Simulating the effects of reforestation on a large catastrophic fire burned landscape in Northeastern China

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Simulating the effects of reforestation on a large catastrophic fire burned landscape in Northeastern China
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  Simulating the effects of reforestation on a large catastrophic fireburned 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, China b 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 ForestBureau 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 a4  2 factorial design over a 300-year period. The results showed that increasing planting intensity positively influenced larch and MongolianScotch pine abundance, but negatively influenced white birch abundance. However, the increased degree of larch abundance with increasingplanting intensity was significantly different between intensities. The difference in larch abundance between the 10% planting intensity scenarioand the 30% planting intensity scenariowas 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 andeconomic costs, 30% planting intensity would be effective for forest recovery. In addition, dispersed planting showed more promising results onforest recovery than aggregated planting. However, the difference of larch abundance between dispersed planting and aggregated planting underintermediate planting intensity scenarios (30% and 50% planting intensity) was greater than that under a low planting intensity scenario and a highplanting intensity scenario. Therefore, it is necessary to incorporate spatial pattern of plantation into planting practice, especially under anintermediate planting intensity scenario. These results have important implications for forest managers to design sound forest restoration projectsfor landscapes affected by large infrequent disturbances. In particular, the results suggest that the current planting strategy (50% planting intensitywith aggregated planting) employed after the catastrophic fire in 1987 could not be optimum, and the dispersed planting strategy covering about30% 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’anMountains in northeastern China provide the most timber of any forested area in the country; simultaneously, this areaencompasses rather unique ecological and environmentalsystems in the region (Zhou, 1991; Xu, 1998). Humanactivities, particularly timber harvesting, have substantiallyaltered the spatial pattern and ecological functions of thesesystems. Decades of fire suppression have reduced fire size,prolonged the fire return interval (i.e., the number of yearsbetween two successive fire events for a specific area), andindirectly influenced forest composition and dynamics (Shuet al., 1996). The success of fire suppression, coupled with awarmer, drier climate due to global warming (Xu, 1998), haveled to a fuel buildupand resulted in fires of greater intensity andextent than those that occurred previously in the region.Catastrophic fires can have disastrous effects on forestcomposition and structure, ecosystem processes, and landscapepattern (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  10 6 ha. Ecology and Management 225 (2006) 82–93* Corresponding author. Tel.: +86 24 83970350; fax: +86 24 83970351. E-mail addresses:, Wang).0378-1127/$ – see front matter # 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.foreco.2005.12.029  This immense, high-intensity fire consumed vegetation coverand precipitated the exposition of mineral soils – as well as thesubsequent erosion and runoff – during post-fire rain events(Xiao et al., 1988; Shu et al., 1996). Forest recovery in suchvastly burned areas is challenging because the long-termlandscape-level vegetation dynamic in a forest landscape iscomplicated by spatial and temporal interactions amongmultiple ecological and anthropogenic processes.In many cases, natural succession can eventually lead topost-fire recovery. This is especially true for cases in whichthere are sufficient residual forests remaining nearby to act asseedsources(Turneretal.,1999;Borchertetal.,2003).Incases such as the 1987 fire in the Great Hing’an Mountains, naturalrecovery is difficult because the severely burned area isextensive, the burn severities are high, and the seed sources arefar removed (Xiao et al., 1988). In these situations, the