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Land cover change on the Isthmus of Karelia 1939–2005: Agricultural abandonment and natural succession
Environmental Science & Policy 55 (2016) 127–134
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Land cover change on the Isthmus of Karelia 1939–2005: Agricultural abandonment and natural succession Aapo Rautiainen a,*, Tarmo Virtanen b, Pekka E. Kauppi b a b
Natural Resources Institute Finland, P.O. Box 18 (Jokiniemenkuja 1), 01301 Vantaa, Finland Department of Environmental Sciences, University of Helsinki, P.O. Box 65 (Viikinkaari 1), 00014 Helsinki, Finland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 December 2014 Received in revised form 14 September 2015 Accepted 23 September 2015 Available online 21 October 2015
The Isthmus of Karelia is located in North-West Russia, between Lake Ladoga and the Baltic Sea. At the end of World War II (WWII), Finland ceded the western part of the Isthmus to the Soviet Union. In 1991 the Soviet Union ceased to exist. The Isthmus became a part of the Russian Federation. Using land cover data from the years 1939, 1987 and 2005 we document and analyze land use change on the Isthmus during and after the Soviet era. Large-scale agricultural abandonment was observed during the Soviet era. The landscape share of agriculture halved from 18 to 9%, as only a part of the land vacated by Finnish farmers during WWII was incorporated into the Soviet agricultural system. Forest expanded onto farmland and its landscape share increased from 72 to 77%. Another observed trend was the recovery from previous forest degradation. As logging on the Isthmus was banned, the development of the forest mosaic followed patterns of natural succession. Some deciduous forests were transformed into mixed forests and spruce forests. Clear cuts and sapling stands became rare. After the collapse of the Soviet Union, logging in old growth forests was resumed. However, its impacts on the regional forest composition remained modest. The recent forest history of the Isthmus is an extreme example of recovery from earlier forest degradation in boreal conditions. To illustrate the magnitude of the changes, we contrast our findings with the contemporary development in Southern Finland, where land use remained stable and forests were intensively utilized for timber production. ß 2015 Elsevier Ltd. All rights reserved.
Keywords: Isthmus of Karelia Land use change Forest Agriculture Agricultural abandonment Forest degradation
1. Introduction Since the collapse of the Soviet Union cropland area in European Russia has decreased by 272 000 km2 (Schierhorn et al., 2013). Forests have spread into abandoned areas, accumulating carbon in expanding biomass and soils. On the Isthmus of Karelia, in Northwest Russia, the contraction of agriculture and the expansion of forests began earlier, when farms and pastures were abandoned by Finnish farmers during World War II (WWII). Due to an extensive logging ban, the region’s forests remained nearly untouched by industrial logging for half a century. Forest cover expanded and the forest mosaic developed according to patterns of natural succession. We document this strong recovery from earlier forest degradation1
* Corresponding author. E-mail address: aapo.rautiainen@luke.fi (A. Rautiainen). 1 The definition of ‘forest degradation’ varies according to context (Simula, 2009). We use the term to refer to the depletion of forest timber and carbon reserves at the landscape level. The opposite development is called ‘recovery from degradation’. http://dx.doi.org/10.1016/j.envsci.2015.09.011 1462-9011/ß 2015 Elsevier Ltd. All rights reserved.
which occurred during the 20th century as the political regime changed twice. The Isthmus covers an area of 15 000 km2 between Lake Ladoga and the Baltic Sea, north of St. Petersburg (Fig. 1). Until WWII, its north-western part was inhabited by a Finnish population and the region shared a common history with Finland. Both were ruled over by Sweden between the 13th and 18th and Russia between 1743 and 1917. When Finland declared independence in 1917, the larger north-western part, including the provincial capital Vyborg, became a part of Finland, while the south-eastern part near St. Petersburg (then Leningrad) became a part of the Soviet Union. At the end of WWII, Finland ceded its share of the Isthmus to the Soviet Union. The Vyborg province was divided. Its western part remained a part of Finland and became the Kymi province. Its eastern part on the Isthmus became a part of the Soviet Union (Fig. 1). Due to the shared history, the pre-war land use was similar in both parts of the former Vyborg province. The population was largely rural and dispersed. Farmland and forests were divided into small private estates. Cattle grazed in agro-forestry ecosystems. As elsewhere in Southern Finland, timber reserves had been severely
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Fig. 1. The study area on the Isthmus of Karelia (in Russia) and the Kymi region (in Finland), to which comparisons are made, are marked dark grey. The pre-WWII border between Finland and the USSR (marked black) dissects the study area. The city of St. Petersburg and other main cities are indicated in mid-tone grey. Other land area is light grey and water is white.
depleted by selective cuttings2 over the past two centuries (Kauppi et al., 2010; Myllyntaus and Mattila, 2002). When the Finnish part of the Isthmus was ceded to the Soviet Union, the patchwork of small privately owned estates was transformed into a centrally planned system of land use (Isachenko, 2004). Logging was banned in 80% of the forests and the area was reserved for recreation (Isachenko, 2004). The Finnish inhabitants left and the area was gradually repopulated by settlers from Belorussia, Ukraine and central European Russia. However, the regional population did not reach its pre-war level until the 1980s (Isachenko, 2004). Another big change in policy followed the dissolution of the USSR in the early 1990s. Land in Russia was partly privatized and agriculture denationalized. Forests remained state-owned, but harvests were resumed (Trubin, 2000; Sta˚hls et al., 2010). We analyze land use change on the Isthmus of Karelia during and after the Soviet era, using land cover data from 1939, 1987 and 2005. The land cover data are interpreted to derive land use and forest type classifications. Utilizing the derived classifications, we estimate the land use and forest type distribution in each year and observe its changes over time. Relying on observations from fixed locations in each of the 3 years, we identify conversion trends between land use and forest type classes and test them for statistical significance. The interval 1939–1987 roughly coincides with the Soviet era. The interval 1987–2005 reflects the development in post-Soviet years. We focus on two aspects: (1) land use dynamics between agriculture and forests, and (2) change in the composition of forest types. The former captures agricultural abandonment in the region. The latter outlines the patterns of natural succession driving the accumulation of forest biomass in the region. We contrast our findings with concurrent land use and 2 Here, ‘selective cutting’ refers to the historical practice of resource extraction without regard for the long-term sustainability of the resource base. The largest trees were cut first. Then smaller and smaller trees were harvested. The harvests were not optimized over time for maximal economic gain. The historical practice thus differs from, e.g. modern continuous cover forestry which is an optimized form of selective cutting (profitability and resource base sustainability are considered). The difference between the practices is outlined in Kuuluvainen et al. (2012).
forest trends in the Kymi province and Southern Finland. The two regions’ divergent political histories serve as a backdrop for the separation of the trends. The study contributes to three discussions. Firstly, we document a piece of European forest history that is linked to the broader framework of European forest transitions. A ‘forest transition’ is a retrospectively observed sustained regional shift from decreasing to increasing forest area (Mather, 1992; Grainger, 1995), growing stock, biomass or carbon (Kauppi et al., 2006). Along with industrialization and urbanization a wave of forest area transitions swept across Europe, starting from France and spreading in all directions (Mather, 1992; Meyfroidt and Lambin, 2011). By early 20th century the wave had reached the southern parts of Fennoscandia. The reduction of forest cover in Southern Finland had come to a halt. However, forest biomass stocks remained severely depleted (Myllyntaus and Mattila, 2002; Kauppi et al., 2010). By documenting the post-transitional development on the Isthmus, we contribute a new piece into the puzzle of the transition’s continental progress. Secondly, we contribute an example of how political regime change may affect land use and forests. Government policies as well as land ownership and governance structures affect land allocation, forest management, and resource use sustainability (e.g. Hardin, 1968; Ostrom, 1990). Alternative forest management regimes imply different forest structures and levels of carbon storage (e.g. Kauppi et al., 2010). The important role of the underlying policies and institutions is exposed when abrupt political changes occur and land use reacts strongly. Recent studies document these changes in post-soviet Russia (Schierhorn et al., 2013), Latvia (Vanwanbeke et al., 2012) and the Ukraine (Baumann et al., 2011). Agricultural area retreated, as the Soviet Union collapsed and state support for agriculture decreased. Subsequently, forests on abandoned agricultural land have become a notable carbon sink European Russia and the Ukraine (Schierhorn et al., 2013). In this study we document the land use change on the Isthmus after two political upheavals in the 20th century. While land use on the Isthmus cannot be interpreted representative of
