Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region

Forest Ecology and Management 257 (2009) 911–922

Contents lists available at ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region L.K. Peterson a,1, K.M. Bergen a,*, D.G. Brown a, L. Vashchuk b, Y. Blam c a

School of Natural Resources and Environment, University of Michigan, 440 Church Street Ann Arbor, MI 48109, USA Forest Service of Irkutsk, Ministry of Agriculture of the Russian Federation 31 Gorky St, Irkutsk, Russia c Department of Economic Informatics, Institute of Economics and Industrial Engineering, Siberian Branch of the Russian Academy of Sciences, 17 Prosp. Akademika Lavrentieva, Novosibirsk, Russia b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 24 July 2008 Received in revised form 22 October 2008 Accepted 24 October 2008

Remote sensing observations over areas of the former Soviet Union suggest that there may be important ongoing influences on forested landscapes resulting from divergent land use and forest management associated with the Soviet versus post-Soviet eras. As the Russian Federation implements its new Forest Code and associated regulations, knowledge of existing forest patterns and trends, plus the development of methods with which to understand the landscape-level influence of different forest management strategies is increasingly important. We developed spatial–temporal models and projections of forest patterns and trends over Soviet and early post-Soviet forest management eras for a study site in the Lake Baikal region in southern Siberia. We used Landsat-derived land-cover data, logistic regressions, and Markov and cellular automata methods (CA–Markov) to characterize patterns and trends 1975–1989 and 1990–2001, and to develop predictive scenarios through 2013. Relationships of forest types (Conifer, Mixed, Deciduous) and Agriculture to other explanatory environmental variables indicated mostly consistent forest–environment relationships, but some different spatial relationships between eras were found for Cut and Regeneration disturbance types. Landscape proportional trends showed greater differences between eras. Cut proportions observed via Landsat in 2001 were approximately 74% lower, and the area of Conifer observed was approximately 14% higher, than modeled proportions predicted for 2001 using 1975–1989 Soviet era transition rates. The proportion of Cut projected for 2013 was about 80% lower when based on early post-Soviet era probabilities. Overall, modeled results indicate that should early post-Soviet trends continue, low rates of logging, some agricultural abandonment, regrowing forests especially near access routes, increases in deciduous cover, along with continued or increased fire events in mixed and conifer forests will define the landscape. Should forest management change, for example to Soviet era rates and patterns of harvest, different outcomes are projected. More broadly, results highlight the real and prospective effects that divergent management strategies can have on forested landscapes, and demonstrate that land-cover data combined with emerging spatial–temporal modeling methods provide an approach to understand and project the complex and ongoing influences associated with changing forest management at landscape scales. ß 2008 Elsevier B.V. All rights reserved.

Keywords: Boreal forest Forest management Forest modeling Logging Russia

1. Introduction In the regions of the former Soviet Union and Eastern Bloc, the past century saw widespread political and socio-economic changes associated with the gradual implementation of communism starting in the early twentieth century, followed by fairly abrupt

* Corresponding author. Tel.: +1 734 615 8834; fax: +1 734 936 2195. E-mail address: [email protected] (K.M. Bergen). 1 Current address: U.S. Forest Service International Programs, 1099 14th Street, NW, Suite 5500W Washington, DC 20005, USA. 0378-1127/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2008.10.037

changes to post-Soviet transitioning market economies near the end of the same century. The latter events marked a distinct turning point in both general economic conditions (World Bank, 1997) and specific institutional factors influencing forest management (Korovin, 1995). Recent studies have used remotely sensed observations to compare Soviet and post-Soviet era forested landcover and provide evidence of divergent patterns and trends between these eras (Krankina et al., 2005; Kuemmerle et al., 2006, 2007; Bergen et al., 2008). The results of these observational studies also suggest that there may be important ongoing influences associated with the different Soviet and post-Soviet socio-economic and forest management eras on landscapes. This is

912

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

due to the intrinsic nature of disturbance, regeneration, and succession such that forest land-use histories may continue to influence the longer-term vegetation compositions, spatial patterns and other processes of a site for decades, even centuries (Foster et al., 1998). In addition to observation of forested landscapes using remote sensing, several spatial and temporal simulation methods have been developed that can be used to devise management strategies that consider a more complete understanding of the historical and current factors driving forest dynamics and the trajectories of those changes (Brown et al., 2004; Sturtevant et al., 2007). Whereas logistic regression and other static modeling approaches have found widespread use revealing the relationships of forested and other vegetation types with environmental and spatial variables (Guisan and Zimmermann, 2000), dynamic modeling approaches, like Markov chains (Brown et al., 2000) and cellular automata (Balzter et al., 1998), provide information about the rates and patterns of change, and can be more useful for understanding the implications of forest landscape processes. When combined and used with land-cover data derived from moderate spatial resolution remote sensing, these methods are appropriate for modeling forested ecosystems and the influence of management at landscape to regional scales (Zhou and Buongiorno, 2006). As such they fill the niche between synoptic broad area observation by remote sensing (Achard et al., 2006), and local stand level modeling of forest dynamics (Sizykh, 2007) or specific management objectives (Zhou et al., 2008). The ongoing influences of forested landscape patterns and trends associated with the Soviet and post-Soviet eras may be particularly evident in certain regions such as Siberia which have long been heavily forest-resource dependent (Krankina et al., 2005). Development of spatial–temporal models and projections for Siberian forests over late Soviet and early Russian Federation eras could provide insight into the real past and prospective future effects that these divergent forest management eras have had and continue to have on the landscape. This type of baseline knowledge coupled with projections may become especially important in order to understand the ongoing influence of past legacies within the emerging forest policy framework associated with Russia’s new Forest Code (Russian Federation, 2006), and how that code can be effectively translated into on-the-ground management strategies for sustainable forest use. 1.1. Research goal and objectives The goal of our research was to characterize patterns and trends of forested land cover in a study site representative of southern Siberian forests, and to develop simulated predictive (through 2013) scenarios to investigate possible differences at the landscape-level resulting from forest dynamics associated with Soviet era management strategies versus those from the early Russian Federation era. We selected a study site in Irkutsk Oblast in the vicinity of Lake Baikal for a combination of practical and scientific reasons, including the availability of moderate spatial resolution time series land-cover data (otherwise generally scarce for Siberia) for a suitable span of years; its location in a region of the Siberian forest that is important to forest management; and its representativeness of forest- and land-cover dynamics typical of the Soviet and early post-Soviet eras. Our specific objectives were to (1) use time series land-cover data derived from Landsat remote sensing with other environmental spatial data in logistic regressions to characterize relationships between forested land-cover spatial patterns and other explanatory environmental variables in both eras, (2) couple the logistic models with Markov land-cover transition probabilities derived from the time series data and a

