mongolica)
210
25
2
3
0
0
Pine-D (Pinus densiflora)
200
15
1
3
0
0
Ash (Fraxinus mandshurica)
250
30
3
5
0.9
50
Walnut (Juglans mandshurica)
250
20
2
4
0.9
60
Yellow cypress (Phellodendron amurense)
250
20
3
4
0.8
60
Oak (Quercus mongolica)
350
30
2
5
1.0
50
Elm (Ulmus japonica)
250
20
3
3
0.5
60
Maple (Acer mono)
200
20
3
3
0.5
50
Birch-C (Betula costata)
250
20
3
3
0.9
40
Black birch (Betula davurica)
150
15
2
5
0.9
30
Basswood (Tilia amurensis)
300
30
3
2
0.8
30
White birch (Betula platyphylla)
150
15
1
2
0.8
50
Aspen-D (Populus davidiana)
150
10
1
1
0.9
30
Note: Long—longevity (year); MTR—age of maturity (year); ST—shade tolerance class; FT—fire tolerance class; VP—vegetative reproduction probability; MVP—minimum age of vegetative reproduction (year).
seed within a particular land type, given the environmental conditions of that land type. The species establishment coefficients were derived from available literature as well as the existing LANDIS parameterizations in this region[27,28]. 2.1.3 Fire regime In LANDIS, fire return interval (FRI) is one of the primary determinants of fire disturbance. FRI is defined as the length of time required to burn an area equal in size to a specified area. In our study, the current fire regime for simulations was parameterized based on a database of 20-year fire records from 1971 to 1990[29]. Current fire return intervals were estimated by calculating the reciprocal of the annual proportion of forest land burned within each fire regime in the region and the FRI of the study landscape was specified to 360 years. Because the purpose of this study was to find out the relationship between aggregation and model behavior, LANDIS was only calibrated at the finest resolution. 2.2 Spatial aggregation scenarios To investigate the effects of changing grain size, the spatial resolution of maps was sequentially changed from the original resolution. As the grain size increased, data were aggregated following the majority rule, which is one of the most commonly used methods for aggregating categorical data in ecology and remote sensing[30]. The
majority rule finds the majority value (the value that appears most often, but not necessarily >50%) for the specified cells on the input grid, and records that value in the corresponding aggregated cell on the output grid. Because LANDIS is capable of simulating forest succession at varied grain sizes (from 10 to 500,m), the resolution for the species composition map and the land type map were degraded in 30,m increments from 30 to 60,m and to 90,m, and so forth up to 480,m × 480,m grain size. Therefore, no pixel was divided by aggregation procedure and data were not distorted owing to the grouping scheme. To examine the effects of spatial aggregation on the simulation of fire disturbance, the number of fires, mean fire size, FRI, and the proportion of cumulative areas damaged by fire were calculated from each model run. In addition, for each of the six main species in the study landscape (pine, spruce, larch, maple, birch, and aspend), the proportion of area (PA) was calculated for each decade over 500 simulation years. The PA describes the relative landscape abundance of each species. Also, to evaluate the effect of spatial aggregation on the spatial pattern of each species, an aggregation index (AI) for each species was calculated using their respective mapped outputs over the 500 simulation years. The AI is a ratio variable and has a range between 0 and 1, with —181—
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higher values showing a higher level of aggregation in the spatial pattern[31]. The indices (PA and AI) were calculated in APACK, a spatial statistics program designed to calculate patch statistics from LANDIS outputs[32]. Since LANDIS is a stochastic model, five replicates of each scenario were simulated for 500 years, and the mean value was recorded.
3
(a) Number of fires
Results
3.1
Fire regime The results showed that spatial aggregation had effects on FRI, the mean fire size and the number of fires (Fig. 2 and Fig. 3). The simulated fire regime (FRI, mean fire size and number of fires) remained relatively steady from 30 m to 270,m. However, the mean FRI substantially decreased while the mean fire size and the number of fires dramatically increased with increased levels of aggregation from 300 m to 480,m in the region. In addition, the proportions of cumulative area damaged by low-intensity fire (Class 1 and 2) were stable under all spatial aggregation scenarios (Fig. 3). The proportions of cumulative area damaged by high-intensity fire (Class 3, 4 and 5) remained stable at the fine and intermediate grains, then increased at the coarse-grained level of aggregation (Fig. 3).
(a) Fire size
(b) FRI
Fig.2 Simulated mean fire size and mean fire return interval over 500 simulation years in the study landscape
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(b) Proportion of landscape Class 1—5 represents the fire severity levels simulated by LANDIS.