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general Soviet-era practices in North-West Russia [forests on the Isthmus were spared, whereas forests in some other parts of boreal North-West Russia were heavily exploited (Nordberg et al., 2013)], it provides an interesting comparison to Southern Finland. The contrast between the privately optimized intensive management (Kauppi et al., 2010), practiced in Finland, and the abandonment, observed on the Isthmus, is stark. Thirdly, the resumption of logging on the Isthmus has sparked concerns about its impacts on Fennoscandian biodiversity and the dispersal of species to the region (Mayer et al., 2005, 2006). For many old-growth forest species, Fennoscandia is an ecological sink that draws migrants from the source populations in Russia. The Isthmus is one of three ecological corridors connecting Fennoscandia to the Russian taiga and allowing dispersal (Linde´n et al., 2000). The link is not only important to Finnish fauna, but also the Scandinavian populations that receive reinforcements through Finland. Old-growth forests are scarce in Finland, especially in the South (Wallenius et al., 2010), but more abundant on the Russian side of the border (Muukkonen et al., 2009). A large-scale shift towards a younger forest age structure on the Isthmus could weaken the dispersal of species and hamper Fennoscandian conservation efforts. The forest and land use dynamics documented in this study provide material for assessing these concerns. 2. Materials and methods 2.1. Data Three land cover data sources were utilized: a 1:20 000 map of the Isthmus in 1939 (available online: http://www.karjalankartat. fi/) and two Landsat images based land cover classifications from 1987 and 2005 (path 185, row 18, acquisition dates 23.5.1987 and 9.6.2005). The map of 1939 is in the WGS84 geodetic system. When the land cover classifications of 1987 and 2005 were compared to 1939, the compared points were transformed to that system. Roughly 89% of the study area (see Fig. 1) was located in former Finnish territory. No visible differences in landscape structure were observed between the two sides of the historical FennoSoviet border in 1939 data. The Landsat images were classified by a supervised method using field data collected in summer 2006. A total of 101 field observation sites were measured and used to classify major land cover types in the study area, with a particular focus on forest types. The locations of the plots were selected at random based on unsupervised satellite image classification and Russian thematic maps. However, all plots were required to be located within land cover patches at a minimum distance of 100 m from the patch border. The spectral signatures for classified land cover types were developed using 20 training areas. The classifications were tested with the whole set of reference plots. The 1987 image was classified and tested using the plots measured in 2006, so that the observations were transformed to describe the land cover in 1987. This reduced the test data set to 90 points. At 11 sites the land cover had changed recently and it was unclear what it had been in 1987. The accuracy of the classification for the year 2005 was 80% (with kappa-value K = 0.76) and for year 1987, 79% (with kappa-value K = 0.73). The methodology and the classifications are presented in more detail in Takala (2007) and Muukkonen et al. (2009). A dataset consisting of land cover observations at fixed points in each year was assembled. The coordinates for the points were obtained through random sampling using the intersection of the land area covered by the Karelian maps and satellite image classifications as the domain. The points were required to be at least 100 m apart. Land cover at each point, in each year, was recorded according to an eight-point land cover classification. For
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1987 and 2005 the observations were made based on land cover in the data pixel indicated by the coordinates. For 1939, the observations were made based on the land cover in the near neighbourhood of the exact point. The sample size was increased until a minimum of 30 observations were obtained for each land cover class. The final sample size was 1232 points. Eight categories were included in the land cover classification: low tree cover stands (clear cuts and young stands with low canopy cover), deciduous forests (mixed species, mostly Betula spp.), mixed forest, spruce forest (Picea abies), pine forest (Pinus sylvestris), open peatland, buildings and infrastructure and agriculture. Water bodies were excluded from the analysis. In the satellite image classifications, all green areas outside forests and peatlands were considered agricultural land (which must have relatively recently been used for farming or grazing, as long-term abandonment leads to forestation). Non-vegetated areas were classified as buildings and infrastructure (as bare areas on the Isthmus are chiefly man-made). With these assumptions, the land cover classification could be interpreted as a land use classification, consisting of built-up area (including heavily disturbed areas without vegetation), peatland, agriculture, and forests divided into five subclasses. The data used for comparisons to contemporary land use trends in Finland were obtained from the Finnish Statistical Yearbook of Forestry (Ylitalo, 2010) and three editions of the Statistical Yearbook of Finland (Central Statistical Office of Finland, 1948, 1990; Statistics and Finland, 2005). The forest data are based on the Finnish National Forest Inventory. The land use data are based on national land surveys. 2.2. Methods Land use change on the Isthmus was analyzed in two ways. First, land use distributions for all three years were derived in order to detect long-term land use trends. Second, flows between land use classes were observed and tested for statistical significance to identify land use dynamics. Both analyses were conducted using the assembled data set. The procedures for estimating the accuracy of observations, deriving the land use distributions and testing the significance of land flows are summarized below. More extensive descriptions of the methods are included in the supplement. Neither the map nor the Landsat classifications were fully accurate. Thus, an assessment of the accuracy of the observations was conducted to enable the calculation of confidence intervals and to allow the testing of the significance of the observed land flows. While the correctness of individual observations could not be established, class-specific estimates of the probability of having misidentified land cover in a given pixel could be made. For the Landsat classifications these probabilities were constructed based on the error matrix and additional assumptions relying on supplementary data from another study covering six Eurasian landscapes (Wallenius et al., 2010). Similar background material was not available for assessing the map from 1939. Based on our expert opinion, the misidentification probabilities were calculated by making the (conservative) assumption that 15% of the points have been misidentified, but the observations are not systematically biased. The land use distributions were constructed as follows. First, the total number of observations belonging to each land use class was recorded. Each data point, at which a particular land use was observed, was counted as an observation belonging to that class. Then, the total numbers were corrected for systematic misidentification bias. The misidentification probabilities were used to estimate the most likely share of misidentified points among the observations allocated to each class. These points were then reallocated to the correct classes in (the most likely) proportions
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indicated by the probabilities. After making the corrections, 95 and 99% confidence intervals for each class’s landscape share were calculated based on a binomial distribution, in which the number of classes was reduced to two (the particular land use of interest vs. all other land uses). An analysis of land use conversions between years was conducted using a 8 8 = 64 matrix. The initial land use was indicated by rows and the final land use was indicated by columns (eight classes in both years). Each of the matrix’s elements thus depicted a particular type of land use change (e.g. agricultural land converted to mixed forest). The number of observations in each of the 64 transition classes was used to test the significance of the type of land use change against a null hypothesis. For transition classes outside the diagonal of the matrix, indicating land use change, the null hypothesis was that the change was fully attributable to observation errors (i.e. the misidentification of the land use at data points at either, or both, end(s) of the time interval). The null hypothesis was rejected if the probability of all observations being wrong was less than 0.05. For classes on the diagonal of the matrix, indicating persistence in land use, the null hypothesis was that the class had remained unchanged throughout the interval. Significant persistence was observed if the null hypothesis could not be rejected. A full description of the testing procedure is provided in the supplement. It is important to note that the procedure can be used to assess the existence of particular types of land use change, but not their magnitude. We can only conclude whether land has been transferred from one class to another. We cannot accurately estimate how much. Therefore, the landscape shares of the transition classes referred to in the analysis should not be interpreted as accurate estimates. 3. Results 3.1. Changes during the Soviet period During the Soviet era, forests and built-up areas expanded, while open peatland and agricultural areas retreated (Fig. 2). The share of forests increased from 72 to 77%. This expansion came at the expense of open peatland and agricultural areas, whose landscape shares declined significantly (Fig. 3). The landscape shares of mixed forests and spruce forests rose sharply, and the share of low tree cover stands declined (Fig. 3). The share of agriculture halved from 18 to 9%. Area was lost to forests and settlements (Fig. 4). Most low tree cover stands from 1939 had matured by 1987 and thus transitioned into other forest classes, especially mixed forest
Fig. 2. Landscape shares of the land cover classes in 1939, 1987 and 2005, with all forests as a single class.
Fig. 3. Landscape shares of the land cover classes in 1939, 1987 and 2005. Forests in five separate classes.
and pine forest (Fig. 4). As low tree cover stands are usually temporary (young stands gain tree cover as they mature), the decline in the landscape share of low tree cover stands suggests a lower rate of logging and afforestation in the 1980s than in the 1930s. The transition patterns between forest classes provide evidence of the important role of natural succession in transforming the landscape (Fig. 4). As expected, deciduous forests transformed into mixed forest and mixed forests transformed into spruce forest. The counter-evidence of pine or spruce forests reverting back to deciduous forests or low tree cover stands was too weak to indicate significant human interference. 3.2. Changes after the Soviet period The changes observed after 1987 were more modest, partly due to the shorter length of the observation interval (18 years from 1987 to 2005, vs. 48 years from 1939 to 1987). Total forest area increased (Fig. 2), while peatlands and agricultural area remained fairly stable (Fig. 3). The extent of built-up and other bare areas declined sharply (Fig. 3), but this change may be (to some extent) attributable to classification errors. Their share in 1987 is likely to have been overestimated, due to a seasonal difference between the Landsat images. The 1987 image was taken in May. The 2005 image was taken in June, when the vegetation is greener. Thus, some areas appearing ‘‘bare’’ in 1987 may have been seen as ‘‘green’’ in 2005, even without actual land cover change. Furthermore, with deciduous foliage camouflaging buildings in 2005, some built-up areas may have been classified as forest despite seasonal corrections. The latter may explain a part of the observed expansion of mixed and deciduous forests into built-up areas (Fig. 4), to which some of the increase in total forest area is attributed. The actual increase in total forest area is thus likely to have been less than the 5% points indicated in Fig. 2. Transitions within the forest mosaic continued to exhibit patterns of natural succession (Fig. 4). Deciduous forest transformed into mixed forest and mixed forest into spruce and pine forests. However, there were some indications of selective logging and clear-cuts. The reversions of spruce forests to mixed forests and mixed forests to deciduous forests appear to have strengthened in the post-Soviet years. Such an effect could have been generated by the selective logging of coniferous species. The revitalization of clear cuts since the 1980s is suggested by the observed flow of land from pine forests into low tree cover stands. A similar flow was not observed during the Soviet era.
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Fig. 4. Land use transitions in the timespans 1939–1987, 1987–2005 and 1939–2005.