processofvegetationrecoveryisslow,increasingtheriskofsoilerosionand environmental degradation. Thus, ecological restorationthrough human mediation is necessary. After the 1987 fire,forestmanagementinthisregionshiftedfromtimberharvestingto reforestation – particularly in the severely burned area – inorder to accelerate forest restoration.Various approaches have been developed to restore forestvegetations for degraded systems where natural recovery isunlikely. Grass seeding provides quick, temporary vegetationground cover; these are typically annuals or short-livedperennials that can hold soil (Beyers, 2004). However, such a treatment does not facilitate long-term ecosystem restoration.Long-term ecosystem restorations are accomplished eitherthrough the planting of a small number of early successionalnursery trees or shrubs to create habitats for seed-dispersingbirds (e.g., Lamb, 1998), or through high-density plantation of  tree species once present in the disturbed areas (e.g., Smaleet al., 2001; Nagashima et al., 2002).Althoughtheseapproacheshaveshownpromisingresultsfordevelopment of forest structure, increase of species richness,and recovery of natural successional processes, they are oftenlimited to relatively small areas (Lamb, 1998). In the Great Hing’an Mountains, it is not sufficient to restore only a fewburned areas, and it is highly impractical to plant trees in thevast severely burned area due to the considerable labor andeconomic resources required. It is therefore necessary to assessthe planting intensity (the proportion of the burned area forreforestation) and spatial patterns of plantation (the spatialallocationof reforestationin the field)forforestrecovery.IntheGreat Hing’an Mountains, the planting strategy after thecatastrophic fire in 1987 was to plant coniferous seedlings in anaggregated fashion over about 50% of the severely burned areawhere coniferous forests used to grow. Others such as dispersedplanting, an alternative to the aggregated planting, have notbeen evaluated and compared. A dispersed planting wouldentail planting trees in a random fashion across the entireseverely burned area.Descriptive studies and field experiments are ofteninadequate sources of input for managers developing andimplementing reforestation plans. Comparing ecologicalrestoration strategies on a large-scale landscape is oftenbeyondthe limits of traditional or experimental studies. Modelshave therefore become an important tool for predicting theeffects of alternative management options. Landscape modelsare particularly important in this study because other types of models, such as gap models and ecosystem process models, areoften limited in spatial extents (He et al., 2002b; Mladenoff,2004). While other anthropogenic disturbances – such as firesuppression and forest harvesting – have been studied usinglandscape 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 beenexplicitly simulated.The purpose of this research is to study the effects of different reforestation scenarios on vegetation dynamics in theseverely burned area of the Great Hing’an Mountains. We willexamine the effects of planting intensities and spatial patternsof plantation (dispersed vs. aggregated) on forest landscaperecovery. We will apply a factorial design of planting intensityand spatial pattern of plantation on the realistically para-meterized forest composition maps of 1987 to identify thecombinations of planting intensity and method that could bestaccelerate forest restoration. Understanding the probable post-fire dynamics of the region under different reforestationscenarios will not only provide insights into landscape scaleprocesses, but also will provide baseline information on forestlandscape restoration in northeast China after catastrophic firedisturbances. 2. Study area The Tuqiang Forest Bureau (Fig. 1), encompassingapproximately 4  10 5 ha on the northern slopes of GreatHing’an Mountains, is in the Mohe County of Heilongjiangprovince in northeast China (from 52 8 15 0 55 00 to 53 8 33 0 40 00 N,and 122 8 18 0 05 00 to 123 8 29 0 00 00 E). It borders Russia to the north(separated by Heilongjiang River), the Xilinji Forest Bureau tothe west, the Amur Forest Bureau to the east, and the InnerMongolia Autonomous Region to the south. The area has acold, continental climate, with an average annual temperature  X. Wang et al./Forest Ecology and Management 225 (2006) 82–93  83Fig. 1. The location of Tuqiang Forest Bureau and burn severities after thecatastrophic fire in 1987 (1, unburned area (MA 1); 2, severely burned areawhere conifers were dominant before the fire (MA 2); 3, severely burned areawhere conifers were not dominant before the fire (MA 3); 4, other burned area(MA 4)).  