Fig.3 Simulated mean number of fires and mean proportion of cumulative areas damaged by fire over 500 simulation years in the study landscape
3.2
Species abundance Spatial aggregation had no substantial effect on initial species abundance (Fig.4). However, moving the finest to coarsest resolution, species abundance showed great changes over simulation time. Abundance for each species maintained a higher proportion of the study area over simulation time at lower level of aggregation. Generally, species abundance at 30,m resolution was greater than that at 240,m and 480,m resolution. In addition for the two pioneer species (birch and aspen-d), the difference in species abundance between the finer and the coarser grains became more pronounced after 100 simulation years. 3.3 Aggregation index The simulated results showed that spatial aggregation also substantially affected the spatial pattern of these species (Fig.5). At the finest resolution, each species had a high AI value (>0.9). However, the AI value for each species decreased with increased levels of spatial aggregation. In addition, the AI for pine at 30,m resolution was considerably high (>0.9) at the start of the simulation, and then decreased and stabilized at 0.6 after about 300 simulation years. However, at coarser resolution, the AI for pine over simulation years was lower than that at finer resolution. Like the pine, other species responded to increased levels of aggregation in a similar way, with its AI higher at finer resolutions than at coarser resolutions.
ZHOU Yufei et al: Effects of Spatial Aggregation on Forest Landscape Model Simulation in Northeastern China
Fig.4 Proportion of sites in the study landscape during each decade at the finest, intermediate, and coarsest resolutions over 500 simulation years
Fig.5
Species aggregation index during each decade at the finest, intermediate, and coarsest resolutions over 500 simulation years
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Discussion
Previous studies showed that spatial aggregation based on majority rule could lead to data distortion or even data loss[5, 33]. The study sequentially changed the resolution of spatial input maps to a forest landscape model, LANDIS, to determine whether spatial aggregation would not only influence species abundance and its spatial pattern, but also alter ecological processes, such as fire disturbance. 4.1 Effect of spatial aggregation on fire regime Our study indicated that spatial aggregation across a broad range of spatial scales (30 m—480 m) had significant effect on fire return interval, fire size and number of fires. The response of fire disturbance to spatial aggregation was the complexity and likely an interaction between fire spread and seed dispersal. Coarsening the grain of spatial data can decrease landscape heterogeneity[4,7], which may retard or enhance the spread of disturbance depending on the probability of the disturbance spreading cross habitat types[34]. In this study, aggregation increased the landscape homogeneity (some land types disappeared), which may enhance the spread of fire disturbance, particularly when grains were larger than 270 m. Supposing that a fire occurred, increased levels of aggregation would result in more burned areas, which then decrease FRI. When fire spread across the cell boundaries, it may spread across the boundaries of fire regimes, and then a new fire occurred. In LANDIS, when a fire spreads into a different fire regime unit, the module will simulate a new ignition[17]. Therefore, the number of fires increased dramatically with increased levels of aggregation. In LANDIS, seed dispersal directly affects species abundance and composition as well as migration rates across the landscape[12]. Aggregation reduced the number of cells available for species establishment because few cells fell within the species’ dispersal radii with grains largened. In our simulation, the shade-tolerant species were also fire-tolerant. Conversely, the shade-intolerant species were fire-intolerant. Therefore, competition became an important impetus of species composition to reduce the abundance of less shade-tolerant species when grains were large enough. Compared with fire-intolerant species, more fire-tolerant species occupied higher proportions of the study area at coarser grains. As a result, the area damaged by high-intensity fire potentially in
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creased at coarser grains. 4.2 Effect of spatial aggregation on species abundance The results described here revealed that spatial aggregation had significant effects on species abundance. Although spatial aggregation had no significant effects on the initial extent of each species, consistent with the previous study in which no substantial change in initial plant species extent with increased levels of spatial aggregation [10], abundance for each species over simulation years was higher at finer resolution than at coarser grains. Therefore, spatial aggregation had a negative influence on species abundance over simulation time. It is hard to conduct the validation of simulation results at different resolutions, for long-term series vegetation data do not exist. Verifying the simulation re-sults by comparing them with other study results could increase confidence in determining appropriate levels of aggregation. Previous study in this region showed that shade-intolerant species, such as birch, dominated in the early stage of succession, while shade-tolerant species, such as Korean pine, spruce and maple, would gradually increase in abundance and ultimately dominate the region[23]. This result was consistent with our simulated results, especially at finer resolution. In addition, in order to effectively simulate forest dynamics, appropriate level of spatial aggregation should ensure that each species could move out the cells[35]. 4.3 Effect of spatial aggregation on spatial pattern The effect of spatial aggregation on species spatial pattern is unexpected and surprising. He[5] found that spatial aggregation based on majority rule tended to make the dominant classes more aggregated and minor classes disaggregated. However, in this study, the initial AI value of each species unexceptionally decreased with the increased levels of spatial aggregation. Since this study endued a pixel with much information including several trees and their age cohorts, numerous classes in the species composition map resulted in a complex initial pattern. As aggregation based on majority rule increased, some classes tended to be aggregated, while others became more disaggregated. Classes aggregated and classes disaggregated may have the same species (age cohorts may not be the same), and the AI for all species decreased as spatial aggregation increased. Sequentially, these changes in initial spatial pattern resulted in changes of fire regime in this region.