cellular automata (CA) method to create spatial–temporal predictive scenarios of land-cover trends based on both eras, and (3) to analyze modeled results and compare future forested landscape scenarios simulating those based on Soviet and early Russian Federation eras. In addition to revealing landscape influences over divergent forest management eras, this work demonstrates a combined spatial–temporal modeling methodology using land-cover data and GIS that is increasingly available and useful to forest scientists and managers at landscape to regional levels. 2. Study site 2.1. Geography and ecology The Baikal study site occupies a territory of approximately 30,000 km2 (Fig. 1). Topography includes low mountains and plateaus dissected by streams, plus broad valleys. Lake Baikal dominates the eastern portion of the site. Elevation ranges from 455 m above sea level (asl) at the lake surface to 2055 m asl within the Baikal Range (USSR, 1991). Soils are mainly Mollisols in broad valleys, and Spodosols or Gelisol Turbels in mountainous areas. Areas of permafrost occur throughout the site (USSR, 1991). The climate is continental with long, cold and dry winters (January mean of 16 to 30 8C), warm summers (June mean of 15 to 25 8C), intermittent precipitation and a short growing season (Kozhova and Izmesteva, 1998). The study site is located in the boreal forest ecoregion (Farber, 2000; Olson et al., 2001). Dominant tree species include Scots pine (Pinus sylvestris L.), Siberian larch (Larix sibirica Ledeb.), Dahurian larch (Larix gmelinii), Siberian pine (referred to as ‘cedar’; Pinus sibirica du Tour), European White birch (Betula pendula Roth), Upright European aspen (Populus tremula L.), Siberian spruce (Picea obovata Ledeb.), and Siberian fir (Abies sibirica Ledeb.; Nikolov and Hellmisaari, 1992; Vashchuk, 1997). ‘‘Light-coniferous’’ forests (dominated by pine and larch) are naturally more prevalent in the Baikal region than are ‘‘dark coniferous’’ forests (comprised of cedar, spruce, and fir; Farber, 2000). Human and natural-driven forest disturbances in the past century have included logging, agriculture, fire and insect pests (Krankina et al., 2005). In natural succession, deciduous birch (with aspen in association) is the typical pioneer species in Siberia; although in the higher and somewhat drier Baikal region, disturbed areas may also regenerate directly to pine or larch. Relatively short-lived birch–aspen forests typically succeed to mixed conifer–deciduous, and eventually to mature conifer forests. 2.2. Forest management over changing eras Since the nationalization of forests after the 1917 communist revolution, and throughout the Soviet era, Russian forests were state-owned and managed in terms of both silviculture and industrial output. High levels of timber production were maintained, reaching a recent high in 1989 (Pappila, 1999), but forest resources suffered from over-harvesting in economically accessible areas (Korovin, 1995) and from inefficient management methods (Blam et al., 2000). The dissolution of the Soviet Union in 1990 brought an abrupt reduction in state support for forestry, and logging rates dropped sharply and remained low in the ensuing years (Krankina and Dixon, 1992; Bergen et al., 2008). Since the establishment of the Russian Federation in 1991, new legislation has been enacted. National-level legislation adopted in 1993–1994 (Zaslavskaja, 1994) succeeded in specifying concrete ecologically sustainable management practices including restrictions on permissible logging areas and discontinuation of the prevalent

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

913

Fig. 1. The location of the study site within Siberia and regional ecosystem types (ESRI, 2004; Federal Geodesy and Cartography Service of Russia, 1991; Olson et al., 2001).

Soviet era practice of logging in large landscape-sized clear-cuts by establishing a maximum cut size of 50 ha and in non-contiguous patches for industrial forests (Korovin, 1995). However, at the same time, regional authorities were given increasing control over forest management and industry (Kortelainen and Kotilainen, 2003; Williams and Kinard, 2003), and subsequent challenges in clarifying national versus regional roles resulted in delay and difficulties in implementing new forest management strategies. A new Forest Code implemented in January 2007 (Russian Federation, 2006) attempts to address the latter by transferring management authority to regions, though this relationship remains operationally complex. The Baikal region forests are representative of the above forest management transitions. The location of Lake Baikal on the TransSiberian Railway has allowed access to the region for industrial timber management and harvesting since 1896 (Matthiessen and Norton, 1992). Since that time, the greater Irkutsk Oblast, with a forest density (defined as forest cover as a percent of the Oblast total area) of 1.7 times the Russian Federation average, has been one of Russia’s most important forest management and timber producing regions (Blam et al., 2000). Immediately after 1989, the forest industry in Irkutsk Oblast dropped sharply to about onefourth of late Soviet era output (All Union Scientific Research Institute of Economics, 1991; Obersteiner, 1999; Fig. 2), and

through the early 2000s post-Soviet era had not recovered. As a result of new legislation, forests in the near coastal zone of Lake Baikal received protective status preventing final felling, and because of the globally important presence of Lake Baikal itself, the Baikal region of Irkutsk Oblast is attracting the interest of Russian and international sustainable forest management and development projects. As focus shifts back to forest sector development, sustainable forest management in the region will need strong information databases, including information on spatial distributions as well as past and continuing responses of regional forests to human management practices and natural disturbances (Korovin, 1995). 3. Methods 3.1. Spatial data A set of forest- and land-cover data of the Baikal study site created using Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM+) images from 1975, 1989, and 2001 respectively were available for this study. The dates of these data corresponded to (1) the Soviet era (1975), (2) the approximate peak of Soviet era forestry (1989) just prior to transition, and (3) a post-Soviet era date at a similar time

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

914

included combining Floodland (riparian) and Wetland cover types and masking Water and the small classes of Urban and Bare out of the dataset. The following environmental features were digitized in ArcGIS (ESRI, 2004) for this study from 1:200,000 scale Russian topographical map quadrangles (USSR, 1991): all hydrologic features (rivers and lakes), roads (paved, dirt, and forest roads), and urban areas (towns and villages). Major infrastructure features, such as urban areas, rivers, lakes, and larger roads (both paved and dirt), were observed on the Landsat imagery to remain mostly constant throughout the study period in this relatively under-developed region; however it is possible that some forest roads were transient. A higher resolution (90 m) hydrologically corrected DEM was interpolated from a 1-km elevation DEM (USGS, 1996) using the digitized river data to enforce valleys and drainage direction. The above land-cover and environmental data comprised the data upon which the modeling process was based (Table 1). Additional variables were created from these data specifically for the logistic regression step and are described in that section. 3.2. Logistic regression

