Search

Abstract Land‐use change is a major driver of biodiversity loss, affecting tropical forests and savannas at an unprecedented rate. To protect these ecosystems and their biodiversity, national conservation areas and international conservation funding have increased considerably in these regions. Understanding of how conservation funding allocation relates to dynamics in deforestation and conservation areas is crucial to identifying what mobilizes and distributes conservation funding. By applying fixed‐effect models on 30 years of dynamics in forest cover, conservation areas and conservation funding in the main deforestation regions of South America, we analysed the conservation funding allocation strategies in relation to deforestation (proactive vs. reactive), conservation areas (expanding or consolidating) and previous investment (agglomerating or not). We also assessed whether allocation strategies vary across regions and the stages of deforestation and conservation. We found that funding allocation followed conservation areas and higher prior funding levels over space and over time, across all regions and deforestation stages. This highlights the important role of recognized conservation areas (including protected areas and Indigenous territories) in mobilizing financial support for conservation. The allocation strategies relating to forest dynamics, however, varied depending on the scale of analysis, whether we look at the temporal or spatial dimension, and in which regions we assessed the allocation patterns. Capturing region‐level biases and within‐region dynamics is crucial for more effective and equitable conservation. As public and private actors commit to mobilizing more conservation funding and conserving more areas of the Earth, our insights on the relations between forest change and conservation efforts can help improve the allocation of conservation areas and conservation funding. Read the free Plain Language Summary for this article on the Journal blog.

Abstract The EU Biodiversity Strategy 2030 aims to better protect natural ecosystems with high biodiversity and climate change mitigation potential. To achieve this goal, it is crucial to identify forests worth protecting, such as those characterized by long continuity and old age. Here, we propose a robust approach that combines historical maps from the mid‐19th century with remote sensing data to identify areas that have been forested for over 150 years, aiming to reduce the need for detailed and time‐consuming field investigations in potential conservation areas. We tested this approach in a 20,000 km2 region in the Polish Carpathians, an area likely containing many unprotected forests of high conservation value, where historical maps from the mid‐19th century are representative of much of Central Europe. Our results showed that an area of approximately 4200 km2 has been continuously forested since at least the mid‐19th century. Currently, 50% of these forests are outside protected areas, representing an important conservation opportunity. More generally, our approach can support the identification of valuable forests worth protecting before they are lost and provide useful insights for policymakers and stakeholders in the ongoing debate on the challenges of improving forest conservation in the Carpathians, and Europe more widely.

Abstract Land use is both a major cause of the biodiversity crises and a potential solution to it. Decisions about land use are made in complex social–ecological systems, yet conservation research, policy, and practice often neglect the diverse and dynamic nature of land use. A deeper integration of land system science and conservation science provides major opportunities in this context, through a transfer of concepts, data, and methodologies. Specifically, a closer exchange between land‐use data developers and users will enable common terminology and better data use, allowing to move beyond coarse land‐cover representations of land use. Similarly, archetyping and regionalization approaches can help to embrace, rather than oversimplify, the diversity of land‐use actors and practices. Finally, systematically linking land‐use actors to portfolios of pressures on biodiversity, beyond their direct impact on habitat, can better represent and map co‐occurring and interacting threats. Together, this will enable conservation policymakers and planners to recognize the often‐complex and wicked nature of conservation challenges related to land, allowing for more context‐specific conservation policymaking and planning, and more targeted conservation interventions.

Land-use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called ‘frontiers’, defined here as places experiencing marked transformations owing to rapid resource exploitation. Understanding the mechanisms shaping these frontiers is crucial for sustainability. Previous work focused mainly on explaining how active frontiers advance, in particular, into tropical forests. Comparatively, our understanding of how frontiers emerge in territories considered marginal in terms of agricultural productivity and global market integration remains weak. We synthesize conceptual tools explaining resource and land-use frontiers, including theories of land rent and agglomeration economies, of frontiers as successive waves, spaces of territorialization, friction and opportunities, anticipation and expectation. We then propose a new theory of frontier emergence, which identifies exogenous pushes, legacies of past waves and actors’ anticipations as key mechanisms by which frontiers emerge. Processes of differential rent creation and capture and the built-up of agglomeration economies then constitute key mechanisms sustaining active frontiers. Finally, we discuss five implications for the governance of frontiers for sustainability. Our theory focuses on agriculture and deforestation frontiers in the tropics but can be inspirational for other frontier processes including for extractive resources, such as minerals.