at  5  8 C. Monthly mean temperature ranges from  47.2  8 C inJanuary to 31.4  8 C in July. The average annual precipitation is432 mm, with great inter-annual variations. Seventy-fivepercent of the rainfall occurs between June and August.Uplands and small hills characterize this region, though itpossesses a relatively smooth topography. Slopes are generallyless than 15 8 ; the maximum slope is less than 45 8 . Hillsundulate throughout this area, and the mountain ranges mostlyrun in north and south directions. Elevations range from 270 mto 1210 m; the mean elevation is 500 m. Brown coniferousforest soil is representative in the Bureau. Vegetation isdominated by larch (  L. gmelini ) forests. White birch (  Betula platyphylla ) is the major broad-leaved species in the region. Inaddition to larch and white birch, the tree species includeMongolian 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  10 6 hatook place on the northern slopes of the Great Hing’anMountains. The burned area covered 2.31  10 5 ha in theBureau, and the severely burned area covered roughly9  10 4 ha (Fig. 2). The conflagration caused incredibledamage to Tuqiang Forest Bureau, and this has led to greatdifficulty in restoring the forest ecosystems. 3. Methods 3.1. Description of LANDIS  LANDIS is a landscape disturbance and succession modelthat facilitates the study of the effects of natural andanthropogenic disturbances on forest landscapes, and has beendescribed 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. InLANDIS, a landscape is organized as grid of cells, withvegetation information stored as attributes for each cell. Cellsize can be varied from 10 m to 500 m depending on theresearch scale. At each cell, the model tracks a matrixcontaining a list of species by rows and the 10-year age cohortsby columns. The model does not track individual trees. Thisdiffers 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 landscapesand avoids any false precision of predicting species abundancemeasures with inadequate input data or parameter information.LANDIS stratifies a heterogeneous landscape into landtypes, which are generated from GIS layers of climate, soil, orterrain attributes (slope, aspect, and landscape position). It isassumed that a single land type contains a somewhat uniformsuite of ecological conditions, resulting in similar speciesestablishment patterns and fire disturbances characteristic,including ignition frequency, mean fire return interval, and fueldecomposition rated (He and Mladenoff, 1999). These assumptions have been supported by many empirical andexperimental studies (e.g., Brown and See, 1981; Kauffmanet al., 1988). Furthermore, land types can be redefined by usersto partition the landscape into strata that are most relevant for aparticular application.Four spatial processes and numerous non-spatial processesare simulated by LANDIS. The spatial processes are fire,windthrow, harvesting, and seed dispersal. Fire is stochasticprocess based on the probability distributions of fire cycle andmean fire sizes for various land types (He and Mladenoff,1999). LANDIS simulates fire levels of fire intensity fromsurface fire to crown fires. Fire intensity is determined by theamount of fuel on a site. Tree species are also grouped into firefire-tolerance classes based ontheir fire-tolerance attributesandfive age-based fire susceptibility classes from young to old,with young trees being more susceptible to damage than oldertrees. Thus, fire severity is the interaction of susceptibilitybased on species age classes, species fire tolerance and fireintensity. A new LANDIS fire module (Yang et al., 2004)employs hierarchical probability theory to allow even moreexplicit simulation of different fire regimes across landscapes.Windthrow is also stochastically simulated. In the LANDISwind module the probability of windthrow mortality increaseswith tree age and size. Windthrow events interact with firedisturbance such that windthrow increases the potential fireintensity class at a site due to increased fuel load.  