Fig. 2. Forest industry metrics of (a) total wood removal and (b) total sawnwood production in Irkutsk Oblast and in the Russian Federation (All Union Scientific Research Institute of Economics, 1991; State Committee of Statistics of the Russian Federation (GOSKOMSTAT), annual).

interval (Table 1). Land-cover data had been compiled at a 30-m spatial resolution, and included the following classes representative of regional forest ecology and land-use: Conifer, Mixed, Deciduous, Bog/Sparse Conifer (Forest group); Cut, Burn and Regeneration (Disturbance and Recovery group); Floodland, Wetland, Agriculture, Bare, Urban, and Water (Other Land Covers group) (Fig. 3; Bergen et al., 2008). Hereafter we use the term cover type to refer to individual forest, disturbance, regeneration, and other land-cover types in the study region. Cover type proportions for each date were extracted from the above dataset (Fig. 4) as was associated classification accuracy data (Table 2). Modifications to the dataset for the present study

The relationships between the existing spatial patterns of cover types and environmental variables were tested for each of the three years using logistic regression analysis. Logistic regression was used to estimate parameters on the independent variables in models of the categorical dependent variables that included: Conifer, Mixed, Deciduous, Bog/Sparse Conifer, Regeneration, Floodland/Wetland, Burn, Cut, and Agriculture. Dichotomous 30m grids representing the presence and absence of the cover types were created from the land-cover data in ArcGIS. Using the DEM and environmental data (Table 1), additional independent variables and grids were derived (Table 3). Distance to rivers (D2River), distance to roads (D2Road), and distance to urban (D2Urban) were hypothesized to influence cover type patterns and trends because of the access they provide to processing facilities and markets. Slope and aspect were derived and topographical wetness index (TWI) was calculated by combining specific catchment area with slope steepness and used as a proxy for soil moisture (Beven and Kirkby, 1979). A data sample was selected from the land-cover and environmental grids for use in logistic regression analysis and imported into SPSS statistical software (SPSS, 2004). A systematic sample with a spacing of 1 km was selected to maximize space between

Table 1 Data sources on which the available Landsat-derived land-cover classifications were based (A) and from which environmental data and variables were created during this study (B). (A) Landsat data Name

WRS

Path

Row

Date

Platform

Sensor

Resolution (m)

ETM+ TM West* TM Center TM East* MSS West MSS East*

2 2 2 2 1 1

133 134 133 132 144 143

23 23 23 23 23 23

8-13-2001 8-19-1989 8-28-1989 8-21-1989 7-28-1975 6-21-1975

Landsat Landsat Landsat Landsat Landsat Landsat

ETM+ TM TM TM MSS MSS

30 30 30 30 60 60

7 4 4 4 2 2

(B) Environmental data sources Feature

Source

Date

Transportation, Hydrology, Urban Elevation

Topographic map. 1:200,000. Federal Geodesy and Cartography Service of Russia, USSR. Digital Elevation Model, USGS.

1991 1996

*

Indicates the primary scene used.

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

915

Fig. 3. Landsat-derived land cover of the study site: (a) 1975 (MSS), (b) 1989 (TM), (c) 2001 (ETM+), (d) cover type names.

sample points and minimize the size of the dataset, while maintaining an adequate sample size for modeling less abundant cover types. For each cover type, a file containing all sample points at which the type was present was combined with an equal number of random sample points at which it was absent. A forward stepwise logistic regression procedure was used in SPSS to first create exploratory models (Miller and Franklin, 2002). To deal with the spatial dependence inherent in data extracted from land-cover maps (Bailey and Gatrell, 1995; Miller et al., 2007),

the sampling scheme described above was intended to reduce spatial autocorrelation. Remaining spatial dependence was estimated by calculating Moran’s I, a measure of spatial autocorrelation (Anselin, 2003), on model residuals, and was addressed through the inclusion of a lag variable in subsequent regression Table 2 Classification accuracy of cover types for each year. Shown is producer’s accuracy in percent for each category plus overall accuracy. Also given are cover type group affiliations used to interpret selected results in this study. Classification accuracy (%)

Fig. 4. Landsat-derived cover type proportions for the study site for the three imaged dates.

Group

Type

1975

1989

Forest

Conifer Mixed Deciduous Bog/Sparse Conifer

94.9 81.1 86.4 67.9

97.0 90.3 80.3 93.4

98.5 81.3 85.0 98.7

Disturbance and Recovery

Cut Burn Regeneration

91.6 59.5 51.0

94.6 92.0 71.0

82.5 99.9 77.6

Other Land Covers

Floodland/Wetland Agriculture Urban Bare Water

76.3 94.9 80.6 99.3 100.0

92.5 92.4 98.7 100.0 100.0

72.8 96.3 98.8 98.5 99.9

84.8

92.5

89.9

Overall

2001

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

916

Table 3 Results and model comparison from logistic regression analysis. Models are grouped by cover type for comparison across dates. Positive and negative coefficients for significant variables are indicated by + or , respectively. Model fitness is indicated by Nagelkerke R2 and overall classification accuracy, both excluding (1) and including (2) a lag variable. Abbreviations: Regen = Regeneration, Elev = Elevation, TWI = Topographical Wetness Index, D2River = Distance to rivers, D2Road = Distance to roads, D2Urban = Distance to urban, Con = Conifer, Mix = Mixed, Dec = Deciduous. Logistic regression analysis: model comparison and selected results Year

N of model pixels

Dependent variable

Nagelkerke R2

Independent variables Elev

Slope

TWI

D2River

D2Road

D2Urban

Previous forest type Con

1975 1989 2001

9454 7468 7680

Conifer

+ + +

1975 1989 2001

14298 14638 12844

Mixed

1975 1989 2001

4058 3948 5800

Deciduous

1975 1989 2001

4092 4398 4510

Bog/Sparse Conifer

1975 1989 2001

368 1002 306

Cut

1975 1989 2001

80 172 1188

Burn

1975 1989 2001

716 624 794

Regen

1975 1989 2001

5950 4278 7212

Floodland/Wetland

1975 1989 2001

7578 7044 6590

Agriculture

+ + +

+ + +

     

  

  