Traffic mortality can pose a serious risk to endangered species that occur in small populations, are mobile and occupy fragmented habitats. This is the case for the European bison (Bison bonasus) yet, how traffic mortality affects this species is unknown. Here, we assessed patterns and trends of European bison mortality on roads and railways in Poland, which harbors a large share of the global free-ranging population of this species. We identified 70 records of European bison mortality due to roads or railways during 2010–2021, which involved three free-ranging populations: Białowieska Forest, Knyszyńska Forest, and Zachodniopomorskie. Most reported mortality (73%) was from the Zachodniopomorskie population, likely an effect of the high traffic volumes on a national road passing through the core range of that population. Furthermore, our analysis revealed an increasing trend of European bison traffic fatalities, which is likely associated with increasing European bison numbers in these populations. We conclude that traffic accidents may pose a risk to both European bison and people, and reintroductions should therefore prioritize roadless areas and avoid areas with busy roads and railways. Although traffic mortality of European bison has been a negligible threat to the species as a whole, our study shows that this threat may be substantial locally. As European bison numbers grow, which is desirable given the still small size of many free-ranging populations, effective mitigation measures are needed to ensure the safety of European bison and people.

Abstract Monitoring is a prerequisite for evidence‐based wildlife management and conservation planning, yet conventional monitoring approaches are often ineffective for species occurring at low densities. However, some species such as large mammals are often observed by lay people and this information can be leveraged through citizen science monitoring schemes. To ensure that such wildlife monitoring efforts provide robust inferences, assessing the quantity, quality, and potential biases of citizen science data is crucial. For Eurasian moose (Alces alces), a species currently recolonizing north‐eastern Germany and occurring in very low numbers, we applied three citizen science tools: a mail/email report system, a smartphone application, and a webpage. Among these monitoring tools, the mail/email report system yielded the greatest number of moose reports in absolute and in standardized (corrected for time effort) terms. The reported moose were predominantly identified as single, adult, male individuals, and reports occurred mostly during late summer. Overlaying citizen science data with independently generated habitat suitability and connectivity maps showed that members of the public detected moose in suitable habitats but not necessarily in movement corridors. Also, moose detections were often recorded near roads, suggestive of spatial bias in the sampling effort. Our results suggest that citizen science‐based data collection can be facilitated by brief, intuitive digital reporting systems. However, inference from the resulting data can be limited due to unquantified and possibly biased sampling effort. To overcome these challenges, we offer specific recommendations such as more structured monitoring efforts involving the public in areas likely to be roamed by moose for improving quantity, quality, and analysis of citizen science‐based data for making robust inferences.

Abstract Pulsed resources have prominent effects on community and ecosystem dynamics; however, there is little research on how resource pulses affect human–wildlife interactions. Tree masting is a common type of pulsed resource that represents a crucial food for many species and has important bottom‐up effects in food webs. In anthropogenic landscapes, years of food shortage after mast years can have negative outcomes for both people and wildlife, for instance when an increased use of anthropogenic foods by animals exacerbates human–wildlife conflicts. Here, we used novel remote sensing indicators of forest productivity and phenology, together with weather cues and ground measures of mast production, to assess whether years of masting and crop failures lead to changes in human–wildlife conflict occurrence. We used a unique 14‐year dataset including the production of European beech Fagus sylvatica seeds and brown bear Ursus arctos damage in the northeastern Carpathians as our model system. Linking these data in a panel regression framework, we found that temporal fluctuations in damage occurrence were sensitive to the year‐to‐year variation in beechnut production. Specifically, the number of damages during bear hyperphagia (i.e., September to December, when bears need to accumulate fat reserves prior to hibernation) was significantly higher in years with low beechnut production than in normal or mast years. Furthermore, we provide evidence that beech masting and failure can be predicted through a combination of remote‐sensing, weather, and field indicators of forest productivity and phenology. We demonstrate how pulsed resources, such as tree masting, can percolate through food webs to amplify human–wildlife conflict in human‐dominated landscapes. Given the recent range expansion of large carnivores and herbivores in many regions, including Europe, predicting years of natural food shortage can provide a pathway to proactive damage prevention, and thus to foster coexistence between wildlife and people.

