Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem services in a watershed in Japan

Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem services in a watershed in Japan Kikuko Shoyama n, Yoshiki Yamagata Center for Global Environmental Research, National Institute for Environmental Studies, Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan a r t i c l e i n f o Article history: Received 26 October 2013 Received in revised form 22 January 2014 Accepted 2 February 2014 Keywords: Scenario analysis Land-use and cover change Rural landscape Ecosystem service trade-offs a b s t r a c t Potential conflicts between biodiversity conservation and climate-change mitigation can result in trade- offs in multiple-use land management. This study aimed to detect possible changes in land-use patterns in response to biodiversity conservation and climate-change mitigation measures and the effects on ecosystem services across a watershed. We analyzed land-cover change based on past and future scenarios in the rural Kushiro watershed in northern Japan. The analysis showed that if no conservation measures were implemented and the timber and agricultural industry remained small until 2060, supporting and provisioning services would decline due to less land management. Although biodiversity conservation measures are predicted to improve three of the ecosystem services that we studied, carbon sequestration and timber production would be improved to a greater degree by climate-change mitigation measures. The greatest land-cover changes are likely to occur in the unprotected area around the middle reaches of the Kushiro River, and such changes could affect the provision of ecosystem services throughout the entire watershed. Thus, our findings indicate that landuse decisions for the middle reaches of the watershed are particularly important for managing the integrated ecosystem services of the entire watershed for the future. & 2014 Elsevier B.V. All rights reserved. 1. Introduction In response to the trade-offs necessary in multiple-use land management, the concept of ecosystem services has been intro- duced to find synergies between nature conservation and other aspects of human welfare improvements (Tallis et al., 2009). However, the concept has not yet been used to support decision- making processes. Thus, researchers must move from devising the conceptual framework to the practical integration of ecosystem services into real decision-making (Daily and Matson, 2008). Several studies have revealed existing spatial trade-offs of ecosys- tem services, which potentially arise in land management choices that influence the provision of ecosystem services within a land- scape (Bennett and Balvanera, 2007; Bennett et al., 2009; Ruijs et al., 2013). Thus, predicting the effects of land-cover change on ecosystem services is crucial in planning (Geneletti, 2013). The United Nations Framework Convention on Climate Change and the Convention on Biological Diversity are key agreements adopted in the early 1990s. Recent studies have clearly revealed a decline in biodiversity globally, and threats and pressures on biodiversity have been increasing, particularly over the past decade (TEEB, 2010). This decline in biodiversity could have a major impact on ecosystems and human society as well as climate change (Cardinale et al., 2012; Maestre Andres et al., 2012; Nagendra et al., 2013). Therefore, efforts to conserve biodiversity need to be strengthened by measures such as integrating biodi- versity conservation into land management decision-making. Mitigating climate change is also a pressing concern for both ecosystem and biodiversity conservation. However, at the local scale, land-use changes for climate-change mitigation may pose threats to biodiversity. For instance, converting diverse natural vegetation to monoculture plantations to capture greenhouse gas emissions may adversely affect biodiversity (Secretariat of the Convention on Biological Diversity, 2009). Therefore, in practical terms, seeking synergy between biodiversity conservation and climate-change mitigation has become necessary. However, few practical strategies have been suggested in terms of applying these global needs to local situations because trade-offs among ecosys- tem services need to be clarified to support decision-making. From the perspective of rural landscape management in Japan, the underuse of natural resources is a direct driving force of environmental degradation and biodiversity loss (JSSA, 2010). Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/ecoser Ecosystem Services http://dx.doi.org/10.1016/j.ecoser.2014.02.004 2212-0416 & 2014 Elsevier B.V. All rights reserved. n Corresponding author. Tel.: þ81 298 50 2567; fax: þ81 298 50 2960. E-mail address: [email protected] (K. Shoyama). Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Depopulation and aging in rural areas have led to reductions in land management and increased land abandonment, both of which have resulted in the degradation of ecosystem services. Because the rural landscape is a key source of natural resources that benefit society, the Japanese government has strengthened measures to maintain natural resources across rural landscapes. However, it is not clear whether ecosystem service trade-offs are being recognized comprehensively in terms of ecosystem manage- ment. In this case, scenario analysis is a fundamental tool of environmental assessment to reduce future uncertainty when choosing among policy alternatives. Future scenarios are derived from different assumptions about the underlying driving forces, which are important factors that affect land management. Quan- tifying the impact of future land-use change on ecosystem services