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Conclusions
［9］ Malanson G P, Armstrong M P. Issues in spatial representation: Effects of number of cells and between-cell step
The results of this study had significant implications size on models of environmental process [J]. Geographifor ecological researchers who use forest landscape modcal and Environmental Modelling, 1997, 1: 47-64. els to identify appropriate levels of spatial aggregation at ［10］ Syphard A D, Franklin J. Spatial aggregation effects on the which the components and processes of forest landscape simulation of landscape pattern and ecological processes in were accurately represented. Particularly, the results sugSouthern California plant communities [J]. Ecological gest that spatial aggregation affected the simulated ecoModelling, 2004, 180(1): 21-40. logical process, species abundance, and spatial pattern. ［11］ Li Junqing, Li Jingwen. Regeneration and restoration of The response of the model to the spatial aggregation was broad-leaved Korean pine forests in Lesser Xing’an Mounless sensitive from 30 m to 270,m. Aggregation resulted tains of Northeast China [J]. Acta Ecologica Sinica, 2003, in more homogeneous landscape that may enhance the 23(7): 1268-1277. spread of fire disturbance, particularly when cell size was larger than 270 m. Appropriate levels of spatial aggrega- ［12］ He H S, Mladenoff D J. The effects of seed dispersal on the simulation of long-term forest landscape change [J]. tion for the model should not be larger than 270,m. References ［1］ Mladenoff D J. The promise of landscape modeling: Suc-
Ecosystems, 1999, 2(4): 308-319. ［13］ Gustafson E J, Shifley S R, Mladenoff D J et al. Spatial simulation of forest succession and timber harvesting using
cesses, failures, and evolution [M]. In: Wiens J A, Moss
LANDIS [J]. Canadian Journal of Forest Research, 2000,
M R, Issues in Landscape Ecology. Cambridge University
30(1): 32-43.
Press, Cambridge, UK, 2005. 90-100. ［2］ Mladenoff D J , Baker W L. Development of forest and landscape modeling approaches [M]. In: Mladenoff D J, Baker W L, Advances in Spatial Modeling of Forest Landscape Change: Approaches and Applications. Cambridge University Press, Cambridge, UK, 1999. 1-13. ［3］ Mladenoff D J, He H S. Design and behavior of LANDIS, an object-oriented model of forest landscape disturbance and succession [M]. In: Mladenoff D J , Baker W L, Advances in Spatial Modeling of Forest Landscape Change: Approaches and Applications. Cambridge University Press, Cambridge, UK, 1999. 125-162. ［4］ Levin S A. The problem of pattern and scale in ecology [J]. Ecology, 1992, 73(6): 1943-1967. ［5］ He H S , Stephen J V, Mladenoff D J. Effects of spatial aggregation approaches on classified satellite imagery [J]. International Journal of Geographical Information Science, 2002, 16(1): 93-109. ［6］ Turner M G, Gardner R H, O’Neill R V. Pattern and Process: Landscape Ecology in Theory and Practice [M]. Springer, New York 2001. ［7］ Costanza R , Maxwell T. Resolution and predictability: An approach to the scaling problem [J]. Landscape Ecology, 1994, 9(1): 47-57. ［8］ Turner M G. Landscape ecology: The effect of pattern on process [J]. Annual Review of Ecology and Systematics, 1989, 20: 171-197.