+ + +

2

1

2

N/A

0.323 0.271 0.261

0.574 0.494 0.491

71.0 68.8 67.8

81.5 78.1 78.2

N/A

0.219 0.239 0.208

0.453 0.415 0.403

67.5 67.5 65.7

75.7 73.8 73.3

Dec

 — 

N/A

0.118 0.158 0.123

0.398 0.367 0.361

58.6 62.2 59.7

73.1 72.4 72.1

  

+ + +

N/A

0.224 0.245 0.176

0.367 0.421 0.338

67.3 69.6 65.3

71.9 75.5 71.8



+

0.330 0.289 0.119

0.763 0.638 0.484

70.1 67.4 65.4

91.0 85.0 78.8

+ + +

0.583 0.152 0.359

0.666 0.563 0.759

78.8 59.9 71.0

81.3 80.8 89.3

N/A

0.284 0.179 0.183

0.442 0.396 0.336

70.3 63.6 61.6

77.7 73.1 71.8

N/A

0.173 0.223 0.235

0.370 0.391 0.372

62.7 66.6 65.5

72.6 73.5 72.0

N/A

0.650 0.655 0.662

0.805 0.806 0.806

85.6 85.8 85.8

91.4 91.6 91.5

+ + +

 

 

+ +

     

  

+ + +







+

  

  



  

  

  

  

analysis (Miller and Franklin, 2002). Exploratory model results, both including and excluding the lag variable, were compared to evaluate the strength and significance of the variables included in the models. Variables were also interpreted to assess if they made sense ecologically. After determining which variables should be included to produce the best model for each cover type, logistic regression analysis was again performed, this time entering the significant variables into the model. Spatial autocorrelation in the residuals was again tested and a new lag variable was created and included. Two final models, including and excluding the spatial lag variable, were created for each cover type at each date. Statistical output included several indicators of model fit and variable significance. A Nagelkerke R2 (Nagelkerke, 1991) greater than 0.2 indicates a relatively fit model; classification results also indicate the relative model fitness, with 100% indicating a perfect model. Once the best-fit models were determined through logistic regression analysis, probability maps for input into the CA–Markov simulation were created for each of the nine cover types in 1975, 1989, and 2001. Probability maps were created at the 30-m resolution using the following Eq. (1): elp 1 þ elp

1

Mix

  

+ +

Cover type probability ¼

+ + +

Classification (%)

(1)

where lp is the linear predictor equation resulting from logistic regression analysis (Miller and Franklin, 2002). Probability maps for all cover types were grouped by date for use in the CA–Markov

  



procedure. For input to CA–Markov analysis, all variable and probability grids were resampled to a 60-m resolution to improve computational efficiency and imported into IDRISI (Clark Labs, 2003). 3.3. CA–Markov With Markov chain analysis, future land cover can be modeled on the basis of the preceding state; that is, a matrix of observed transition probabilities between states can be used to project future changes in the landscape from current patterns (Brown et al., 2000). Because spatially proximate objects are often more likely to exhibit similar attributes (Miller and Franklin, 2002), the incorporation of neighboring states through combination of Markov and cellular automata (CA–Markov) approaches has been shown to improve models that describe complex natural patterns (Baker, 1989; Deadman and Brown, 1993). Transition matrices, representing probability of change between individual cover types, were calculated for the two periods 1975–1989 and 1989–2001 using the corresponding grids. Future cover types were then predicted using a CA–Markov model based on the 1989 or 2001 land-cover classifications, plus the transition matrices, probability maps, and contiguity. Calculations were made in yearly increments, incorporating a classification error value based on the input land-cover data (0.15 for the period 1975–1989 and 0.10 for the period 1989–2001). As transition rates were calculated for change occurring over a period

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

slightly longer than a decade, predictions were made using one 12year time step. Contiguity filters of several sizes were tested and the 5  5 contiguity filter was applied to define the neighborhood of each cell and used to weight the suitability of areas near each existing cover type higher for the establishment of that cover type. Three maps were generated: (1) as a test of the temporal stationarity in the forest- and land-cover trends, 2001 cover was predicted from 1989 cover and the 1975–1989 transition rates and compared with the observed 2001 data; (2) future (2013) cover was predicted from the 2001 data using 1989–2001 transition rates, i.e., assuming post-Soviet forest- and land-change patterns and trends continue; and (3) future cover was again predicted from 2001 data based on 1975–1989 transition rates adjusted to 2001 cover proportions, i.e., assuming a return to Soviet era patterns and trends. 4. Results and discussion 4.1. Logistic regression models Twenty-seven models, both including and excluding the lag variable, were created that described the relationship between several explanatory environmental variables and study site cover types existing in 1975, 1989, and 2001 (Table 3). Random permutation tests for each model (excluding the lag variable) had indicated that Moran’s I for the residuals departed significantly from zero in all models (p < 0.001), providing evidence that spatial autocorrelation was probably an important factor that should be considered in analyses. All models improved significantly with the inclusion of the lag variable, indicated by higher Nagelkerke R2 and higher classification accuracy in the autoregressive models. 4.2. Forest- and land-cover patterns by era Among the cover types in the Forest group (see Table 2), Conifer was fit well by the models (Table 3) and relationships did not vary by era. At all dates, Conifer forests, in the form of pine and/or larchdominant stands, are generally concentrated in the higher elevation and steeper slope regions of the study site. The relationship with D2Urban (+) (Table 3) is also logical because areas near cities are more likely to be used for agriculture or other more intensive uses. Intuitively, it seems that coniferous forests should similarly be associated with an absence of roads; however, the minor and forest road networks are, by nature, built into forest management areas used for harvesting and the D2Road relationship () is likely indicative of the importance of Conifer to the forest industry of the region. Mixed forest models also fit well. Mixed forests are similarly concentrated at higher elevations in the study site at all dates; these areas have a lower TWI (). Because Mixed can include secondary succession, its closeness to access routes of roads () and rivers () may represent re-growing accessible forests logged earlier in the Soviet era. In addition to road transportation, much Soviet era logging was floated on rivers, a management practice no longer widely allowed (Shvidenko and Nilsson, 1996). Models for Deciduous fit slightly less well but were consistent over all dates. Correlation with Elevation (+) may be explained by the fact that other land-cover types are more suited to conditions at lower elevations (i.e., Floodland/Wetland); additionally, deciduous forests often occupy previously disturbed areas that preferentially occur at higher elevations where fire- and logging-prone conifers are more prevalent. Correlation with D2Urban () and D2Road () is also likely due to the fact that in this region deciduous forests tend to occupy previously disturbed areas; regions with developed infrastructure are also more likely to have been disturbed. More remote, less developed