and human well-being at the local scale has been addressed in recent studies (Estoque and Murayama, 2012; Geneletti, 2013; Goldstein et al., 2012; Leh et al., 2013; Nelson et al., 2010). Here, we present a case study that explores empirically how land-use policies for biodiversity conservation and climate-change mitigation will affect the future provision of ecosystem services within a rural landscape in Japan. The effects of the land-use change on selected ecosystem services are then quantified and compared using indicators. The aim of this study is to analyze the effects of some rural landscape conservation practices on the target area. Specific objectives are two-fold: (1) to detect land- use change patterns across a watershed based on past changes and future scenarios; and (2) to quantify changes in the provision of ecosystem services caused by predicted future land-use change and then identify areas susceptible to ecosystem degradation. We first constructed land-use scenarios associated with rural land management. Next, the effects of the land-use change on the provision of selected ecosystem services were evaluated using GIS- based models. Finally, the selected indicators were compared to assess spatial ecosystem service trade-offs in the target area. Our findings can be used to support spatial natural resource planning in this rural landscape. 2. Methods 2.1. Study area The study area is the Kushiro watershed, which is located in Hokkaido Prefecture on Japan's northernmost island (Fig. 1). The total length of the Kushiro River is 154 km, and the total area of the watershed is 2510 km2. The watershed includes a population of about 200,000, located in a city, three towns, and a village. By 2050, the population in the area is predicted to decline by 55% from that in 2005; in particular, the working-age population (age 15–64) is predicted to decline from 63% to 42% of the total and the aged population (age 64þ) is predicted to increase from 23% to 51% (Fig. 2). Population decline and aging are the greatest impediments to the continuation of local communities and rural landscape management. The area is considered to have a high degree of natural capital and includes two national parks, Akan Forest National Park in the upper reaches and Kushiro Wetland National Park from the middle to lower reaches, with the latter designated as a Ramsar Conven- tion site in 1980. In the past few decades, however, most of the Fig. 1. Map of the Kushiro watershed showing land-use changes that occurred between 1977 and 2011. 0 20 40 60 80 100 2005 2010 2020 2030 2040 2050 Po pu la tio n ch an ge (% ) Total population Child population (Age 0 - 14) Working-age population (Age 15 - 64) Aged population (Age 64 + ) Fig. 2. Predicted population changes in the Kushiro watershed between 2005 and 2050 (data source: National Institute of Population and Social Security Research). K. Shoyama, Y. Yamagata / Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎e2 Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i middle-reaches area has been converted to pasture land and plantation forests for rural development. The current land cover includes natural forest, managed forest, wetland, and agricultural land. Natural forest covers about 30% of the area and is distributed mainly in the upper reaches. The dominant vegetation is broadleaved forest and conifer – broad- leaved mixed forest, both of which provide rich habitat for wild animals. Managed forest covers about 20% of the area and is distributed in the upper and middle reaches. The managed forest is mostly monocultural plantations of larch (Larix kaempferi) and fir (Abies sachalinensis). These plantations have been managed to produce timber for the past few decades. Currently, there is a need for this forest to provide woody biomass for bioenergy and contribute to carbon sequestration through the growth of planted trees. Agricultural land covers 20% of the area and is mostly pasture land. Pastures are concentrated in the middle reaches and are associated with small villages and settlements. Dairy farming is an important industry in the watershed. Wetlands account for less than 10% of the area and are located from the middle to lower reaches. The area provides rich habitat for wild animals and is highly valued for its biodiversity assets, including endangered species listed on the International Union for Conser- vation of Nature Red List. Most of the area is protected as a national park designated by the Ramsar Convention and is considered an important tourism resource. 2.2. Land-use change and scenario analysis 2.2.1. Detecting past land-use change To detect the trends in past land-use change, maps of land use in 1977 and 2011 were prepared. The land-cover data were developed by using satellite images and aerial photographs that were validated with field observations (data source: Japan Agency for Marine-Earth Science and Technology and the Ministry of the Environment Japan, unpublished). The original data had 12 land- use classes, which we condensed into seven classes: (1) natural forest; (2) managed forest; (3) agricultural land (mainly pasture land); (4) shrub/grassland; (5) wetland; (6) residential area; and (7) others (e.g., water body). A transition matrix was constructed to show the amount of change in each land-use between 1977 and 2011. The spatial analyses were carried out using ArcGIS ver. 10.1 with a spatial resolution of 100 m. Change was detected by using the cross-tabulation method (Pontius et al., 2004). The cross-tabulation matrices consist of seven columns displaying the land-use types at time t1 and seven rows displaying the land-use types at time t2. Entries Pij indicate the proportion of the landscape that