Commodity agriculture continues to spread into tropical dry forests globally, eroding their social-ecological integrity. Understanding where deforestation frontiers expand, and which impacts this process triggers, is thus important for sustainability planning. We reconstructed past land-system change (1985–2015) and simulated alternative land-system futures (2015–2045) for the Gran Chaco, a 1.1 million km ^2 global deforestation hotspot with high biological and cultural diversity. We co-developed nine plausible future land-system scenarios, consisting of three contrasting policy narratives (Agribusiness, Ecomodernism, and Integration) and three agricultural expansion rates (high, medium, and low). We assessed the social-ecological impacts of our scenarios by comparing them with current biodiversity, carbon density, and areas used by forest-dependent people. Our analyses revealed four major insights. First, intensified agriculture and mosaics of agriculture and remaining natural vegetation have replaced large swaths of woodland since 1985. Second, simulated land-system futures until 2045 revealed potential hotspots of natural vegetation loss (e.g. western and southern Argentinian Chaco, western Paraguayan Chaco), both due to the continued expansion of existing agricultural frontiers and the emergence of new ones. Third, the strongest social-ecological impacts were consistently connected to the Agribusiness scenarios, while impacts were lower for the Ecomodernism and Integration scenarios. Scenarios based on our Integration narrative led to lower social impacts, while Ecomodernism had lower ecological impacts. Fourth, comparing recent land change with our simulations showed that 10% of the Chaco is on a pathway consistent with our Agribusiness narrative, associated with adverse social-ecological impacts. Our results highlight that much is still at stake in the Chaco. Stricter land-use and conservation planning are urgently needed to avoid adverse social-ecological outcomes, and our results charting the option space of plausible land-system futures can support such planning.

Abstract Unprecedented amounts of analysis‐ready Earth Observation (EO) data, combined with increasing computational power and new algorithms, offer novel opportunities for analysing ecosystem dynamics across large geographic extents, and to support conservation planning and action. Much research effort has gone into developing global EO‐based land‐cover and land‐use datasets, including tree cover, crop types, and surface water dynamics. Yet there are inherent trade‐offs between regional and global EO products pertaining to class legends, availability of training/validation data, and accuracy. Acknowledging and understanding these trade‐offs is paramount for both developing EO products and for answering science questions relevant for ecology or conservation studies based on these data. Here we provide context on the development of global EO‐based land‐cover and land‐use datasets, and outline advantages and disadvantages of both regional and global datasets. We argue that both types of EO‐derived land‐cover datasets can be preferable, with regional data providing the context‐specificity that is often required for policy making and implementation (e.g., land‐use and management, conservation planning, payment schemes for ecosystem services), making use of regional knowledge, particularly important when moving from land cover to actors. Ensuring that global and regional land‐cover and land‐use products derived based on EO data are compatible and nested, both in terms of class legends and accuracy assessment, should be a key consideration when developing such data. Open access high‐quality training and validation data derived as part of such efforts are of utmost importance. Likewise, global efforts to generate sets of essential variables for climate change, biodiversity, or eventually land use, which often require land‐cover maps as inputs, should consider regionalized, hierarchical approaches to not sacrifice regional context. Global change impacts manifest in regions, and so must the policy and planning responses to these challenges. EO data should embrace that regions matter, perhaps more than ever, in an age of global data availability and processing.

Syrian agriculture recovered after the 2007-2009 drought, before the 2011 civil conflict, according to remotely sensed data on agricultural activity in Syria, which calls into question the influence of climate stress on migration and conflict risk.

ABSTRACTLand-use frontiers, such as agriculture expanding into forests, remain a major driver of biodiversity loss, and often lead to conservation responses. To better understand the geographies of conservation, connecting conservation with tools used widely in Land System Science – particularly the frontier concept – allows assessing the patterns, actors, and drivers of conservation. We propose that land conservation can be analysed through three different perspectives. First, conservation can be framed as efforts to slow or stop other frontiers. Second, the expansion of conservation could itself be described as a frontier process, similarly leading to institutional and cultural reorganization, and sometimes conflicts (e.g. green grabbing). Third, frontiers can be seen as spaces where multiple land uses, including conservation, interact. Analysing conservation through these perspectives could be particularly powerful to thoroughly consider the social-ecological contexts in which conservation happens, and thus to bridge the disciplines of Land System Science and Conservation Science.