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3.3. Changes on the Isthmus compared to contemporary development in Finland While forests on the Isthmus expanded and agricultural area declined sharply during the Soviet era, both remained relatively stable in neighbouring Finland (Fig. 5). The landscape share of agricultural land in the Kymi province remained above national average due to its Southern location that is favourable for agriculture. 4. Discussion 4.1. Agricultural Abandonment Agricultural abandonment on the Isthmus occurred during the Soviet era. Agricultural area did not change much after 1987. On the one hand, the strong abandonment in the Soviet years contrasts with the stable trend in Finland. In the (former) Kymi province across the border, the landscape share of agriculture in fact increased from 13.5% in 1946 to 14.5% in 1987 (Fig. 5). On the other hand, the relatively stable trend on the Isthmus after 1987 contrasts with the contemporary development further south in European Russia and Ukraine, where agricultural area decreased strongly after the collapse of the Soviet Union (Schierhorn et al., 2013). Why did the land use histories of Southern Finland and the Isthmus diverge so strongly after the territory was ceded and why was not there a strong wave of agricultural abandonment in the post-Soviet years? Several factors explain the agricultural decline in the Soviet years. During WWII, the Finnish population of the Isthmus was evacuated to Finland. The farms were left unattended. The area was repopulated by settlers from elsewhere within the Soviet Union, but population growth was slow and predominantly urban (Isachenko, 2004). As a result, the farming tradition in the region was disrupted and only a fraction of the vacated farmland was incorporated into the Sovkhozes and Kolkhozes established in the area during the following decades (Isachenko, 2004). The slow rural repopulation went hand in hand with the state policies of agricultural development. For instance, the centrally planned efforts to expand farmland and boost agricultural yields to tackle USSR’s deficit in cereal production in its last decades, primarily targeted other more productive regions in the Soviet Union (Johnson, 1982; Jones et al., 1996). The Isthmus was inferior due to its northern location, and the repopulation of the countryside was not a priority. Meanwhile in Finland, agriculture continued uninterrupted. A broad system of import tariffs and agricultural subsidies was established in the decades following WWII to compensate for the climatic disadvantage and shelter domestic production from
international competition. The policies were motivated by food security and the desire to maintain farmers’ incomes. By 2005, agricultural subsidies made up over 40% of all agricultural turnover (MTT, 2014). The level of government support for agriculture was substantially higher in Finland than in Russia (OECD, 2013). The early agricultural abandonment on the Isthmus explains its lack in post-Soviet years. Government support for agriculture in Russia plummeted in the early 1990s (OECD, 2013). The denationalization of agriculture and the decrease in government support led to a decline in agricultural area in European Russia (Schierhorn et al., 2013) and elsewhere in the former Soviet Union (Baumann et al., 2011; Vanwanbeke et al., 2012; Schierhorn et al., 2013). As agriculture on the Isthmus had already contracted half a century earlier, a similarly strong trend was not observed in the 1990s. However, due to the rather loose definition of agricultural area applied in our classification, it is possible that some agricultural abandonment may have gone unobserved, given that the abandoned areas had not yet gained tree cover by 2005. 4.2. Change in forest area and structure Forests expanded as agriculture on the Isthmus retreated. The drainage of bogs during the Soviet years also contributed to the increase in forest cover (Isachenko, 2004). The expansion of forests onto open peatland is seen in Fig. 4. Apart from drainage, active forest management was not practiced. Forests in the Soviet Union were zoned into three groups: (i) forests with protective and social functions, (ii) forests with multiple functions, and (iii) exploitable forests (Nordberg et al., 2013). When the land was claimed by the state, approximately 80% of the forests in Vyborg Karelia were classified into the first category (Isachenko, 2004). Officially, the forests were reserved for recreational use for the people of St. Petersburg (Isachenko, 2004). However, given the proximity to the border, keeping the Isthmus forested may have also had a military strategic purpose in the city’s defence. Either way, industrial treecutting was forbidden. Where clear-cuts were not banned, harvesting and management resembled the practices observed elsewhere in boreal North-West Russia: harvests were clear-cuts, regeneration was accomplished through natural seeding from adjacent forests, and active forest management between regeneration and clear cut was not practiced (Nordberg et al., 2013). Due to the logging ban and the management regime, the change in the forest mosaic was mostly driven by natural succession. In boreal forests on fertile soils, deciduous species are usually the first to arrive after disturbance. The canopy closes as the trees grow. The amount of light in the understory is reduced. This favours the shade-tolerant Norway spruce. Gradually, the deciduous canopy is replaced by a coniferous one as spruce trees fill tree
Fig. 5. The landscape share of agriculture (panel A) and forests (panel B) in Finland and the Isthmus of Karelia.
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fall gaps. On less fertile and sandy soils, Scots pine can also be a pioneer tree leading to relatively pure pine stands. As boreal coniferous species have a longer lifespan than their deciduous counterparts, eventually all old-growth forest become coniferous. The natural disturbance dynamics in boreal forests are reviewed in more detail in, e.g. Gromtsev (2002) and Angelstam and Kuuluviainen (2004). The landscape-level forest dynamics observed during the Soviet times match the described pattern of natural succession. Areas that were farmland in 1939, but were later abandoned, were mostly covered by mixed or deciduous forests in 1987. The Deciduous forests of 1939 gradually transformed into mixed forests and mixed forests became coniferous. The same pattern continued after 1987, but there was some evidence of selective logging and clearcuts, implied by observed reversions to earlier successional stages. Logging on the Russian side of the international border restarted after the collapse of the Soviet Union, partly driven by Finnish wood imports (Sta˚hls et al., 2010). The impact of the harvests on the dispersal of old-growth forest species to Fennoscandia has been a point of concern (Mayer et al., 2005, 2006), as logging in the ecological corridor may negatively impact the size and genetic diversity of Fennoscandian populations (Mayer et al., 2005). However, old-growth forests remain more common on the Isthmus than in Southern Finland (Muukkonen et al., 2009). Also, the forest landscape on the Isthmus is less fragmented than in Finland (Muukkonen et al., 2009). The road network is sparse and, unlike in Finland, the forests are not divided into small estates, as they remain government-owned (Trubin, 2000). We find that, despite resumed logging, the forest landscape has largely followed patterns of natural succession in the postSoviet years. We therefore do not find any strong evidence in support of an increased threat to the dispersal of old growth forest species in particular. The overall biodiversity impacts of the longterm landscape change and the recent resumption of logging cannot be comprehensively assessed in the scope of this study. The forested landscape on the Isthmus has matured considerably since 1939. All species do not thrive in old-growth forests. Policies that promote the accumulation of biomass and carbon in forests may not be optimal for species conservation (e.g. Nelson et al., 2008). The biodiversity impacts of the observed changes may thus be multifaceted. 4.3. Changes in forest biomass and carbon A large amount of carbon has been stored by the expanding forest biomass on abandoned agricultural lands in the former Soviet Union since 1990 (Schierhorn et al., 2013). On the Isthmus, the relative accumulation of carbon (per area) must have been even greater as it has persisted for half a century longer. For comparison, Kauppi et al. (2010) reported the doubling of the forest biomass carbon stock in an area in Southern Finland between 1912 and 2005. Two factors suggest an even greater relative accumulation of carbon reserves on the Isthmus. First, notable agricultural abandonment took place on the Isthmus after WWII. The abandoned land turned into forest. In southern Finland forest area changed little. Thus, forests on the Isthmus gained carbon via areal expansion, while forests in Finland did not. Second, also the accumulation of carbon in preexisting forests was greater on the Isthmus. By the early 20th century forest biomass stocks all over Southern Finland had been depleted due to over-harvesting and suboptimal selective harvests (Siiskonen, 2007; Kauppi et al., 2010). Over the following century, biomass carbon in Finnish forests doubled as a sideeffect of management changes (Liski et al., 2006; Kauppi et al., 2010; Siiskonen, 2007): the management regime shifted from
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selective cutting to yield-maximizing, and later profit-maximizing, even-aged forestry. On the Isthmus, however, harvests nearly stopped for half a century due to the Soviet era logging ban. Unlike in Finland, stands were not cut when they reached the end of their economically optimal rotation. Instead they continued to grow and accumulate timber. Therefore, higher average per-hectare growing stock volumes (of merchantable timber) have been measured in all forest types on the Russian side of the border (Muukkonen et al., 2009). The change in growing stock can be used as a rough proxy for the change in biomass carbon (e.g. Kauppi et al., 2006). The average growing stock per hectare in the Vyborg province (including Kymi and the Isthmus of Karelia), as measured in the Finnish National Forest Inventory 1936–1938 was 71 m3 ha 1 (Ilvessalo, 1943), which implies a biomass carbon stock3 of roughly 26 tC ha 1. The average growing stock per hectare in the Leningrad Region4, of which the Isthmus is currently a part of, was 175 m3 ha 1 (65 tC ha 1) in the mid-1990s (Pa¨ivinen et al., 1999). Thus, growing stock and carbon increased 2.4-fold. The corresponding figure for Southern Finland in 2009–2012 was lower: 138 m3 ha 1 (51 tC ha 1) (Ylitalo, 2013), implying a 1.9-fold increase since the 1930s. Accounting for the expansion of forest area, the forest biomass carbon stock on the Isthmus may have expanded as much as threefold since 1939. Land use changes, such as afforestation and deforestation, affect soil carbon. The stock decreases when boreal forest is cleared for agriculture (Karhu et al., 2011). The depletion is caused by a reduction in soil carbon inputs. After an initial emission pulse, a weak net flux into the atmosphere may be sustained for a long time after clearance (Heikkinen et al., 2013). The soil carbon stock may also initially decrease when agricultural land is afforested (Karhu et al., 2011). This is also due to a reduction in soil carbon inputs, as young forests produce little dead wood and litter. However, the inputs increase and soil carbon stocks accumulate with stand age (Peltoniemi et al., 2004; Karhu et al., 2011). The afforestation and maturing of forests observed on the Isthmus suggest increased soil carbon storage. However, a notable amount of carbon may have been released due to the drainage of bogs. We therefore cannot conclude the direction of the net change in soil carbon. 5. Conclusions Large-scale agricultural abandonment followed the Finnish cession of the western part of the Isthmus to the Soviet Union during WWII. Former agricultural areas gained forest cover. Meanwhile, the utilization of the region’s forest resources ceased due to a logging ban. The abstinence from logging enabled the region’s previously degraded forests to recover according to patterns of natural succession. Old-growth forests became more abundant and the forest biomass carbon stock accumulated. The accumulation was stronger than in neighbouring Finland, where the increase in forest biomass was achieved by intensifying agriculture and forestry, rather than by abandoning land and restraining from logging. The two divergent development paths 3 Assuming a conversion factor 0.3715. The conversion factor includes the conversion of growing stock volume to total tree biomass, the conversion of biomass into dry matter and accounting for the dry-matter share of carbon. 0.3715 is the conversion factor for Norway spruce (Tomppo, 2000). The respective factors for Scots pine and deciduous species are 0.3091 and 0.4152, respectively. The factor for spruce was applied in the calculations as it most closely reflects the landscape level average. 4 Calculated for Forest Fund area administered by the Forest Committee. The total area of the Forest Fund is 57 890 km2. A majority of it (45 770 km2) is administered by the Committee. Non-forest land (open peatlands, water bodies, hay fields, roads, etc.) makes up 9281 km2 of the administered area. All other area is considered forest in our calculations. The total growing stock volume in these forests is 639.5 M m3, which implies 175 m3 ha 1.