transitions from land-use type i at time t1 to land-use type j at time t2, where ∑i∑jPij ¼ 1. Entries along the diagonal Pii indicate the persistence of land-use type i. The total of column Piþ is the proportion of land-use type i at time t1, and the total of row Pþ j is the proportion of land-use type j at time t2. The gross gain and loss of land-use type j are calculated as Pþ j�Pjj and Pjþ �Pjj respectively. The concepts of systematic change (Pontius et al., 2004) were applied to the transition matrices to identify the significant changes in the landscape. The expected value is the amount that would be expected if the gain or loss in each land-use type were to occur randomly. The expected value in terms of gain, Gij, and that in terms of loss, Lij, were calculated as follows for ia j, Gij ¼ ðPþ j�PjjÞPiþ 1�Pjþ ð1Þ Lij ¼ ðPiþ �PiiÞPþ j 1�Pþ i ð2Þ Transitions with observed values that were larger than the expected values were identified as systematic transitions. The analysis was conducted using IDRISI Geospatial software (Clark Labs at Clark University, Worcester, MA, USA). 2.2.2. Future scenario analysis To explore the changes in future land-cover patterns, we developed three land-use scenarios: (1) the trend scenario (based on a continuation of current trends); (2) a biodiversity conserva- tion scenario; and (3) a climate-change mitigation scenario (for details, see Section 3.2.1). The Dyna-CLUE model (Verburg and Overmars, 2009) was used to project land-use transitions for each scenario from 2011 to 2060. The model was developed for the spatially explicit simulation of land-use change based on an empirical analysis of location suitability combined with the dynamic simulation of competition between the spatial and temporal dynamics of land use. It has been applied in various case studies and has been proven to simulate accurately the land- use dynamics in some regions in Asia (Castella and Verburg, 2007; Pontius et al., 2008; Trisurat et al., 2010; Verburg and Veldkamp, 2004). The following information was provided, and the model calculated the best solution by an iterative procedure. First, future land-use demand (requirement) was defined across the entire area of the watershed for each land-use type based on the specifics of each scenario. Second, conservation restrictions influen- cing the pattern of land-use change were provided as model input. These indicate areas where land-use changes are restricted through conservation policies. The areas protected as national parks were set as having spatial restrictions (the area is shown in Fig. 1). Third, the parameters of conversion elasticity for each land-use type were estimated based on the capital investment and time required for the conversion, with values ranging from 0 for easy conversion to 1 for irreversible change. High values (0.8–1) were assigned to natural and managed forests because a long period of time would be required to establish these land uses. Medium values (0.5) were given to wetland. Because agricultural land and shrub/grassland are highly dynamic land uses, a low value (0.2) was assigned. In the final step, location characteristics (location probability of each land-use type) were addressed based on the following logistic regression model: LogitðpiÞ ¼ lnðpiÞ=ð1�piÞ ¼ β0þβ1X1þβ2X2þ⋯þβnXn ð3Þ where pi is the probability of a grid cell containing land-use type k at location i in the land use in 2011. The X parameters are the driving factors including physical and social factors and the β coefficients were estimated by logistic regression. The goodness- of-fit of a logistic regression model was evaluated using the receiver operating characteristic (ROC) curves (Swets, 1988). The physical factors included elevation, slope, soil depth, and annual precipitation, which influence the distribution of natural land cover. The socioeconomic factors included distance to a stream, distance to a village, distance to a road, and population density, which influence the distribution of human-dominated land covers. Elevation, slope, soil depth, and distances to a stream, village, and road were calculated by using topographic maps from the National Land Numerical Information (National Land Information Division, National and Regional Policy Bureau: http://nlftp.mlit.go.jp/ksj-e). The average annual precipitation (1976–2010) for the region was obtained from the meteorological observation database of the Japan Meteorological Agency. Population was obtained from national census data in 2010 (Statistics Bureau of Japan: http:// www.stat.go.jp/english/data/kokusei/). For each grid cell, the Dyna-CLUE model calculated the total probability of each land-use type based on the location suitability derived from the logit model and the conversion elasticity. In those cases where no constraints were specified for the conversion, a grid K. Shoyama, Y. Yamagata / Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎ e3 Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i cell was allocated to the land use with given highest total suitability. By using an iterative process (Verburg and Overmars, 2009), the competitive strength of each land-use type was adapted until the total allocated grid cells of each land-use type equaled the total land required by each land-use scenario. 