917

areas in this region should tend towards non-harvested mature forests or forest types not suited for harvest. In the Disturbance and Recovery group, variables significant in predicting Cut differed by date. In 1975, only D2River () was significant. Historically, logging would preferentially occur in the more easily accessible river valleys where access routes existed; however, D2Road did not show correlation to Cut areas in the 1975 model. The 1989 model again included significant correlation with D2River () and also D2Road (). Data from 2001 produced the weakest model describing cut areas; Cut was associated only with Elevation (+). Poorer model fit was likely associated with the limited territory affected by Cut in 2001 due to decreases in logging activity. Analysis of Burn produced relatively good models. Models of Burn for both 1975 and 1989 included Elevation (+) as the only explanatory variable. There were few recent Burn pixels in the land-cover data for these dates, and therefore presence points available for the analysis were concentrated in a few small areas. A larger area was affected by Burn in 2001 and this date produced the best fitting model. Similar to 1975 and 1989 models, Elevation was significant (+). In addition, 2001 Burn occurred closer to transportation routes, where logging and other human-driven factors are more likely to affect the landscape. Burn showed a positive relationship with D2Urban; in the study region and dates, fires seem to be more associated with forest use than human settlements. Models describing the probability of occurrence of Regeneration also differed by date. This was expected for a cover type dependent on various disturbance types. Correlation with D2River () and D2Road () in all models is consistent with the hypothesis that disturbance (whether fire, logging, agriculture), and thus regeneration, occurs more frequently near access routes. Positive correlation of Regeneration to Elevation (+) and Slope (+) at 1989 and 2001 dates indicates that disturbance preceding forest regeneration occurred in higher elevation, dissected areas. In the Other Land Covers group, Agriculture produced strong models for all three dates. The most suitable land for agriculture during both eras is found in lower elevation flat-lands near river networks and in close association with infrastructure, indicated by roads and urban. Models for Bog/Sparse Conifer fit somewhat well in 1975 and 1989 and less well in 2001. Models predicting Bog/ Sparse Conifer in 1975 and 1989 showed a positive correlation with Elevation and D2Urban and a negative correlation with both D2River and D2Road. Based on these results, positive correlation with high elevation is likely due to inclusion of sparse conifer forests in this mixed cover type category. This cover type is also generally located in the more remote regions of the study site, farther from urban areas. Analysis of Floodland/Wetland resulted in logical albeit relatively weak models. Floodland/Wetland was associated with Elevation () in 1975 and 1989 and with TWI (+), D2Road () and D2River () in all dates, logically occurring in moist, accessible valleys. In 2001, Elevation was not significant, though Slope was associated with Floodland/Wetland (), which typically occurs in flatter areas. 4.3. Projections of forest- and land-cover trends Three maps and associated statistics of forest- and land-cover proportions resulted from the CA–Markov analysis: (1) 2001 cover projected from 1989 data using 1975–1989 transition rates; (2) 2013 cover projected from observed 2001 data using 1989–2001 transition rates; (3) 2013 cover projected from 2001 data using 1975–1989 transition rates adjusted to 2001 land-cover proportions (Figs. 5 and 6). When predictions for 2001 using 1975–1989 Soviet era transition rates were compared with observed 2001 land cover, results revealed some potential differences that might have

918

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

Fig. 5. Predicted maps from CA–Markov models: (a) predicted 2001 cover based on 1975–1989 transition probabilities, (b) observed 2001 cover, (c) predicted 2013 cover based on 1975–1989 transition probabilities, (d) predicted 2013 cover based on 1989–2001 transition probabilities.

occurred had pre-transition forest management and other conditions continued to shape the landscape. The actual Cut proportion observed in 2001 was approximately 74% lower than that predicted by 1975–1989 transition rates. This reflects the decreases in timber harvest and forest industry production. Conifer forest was a somewhat lower proportion (by about 14%) in the modeled 2001 scenario compared to the observed, likely indicative of continued higher rates logging in Conifer should Soviet era trends have continued. Mixed was predicted as a greater proportion in the modeled 2001 scenario. This was likely influenced by a more severe fire year in 2001 observed by the land-cover data, and where fires may have actually burned in these mixed forests; some may also be due to continued growth of secondary forests. Presence of Agriculture was predicted at a slightly higher proportion based on the 1975–1989 era data,

possibly indicative of less abandonment of collective agriculture (or less visible forest regrowth on these sites) compared to the post-Soviet era. Forest- and land cover predicted for 2013 based on 1975–1989 transition rates and the observed 2001 data proportions, indicates the potential state of the landscape if dynamics were to revert to pre-1990 conditions. In this model, Conifer proportion decreased by about 16% from the 2001 observed proportion while the proportion of Mixed increased (again, in part likely due to fires in 2001 observed). The proportion of Cut was more than three times as great in the 2013 modeled data compared to 2001 observed. The proportion of Deciduous predicted for 2013 increased slightly from that predicted for 2001 using the same 1975–1989 transition data, however it did not increase as much as in observations and predictions based on the early Russian Federation era trends.

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

919

Fig. 6. Projected cover type proportions resulting from CA–Markov models.

Agriculture proportions continued to decline slightly by 2013 when based on these Soviet era trends, but the decline was not as great as that in predictions based on the early Russian Federation era trends. Forest- and land-cover proportions predicted for 2013 from the 1989–2001 early Russian Federation era rates indicate patterns that could result if forest management and associated conditions observed over the early Russian Federation era continue into the future. In this model, 2013 Cut proportions showed a continued decrease (by approximately 10%) from observed already low 2001 proportions. Agriculture proportions also continued to decrease (by about 13%) with respect to observed 2001 proportions, and Conifer proportions decreased slightly (about 5%). Lower Mixed predictions are, again, likely influenced by large fires on the 2001 image. Deciduous occupied a greater proportion of the landscape than its proportion based on Soviet era projections. 4.4. Comparisons with other studies in Siberia This study is the first to undertake landscape-scale analyses of Siberian forests focusing on both forest–environmental relationships and spatial–temporal projections of trends over divergent forest management eras. A comparison of the patterns and trends resulting from this analysis with those found in other studies of Siberian forests provides context and support for the overall results of the present study. Several studies have used finer scale field data to investigate forest–environmental relationships in the broader Baikal region (Chytry et al., 2008) as well as other regions of Siberia (Cushman and Wallin, 2002). Working over an elevation gradient in a site to the southwest of the Baikal study site, Chytry et al. (2008) found topography to be the main source of variation in forest types. Their results indicated that light versus dark coniferous forests, or even different species, may occupy different topographical aspects. Although our remote sensing derived land-cover data was of coarser thematic resolution than their community- and speciesspecific field data, logistic regression results for the Baikal site also demonstrated that topography played a major role in the presence of forest cover types. Elevation was positively associated with Conifer, Mixed, and Deciduous; Conifer was also associated with steeper slopes, as was Regeneration at the second and third dates, presumably in harvested Conifer areas. Lack of species-level data in the Baikal site forest-cover dataset, plus the overall prevalence of light-coniferous forests in the Baikal site are likely reasons why Aspect, although it was tested as an independent variable, was not included in final models.