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initiating from a similar state provide an empirical example of forest biomass recovery potentials under optimized intensive land management and nearly full abandonment. Acknowledgements We thank Terhi Takala for her work on the land cover classifications and Julia Luotola for collecting the 1939 data points. We acknowledge the contributions of Audrey Mayer, Pa¨ivi Tikka and Leena Vihermaa to The Boomerang project (2006-2009). The project was supported by Grant No. 109942 from the Academy of Finland. A.R. acknowledges financial support from the Finnish Cultural Foundation.
Appendix A. Supplementary data A methodological supplement associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. envsci.2015.09.011. References Angelstam, P., Kuuluviainen, T., 2004. Boreal forest disturbance regimes, successional dynamics and landscape structures – a European perspective. Ecol. Bull. 51, 117–136. Baumann, M., Kuemmerle, T., Elbakidze, M., Ozdogan, M., Radeloff, V.C., Keuler, N.S., Prishchepov, A.V., Kruhlov, I., Hostert, P., 2011. Patterns and drivers of post-socialist farmland abandonement in Western Ukraine. Land Use Policy 28, 552–562. Central Statistical Office of Finland, 1948. Statistical Yearbook of Finland 1946– 1947. Central Statistical Office of Finland, Helsinki 283 pp. Central Statistical Office of Finland, 1990. Statistical Year Book of Finland 1990. Central Statistical Office of Finland, Helsinki 586 pp. Grainger, A., 1995. The forest transition: an alternative approach. Area 27, 242– 251. Gromtsev, A., 2002. Natural disturbance dynamics in the boreal forests of European Russia: a review. Silva Fenn. 36, 41–55. Hardin, G., 1968. The tragedy of the commons. Science 162, 1243–1248. Heikkinen, J., Ketoja, E., Nuutinen, V., Regina, K., 2013. Declining trend of carbon in Finnish cropland soils in 1974–2009. Glob. Change Biol. 19, 1456–1469. Ilvessalo, Y., 1943. The forest resources and the condition of the forests of Finland. The second national forest survey. Commun. Inst. For. Fenn. 30, Valtioneuvoston Kirjapaino, Helsinki 446 p. Isachenko, G.A., 2004. The landscape of the Karelian Isthmus and its imagery since 1944. Fennia 182, 47–59. Johnson, D.G., 1982. Agriculture in the centrally planned economies. Am. J. Agric. Econ. 64, 845–853. Jones, J.R., Li, S.L., Devadoss, S., Fedane, C.J., 1996. The former Soviet Union and the world wheat economy. Am. J. Agric. Econ. 78, 869–878. Karhu, K., Wall, A., Vanhala, P., Liski, J., Esala, M., Regina, K., 2011. Effects of afforestation and deforestation on boreal soil carbon stocks – comparison of measured C stocks with Yasso07 model results. Geoderma 164, 33–45. Kauppi, P.E., Ausubel, J.H., Fang, J., Mather, A.S., Sedjo, R.A., Waggoner, P.E., 2006. Returning forests analyzed with the forest identity. Proc. Natl. Acad. Sci. U. S. A. 103, 17574–17579. Kauppi, P.E., Rautiainen, A., Korhonen, K.T., Lehtonen, A., Liski, J., No¨jd, P., Tuominen, S., Haakana, M., Virtanen, T., 2010. Changing stock of biomass carbon in a boreal forest over 93 years. For. Ecol. Manag. 259, 1239–1244. Kuuluvainen, T., Tahvonen, O., Aakala, T., 2012. Even-aged and uneven-aged forest management in boreal Fennoscandia: a review. Ambio 41, 720–737. Linde´n, H., Danilov, P.I., Gromtsev, A.N., Helle, P., Ivanter, E.V., Kurhinen, J., 2000. Large-scale forest corridors to connect the taiga fauna to Fennoscandia. Wildl. Biol. 6, 179–188.
Liski, J., Lehtonen, A., Palosuo, T., Peltoniemi, M., Eggers, T., Muukkonen, P., Ma¨kipa¨a¨, R., 2006. Carbon accumulation in Finland’s forests 1922–2004 – an estimate obtained by combination of forest inventory data with modelling of biomass, litter and soil. Ann. For. Sci. 63, 687–697. Mather, A.S., 1992. The forest transition. Area 24, 367–379. Mayer, A.L., Kauppi, P.E., Angelstam, P.K., Zhang, Y., Tikka, P.M., 2005. Importing timber, exporting ecological impact. Science 308, 359–360. Mayer, A.L., Kauppi, P.E., Tikka, P.M., Angelstam, P.K., 2006. Conservation implications of exporting domestic wood harvest to neighbouring countries. Environ. Sci. Policy 9, 228–236. Meyfroidt, P., Lambin, E.F., 2011. Global forest transition: prospects for an end to deforestation. Ann. Rev. Environ. Resour. 36, 343–371. MTT, 2014. Income Statement: Profitability Bookkeeping Results of MTT Economic Research. www.mtt.fi/economydoctor (retrieved 19.3.2014). Muukkonen, P., Takala, T., Virtanen, T., 2009. Differences in the forest landscape structure along the Finnish-Russian border in Southern Karelia. Scand. J. For. Res. 24, 140–148. Myllyntaus, M., Mattila, M., 2002. Decline or increase? The standing timber stock in Finland, 1800–1997. Ecol. Econ. 41, 271–288. Nelson, E., Polasky, S., Lewis, D.J., Plantinga, A.J., Lonsdorf, E., White, D., Bael, D., Lawler, J.J., 2008. Efficiency of incentives to jointly increase carbon sequestration and species conservation on a landscape. Proc. Natl. Acad. Sci. U. S. A. 105 (28), 9471–9476. Nordberg, M., Angelstam, P., Elbakidze, M., Axelsson, R., 2013. From logging frontier towards sustainable forest management: experiences from boreal regions of North-West Russia and North Sweden. Scand. J. For. Res. 28, 797– 810. OECD (Organization for Economic Co-operation and Development), 2013. Agricultural Policies and Support: Producer and Consumer Support Estimates Database. www.oecd.org/agriculture/agricultural-policies/ producerandconsumersupportestimatesdatabase.htm. Ostrom, E., 1990. Governing the Commons. Cambridge University Press 280 pp. Pa¨ivinen, R., Nabuurs, G.-J., Lioubimov, A.V., Kuusela, K., 1999. The State Utilisation and Possible Future Developments of Leningrad Region Forests. EFI Working Paper 18. European Forest Institute. Peltoniemi, M., Ma¨kipa¨a¨, R., Liski, J., Tamminen, P., 2004. Changes in soil carbon with stand age – an evaluation of modelling method with empirical data. Glob. Change Biol. 10, 2078–2091. Schierhorn, F., Mu¨ller, D., Beringer, T., Prishchepov, A.V., Kuemmerle, T., Balmann, A., 2013. Post-Soviet cropland abandonement and carbon sequestration in European Russia, Ukraine and Belarus. Glob. Biochem. Cycles 27, 1–11. Siiskonen, H., 2007. The conflict between traditional and scientific forest management in 20th century Finland. For. Ecol. Manag. 249, 125–133. Simula, M., 2009. Towards Defining Forest Degradation: Comparative Analysis of Existing Definitions. Forest Resources Assessment Working Paper 154. UNFAO, Rome. Sta˚hls, M.H., Mayer, A.L., Tikka, P.M., Kauppi, P.E., 2010. Disparate geography of consumption, production and environmental impacts: forest products in Finland 1991–2007. J. Ind. Ecol. 14, 576–585. Statistics Finland, 2005. Statistical Yearbook of Finland 2005. Statistics Finland, Helsinki 702 pp. Takala, T., 2007. Metsa¨maiseman rakenteen muutokset Karjalankannaksella (Changes of structure of forest landscapes on Karelian Isthmus). (Master’sthesis) Department of Biological and Environmental Sciences, University of Helsinki (in Finnish). Tomppo, E., 2000. National forest inventory of Finland and its role estimating the carbon balance of forests. Biotechnol. Agron. Soc. Environ. 4, 281–284. Trubin, D.V., 2000. Forest management in the North of the European Russia. In: Ma¨lko¨nen, E., Babich, N.A., Krutov, V.I., Markova, I.A. (Eds.), Forest Regeneration in the Northern Parts of Europe, 790. Finnish Forest Research Institute Research Papers, pp. 17–22. Vanwanbeke, S.O., Meyfroidt, P., Nikodemus, O., 2012. From USSR to EU: 20 years of rural landscape changes in Vidzeme, Latvia. Landsc. Urban Plan. 105, 241–249. Wallenius, T., Niskanen, L., Virtanen, T., Hottola, J., Brumelis, G., Angervuori, A., Julkunen, J., Pihlstro¨m, M., 2010. Loss of habitats, naturalness and species diversity in Eurasian forest landscapes. Ecol. Indic. 10, 1093–1101. Ylitalo, E. (Ed.), 2010. Finnish Statistical Yearbook of Forestry. Finnish Forest Research Institute, Vantaa, 472 pp. Ylitalo, E. (Ed.), 2013. Finnish Statistical Yearbook of Forestry. Finnish Forest Research Institute, Vantaa, 448 pp.