2.3. Mapping ecosystem service provisioning Using the maps of future land-use patterns based on the Dyna- CLUE simulation, indicators were calculated to assess the change in ecosystem service provisioning with land-use changes. Four indicators were selected based on the characteristics of the study area: (1) habitat quality index; (2) carbon sequestration; (3) timber production; and (4) water yield. These ecosystem services repre- sent different categories, namely supporting (habitat for target species), regulating (carbon sequestration), and provisioning (tim- ber production, water yield) services. The selected indicators were calculated across the three land-use scenarios using the 2011 land- use map, which served as a baseline. The calculation was per- formed using a GIS-based model, the Integrated Valuation of Ecosystem Services and Tradeoffs tool (InVEST) (Tallis et al., 2011). The habitat quality index indicates how well the grid cell can support wildlife and natural vegetation over time. The index reflects the assumption that areas with high-quality habitat would better support biodiversity at multiple scales. The habitat quality index is calculated based on four factors: (1) relative impact of each threat; (2) relative sensitivity of each habitat type to each threat; (3) dis- tance between the habitat and threats; and (4) degree to which the land is protected (Nelson et al., 2010; Tallis et al., 2011). We defined the threats as urban development, residential areas, and paved roads, which represent anthropogenic drivers in the landscape. The endangered birds were set as target species. Carbon storage within terrestrial ecosystems has the effect of removing carbon dioxide from the atmosphere, which can help to mitigate climate change. Terrestrial ecosystems currently store four times more carbon than that in the atmosphere (Lal, 2004), and land-use and cover change due to development and timber harvesting releases substantial amounts of stored carbon. Thus, the carbon balance within a landscape must be assessed to evaluate the ecosystem service of climate regulation. The amount of carbon sequestered within the landscape during the simulation period was calculated by using maps of land-use types and data on stocks in four carbon pools: aboveground biomass, belowground biomass, soil, and dead organic matter. Timber production is commonly used to assess the productive capacity of forests. The timber production model estimates the volume of harvested timber from managed plantations based on harvest level and cycle. The information from harvest plans by the Forest Administration Department was used to estimate the volume of harvested timber. Water yield is defined as the amount of water runoff from the landscape, and its relative volume affects the quality of life of local residents. The water yield model calculates the average annual water yield in each grid cell using average annual precipitation, annual reference evapotranspiration, soil depth, plant-available water content, plant root depth, and land-use characteristics. Annual reference evapotranspiration was obtained from the FAO GeoNetwork database (http://www.fao.org/geonetwork/srv/en/). 3. Results 3.1. Past land-use change The proportions of each land use in 1977 and 2011 are shown in Table 1. Natural forest was the dominant land use in each year, and its proportion declined from about 46% in 1977 to 38% in 2011. Managed forest increased from about 10% to 17%. Agricultural land increased from about 15% to 19%. Wetland decreased from about 9% to 7%. Shrub/grassland and residential areas increased only slightly, by o0.1% in both cases. Three types of interclass transitions are possible: gain, loss, and a swap (Table 2). Swap refers to a situation in which a given quantity of loss (gain) at one grid cell is accompanied by the same quantity of gain (loss) at another grid cell. Thus, whereas net change is attributable to a change in quantity, swap change is attributable to a change in location. Most land-use change in this area was attributable to change in location. The total landscape change due to location was about 72%, whereas the total landscape change due to quantity was only 27% of the total change. This indicates that land-use change in this area is characterized by change in location between land-use categories. Natural forest, managed forest, agricultural land, shrub/grassland, and residential area experienced more swap than net change, indicating that location change occurred more than quantity change. Only wet- land experienced more loss than gain (except “others”), resulting in a net change of 2.43% compared to a swap of 1.41% of the landscape. The land-use transitions occurring in the landscape are sum- marized in Table 3. Fourteen transitions were identified as sys- tematic (indicated as bold in Table 3), including mutual transitions between natural forest and managed forest; mutual transitions between shrub/grassland and natural forest, managed forest, agricultural land, wetland, and residential area; and mutual transitions between agricultural land and residential area. These trends in past land-use change imply that natural forest decreased as a result of conversion to managed forest and shrub/grassland and that managed forest increased as a result of conversion from natural forest and shrub/grassland. Agricultural land was mainly developed from shrub/grassland and residential areas, but reverse transitions also occurred during the same period. In addition, the results indicated that shrub/grassland is easy to convert to other categories, which suggests higher elasticity. Table 1 Proportions of land uses in 1977 and 2011 (% of landscape). Land-use category 1977 2011 2011–1977 Natural forest 45.56 38.42 �7.14 Managed forest 9.87 17.41 7.54 Agricultural land 15.35 19.15 3.80 Shrub/grassland 13.71 13.74 0.03 Wetland 9.25 6.82 �2.43 Residential area 0.90 0.99 0.09 Others 5.37 3.47 �1.90 Total 100.00 100.00 Table 2 Summary of land-use changes from 1977 to 2011 (% of landscape). Land-use category Gain Loss Total change Swap Net change (1) (2) (3) (4) (5) (1)þ(2) 2�min[(1),(2)] (3)�(4) Natural forest 10.35 17.46 27.81 20.69 7.12 Managed