X. Wang et al./Forest Ecology and Management 225 (2006) 82–93 84Fig. 2. Landtype maps of Tuqiang Forest Bureau.  The LANDIS harvest module simulates forest harvestingactivities based on management area and stand boundaries(Gustafson etal.,2000). These maps are predefinedand are only usedbytheLANDISharvestandfuelmodules.Harvestactivitiesare specified through rules relative to spatial, temporal, andspecies age-cohort information tracked in LANDIS. The spatialcomponentdetermineswhereharvestactivitiesoccurandmaybeused to enforce stand boundary and adjacency constraints. Thetemporal component determines the timing (rotations) andmanner (single versus multiple-entry treatments) of harvestactivities. The species age-cohort component allows specifica-tion of the species and age cohorts removed by the harvestactivities. For example, a clearcut removes all species and allages, whereas a selection harvest typically removes only a fewspecies and age cohorts. The ability to use a combination of spatial,temporal,species,andageinformationtospecifyharvestaction 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 speciesusing an exponential distribution that defines the effective andmaximum seed dispersal distances.Non-spatial processes of succession and seedling establish-ment are simulated independently at each site. They alsointeract with spatial processes such as seed dispersal, harvest-ing, and disturbances. Succession is a competitive processdriven by species life history parameters in LANDIS. It iscomprised of a set of logical rules primarily using thecombination of shade tolerance, seeding ability, longevity,vegetative reproduction capability, and the suitability of theland 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 sitewhere species with greater shade tolerance are present. On theotherhand,themostshadetolerantspeciesareunabletooccupyan open site. Without disturbance, shade tolerant species willdominate the landscape given that other attributes (e.g.,dispersal distances) are not highly limiting and the environ-mentalconditionsare otherwisesuitable.Speciesestablishmentis regulated by a species-specific establishment coefficient(ranging from 0 to 1.00), which quantifies how different landtypes favor or inhibit the establishment of a particular species(Mladenoff and He, 1999). These coefficients, which areprovided as input to LANDIS, are derived either from thesimulation 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,LANDISisnotdesignedtopredictthespecifictimeorplacethatindividual disturbance events will occur. Rather, it is a cause–response type of scenario model that simulates landscapepatterns over time in response to the combined and interactiveoutcomesofsuccessionanddisturbance.Itcanprovidemanagerswith guidance about management practices that can mitigatecurrent or anticipated problems on the forest landscape, andprovide a better understanding of long-term, cumulative effectsthatmayresultfromthecombinationofnaturaldisturbancesandmanagement practices. LANDIS has been applied and testedwith different species and environmental settings (Gustafsonet al., 2000; Shifley et al., 2000; Franklin et al., 2001; He et al.,2002a,b; Mehta et al., 2004; Wimberly, 2004). In addition, thecurrent version, LANDIS 4.0, added new capabilities thatsimulate explicit fuel dynamics, fuel-fire interactions, andbiological disturbances (He et al., 2004). Parameterization of LANDIS 4.0 for the Tuqiang Forest Bureau involved severalaspects: species’ vital attributes, a forest composition map thatcontains individual species presence/absence and age classes ateach cell, a land type map, establishment probabilities for eachlandtype, fire disturbance regimes for each landtype, and forestmanagement scenarios. 3.1.1. Species attributes and forest composition map AtotalofeighttreespecieswereincorporatedintoLANDIS.Species’ vital attributes (Table 1) were estimated based onexisting studies of the region (Ai et al., 1985; Duan, 1991; Huet al., 1991; Xu, 1998; He et al., 2002a; Xu et al., 2004) as wellas consultation with local experts. A forest composition mapwas derived from an extant forest stand map of 1987, a standattribute database, and one scene of Landsat TM imagery takenin 1987. The forest stand map recorded boundaries of standsandcompartments.