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Land cover change on the Isthmus of Karelia 1939–2005: Agricultural abandonment and natural succession Aapo Rautiainen a,*, Tarmo Virtanen b, Pekka E. Kauppi b a b
Natural Resources Institute Finland, P.O. Box 18 (Jokiniemenkuja 1), 01301 Vantaa, Finland Department of Environmental Sciences, University of Helsinki, P.O. Box 65 (Viikinkaari 1), 00014 Helsinki, Finland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 December 2014 Received in revised form 14 September 2015 Accepted 23 September 2015 Available online 21 October 2015
The Isthmus of Karelia is located in North-West Russia, between Lake Ladoga and the Baltic Sea. At the end of World War II (WWII), Finland ceded the western part of the Isthmus to the Soviet Union. In 1991 the Soviet Union ceased to exist. The Isthmus became a part of the Russian Federation. Using land cover data from the years 1939, 1987 and 2005 we document and analyze land use change on the Isthmus during and after the Soviet era. Large-scale agricultural abandonment was observed during the Soviet era. The landscape share of agriculture halved from 18 to 9%, as only a part of the land vacated by Finnish farmers during WWII was incorporated into the Soviet agricultural system. Forest expanded onto farmland and its landscape share increased from 72 to 77%. Another observed trend was the recovery from previous forest degradation. As logging on the Isthmus was banned, the development of the forest mosaic followed patterns of natural succession. Some deciduous forests were transformed into mixed forests and spruce forests. Clear cuts and sapling stands became rare. After the collapse of the Soviet Union, logging in old growth forests was resumed. However, its impacts on the regional forest composition remained modest. The recent forest history of the Isthmus is an extreme example of recovery from earlier forest degradation in boreal conditions. To illustrate the magnitude of the changes, we contrast our findings with the contemporary development in Southern Finland, where land use remained stable and forests were intensively utilized for timber production. ß 2015 Elsevier Ltd. All rights reserved.
Keywords: Isthmus of Karelia Land use change Forest Agriculture Agricultural abandonment Forest degradation
1. Introduction Since the collapse of the Soviet Union cropland area in European Russia has decreased by 272 000 km2 (Schierhorn et al., 2013). Forests have spread into abandoned areas, accumulating carbon in expanding biomass and soils. On the Isthmus of Karelia, in Northwest Russia, the contraction of agriculture and the expansion of forests began earlier, when farms and pastures were abandoned by Finnish farmers during World War II (WWII). Due to an extensive logging ban, the region’s forests remained nearly untouched by industrial logging for half a century. Forest cover expanded and the forest mosaic developed according to patterns of natural succession. We document this strong recovery from earlier forest degradation1
* Corresponding author. E-mail address: aapo.rautiainen@luke.fi (A. Rautiainen). 1 The definition of ‘forest degradation’ varies according to context (Simula, 2009). We use the term to refer to the depletion of forest timber and carbon reserves at the landscape level. The opposite development is called ‘recovery from degradation’. http://dx.doi.org/10.1016/j.envsci.2015.09.011 1462-9011/ß 2015 Elsevier Ltd. All rights reserved.
which occurred during the 20th century as the political regime changed twice. The Isthmus covers an area of 15 000 km2 between Lake Ladoga and the Baltic Sea, north of St. Petersburg (Fig. 1). Until WWII, its north-western part was inhabited by a Finnish population and the region shared a common history with Finland. Both were ruled over by Sweden between the 13th and 18th and Russia between 1743 and 1917. When Finland declared independence in 1917, the larger north-western part, including the provincial capital Vyborg, became a part of Finland, while the south-eastern part near St. Petersburg (then Leningrad) became a part of the Soviet Union. At the end of WWII, Finland ceded its share of the Isthmus to the Soviet Union. The Vyborg province was divided. Its western part remained a part of Finland and became the Kymi province. Its eastern part on the Isthmus became a part of the Soviet Union (Fig. 1). Due to the shared history, the pre-war land use was similar in both parts of the former Vyborg province. The population was largely rural and dispersed. Farmland and forests were divided into small private estates. Cattle grazed in agro-forestry ecosystems. As elsewhere in Southern Finland, timber reserves had been severely
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Fig. 1. The study area on the Isthmus of Karelia (in Russia) and the Kymi region (in Finland), to which comparisons are made, are marked dark grey. The pre-WWII border between Finland and the USSR (marked black) dissects the study area. The city of St. Petersburg and other main cities are indicated in mid-tone grey. Other land area is light grey and water is white.
depleted by selective cuttings2 over the past two centuries (Kauppi et al., 2010; Myllyntaus and Mattila, 2002). When the Finnish part of the Isthmus was ceded to the Soviet Union, the patchwork of small privately owned estates was transformed into a centrally planned system of land use (Isachenko, 2004). Logging was banned in 80% of the forests and the area was reserved for recreation (Isachenko, 2004). The Finnish inhabitants left and the area was gradually repopulated by settlers from Belorussia, Ukraine and central European Russia. However, the regional population did not reach its pre-war level until the 1980s (Isachenko, 2004). Another big change in policy followed the dissolution of the USSR in the early 1990s. Land in Russia was partly privatized and agriculture denationalized. Forests remained state-owned, but harvests were resumed (Trubin, 2000; Sta˚hls et al., 2010). We analyze land use change on the Isthmus of Karelia during and after the Soviet era, using land cover data from 1939, 1987 and 2005. The land cover data are interpreted to derive land use and forest type classifications. Utilizing the derived classifications, we estimate the land use and forest type distribution in each year and observe its changes over time. Relying on observations from fixed locations in each of the 3 years, we identify conversion trends between land use and forest type classes and test them for statistical significance. The interval 1939–1987 roughly coincides with the Soviet era. The interval 1987–2005 reflects the development in post-Soviet years. We focus on two aspects: (1) land use dynamics between agriculture and forests, and (2) change in the composition of forest types. The former captures agricultural abandonment in the region. The latter outlines the patterns of natural succession driving the accumulation of forest biomass in the region. We contrast our findings with concurrent land use and 2 Here, ‘selective cutting’ refers to the historical practice of resource extraction without regard for the long-term sustainability of the resource base. The largest trees were cut first. Then smaller and smaller trees were harvested. The harvests were not optimized over time for maximal economic gain. The historical practice thus differs from, e.g. modern continuous cover forestry which is an optimized form of selective cutting (profitability and resource base sustainability are considered). The difference between the practices is outlined in Kuuluvainen et al. (2012).
forest trends in the Kymi province and Southern Finland. The two regions’ divergent political histories serve as a backdrop for the separation of the trends. The study contributes to three discussions. Firstly, we document a piece of European forest history that is linked to the broader framework of European forest transitions. A ‘forest transition’ is a retrospectively observed sustained regional shift from decreasing to increasing forest area (Mather, 1992; Grainger, 1995), growing stock, biomass or carbon (Kauppi et al., 2006). Along with industrialization and urbanization a wave of forest area transitions swept across Europe, starting from France and spreading in all directions (Mather, 1992; Meyfroidt and Lambin, 2011). By early 20th century the wave had reached the southern parts of Fennoscandia. The reduction of forest cover in Southern Finland had come to a halt. However, forest biomass stocks remained severely depleted (Myllyntaus and Mattila, 2002; Kauppi et al., 2010). By documenting the post-transitional development on the Isthmus, we contribute a new piece into the puzzle of the transition’s continental progress. Secondly, we contribute an example of how political regime change may affect land use and forests. Government policies as well as land ownership and governance structures affect land allocation, forest management, and resource use sustainability (e.g. Hardin, 1968; Ostrom, 1990). Alternative forest management regimes imply different forest structures and levels of carbon storage (e.g. Kauppi et al., 2010). The important role of the underlying policies and institutions is exposed when abrupt political changes occur and land use reacts strongly. Recent studies document these changes in post-soviet Russia (Schierhorn et al., 2013), Latvia (Vanwanbeke et al., 2012) and the Ukraine (Baumann et al., 2011). Agricultural area retreated, as the Soviet Union collapsed and state support for agriculture decreased. Subsequently, forests on abandoned agricultural land have become a notable carbon sink European Russia and the Ukraine (Schierhorn et al., 2013). In this study we document the land use change on the Isthmus after two political upheavals in the 20th century. While land use on the Isthmus cannot be interpreted representative of
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general Soviet-era practices in North-West Russia [forests on the Isthmus were spared, whereas forests in some other parts of boreal North-West Russia were heavily exploited (Nordberg et al., 2013)], it provides an interesting comparison to Southern Finland. The contrast between the privately optimized intensive management (Kauppi et al., 2010), practiced in Finland, and the abandonment, observed on the Isthmus, is stark. Thirdly, the resumption of logging on the Isthmus has sparked concerns about its impacts on Fennoscandian biodiversity and the dispersal of species to the region (Mayer et al., 2005, 2006). For many old-growth forest species, Fennoscandia is an ecological sink that draws migrants from the source populations in Russia. The Isthmus is one of three ecological corridors connecting Fennoscandia to the Russian taiga and allowing dispersal (Linde´n et al., 2000). The link is not only important to Finnish fauna, but also the Scandinavian populations that receive reinforcements through Finland. Old-growth forests are scarce in Finland, especially in the South (Wallenius et al., 2010), but more abundant on the Russian side of the border (Muukkonen et al., 2009). A large-scale shift towards a younger forest age structure on the Isthmus could weaken the dispersal of species and hamper Fennoscandian conservation efforts. The forest and land use dynamics documented in this study provide material for assessing these concerns. 