forest 11.60 4.05 15.65 8.11 7.55 Agricultural land 7.38 3.60 10.97 7.19 3.78 Shrub/grassland 10.57 10.54 21.11 21.08 0.03 Wetland 0.71 3.14 3.84 1.41 2.43 Residential area 0.70 0.61 1.31 1.22 0.09 Others 0.04 1.94 1.98 0.08 1.90 Total 41.34 41.34 82.68 59.78 22.90 K. Shoyama, Y. Yamagata / Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎e4 Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i 3.2. Land-use projection 3.2.1. Scenario definition Based on past land use and detected change, future land-use settings were defined as follows. By setting the land cover in 2011 as the baseline (Table 1), three land-use scenarios in 2060 were developed: (1) the trend scenario; (2) a biodiversity conservation scenario; and (3) a climate-change mitigation scenario (Table 4). The trend scenario was based on the continuation of past land-use change trends, and the area of shrub/grassland was set to increase from 14% in 2011 to 20% in 2060 based on the increasing rate of farm abandonment (Ministry of Agriculture, Forestry, and Fisheries of Japan). The biodiversity conservation scenario aimed to improve the wildlife habitat of the area over the long term through nature restoration projects, the details of which were based on national environmental policy (National Strategy for the Conservation and Sustainable Use of Biological Diversity, initiated in 2010). This scenario aimed to increase natural forest cover from 38% to 46%, the same area as in 1977, and to rehabilitate the degraded wetland from 7% to 9%, the same area as in 1977. The climate-change mitigation scenario aimed to replace the low-carbon community that results from timber harvesting with new forest that would increase carbon sequestration as woody biomass is removed during timber production. The scenario aimed to increase mana- ged forest cover from 17% to 25% in a similar manner in the past few decades. 3.2.2. Location suitability Table 5 lists the significant factors that determined the suit- ability of a location for six land-use types (i.e., excluding the “others” category), as derived from the logistic regression analysis. The spatial distributions of the six land-use types were explained by the selected location factors, with ROC values indicating the goodness-of-fit of the logistic regression models. The land-use types with the highest ROC values were residential area (0.994), agricultural land (0.880), and wetland (0.885), indicating that these land-use types tend to be distributed in locations with specific characteristics. For instance, the distribution of residential areas was positively correlated with population density and precipitation and negatively correlated with elevation, slope, soil depth, and distances to the river and villages. The distribution of agricultural land was positively correlated with precipitation and elevation and negatively correlated with slope and distances to the river, villages, and roads. These results imply that human invest- ments in agricultural and residential development are restricted by these physical and social factors. The distribution of wetland is strongly restricted by physical factors such as elevation and slope, which describes the location of wetlands in low-lying areas. In contrast, the models for natural forest and managed forest showed relatively low ROC values (0.645 and 0.676, respectively), reflect- ing the lack of significance for the social factors distance to the river and population density. 3.2.3. Future land-use and cover change The simulation results for 2060 and the changes in target land uses in the intervening decades for each scenario are shown in Fig. 3. The land-cover change of the trend scenario, which targets the high rate of farmland abandonment, showed a decrease of agricultural land from about 19% to 11% (Table 6) corresponding with an increase in shrub/ grassland area. The dominant expansion of shrub/grassland was particularly apparent in the middle of the watershed (Fig. 3a). These flatlands along the river were suitable for agricultural development, whereas farmlands remote from roads and populated areas tend to be abandoned, becoming shrub/grassland due to inefficient landmanage- ment. In the biodiversity conservation scenario, which targets a high rate of natural forest and wetland restoration, these land uses were expected to recover to the same levels as those in 1977. The results showed decreases of managed forest and agricultural land from about 17% to 11% and 19% to 14%, respectively (Table 6). Natural vegetation is restricted by physical factors. For instance, the dominant expansion of natural forest occurred in the area from the middle to upper reaches (Fig. 3b). These sloping highlands with deep soil are suitable for natural forests. In contrast, the result of the climate-change mitigation scenario showed the expansion of managed forest in the same sloping highlands from the middle to upper reaches (Fig. 3c). This scenario assumed more demand for managed forest (i.e., substituting for natural revegetation in the conservation scenario), such that managed forest was expected to increase from about 17% to 25% (Table 6). Table 3 Transition of land uses from 1977 to 2011 (% of landscape). Land use in 1977 Land use in 2011 Natural forest Managed forest Agricultural land Shrub/grassland Wetland Residential area Others Total Natural forest 28.02 8.15 3.35 5.53 0.34 0.05 0.02 45.46 Managed forest 2.79 5.80 0.53 0.72 0.00 0.01 0.00 9.85 Agricultural land 1.24 0.36 11.75 1.86 0.01 0.12 0.00 15.34 Shrub/grassland 5.38 2.62 2.07 3.16 0.28 0.16 0.01 13.68 Wetland 0.33 0.11 0.57 1.87 6.11 0.25 0.01 9.25 Residential area 0.08 0.05 0.31 0.16 0.00 0.29 0.00 0.89 Others 0.51 0.29 0.54 0.41 0.07 0.11 3.43 5.36 Total 38.35 17.38 19.12 