(Acompartmentisaunitofforestinventory,generally containing 10–100 stands.) The stand attributedatabase provided information on the relative percentage of canopy species, the average age of dominant canopy species,timber production, and crown density. To reduce computationalloads during model simulations, the forest composition map  X. Wang et al./Forest Ecology and Management 225 (2006) 82–93  85Table 1Species’ life attributes for Tuqiang Forest Bureau in northeastern ChinaSpecies LONG MTR ST FT ED MD VP MVPLarch (  Larix gmelini ) 300 20 3 3 150 300 0 0Mongolian Scotch pine ( Pinus sylvestris  var.  mongolica ) 250 40 1 1 100 200 0 0Spruce ( Picea koraiensis ) 300 30 4 2 10 150 0 0White birch (  Betula platyphylla ) 150 15 1 3 200 4000 0.8 40Aspen-D ( Populus davidiana ) 180 20 1 3   1   1 1 40Black birch (  Betula davurica ) 150 15 1 4 200 1000 0.8 40Aspen-S ( Populus suaveolens ) 150 25 1 4   1   1 0.9 40Willow ( Chosenia arbutifolia ) 200 30 2 2   1   1 0.9 30LONG: longevity (years); MTR: age of maturity (years); ST: shade tolerance (1–5); FT: fire tolerance (1–5); ED: effective seeding distance (m); MD: maximumseeding distance (m); VP: vegetative reproduction probability; MVP: minimum age of vegetative reproduction (years).   1 represents unlimited seeding range.  was processed at 90 m  90 m resolution, which yielded 1604rows  873 columns. Each cell contains the presence/absenceand 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 theapproach, and their results demonstrated that uncertainty wasrelatively low at the cell level during the beginning of thesimulation. The uncertainty increased with simulation years,butthe uncertainty finallyreached anequilibrium stateinwhichinput errors in srcinal species age cohorts had little effect onthe simulation outcomes. At the landscape level, speciesabundance and spatial patterns were not substantially affectedby the uncertainties in species age structure at the cell level.Since the typical application of LANDIS is topredict long-termlandscape pattern changes, SBA can be used to parameterizespecies age cohorts for individual cells. 3.1.2. Landtype map LANDISstratifiestheheterogeneouslandscapeintorelativelyhomogeneous units (landtypes or ecoregions). Within eachlandtype, similar environments for species establishment areassumed (Mladenoff and He, 1999). In the study, we derived eightlandtypes,primarilybasedonterrainattributes,TMimagestaken in 1987, and the catastrophic fire of 1987 (Fig. 3). Theselandtypes includewater, residential land, terrace, southern slope(SS), northern slope (NS), burned terrace (BT), burned southernslope (BSS), and burned northern slope (BNS). Because largeburned areas came into existence after the 1987 fire, fire/fuelcharacteristics – such as fuel accumulation and the time elapsedsince the previous fire – were different for unburned areas.Therefore, we differentiated BT, BSS, and BNS from unburnedareas. All landtypes were interpreted from the previous forestinventory and the TM images taken in 1987. Non-activelandtypes (water and residential land) account for 0.90% of thetotalarea,whileactivelandtypes (includingterrace, 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 specieswill successfully establish on a landtype, given the environ-mental conditions encapsulated by that landtype. The speciesestablishment coefficients (Table 2) were derived fromavailable literature as well as the existing LANDIS para-meterizationsonnortheasternChina(Lietal.,1987;Zhaoetal.,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 86Fig. 3. Spatial distribution of part landscape occupied by larch at simulation year 200. (  I  1 ,  I  2 ,  I  3 , and  I  4  represent simulation scenarios where the planting intensitycovers 10%, 30%, 50%, and 70% of the severely burned area, respectively.  M  1  and  M  2  represent dispersal planting and aggregated planting, respectively.)Table 2Attributes for each land type of Tuqiang Forest Bureau in ChinaLand type MFRI TSLF EC1 EC2 EC3 EC4 EC5 EC6 EC7 EC8SS 300 150 0.3 0.25 0.03 0.3 0.01 0.1 0 0NS 350 180 0.25 0.2 0.05 0.25 0.01 0.05 0 0T 1000 500 0.002 0 0 0.004 0.001 0 0.01 0.05BSS 300 10 0.3 0.25 0.03 0.3 0.01 0.1 0 0BNS 350 10 0.25 0.2 0.05 0.25 0.01 0.05 0 0BT 1000 10 0.002 0 0 0.004 0.001 0 0.01 0.05MFRI:meanfirereturnintervalsinyears;TSLF:timesincelastfiredisturbance;EC1, EC2, EC3, EC4, EC5, EC6, EC7, EC8 are the establishment coefficientfor 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.
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