2. Materials and methods 2.1. Data Three land cover data sources were utilized: a 1:20 000 map of the Isthmus in 1939 (available online: http://www.karjalankartat. fi/) and two Landsat images based land cover classifications from 1987 and 2005 (path 185, row 18, acquisition dates 23.5.1987 and 9.6.2005). The map of 1939 is in the WGS84 geodetic system. When the land cover classifications of 1987 and 2005 were compared to 1939, the compared points were transformed to that system. Roughly 89% of the study area (see Fig. 1) was located in former Finnish territory. No visible differences in landscape structure were observed between the two sides of the historical FennoSoviet border in 1939 data. The Landsat images were classified by a supervised method using field data collected in summer 2006. A total of 101 field observation sites were measured and used to classify major land cover types in the study area, with a particular focus on forest types. The locations of the plots were selected at random based on unsupervised satellite image classification and Russian thematic maps. However, all plots were required to be located within land cover patches at a minimum distance of 100 m from the patch border. The spectral signatures for classified land cover types were developed using 20 training areas. The classifications were tested with the whole set of reference plots. The 1987 image was classified and tested using the plots measured in 2006, so that the observations were transformed to describe the land cover in 1987. This reduced the test data set to 90 points. At 11 sites the land cover had changed recently and it was unclear what it had been in 1987. The accuracy of the classification for the year 2005 was 80% (with kappa-value K = 0.76) and for year 1987, 79% (with kappa-value K = 0.73). The methodology and the classifications are presented in more detail in Takala (2007) and Muukkonen et al. (2009). A dataset consisting of land cover observations at fixed points in each year was assembled. The coordinates for the points were obtained through random sampling using the intersection of the land area covered by the Karelian maps and satellite image classifications as the domain. The points were required to be at least 100 m apart. Land cover at each point, in each year, was recorded according to an eight-point land cover classification. For
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1987 and 2005 the observations were made based on land cover in the data pixel indicated by the coordinates. For 1939, the observations were made based on the land cover in the near neighbourhood of the exact point. The sample size was increased until a minimum of 30 observations were obtained for each land cover class. The final sample size was 1232 points. Eight categories were included in the land cover classification: low tree cover stands (clear cuts and young stands with low canopy cover), deciduous forests (mixed species, mostly Betula spp.), mixed forest, spruce forest (Picea abies), pine forest (Pinus sylvestris), open peatland, buildings and infrastructure and agriculture. Water bodies were excluded from the analysis. In the satellite image classifications, all green areas outside forests and peatlands were considered agricultural land (which must have relatively recently been used for farming or grazing, as long-term abandonment leads to forestation). Non-vegetated areas were classified as buildings and infrastructure (as bare areas on the Isthmus are chiefly man-made). With these assumptions, the land cover classification could be interpreted as a land use classification, consisting of built-up area (including heavily disturbed areas without vegetation), peatland, agriculture, and forests divided into five subclasses. The data used for comparisons to contemporary land use trends in Finland were obtained from the Finnish Statistical Yearbook of Forestry (Ylitalo, 2010) and three editions of the Statistical Yearbook of Finland (Central Statistical Office of Finland, 1948, 1990; Statistics and Finland, 2005). The forest data are based on the Finnish National Forest Inventory. The land use data are based on national land surveys. 2.2. Methods Land use change on the Isthmus was analyzed in two ways. First, land use distributions for all three years were derived in order to detect long-term land use trends. Second, flows between land use classes were observed and tested for statistical significance to identify land use dynamics. Both analyses were conducted using the assembled data set. The procedures for estimating the accuracy of observations, deriving the land use distributions and testing the significance of land flows are summarized below. More extensive descriptions of the methods are included in the supplement. Neither the map nor the Landsat classifications were fully accurate. Thus, an assessment of the accuracy of the observations was conducted to enable the calculation of confidence intervals and to allow the testing of the significance of the observed land flows. While the correctness of individual observations could not be established, class-specific estimates of the probability of having misidentified land cover in a given pixel could be made. For the Landsat classifications these probabilities were constructed based on the error matrix and additional assumptions relying on supplementary data from another study covering six Eurasian landscapes (Wallenius et al., 2010). Similar background material was not available for assessing the map from 1939. Based on our expert opinion, the misidentification probabilities were calculated by making the (conservative) assumption that 15% of the points have been misidentified, but the observations are not systematically biased. The land use distributions were constructed as follows. First, the total number of observations belonging to each land use class was recorded. Each data point, at which a particular land use was observed, was counted as an observation belonging to that class. Then, the total numbers were corrected for systematic misidentification bias. The misidentification probabilities were used to estimate the most likely share of misidentified points among the observations allocated to each class. These points were then reallocated to the correct classes in (the most likely) proportions
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indicated by the probabilities. After making the corrections, 95 and 99% confidence intervals for each class’s landscape share were calculated based on a binomial distribution, in which the number of classes was reduced to two (the particular land use of interest vs. all other land uses). An analysis of land use conversions between years was conducted using a 8 8 = 64 matrix. The initial land use was indicated by rows and the final land use was indicated by columns (eight classes in both years). Each of the matrix’s elements thus depicted a particular type of land use change (e.g. agricultural land converted to mixed forest). The number of observations in each of the 64 transition classes was used to test the significance of the type of land use change against a null hypothesis. For transition classes outside the diagonal of the matrix, indicating land use change, the null hypothesis was that the change was fully attributable to observation errors (i.e. the misidentification of the land use at data points at either, or both, end(s) of the time interval). The null hypothesis was rejected if the probability of all observations being wrong was less than 0.05. For classes on the diagonal of the matrix, indicating persistence in land use, the null hypothesis was that the class had remained unchanged throughout the interval. Significant persistence was observed if the null hypothesis could not be rejected. A full description of the testing procedure is provided in the supplement. It is important to note that the procedure can be used to assess the existence of particular types of land use change, but not their magnitude. We can only conclude whether land has been transferred from one class to another. We cannot accurately estimate how much. Therefore, the landscape shares of the transition classes referred to in the analysis should not be interpreted as accurate estimates. 3. Results 3.1. Changes during the Soviet period During the Soviet era, forests and built-up areas expanded, while open peatland and agricultural areas retreated (Fig. 2). The share of forests increased from 72 to 77%. This expansion came at the expense of open peatland and agricultural areas, whose landscape shares declined significantly (Fig. 3). The landscape shares of mixed forests and spruce forests rose sharply, and the share of low tree cover stands declined (Fig. 3). The share of agriculture halved from 18 to 9%. Area was lost to forests and settlements (Fig. 4). Most low tree cover stands from 1939 had matured by 1987 and thus transitioned into other forest classes, especially mixed forest
Fig. 2. Landscape shares of the land cover classes in 1939, 1987 and 2005, with all forests as a single class.
Fig. 3. Landscape shares of the land cover classes in 1939, 1987 and 2005. Forests in five separate classes.
and pine forest (Fig. 4). As low tree cover stands are usually temporary (young stands gain tree cover as they mature), the decline in the landscape share of low tree cover stands suggests a lower rate of logging and afforestation in the 1980s than in the 1930s. The transition patterns between forest classes provide evidence of the important role of natural succession in transforming the landscape (Fig. 4). As expected, deciduous forests transformed into mixed forest and mixed forests transformed into spruce forest. The counter-evidence of pine or spruce forests reverting back to deciduous forests or low tree cover stands was too weak to indicate significant human interference. 3.2. Changes after the Soviet period The changes observed after 1987 were more modest, partly due to the shorter length of the observation interval (18 years from 1987 to 2005, vs. 48 years from 1939 to 1987). Total forest area increased (Fig. 2), while peatlands and agricultural area remained fairly stable (Fig. 3). The extent of built-up and other bare areas declined sharply (Fig. 3), but this change may be (to some extent) attributable to classification errors. Their share in 1987 is likely to have been overestimated, due to a seasonal difference between the Landsat images. The 1987 image was taken in May. The 2005 image was taken in June, when the vegetation is greener. Thus, some areas appearing ‘‘bare’’ in 1987 may have been seen as ‘‘green’’ in 2005, even without actual land cover change. Furthermore, with deciduous foliage camouflaging buildings in 2005, some built-up areas may have been classified as forest despite seasonal corrections. The latter may explain a part of the observed expansion of mixed and deciduous forests into built-up areas (Fig. 4), to which some of the increase in total forest area is attributed. The actual increase in total forest area is thus likely to have been less than the 5% points indicated in Fig. 2. Transitions within the forest mosaic continued to exhibit patterns of natural succession (Fig. 4). Deciduous forest transformed into mixed forest and mixed forest into spruce and pine forests. However, there were some indications of selective logging and clear-cuts. The reversions of spruce forests to mixed forests and mixed forests to deciduous forests appear to have strengthened in the post-Soviet years. Such an effect could have been generated by the selective logging of coniferous species. The revitalization of clear cuts since the 1980s is suggested by the observed flow of land from pine forests into low tree cover stands. A similar flow was not observed during the Soviet era.
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Fig. 4. Land use transitions in the timespans 1939–1987, 1987–2005 and 1939–2005.