13.71 6.81 0.99 3.47 99.83 Note: Bold indicates a systematic transition. Table 4 Determined land demand in the Kushiro watershed for each scenarios in 2060. Descriptions Trend scenario Biodiversity conservation scenario Climate-change mitigation scenario Local economy Stagnant agricultural industry with smaller working population Development of tourism industry with biodiversity conservation Development of forest industry with renewable energy Human population Decrease and aging in population Maintain population Maintain population Land-use policies Continue land-use change of recent decades Restoration of natural forest and wetland Establishment of forest plantations Target land-cover change Shrub/grassland from 14% to 20% Natural forest from 38% to 46% Managed forest from 17% to 25% Wetland from 7% to 9% K. Shoyama, Y. Yamagata / Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎ e5 Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i Two additional aims of the conservation and climate-change mitigation scenarios are the development of local industries and maintaining population in local communities (Table 4). The results of the trend scenario showed that small villages and settlements in the middle of the watershed could disappear by 2060, although total residential area was predicted to increase slightly from 1.0% to 1.4% (Table 6, Fig. 3a). In contrast, the land-change patterns of the conservation and climate-change mitigation scenarios showed that these small residential areas could remain intact (Fig. 3b and c). Table 5 Regression coefficients of the significant factors (po0.1) determining location suitability for the land-use types. Variable Natural forest Managed forest Agricultural land Shrub/grassland Wetland Residential area Physical factors Elevation 0.00257 0.00234 0.00067 0.00183 �0.02039 �0.00291 Slope 0.11510 �0.01114 �0.03877 �0.00849 �0.08866 �0.04149 Soil depth 0.00020 0.00015 �0.00073 �0.00042 �0.00044 �0.01038 Precipitation �0.00003 �0.00016 0.00041 0.00046 0.00075 0.00251 Social factors Distance to river n.s. n.s. �0.00069 �0.00017 �0.00048 �0.00071 Distance to village �0.00008 0.00023 �0.00044 n.s. 0.00066 �0.00111 Distance to road 0.00004 0.00005 �0.00002 0.00007 n.s. n.s. Population density n.s. n.s. �0.00377 0.00095 �0.00282 0.00138 Constant �0.48510 �2.72000 1.13200 �1.52000 �1.28400 �2.49000 ROC 0.645 0.676 0.880 0.755 0.885 0.994 Note: n.s., not significant at 0.1 level. 2011 2020 2030 2040 2050 2060 Trend scenario Biodiversity scenario Climate scenario Fig. 3. Simulated patterns of change from 2011 to 2060 in the target land use in each scenario. K. Shoyama, Y. Yamagata / Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎e6 Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i 3.3. Changes in the provision of ecosystem services Fig. 4 shows changes in the provision of the four ecosystem service indicators, which were calculated by summing the value of each indicator in each cell across the landscape. The results of the trend scenario showed slight declines in habitat quality and timber production and small increases in carbon sequestration and water yield due to an increase in abandoned farmland. In contrast, the biodiversity conservation and climate-change mitigation scenarios showed some improvement in the ecosystem service provisioning. Habitat quality was dramatically improved in the biodiversity conservation scenario by expansion of natural forest, whereas carbon sequestration was greatly enhanced in the climate- change mitigation scenario by expansion of managed forest to improve timber production. However, trade-offs between ecosys- tem services were also predicted. Timber production was expected to decrease with the reduction of managed forest for biodiversity conservation, and water yield was expected to markedly decrease with expansion of managed forest for climate-change mitigation. Fig. 5 illustrates changes in the distribution of ecosystem services in 2060 for each scenario as compared to the baseline in 2011. Habitat quality, carbon sequestration, and timber production are strongly related to the size of forest patches in the sloping highlands from the middle to upper reaches (Fig. 5b and c). The spatial patterns of carbon sequestration and timber production are clearly associated with land-use changes, especially the changes in forest type and distribution that occurred in the different scenar- ios. The protected areas in the upper and lower reaches show a strong potential for improved habitat quality (Fig. 5b), whereas the area with the greatest potential for enhanced carbon sequestration and timber production is around the sloping highlands in the middle reaches (Fig. 5c). The changes in water yield are predicted in the upper and lower reaches (Fig. 5), where less land-use change will occur because these areas are protected as national parks. 4. Discussion We identified areas likely to experience ecosystem degradation or improvement due to land-use changes based on the policies that have been implemented. Land-use planning that takes into account potential areas of improvement of each ecosystem service will help to reveal synergies of multiple ecosystem services. As suggested by predicted changes in the distribution of ecosystem services (Fig. 5), biodiversity conservation should be given priority in areas surrounding the national parks in the upper and lower reaches, whereas climate-change mitigation should be prioritized in the sloping highlands outside of the protected parks. This allocation plan would optimize natural resource management across this landscape. However, the predicted changes in water yield appear to result from sub-watershed-scale phenomena rather than from land-use changes, although