Abstract Understanding the spatio‐temporal distribution of ungulates is important for effective wildlife management, particularly for economically and ecologically important species such as wild boar (Sus scrofa). Wild boars are generally considered to exhibit substantial behavioral flexibility, but it is unclear how their behavior varies across different conservation management regimes and levels of human pressure. To analyze if and how wild boars adjust their space use or their temporal niche, we surveyed wild boars across the core and buffer zones (collectively referred to as the conservation zone) and the transition zone of a biosphere reserve. These zones represent low and high levels of human pressure, respectively. Specifically, we employed a network of 53 camera traps distributed in the Schaalsee UNESCO Biosphere Reserve over a 14‐month period (19,062 trap nights) and estimated circadian activity patterns, diel activity levels, and occupancy of wild boars in both zones. To account for differences in environmental conditions and day length, we estimated these parameters separately for seven 2‐month periods. Our results showed that the wild boars were primarily nocturnal, with diurnal activity occurring dominantly during the summer months. The diel activity patterns in the two zones were very similar overall, although the wild boars were slightly less active in the transition zone than in the conservation zone. Diel activity levels also varied seasonally, ranging from 7.5 to 11.0 h day−1, and scaled positively with the length of the night (R2 = 0.66–0.67). Seasonal occupancy estimates were exceptionally high (point estimates ranged from 0.65 to 0.99) and similar across zones, suggesting that the wild boars used most of the biosphere reserve. Overall, this result suggests that different conservation management regimes (in this case, the zoning of a biosphere reserve) have little impact on wild boar behavior. This finding is relevant for wildlife management in protected areas where possibly high wild boar densities could interfere with conservation goals within these areas and those of agricultural land use in their vicinity.

Abstract Livestock husbandry exerts major pressures on wildlife across the world. Large carnivores are particularly at risk because they are often killed by pastoralists as a preventive or precautionary response to livestock depredation. Minimizing the overlap between pastures and carnivore habitat can thus be a conservation strategy, but it remains often unclear which pastures should be targeted to maximize conservation benefits given a limited budget. We addressed this question for the last viable population of the Asiatic cheetah (Acinonyx jubatus venaticus) in northeastern Iran. By combining species distribution modeling with a spatial prioritization framework, we aimed to identify where grazing right buyouts should take place to reduce cheetah killing by herders and their dogs. We assessed the Asiatic cheetah habitat using species distribution models, highlighting large, contiguous areas that overlap with livestock pastures (5792 km2, equaling 72% of the total predicted suitable habitat). Subsequently, we used data on the number and distribution of livestock (~47,000 animals in 80 pastures) and applied a spatial prioritization method to identify pastures for grazing right buyouts for a range of budget scenarios (US$100,000–600,000). Pastures selected had a high level of irreplaceability and were generally stable across budget scenarios. Our results provide a novel approach to minimize encounter rates between cheetah and livestock, and thus the mortality risk, for one of the world's most endangered felids and highlight the potential of spatial prioritization as a tool to devise urgent conservation actions.

Measurement(s) Geographic Distribution Technology Type(s) digital curation Factor Type(s) country Sample Characteristic - Environment primary forest Sample Characteristic - Location Europe Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14540625

Abstract Social, burrowing mammals such as prairie dogs, ground squirrels or marmots are keystone species in grassland ecosystems. Grasslands have been converted into cropland or pastures globally, yet it remains virtually unknown how this has affected the biogeography of burrowing mammals, as efficient, broad‐scale survey methods are lacking. We aimed to test whether structures created by burrowing rodents can be reliably detected on publicly available, very‐high‐resolution satellite images, in order to assess rodent distribution and abundance. We identified burrows of Bobak marmot (Marmota bobak), a keystone burrowing steppe rodent, on 1300 randomly selected plots of 1 km diameter (78.53 ha) across the species’ range (~950 000 km²) in Kazakhstan and southern Russia using Google Earth and Bing images. We then used burrow occurrences and species distribution models to map marmot distribution. We assessed how marmot occurrence and density vary across land‐use types. We also combined satellite‐based burrow densities and ground‐survey data to derive a new population estimate for the species across Kazakhstan. We mapped a total of 7425 burrows from the satellite imagery. Field visits at a subsample of burrows suggested that burrow occurrence was detected reliably. Broad‐scale marmot distribution was mainly determined by summer rainfall, land use and elevation. Occurrence probability was highest on arable croplands, followed by abandoned croplands and grazed steppe. The current Bobak marmot population size for Kazakhstan was estimated at 6.1 (±2.4) million individuals. Our results demonstrate that publicly available, very‐high‐resolution images can be used to reliably map the distribution of burrowing mammals across large geographic scales. The observed and predicted distributions indicate that the Bobak's range has remained almost unchanged in Kazakhstan since the 1950s, despite several drastic episode of land‐use change. This suggests that burrowing mammals can be remarkably resilient to land‐use pressure, questioning prevailing narratives of population collapse in these species following agricultural expansion.

Abstract Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.