［14］ Brown J K, See T E. Downed Dead Woody Fuel and Biomass in the Northern Rocky Mountains [R]. USDA Forest Service General Technical Report INT-117, 1981. 1-47. ［15］ Kauffman J B, Uhl C, Cummings D L. Fire in the Venezuelan Amazon (1): Fuel biomass and tree chemistry in the evergreen rainforest of Venezuela [J]. Oikos, 1988, 53: 167-175. ［16］ Shifley S R, ThompsonⅢ F R, Larson D R et al. Modeling forest landscape change in the Missouri Ozarks under alternative management practices [J]. Computers and Electronics in Agriculture, 2000, 27(1/2/3): 7-27. ［17］ He H S, Li W, Sturtevant B R et al. LANDIS 4. 0 User’s Guide [M]. School of Natural Resources, University of Missouri-Columbia, Columbia, USA, 2004. ［18］ He H S, Mladenoff D J, Radeloff V C et al. Integration of GIS data and classified satellite imagery for regional forest assessment [J]. Ecological Applications, 1998, 8(4): 10721083. ［19］ Grimm V. Ten years of individual-based modelling in ecology: What have we learned and what could we learn in the future [J]. Ecological Modelling, 1999, 115(2): 129148. ［20］ Cattelino P J, Noble I R, Slatyer R O et al. Predicting the multiple pathways of plant succession [J]. Environmental Management, 1979, 3(1): 41-50. ［21］ Moore A D, Noble I R. An individualistic model of vegetation stand dynamics [J]. Journal of Environmental Man-
—185—
Transactions of Tianjin University
Vol.15 No.3 2009
agement, 1990, 31(1): 61-81. ［22］ Zhou Yiliang. Vegetation in Small Khingan Mountains of China [M]. Science Press, Beijing, 1994(in Chinese). ［23］ Sang Weiguo, Li Jingwen. Dynamics modeling of Korean
13 (in Chinese). ［29］ Zheng Huanneng. Forest Fire in Northeastern China [M]. Northeast Forestry University Press, Harbin, 2000 (in Chinese).
pine forest in southern Lesser Xingan Mountains of China
［30］ Wu J. Effects of changing scale on landscape pattern
[J]. Acta Ecologica Sinica, 1998, 18(1): 38-47(in Chi-
analysis: Scaling relations [J]. Landscape Ecology, 2004,
nese).
19(2): 125-138.
［24］ Yan Xiaodong, Fu Congbin, Shugart H H. Simulating the
［31］ He H S, DeZonia B, Mladenoff D J. An aggregation index
effects of climate changes on Xiaoxing’an Mountain for-
(AI) to quantify spatial patterns of landscapes [J]. Land-
ests [J]. Acta Phytoecologica Sinica, 2000, 24(3): 312-
scape Ecology, 2000, 15(7): 591-601.
319.
［32］ Mladenoff D J, DeZonia B. APACK 2.0 User’s Guide
［25］ Xu Huacheng. Natural Forests of Pinus koraiensis in
[M]. Department of Forest Ecology and Management,
China [M]. China Forestry Publishing House, Beijing,
University of Wisconsin-Madison, Madison, WI, USA,
2001(in Chinese).
1997.
［26］ Han Youzhi, Wang Zhengquan. The spatial pattern and
［33］ Moody A, Woodcock C E. The influence of scale and the
process of seed bank of Fraxinus mandshurica in secon-
spatial characteristics of landscapes on land-cover mapping
dary hardwood forests [J]. Acta Phytoecologica Sinica,
using remote sensing [J]. Landscape Ecology, 1995,
2002, 26(2): 170-176(in Chinese).
10(6): 363-379.
［27］ Ma Jianlu, Zhuang Liwen, Cheng Dong et al. Geographic
［34］ Turner M G, O’Neill R V, Gardner R H et al. Effects of
distribution of Pinus koraiensis in the world [J]. Journal
changing spatial scale on the analysis of landscape pattern
of Northeast Forestry University, 1992, 20(5): 40-48(in Chinese).
[J]. Landscape Ecology, 1989, 3(3/4): 153-162. ［35］ Xu Chonggang, Hu Yuanman, Chang Yu et al. Spatially
［28］ Ma Jianlu, Liu Dejun, Zhuang Liwen et al. A study on
explicit landscape model—LANDIS mechanism [J]. Chi-
correlativity between community types for natural Korean
nese Journal of Applied Ecology, 2004, 15(5): 837-844(in
pine forest and site factors in Xiao Xing’an Mountains [J].
Chinese).
Journal of Northeast Forestry University, 1994, 22(5): 7-
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