more process-based modeling will be necessary to confirm this conclusion. The land management in the upper reaches and sloping highland may affect the hydrology in the lower reaches. Thus, conservation and mitigation policies should be implemented based on more critical monitoring and prediction of the hydrological phenomena in this region. We focused on the provision of ecosystem services in our analysis. However, without human beneficiaries, they are not services. For instance, water provision is an ecosystem service but water consumption is a benefit (Fisher et al., 2009). Thus, we must consider the relationship between the studied indicators and the actual benefit to local communities. Habitat quality is related to characteristics of the natural landscape, and in our study area, habitat quality is especially relevant to the local tourism industry. The annual number of Table 6 Predicted land-cover change by 2060 for the three scenarios (% of landscape). Category 2011 (Baseline) Trend scenario Biodiversity conservation scenario Climate-change mitigation scenario Natural forest 38.42 39.85 46.26 35.98 Managed forest 17.41 15.85 11.37 25.24 Agricultural land 19.15 10.90 14.25 14.48 Shrub/grassland 13.74 21.76 12.61 11.89 Wetland 6.82 6.81 9.85 6.81 Residential area 0.99 1.36 2.20 2.13 Others 3.47 3.47 3.46 3.47 Total 100.00 100.00 100.00 100.00 Note: Bold indicates the target land-use in the scenario. 80,000 90,000 100,000 110,000 120,000 130,000 140,000 150,000 To ta l h ab ita t q ua lit y sc or e Trend Biodiversity Climate 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 C ar bo n se qu es tra tio n (M gC ) Trend Biodiversity Climate 0 50,000 100,000 150,000 200,000 250,000 300,000 Ti m be r p ro du ct io n (m 3 ) Trend Biodiversity Climate 1,280,000,000 1,300,000,000 1,320,000,000 1,340,000,000 1,360,000,000 1,380,000,000 W at er y ie ld (m 3 ) Trend Biodiversity Climate 2010 207020602050204020302020 2010 207020602050204020302020 2010 207020602050204020302020 2010 207020602050204020302020 Fig. 4. Trends of the four ecosystem services indicators from 2020 to 2060 according to the three scenarios. K. Shoyama, Y. Yamagata / Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎ e7 Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i visitors to the area in 2011 was more than 4.1 million people (data source: Bureau of Tourism in Hokkaido government, http://www. pref.hokkaido.lg.jp/kz/kkd/irikomi.htm, cited 2014-01-06). The tourists mainly visit for activities in mature ecosystems, especially walking and watching wildlife within and near the national parks. The direct effect of the tourist's consumption is estimated to be 16.3 billion JPY (Kushiro City, 2010). In addition, the added value effect of tourism is predicted to increase from 2.2% of total GDP in 2009 to 4.7% in 2035 (Kushiro City, 2010). This suggests that ecosystems as wild habitats are considered to be more important to support local industry than ever before. Timber production is the base of the timber industry, and the annual timber output of the watershed is 1.0 billion JPY (data source: Timber industry statistics of Hokkaido prefecture, http:// www.pref.hokkaido.lg.jp/sr/sum/kcs/rin-toukei/rin-toukei-index. htm, cited 2014-01-06). Although the current timber industry makes only a small contribution to the total GDP (0.14%), due to competition with imported timber, living in a low-carbon society that utilizes the managed forest, which occupies 17% of the area (e.g., enhancing woody biomass for energy production), is a priority in the region. Water yield is closely related to the main industries and livelihoods of the local community. The water consumed by humans within the watershed, 35% is for city water and 63% is for industrial supply (River Bureau of Ministry of Land, Infrastructure, Transport and Tourism, 2006). The predicted change in water yield will affect various industries in the watershed, especially manufacturing industry and agriculture (which contribute 13.3% and 3.46% to total GDP in the area, respectively). According to the Basic Policy for Kushiro River Develop- ment, the quantity of water intake from the river is about 3.9 m3/s of the annual mean flow of 26.3 m3/s. Thus, there is little concern about a water shortage in this area. However, it will be necessary to monitor changes in the water yield with flow control relating to these industries and to verify the hydrological phenomena. Among the ecosystem services, only carbon sequestration is not directly related to local livelihoods because this concern is a global issue. Until low-carbon businesses are introduced to the community (e.g., a Carbon Sequestration Certification Program), this indicator will have less meaning for the local community. Although climate-change mitigation and biodiversity conservation are the most pressing global issues, the relationship between ecosystem services from a global perspective and quality of life of the local community can be revealed by case studies at the local level. Because ecosystem services are benefit-dependent (Boyd and Banzhaf, 2007; Wallace, 2007), the relevance of indicators based on the provision of ecosystem services varies according to location Trend scenario Biodiversity scenario Climate scenario Fig. 5. Changes in the distribution of ecosystem services between 2011 and 2060 according to the three scenarios. K. Shoyama, Y. Yamagata / Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎e8 Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i and social context. Our study area is at a turning point from growth to stagnation of the local economy; that is, it is undergoing a transition from development to sustainable use of natural resources. At this time, long-sighted