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3.3. Changes on the Isthmus compared to contemporary development in Finland While forests on the Isthmus expanded and agricultural area declined sharply during the Soviet era, both remained relatively stable in neighbouring Finland (Fig. 5). The landscape share of agricultural land in the Kymi province remained above national average due to its Southern location that is favourable for agriculture. 4. Discussion 4.1. Agricultural Abandonment Agricultural abandonment on the Isthmus occurred during the Soviet era. Agricultural area did not change much after 1987. On the one hand, the strong abandonment in the Soviet years contrasts with the stable trend in Finland. In the (former) Kymi province across the border, the landscape share of agriculture in fact increased from 13.5% in 1946 to 14.5% in 1987 (Fig. 5). On the other hand, the relatively stable trend on the Isthmus after 1987 contrasts with the contemporary development further south in European Russia and Ukraine, where agricultural area decreased strongly after the collapse of the Soviet Union (Schierhorn et al., 2013). Why did the land use histories of Southern Finland and the Isthmus diverge so strongly after the territory was ceded and why was not there a strong wave of agricultural abandonment in the post-Soviet years? Several factors explain the agricultural decline in the Soviet years. During WWII, the Finnish population of the Isthmus was evacuated to Finland. The farms were left unattended. The area was repopulated by settlers from elsewhere within the Soviet Union, but population growth was slow and predominantly urban (Isachenko, 2004). As a result, the farming tradition in the region was disrupted and only a fraction of the vacated farmland was incorporated into the Sovkhozes and Kolkhozes established in the area during the following decades (Isachenko, 2004). The slow rural repopulation went hand in hand with the state policies of agricultural development. For instance, the centrally planned efforts to expand farmland and boost agricultural yields to tackle USSR’s deficit in cereal production in its last decades, primarily targeted other more productive regions in the Soviet Union (Johnson, 1982; Jones et al., 1996). The Isthmus was inferior due to its northern location, and the repopulation of the countryside was not a priority. Meanwhile in Finland, agriculture continued uninterrupted. A broad system of import tariffs and agricultural subsidies was established in the decades following WWII to compensate for the climatic disadvantage and shelter domestic production from
international competition. The policies were motivated by food security and the desire to maintain farmers’ incomes. By 2005, agricultural subsidies made up over 40% of all agricultural turnover (MTT, 2014). The level of government support for agriculture was substantially higher in Finland than in Russia (OECD, 2013). The early agricultural abandonment on the Isthmus explains its lack in post-Soviet years. Government support for agriculture in Russia plummeted in the early 1990s (OECD, 2013). The denationalization of agriculture and the decrease in government support led to a decline in agricultural area in European Russia (Schierhorn et al., 2013) and elsewhere in the former Soviet Union (Baumann et al., 2011; Vanwanbeke et al., 2012; Schierhorn et al., 2013). As agriculture on the Isthmus had already contracted half a century earlier, a similarly strong trend was not observed in the 1990s. However, due to the rather loose definition of agricultural area applied in our classification, it is possible that some agricultural abandonment may have gone unobserved, given that the abandoned areas had not yet gained tree cover by 2005. 4.2. Change in forest area and structure Forests expanded as agriculture on the Isthmus retreated. The drainage of bogs during the Soviet years also contributed to the increase in forest cover (Isachenko, 2004). The expansion of forests onto open peatland is seen in Fig. 4. Apart from drainage, active forest management was not practiced. Forests in the Soviet Union were zoned into three groups: (i) forests with protective and social functions, (ii) forests with multiple functions, and (iii) exploitable forests (Nordberg et al., 2013). When the land was claimed by the state, approximately 80% of the forests in Vyborg Karelia were classified into the first category (Isachenko, 2004). Officially, the forests were reserved for recreational use for the people of St. Petersburg (Isachenko, 2004). However, given the proximity to the border, keeping the Isthmus forested may have also had a military strategic purpose in the city’s defence. Either way, industrial treecutting was forbidden. Where clear-cuts were not banned, harvesting and management resembled the practices observed elsewhere in boreal North-West Russia: harvests were clear-cuts, regeneration was accomplished through natural seeding from adjacent forests, and active forest management between regeneration and clear cut was not practiced (Nordberg et al., 2013). Due to the logging ban and the management regime, the change in the forest mosaic was mostly driven by natural succession. In boreal forests on fertile soils, deciduous species are usually the first to arrive after disturbance. The canopy closes as the trees grow. The amount of light in the understory is reduced. This favours the shade-tolerant Norway spruce. Gradually, the deciduous canopy is replaced by a coniferous one as spruce trees fill tree
Fig. 5. The landscape share of agriculture (panel A) and forests (panel B) in Finland and the Isthmus of Karelia.
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fall gaps. On less fertile and sandy soils, Scots pine can also be a pioneer tree leading to relatively pure pine stands. As boreal coniferous species have a longer lifespan than their deciduous counterparts, eventually all old-growth forest become coniferous. The natural disturbance dynamics in boreal forests are reviewed in more detail in, e.g. Gromtsev (2002) and Angelstam and Kuuluviainen (2004). The landscape-level forest dynamics observed during the Soviet times match the described pattern of natural succession. Areas that were farmland in 1939, but were later abandoned, were mostly covered by mixed or deciduous forests in 1987. The Deciduous forests of 1939 gradually transformed into mixed forests and mixed forests became coniferous. The same pattern continued after 1987, but there was some evidence of selective logging and clearcuts, implied by observed reversions to earlier successional stages. Logging on the Russian side of the international border restarted after the collapse of the Soviet Union, partly driven by Finnish wood imports (Sta˚hls et al., 2010). The impact of the harvests on the dispersal of old-growth forest species to Fennoscandia has been a point of concern (Mayer et al., 2005, 2006), as logging in the ecological corridor may negatively impact the size and genetic diversity of Fennoscandian populations (Mayer et al., 2005). However, old-growth forests remain more common on the Isthmus than in Southern Finland (Muukkonen et al., 2009). Also, the forest landscape on the Isthmus is less fragmented than in Finland (Muukkonen et al., 2009). The road network is sparse and, unlike in Finland, the forests are not divided into small estates, as they remain government-owned (Trubin, 2000). We find that, despite resumed logging, the forest landscape has largely followed patterns of natural succession in the postSoviet years. We therefore do not find any strong evidence in support of an increased threat to the dispersal of old growth forest species in particular. The overall biodiversity impacts of the longterm landscape change and the recent resumption of logging cannot be comprehensively assessed in the scope of this study. The forested landscape on the Isthmus has matured considerably since 1939. All species do not thrive in old-growth forests. Policies that promote the accumulation of biomass and carbon in forests may not be optimal for species conservation (e.g. Nelson et al., 2008). The biodiversity impacts of the observed changes may thus be multifaceted. 4.3. Changes in forest biomass and carbon A large amount of carbon has been stored by the expanding forest biomass on abandoned agricultural lands in the former Soviet Union since 1990 (Schierhorn et al., 2013). On the Isthmus, the relative accumulation of carbon (per area) must have been even greater as it has persisted for half a century longer. For comparison, Kauppi et al. (2010) reported the doubling of the forest biomass carbon stock in an area in Southern Finland between 1912 and 2005. Two factors suggest an even greater relative accumulation of carbon reserves on the Isthmus. First, notable agricultural abandonment took place on the Isthmus after WWII. The abandoned land turned into forest. In southern Finland forest area changed little. Thus, forests on the Isthmus gained carbon via areal expansion, while forests in Finland did not. Second, also the accumulation of carbon in preexisting forests was greater on the Isthmus. By the early 20th century forest biomass stocks all over Southern Finland had been depleted due to over-harvesting and suboptimal selective harvests (Siiskonen, 2007; Kauppi et al., 2010). Over the following century, biomass carbon in Finnish forests doubled as a sideeffect of management changes (Liski et al., 2006; Kauppi et al., 2010; Siiskonen, 2007): the management regime shifted from
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selective cutting to yield-maximizing, and later profit-maximizing, even-aged forestry. On the Isthmus, however, harvests nearly stopped for half a century due to the Soviet era logging ban. Unlike in Finland, stands were not cut when they reached the end of their economically optimal rotation. Instead they continued to grow and accumulate timber. Therefore, higher average per-hectare growing stock volumes (of merchantable timber) have been measured in all forest types on the Russian side of the border (Muukkonen et al., 2009). The change in growing stock can be used as a rough proxy for the change in biomass carbon (e.g. Kauppi et al., 2006). The average growing stock per hectare in the Vyborg province (including Kymi and the Isthmus of Karelia), as measured in the Finnish National Forest Inventory 1936–1938 was 71 m3 ha 1 (Ilvessalo, 1943), which implies a biomass carbon stock3 of roughly 26 tC ha 1. The average growing stock per hectare in the Leningrad Region4, of which the Isthmus is currently a part of, was 175 m3 ha 1 (65 tC ha 1) in the mid-1990s (Pa¨ivinen et al., 1999). Thus, growing stock and carbon increased 2.4-fold. The corresponding figure for Southern Finland in 2009–2012 was lower: 138 m3 ha 1 (51 tC ha 1) (Ylitalo, 2013), implying a 1.9-fold increase since the 1930s. Accounting for the expansion of forest area, the forest biomass carbon stock on the Isthmus may have expanded as much as threefold since 1939. Land use changes, such as afforestation and deforestation, affect soil carbon. The stock decreases when boreal forest is cleared for agriculture (Karhu et al., 2011). The depletion is caused by a reduction in soil carbon inputs. After an initial emission pulse, a weak net flux into the atmosphere may be sustained for a long time after clearance (Heikkinen et al., 2013). The soil carbon stock may also initially decrease when agricultural land is afforested (Karhu et al., 2011). This is also due to a reduction in soil carbon inputs, as young forests produce little dead wood and litter. However, the inputs increase and soil carbon stocks accumulate with stand age (Peltoniemi et al., 2004; Karhu et al., 2011). The afforestation and maturing of forests observed on the Isthmus suggest increased soil carbon storage. However, a notable amount of carbon may have been released due to the drainage of bogs. We therefore cannot conclude the direction of the net change in soil carbon. 5. Conclusions Large-scale agricultural abandonment followed the Finnish cession of the western part of the Isthmus to the Soviet Union during WWII. Former agricultural areas gained forest cover. Meanwhile, the utilization of the region’s forest resources ceased due to a logging ban. The abstinence from logging enabled the region’s previously degraded forests to recover according to patterns of natural succession. Old-growth forests became more abundant and the forest biomass carbon stock accumulated. The accumulation was stronger than in neighbouring Finland, where the increase in forest biomass was achieved by intensifying agriculture and forestry, rather than by abandoning land and restraining from logging. The two divergent development paths 3 Assuming a conversion factor 0.3715. The conversion factor includes the conversion of growing stock volume to total tree biomass, the conversion of biomass into dry matter and accounting for the dry-matter share of carbon. 0.3715 is the conversion factor for Norway spruce (Tomppo, 2000). The respective factors for Scots pine and deciduous species are 0.3091 and 0.4152, respectively. The factor for spruce was applied in the calculations as it most closely reflects the landscape level average. 4 Calculated for Forest Fund area administered by the Forest Committee. The total area of the Forest Fund is 57 890 km2. A majority of it (45 770 km2) is administered by the Committee. Non-forest land (open peatlands, water bodies, hay fields, roads, etc.) makes up 9281 km2 of the administered area. All other area is considered forest in our calculations. The total growing stock volume in these forests is 639.5 M m3, which implies 175 m3 ha 1.