As early as the mid-1990s, studies in Siberia suggested correlations between fires and closeness to roads (Korovin, 1996). Recently, Kovacs et al. (2004) found a positive spatial 2 2 correlation of fire with roads (r2001 ¼ 0:81, r2002 ¼ 0:90, 2 r2003 ¼ 0:88) in central Siberia; this was also the case with forests near railroads, settlements and mining industry locations. These results agree with our logistic models which support a strong relationship between fires and proximity to roads, but diverge somewhat in that fires in the Baikal site tended to be either not related to or found away from settlements, indicating stronger association with forest use. Crevoisier et al. (2007) found that while climate variables were the most important drivers of fire, that fire had a positive association with road density. We found similar relationships between fire occurrence and D2Road () in the 2001 model (i.e., the year with significant fire presence). The former study also found that above a given road density threshold, no burning is expected to happen. While they attribute this to better fire suppression, this agrees with our Baikal site findings that Burn in 2001 occurred at greater distances from settlements (which in turn have denser transportation networks). While fire frequency trends cannot be inferred from the three dates in our dataset, Baikal site statistics do not disagree with studies indicating that fire return intervals (FRI) may be decreasing (i.e., more frequent fires) in the Siberian forest, attributed primarily to increasingly more favorable weather conditions (Cushman and Wallin, 2002; Soja et al., 2007; Kharuk et al., 2008), and to combined human–climate inter-relationships (Achard et al., 2008), with potential implications for forest and fire management. An additional disturbance, not present in our dataset is infestation by the Siberian silkmoth (Dendrolimus superans sibiricus Tschetw.). Kharuk et al. (2007) showed that tree mortality from this forest pest was related to the conifer cover type and to topographic features, especially elevation and also steepness, relationships of forest management interest that lend themselves to the methods used in this study. With respect to forest-cover trends, in a project using 1-km land-cover data from 1990 and 2000, Achard et al. (2006) characterized types of rapid post-Soviet era forest-cover change over boreal Eurasia. In sample sites near Lake Baikal they found timber industry with moderate intensity clear-cut or selective logging but at small to moderate change rates; moderate levels of change in species composition including natural regrowth with change in species towards deciduous; and increased fire frequency. Low post-Soviet era logging rates, trends toward greater deciduous forest compositions and increased fire proportions agree with our findings during an equivalent era. In a study

920

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

that combined official Russian Federation forestry statistics with remote sensing-derived land-cover statistics, forested landscapes in Tomsk Oblast and Krasnoyarsk Krai showed significantly reduced logging proportions, a decrease in agriculture proportions, and an increase in new regrowth/deciduous forests proportions at a post-Soviet (2000) date compared to Soviet era (1974) proportions (Bergen et al., 2008). Shvidenko and Nilsson (1996) found declines in mature coniferous forests in Russia including in Siberian Russia, results that are also consistent with this study. 4.5. Spatial–temporal modeling methods The approach implemented in this study provided a way to investigate and project the forest- and land-cover patterns and trends characteristic of a southern Siberian boreal forest over a 35 year period surrounding the transition from the Soviet Union to the Russian Federation, and to infer possible influences of the different forest management eras on regional ecology. Use of logistic regression analysis revealed the relationships between each cover type and explanatory environmental variables and over the different eras. Probability maps from logistic regression and transition rates derived from time series land-cover data were used in a CA–Markov model to project trends and to include a spatial component. This combined dynamic approach improves on static models and allowed us to project landscape conditions under two different trend scenarios based on Soviet and post-Soviet era conditions. Some limitations that introduce uncertainty into results should be noted. Land-cover data were available on slightly longer than a decadal basis; therefore only large-scale, long-term change could be modeled for a single increment 12 years into the future. The CA– Markov contiguity filter prevented randomly dispersed changes from occurring; however neighborhood constraints were equally defined for all cover types. In reality, the different cover types modeled occur with different degrees of spatial dependence. In terms of spatial datasets, GIS data were not widely available for Siberia and the inability to accurately map the class of transient minor forest roads for the 1975 and 2001 dates may have slightly biased some results. While remote sensing-derived data typically has error, those present in the land-cover dataset were probably less influential in logistic regressions given that the majority of pixels were classed correctly at each date (Table 2), but pixel-level classification confusion between similar forest cover types between dates may underlie the sometimes non-inclusion of previous forest type as significant explanatory variables for disturbance models (although where these variables were included in models they were as expected ecologically). To address these issues and to further reduce uncertainty in future studies, logical steps to refine the models would ideally include (1) use of further refined land-cover and GIS data, (2) additional dates of land-cover data to create models at finer temporal resolutions; and (3) more flexibility to tailor projections to the differences in the spatial relationships inherent in the different cover types (i.e., variable contiguity filters). 4.6. Information for forest management Modeled results from this study indicate that should early post-Soviet trends continue, low rates of logging, some agricultural abandonment, re-growing forests especially near access routes but away from settlements, some increase in deciduous (although perhaps not as great as in some other Siberian regions), along with continued or increased fire events in mixed and coniferous forests will define the landscape. However, it is also