decisions are being discussed in each municipality, but region-wide decisions are sometimes difficult to make. The use of spatial analysis to illustrate long-term changes in ecosystems is useful in fostering awareness of the need for decision-making at the watershed level. Another consideration is that the beneficiaries of the region's ecosystem services are distributed widely outside the watershed, such that the percep- tions of people in remote places influence the local management policies. Thus, the public value of conserving natural assets in the Kushiro watershed has been estimated using a stated-preference approach (Kuriyama, 1996, 2000; Shoyama et al., 2013). According to these studies, the public has a strong willingness to pay for land management of the Kushiro watershed. Notably, the public strongly wishes to avoid the extinction of endangered species in the area (Shoyama et al., 2013). Compared to people in remote places, however, local people are more interested in the use value rather than non-use value of ecosystems (Yoshida, 2013). The value of an ecosystem with regard to global issues is sometime difficult for local people to perceive. Thus, compatible schemes that connect global issues and local situations are necessary to support beneficiaries in local communities. 5. Conclusions This study combined a land-use change model with ecosystem service assessment models in the GIS-based tool InVEST to determine the patterns of land-use change in three scenarios and their effects on the habitat quality, carbon sequestration, timber production, and water yield in the Kushiro watershed. The empirical land-use modeling approach is based on analysis of the factors that have driven past land-use changes. Thus, this approach allowed us to predict possible future land-use change in a spatially explicit way as a practical matter for local communities. Under the trend scenario, carbon sequestration and water yield are predicted to increase slightly by 2060, with small declines expected in habitat quality and timber production. Thus, with no conservation policy and small carbon sequestration, the Kushiro watershed will experience a decline in the supporting and provi- sioning services as local communities disappear. This will result in less land management, thus leading to biodiversity degradation in the rural landscape. The absence of communities that play a role in land management could have a negative impact on productivity in the area. As noted by the Japan Satoyama Satoumi Assessment (JSSA) (2010), the underuse of natural resources is one of the greatest threats to socio-ecological production landscapes. The local management practices often play an important role in maintaining biodiversity and ecosystem functions (Koyanagi and Furukawa, 2013; Toda et al., 2014). In contrast, natural forest cover could persist in the protected upper reaches and additional reforestation could be expected in the middle reaches by implementing biodiversity conservation policies to enhance regional ecosystem services. Although the biodiversity conservation scenario is predicted to improve three ecosystem service indicators, with the exception of timber pro- duction, the climate-change mitigation scenario is expected to improve carbon sequestration and timber production to 2.3 and 2.5 times those of the conservation scenario, respectively. Improved water yield is expected with biodiversity conservation, but a decline in water yield in the upper to middle reaches is a concern under all three scenarios (Fig. 5). The greatest degree of land-use and cover change is likely to occur around the middle reaches, which is outside of the protected areas, and the land-use changes in this area could affect the provision of ecosystem services throughout the entire watershed. Thus, our findings indicate that land-use decisions for the middle reaches of the watershed are particularly important for managing the integrated ecosystem services of the entire watershed for the future. Based on the model results, conservation measures are recom- mended for improving the provision of ecosystem services in the region with surviving communities, which have a key role in management of the rural landscape. Spatial planning aims to solve conflicts between competing demands for limited resources in a given landscape, and land-use decisions affect all ecosystem services within a landscape by determining the trade-offs among them (Geneletti, 2013). Our findings illustrate the trade-offs between supporting, provisioning, and regulating services that occur within a watershed as a result of land-use decisions related to biodiversity conservation and climate-change mitigation. This valuable information will support decision-making and planning by highlighting the potential benefits and losses in the region. One limitation of the scenario analysis in this study is that the provision of ecosystem services could not be clearly linked to actual local benefits. 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Yamagata / Ecosystem Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎e10 Please cite this article as: Shoyama, K., Yamagata, Y., Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem.... Ecosystem Services (2014), http://dx.doi.org/10.1016/j.ecoser.2014.02.004i Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem... Introduction Methods Study area Land-use change and scenario analysis Detecting past land-use change Future scenario analysis Mapping ecosystem service provisioning Results Past land-use change Land-use projection Scenario definition Location suitability Future land-use and cover change Changes in the provision of ecosystem services Discussion Conclusions Acknowledgments References