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initiating from a similar state provide an empirical example of forest biomass recovery potentials under optimized intensive land management and nearly full abandonment. Acknowledgements We thank Terhi Takala for her work on the land cover classifications and Julia Luotola for collecting the 1939 data points. We acknowledge the contributions of Audrey Mayer, Pa¨ivi Tikka and Leena Vihermaa to The Boomerang project (2006-2009). The project was supported by Grant No. 109942 from the Academy of Finland. A.R. acknowledges financial support from the Finnish Cultural Foundation.
Appendix A. Supplementary data A methodological supplement associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. envsci.2015.09.011. References Angelstam, P., Kuuluviainen, T., 2004. Boreal forest disturbance regimes, successional dynamics and landscape structures – a European perspective. Ecol. Bull. 51, 117–136. Baumann, M., Kuemmerle, T., Elbakidze, M., Ozdogan, M., Radeloff, V.C., Keuler, N.S., Prishchepov, A.V., Kruhlov, I., Hostert, P., 2011. Patterns and drivers of post-socialist farmland abandonement in Western Ukraine. Land Use Policy 28, 552–562. Central Statistical Office of Finland, 1948. Statistical Yearbook of Finland 1946– 1947. Central Statistical Office of Finland, Helsinki 283 pp. Central Statistical Office of Finland, 1990. Statistical Year Book of Finland 1990. Central Statistical Office of Finland, Helsinki 586 pp. Grainger, A., 1995. The forest transition: an alternative approach. Area 27, 242– 251. Gromtsev, A., 2002. Natural disturbance dynamics in the boreal forests of European Russia: a review. Silva Fenn. 36, 41–55. Hardin, G., 1968. The tragedy of the commons. Science 162, 1243–1248. Heikkinen, J., Ketoja, E., Nuutinen, V., Regina, K., 2013. Declining trend of carbon in Finnish cropland soils in 1974–2009. Glob. Change Biol. 19, 1456–1469. Ilvessalo, Y., 1943. The forest resources and the condition of the forests of Finland. The second national forest survey. Commun. Inst. For. Fenn. 30, Valtioneuvoston Kirjapaino, Helsinki 446 p. Isachenko, G.A., 2004. The landscape of the Karelian Isthmus and its imagery since 1944. Fennia 182, 47–59. Johnson, D.G., 1982. Agriculture in the centrally planned economies. Am. J. Agric. Econ. 64, 845–853. Jones, J.R., Li, S.L., Devadoss, S., Fedane, C.J., 1996. The former Soviet Union and the world wheat economy. Am. J. Agric. Econ. 78, 869–878. Karhu, K., Wall, A., Vanhala, P., Liski, J., Esala, M., Regina, K., 2011. Effects of afforestation and deforestation on boreal soil carbon stocks – comparison of measured C stocks with Yasso07 model results. Geoderma 164, 33–45. Kauppi, P.E., Ausubel, J.H., Fang, J., Mather, A.S., Sedjo, R.A., Waggoner, P.E., 2006. Returning forests analyzed with the forest identity. Proc. Natl. Acad. Sci. U. S. A. 103, 17574–17579. Kauppi, P.E., Rautiainen, A., Korhonen, K.T., Lehtonen, A., Liski, J., No¨jd, P., Tuominen, S., Haakana, M., Virtanen, T., 2010. Changing stock of biomass carbon in a boreal forest over 93 years. For. Ecol. Manag. 259, 1239–1244. Kuuluvainen, T., Tahvonen, O., Aakala, T., 2012. Even-aged and uneven-aged forest management in boreal Fennoscandia: a review. Ambio 41, 720–737. Linde´n, H., Danilov, P.I., Gromtsev, A.N., Helle, P., Ivanter, E.V., Kurhinen, J., 2000. Large-scale forest corridors to connect the taiga fauna to Fennoscandia. Wildl. Biol. 6, 179–188.
Liski, J., Lehtonen, A., Palosuo, T., Peltoniemi, M., Eggers, T., Muukkonen, P., Ma¨kipa¨a¨, R., 2006. Carbon accumulation in Finland’s forests 1922–2004 – an estimate obtained by combination of forest inventory data with modelling of biomass, litter and soil. Ann. For. Sci. 63, 687–697. Mather, A.S., 1992. The forest transition. Area 24, 367–379. Mayer, A.L., Kauppi, P.E., Angelstam, P.K., Zhang, Y., Tikka, P.M., 2005. Importing timber, exporting ecological impact. Science 308, 359–360. Mayer, A.L., Kauppi, P.E., Tikka, P.M., Angelstam, P.K., 2006. Conservation implications of exporting domestic wood harvest to neighbouring countries. Environ. Sci. Policy 9, 228–236. Meyfroidt, P., Lambin, E.F., 2011. Global forest transition: prospects for an end to deforestation. Ann. Rev. Environ. Resour. 36, 343–371. MTT, 2014. Income Statement: Profitability Bookkeeping Results of MTT Economic Research. www.mtt.fi/economydoctor (retrieved 19.3.2014). Muukkonen, P., Takala, T., Virtanen, T., 2009. Differences in the forest landscape structure along the Finnish-Russian border in Southern Karelia. Scand. J. For. Res. 24, 140–148. Myllyntaus, M., Mattila, M., 2002. Decline or increase? The standing timber stock in Finland, 1800–1997. Ecol. Econ. 41, 271–288. Nelson, E., Polasky, S., Lewis, D.J., Plantinga, A.J., Lonsdorf, E., White, D., Bael, D., Lawler, J.J., 2008. Efficiency of incentives to jointly increase carbon sequestration and species conservation on a landscape. Proc. Natl. Acad. Sci. U. S. A. 105 (28), 9471–9476. Nordberg, M., Angelstam, P., Elbakidze, M., Axelsson, R., 2013. From logging frontier towards sustainable forest management: experiences from boreal regions of North-West Russia and North Sweden. Scand. J. For. Res. 28, 797– 810. OECD (Organization for Economic Co-operation and Development), 2013. Agricultural Policies and Support: Producer and Consumer Support Estimates Database. www.oecd.org/agriculture/agricultural-policies/ producerandconsumersupportestimatesdatabase.htm. Ostrom, E., 1990. Governing the Commons. Cambridge University Press 280 pp. Pa¨ivinen, R., Nabuurs, G.-J., Lioubimov, A.V., Kuusela, K., 1999. The State Utilisation and Possible Future Developments of Leningrad Region Forests. EFI Working Paper 18. European Forest Institute. Peltoniemi, M., Ma¨kipa¨a¨, R., Liski, J., Tamminen, P., 2004. Changes in soil carbon with stand age – an evaluation of modelling method with empirical data. Glob. Change Biol. 10, 2078–2091. Schierhorn, F., Mu¨ller, D., Beringer, T., Prishchepov, A.V., Kuemmerle, T., Balmann, A., 2013. Post-Soviet cropland abandonement and carbon sequestration in European Russia, Ukraine and Belarus. Glob. Biochem. Cycles 27, 1–11. Siiskonen, H., 2007. The conflict between traditional and scientific forest management in 20th century Finland. For. Ecol. Manag. 249, 125–133. Simula, M., 2009. Towards Defining Forest Degradation: Comparative Analysis of Existing Definitions. Forest Resources Assessment Working Paper 154. UNFAO, Rome. Sta˚hls, M.H., Mayer, A.L., Tikka, P.M., Kauppi, P.E., 2010. Disparate geography of consumption, production and environmental impacts: forest products in Finland 1991–2007. J. Ind. Ecol. 14, 576–585. Statistics Finland, 2005. Statistical Yearbook of Finland 2005. Statistics Finland, Helsinki 702 pp. Takala, T., 2007. Metsa¨maiseman rakenteen muutokset Karjalankannaksella (Changes of structure of forest landscapes on Karelian Isthmus). (Master’sthesis) Department of Biological and Environmental Sciences, University of Helsinki (in Finnish). Tomppo, E., 2000. National forest inventory of Finland and its role estimating the carbon balance of forests. Biotechnol. Agron. Soc. Environ. 4, 281–284. Trubin, D.V., 2000. Forest management in the North of the European Russia. In: Ma¨lko¨nen, E., Babich, N.A., Krutov, V.I., Markova, I.A. (Eds.), Forest Regeneration in the Northern Parts of Europe, 790. Finnish Forest Research Institute Research Papers, pp. 17–22. Vanwanbeke, S.O., Meyfroidt, P., Nikodemus, O., 2012. From USSR to EU: 20 years of rural landscape changes in Vidzeme, Latvia. Landsc. Urban Plan. 105, 241–249. Wallenius, T., Niskanen, L., Virtanen, T., Hottola, J., Brumelis, G., Angervuori, A., Julkunen, J., Pihlstro¨m, M., 2010. Loss of habitats, naturalness and species diversity in Eurasian forest landscapes. Ecol. Indic. 10, 1093–1101. Ylitalo, E. (Ed.), 2010. Finnish Statistical Yearbook of Forestry. Finnish Forest Research Institute, Vantaa, 472 pp. Ylitalo, E. (Ed.), 2013. Finnish Statistical Yearbook of Forestry. Finnish Forest Research Institute, Vantaa, 448 pp.