likely that new developments will influence forest trends in the region. Russia’s new Forest Code contains two significant changes: (1) transfer of forest management and administrative authority from the Federal Forestry Agency to regional authorities, and (2) emphasis on long-term leases that shift management responsibilities (for example reforestation, forest protection, and other management functions) to lease-holders. Current forest sector development plans seek to increase timber production and economic potential of forests through investment in road infrastructure and local value-added processing, combined with improved forest management practices (Vashchuk, 1997; Williams and Kinard, 2003). This study shows that an increase in production approaching that of the Soviet era would likely have an influence on certain forested landscape patterns and trends. Infrastructure, in terms of distance to roads (D2Road), was one of the most frequently significant study explanatory variables, in particular in predicting logging activity locations subsequent to the decline of Soviet era practices which also may have included river transportation. A new focus on road-building to increase timber production would likely target areas associated with coniferous forests, which are found at higher elevations, with steeper slopes and away from settlements. In turn, this may necessitate increased attention to restrictions on logging on steeper slopes in order to protect fragile soils and watersheds (Krankina and Dixon, 1992). Areas of regeneration, deciduous and mixed forests are presently found closer to existing roads and settlements, but the deciduous species which comprise a major component of these cover types are considered of low-quality for forest industry (Shvidenko and Nilsson, 1996). Some newer developments may influence landscapes over the longer-term in ways not yet well-known. Recent legislation has changed certain harvesting practices including requirements for fewer and smaller clear-cuts, and therefore even if logging rates reverted to Soviet era levels, these requirements could result in more sustainable landscapes than those created during the Soviet era where large landscape-scale clear-cuts were prevalent. In acknowledgement of potentially complex forest management considerations such as the above and others, Russia is seeking to better assess existing forest resources through a new National Forest Inventory system and to incorporate increased reliance on remote sensing and GIS data. This information, along with methods such as those demonstrated through this study, could provide valuable information to enable sustainable forest management at multiple scales, including regional landscape scales. 5. Conclusions This analysis provides significant insight into observed and projected landscape patterns and trends occurring in a largely forested Baikal region study site over Soviet and post-Soviet socioeconomic and forest management eras. Although inference of specific causality of trends for the period 1975–1989 as compared to 1989–2001 is beyond the scope of this study, our results suggest that while some patterns are associated primarily with environmental variables, other differences in land-cover patterns and trends are likely related to the institutional changes associated with the different forest management eras. The results corroborate those from other studies of remotely sensed observations of patterns and trends in Siberian forests; they also extend static analyses to provide models of future landscape scenarios. As the situation in Russia stabilizes, natural resource legislation and management is increasingly codified and resulting practices

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

will affect future forest patterns and dynamics in the region. Russia’s new Forest Code, which now officially includes transfer of administrative and management functions to regional authorities and emphasis on long-term leases, continues the early post-Soviet era trend of less central control and will undoubtedly further influence forest dynamics in Siberia and the Baikal region of study. Because Russia contains a large proportion of the world’s forests, the management and dynamics of forests of the study region and of Siberia more broadly may have considerable impact on carbon, climate change, biodiversity, and forest products. Understanding the potential legacy of the different forest management eras on forested land-cover trends, as well as continued development of spatial–temporal methods with which to understand them, will be important as the Russian Federation moves into new era of forest management. Acknowledgements This project was supported by the NASA Land-Cover Land-Use Change (LCLUC) Program through contract NAG5-11084. We extend appreciation to Dr. Garik Gutman, NASA LCLUC Program; Vasily Olenik of the Irkutsk Ministry of Natural Resources; and Shannon Brines, Stephanie Hitztaler, Tingting Zhao, and Bryan Emmett at the University of Michigan ESALab. References Achard, F., Eva, H.D., Mollicone, D., Beuchle, R., 2008. The effect of climate anomalies and human ignition factor on wildfires in Russian boreal forests. Philosophical Transactions of the Royal Society B-Biological Sciences 363, 2331–2339. Achard, F., Mollicone, D., Stibig, H.J., Aksenov, D., Laestadius, L., Li, Z.Y., Popatov, P., Yaroshenko, A., 2006. Areas of rapid forest-cover change in boreal Eurasia. Forest Ecology and Management 237, 322–334. All Union Scientific Research Institute of Economics, 1991. Forest Complex of the USSR, Novosibirsk Akademgorodok. Anselin, L., 2003. GeoDa version 0.9.5-i. Spatial Analysis Laboratory, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign. http://www.geoda.uiuc.edu/, (last accessed 10-1-08). Bailey, T.C., Gatrell, A.C., 1995. Interactive Spatial Data Analysis. Addison Wesley Longman Limited, Essex. Baker, W.L., 1989. A review of models of landscape change. Landscape Ecology 2, 111–133. Balzter, H., Braun, P.W., Kohler, W., 1998. Cellular automata models for vegetation dynamics. Ecological Modelling 107, 113–125. Bergen, K.M., Zhao, T.T., Kharuk, V., Blam, Y., Brown, D.G., Peterson, L., Miller, N., 2008. Changing regimes: forested land-cover dynamics in Central Siberia 1974– 2001. Photogrammetric Engineering and Remote Sensing 74, 787–798. Beven, K.J., Kirkby, M.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24, 43–69. Blam, Y., Carlsson, L., Olsson, M.O., 2000. Institutions and the Emergence of Markets—Transition in the Irkutsk Forest Sector. International Institute for Applied Systems Analysis, Laxenburg. Brown, D.G., Pijanowski, B.C., Duh, J.D., 2000. Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management 59, 247–263. Brown, D.G., Walker, R., Manson, S., Seto, K., 2004. Chapter 23: Modeling land use and land cover change. In: Gutman, G., Janetos, A., Justice, C., Moran, E., Mustard, J., Rindfuss, R., Skole, D., Turner, B., Cochrane, M. (Eds.), Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the Earth’s Surface. Kluwer, Dordrecht, pp. 395–409. Chytry, M., Danihelka, J., Kubesova, S., Lustyk, P., Ermakov, N., Hajek, M., Hajkova, P., Koci, M., Otypkova, Z., Rolecek, J., Reznickova, M., Smarda, P., Valachovic, M., Popov, D., Pisut, I., 2008. Diversity of forest vegetation across a strong gradient of climatic continentality: western Sayan Mountains, southern Siberia. Plant Ecology 196, 61–83. Clark Labs, 2003. IDRISI. Worcester. Crevoisier, C., Shevliakova, E., Gloor, M., Wirth, C., Pacala, S., 2007. Drivers of fire in the boreal forests: data constrained design of a prognostic model of burned area for use in dynamic global vegetation models. Journal of Geophysical ResearchAtmospheres 112, D24112. Cushman, S.A., Wallin, D.O., 2002. Separating the effects of environmental, spatial and disturbance factors on forest community structure in the Russian Far East. Forest Ecology and Management 168, 201–215. Deadman, P., Brown, R.D., 1993. Modelling rural residential settlement patterns with cellular automata. Journal of Environmental Management 37, 147–160. ESRI, 2004. ArcGIS 9. Redlands.

921

Farber, S.K., 2000. The Formation of Forest Stands in Eastern Siberia. Siberian Branch of the Russian Academy of Sciences, Novosibirsk, in Russian. Foster, D.R., Motzkin, G., Slater, B., 1998. Land-use history as long-term broad-scale disturbance: regional forest dynamics in central New England. Ecosystems 1, 96–119. Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135, 147–186. Kharuk, V.I., Ranson, K.J., Dvinskaya, M.L., 2008. Wildfires dynamic in the larch dominance zone. Geophysical Research Letters 35, L01402. Kharuk, V.I., Ranson, K.J., Fedotova, E.V., 2007. Spatial pattern of Siberian silkmoth outbreak and taiga mortality. Scandinavian Journal of Forest Research 22, 531–536. Kortelainen, J., Kotilainen, J., 2003. Ownership changes and transformation of the Russian pulp and paper industry. Eurasian Geography and Economics 44, 384–402. Korovin, G.N., 1996. Analysis of the distribution of forest fires in Russia. In: Goldammer, J.G., Furyaev, V.V. (Eds.), Fire in Ecosystems of Boreal Eurasia. Kluwer Academic Publishers, Boston, pp. 112–128. Korovin, G.N., 1995. Problems of forest management in Russia. Water Air and Soil Pollution 82, 13–23. Kovacs, K., Ranson, K.J., Sun, G., Kharuk, V.I., 2004. The relationship of the Terra MODIS Fire Product and anthropogenic features in the Central Siberian landscape. Earth Interactions 8, 1–25. Kozhova, O.M., Izmesteva, L.R., 1998. Lake Baikal: Evolution and Biodiversity. Bakhuys Publishers, Leiden. Krankina, O.N., Dixon, R.K., 1992. Forest management in Russia—challenges and opportunities in the era of perestroika. Journal of Forestry 90, 29–34. Krankina, O., Sun, G., Shugart, H.H., Kasischke, E., Kharuk, V.I., Bergen, K.M., Masek, J., Cohen, W.B., Duane, M., 2005. Northern Eurasia. In: Gutman, G., Janetos, A., Justice, C., Moran, E., Mustard, J., Rindfuss, R., Skole, D., Turner, B., Cochrane, M. (Eds.), Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the Earth’s Surface. Kluwer, Dordrecht, pp. 123–138. Kuemmerle, T., Radeloff, V.C., Perzanowski, K., Hostert, P., 2006. Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique. Remote Sensing of Environment 103, 449–464. Kuemmerle, T., Hostert, P., Radeloff, V.C., Perzanowski, K., Kruhlov, I., 2007. Postsocialist forest disturbance in the Carpathian border region of Poland, Slovakia, and Ukraine. Ecological Applications 17, 1279–1295. Matthiessen, P., Norton, B., 1992. Baikal: Sacred Sea of Siberia. Sierra Club Books, San Francisco. Miller, J., Franklin, J., Aspinall, R., 2007. Incorporating spatial dependence in predictive vegetation models. Ecological Modelling 202, 225–242. Miller, J., Franklin, J., 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecological Modeling 157, 227–247. Nagelkerke, N.J.D., 1991. A note on a general definition of the coefficient of determination. Biometrika 78, 691–692. Nikolov, N., Hellmisaari, H., 1992. Silvics of the circumpolar boreal forest tree species. In: Shugart, H.H., Leemans, R., Bonan, G.B. (Eds.), A Systems Analysis of the Global Boreal Forest. Cambridge University Press, Cambridge, pp. 133– 184. Obersteiner, M., 1999. Production Functions and Efficiency Analysis of the Siberian Forest Industry: An Enterprise Survey 1989 and 1992. International Institute for Applied Systems Analysis, Laxenburg. Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G., Underwood, E.C., 2001. Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51, 933–938. Pappila, M., 1999. The Russian forest sector and legislation in transition. International Institute for Applied Systems Analysis, Laxenburg. Russian Federation, 2006. Forest Code of the Russian Federation. Translated from the Russian by the World Bank. Moscow. http://www.rosleshoz.gov.ru/english/ docs/codex, (last accessed 10-15-08). Shvidenko, A., Nilsson, S., 1996. Expanding Forests but Declining Mature Coniferous Forests in Russia. International Institute for Applied Systems Analysis, Laxenburg. Sizykh, A.P., 2007. Models of taiga-steppe communities on the western coast of Lake Baikal. Russian Journal of Ecology 38, 234–237. Soja, A.J., Tchebakova, N.M., French, N.H.F., Flannigan, M.D., Shugart, H.H., Stocks, B.J., Sukhinin, A.I., Varfenova, E.I., Chapin, F.S., Stackhouse, P.W., 2007. Climateinduced boreal forest change: predictions versus current observations. Global and Planetary Change 56, 274–296. SPSS, 2004. SPSS 12.0. http://www.spss.com, (last accessed 10-15-08). State Committee for Statistics of the Russian Federation (GOSKOMSTAT). Annual Statistical Handbook. Republican Information-Publication Center, Moscow, 1993. Sturtevant, B.R., Fall, A., Kneeshaw, D.D., Simon, N.P., Papaik, M.J., Berninger, K., Doyon, F., Morgan, D.G., Messier, C., 2007. A toolkit modeling approach for sustainable forest management planning: achieving balance between science and local needs. Ecology and Society 12 (2), In: http://www.ecologyandsociety.org/vol12/iss2/art7/. USSR, 1991. Topographic Map 1:200,000. Federal Geodesy and Cartography Service of Russia, Moscow.(in Russian). USGS, 1996. GTOPO30. Sioux Falls. Vashchuk, L.N., 1997. Forests and forestry in the Irkutsk Region. Irkutsk Forest Administration, Irkutsk, in Russian.

922

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922

Williams, R.A., Kinard, J.C., 2003. A strategy for economic development of the forestry sector in Tomsk, Russia. Journal of Forestry 101, 36–41. World Bank, 1997. Russia: Forest Policy During Transition. The World Bank, Washington, DC. Zaslavskaja, L.A, 1994. Forest codes and laws in the republics of the Russian Federation, Legislation and Economy 3–4. Moscow, pp. 105–109.(in Russian).

Zhou, M., Buongiorno, J., 2006. Forest landscape management in a stochastic environment, with an application to mixed loblolly pine-hardwood forests. Forest Ecology and Management 223, 170–182. Zhou, M., Liang, J.J., Buongiorno, J., 2008. Adaptive versus fixed policies for economic or ecological objectives in forest management. Forest Ecology and Management 254, 178–218.