Abstract International funding is increasingly important in supporting conservation in mega‐biodiverse countries. However, it remains unclear which donors invest in which conservation objectives and where, making it difficult to identify gaps and key actors to influence. Here we identified 1947 foreign‐aided conservation projects in South America's major deforestation frontiers and summarized their objectives and interventions over time and space. We found that conserving nature for its own sake and for ecosystem services remained key objectives, but the types of interventions varied considerably over time. Geographically, international conservation prioritized moist forests over drier biomes, despite equally high deforestation risk. Different donor groups emphasized specific objectives and interventions that reflected socioecological links (e.g., bird migration, colonial history) between donating and receiving regions, as well as the donors’ values (e.g., iconic/endangered species, human rights). These telecoupled patterns provide both opportunities and barriers for conservation and have implications for conservation prioritization strategies.

Grasslands are important for global biodiversity, food security, and climate change analyses, which makes mapping and monitoring of vegetation changes in grasslands necessary to better understand, sustainably manage, and protect these ecosystems. However, grassland vegetation monitoring at spatial and temporal resolution relevant to land management (e.g., ca. 30-m, and at least annually over long time periods) is challenging due to complex spatio-temporal pattern of changes and often limited data availability. Here we assess both short- and long-term changes in grassland vegetation cover from 1987 to 2019 across the Caucasus ecoregion at 30-m resolution based on Cumulative Endmember Fractions (i.e., annual sums of monthly ground cover fractions) derived from the full Landsat record, and temporal segmentation with LandTrendr. Our approach combines the benefits of physically-based analyses, missing data prediction, annual aggregations, and adaptive identification of changes in the time-series. We analyzed changes in vegetation fraction cover to infer the location, timing, and magnitude of vegetation change episodes of any length, quantified shifts among all ground cover fractions (i.e., green vegetation, non-photosynthetic vegetation, soil, and shade), and identified change pathways (i.e., green vegetation loss, desiccation, dry vegetation loss, revegetation green fraction, greening, or revegetation dry fraction). We found widespread long-term positive changes in grassland vegetation (32.7% of grasslands), especially in the early 2000s, but negative changes pathways were most common before the year 2000. We found little association between changes in green vegetation and meteorological conditions, and varied relationships with livestock populations. However, we also found strong spatial heterogeneity in vegetation dynamics among neighboring fields and pastures, demonstrating capability of our approach for grassland management at local levels. Our results provide a detailed assessment of grassland vegetation change in the Caucasus Ecoregion, and present an approach to map changes in grasslands even where availability of Landsat data is limited.

Forest loss in the tropics affects large areas, but whereas full forest conversions are routinely assessed, forest degradation patters remain often unclear. This is particularly so for the world’s tropical dry forests, where remote sensing of forest disturbances is challenging due to high canopy complexity, strong phenology and climate variability, and diverse degradation drivers. Here, we used the full depth of the Landsat archive and devised an approach to detect disturbances related to forest degradation across the entire Argentine Dry Chaco (about 489,000 km2) over a 30-year timespan. We used annual time series of different spectral indices, summarized for three seasonal windows, and applied LandTrendr to temporally segment each time series. The resulting pixel-level forest disturbance metrics then served as input for a Random Forests classification which we used to produce an area-wide disturbance map, and associated yearly area estimates of disturbed forest. Finally, we evaluated disturbance trends in relation to climate and soil conditions. Our best model produced a disturbance map with an overall accuracy of 79%, with a balanced error distribution. A total of 8% (24,877 ± 860 km2) of the remaining forest in the Argentine Dry Chaco have been affected by forest disturbances between 1990 and 2017. Diverse spatial patterns of forest disturbances indicate a variety of agents driving disturbances. We also found the disturbed area to vary strongly between years, with larger areas being disturbed during drought years. Our approach shows that it is possible to robustly map forest disturbances in tropical dry forests using Landsat time series, and demonstrates the value of ensemble approaches to capture spectrally-complex and heterogeneous land-change processes. For the Chaco, a global deforestation hotspot, our analyses provide the first Landsat-based assessment of forest disturbance in remaining forests, highlighting the need to better consider such disturbances in assessments of carbon budgets and biodiversity change.

Abstract Climate change disproportionately threatens alpine species, by reducing available habitat and by isolating their populations. These pressures are particularly relevant for rear‐edge populations, which typically occupy more marginal habitat compared to populations at the core of species' ranges. We studied Caucasian grouse Lyrurus mlokosiewiczi in the Caucasus ecoregion, a global biodiversity hotspot where this species is endemic, to understand potential climate change impacts on the species. Specifically, we assessed how climate change impacts rear‐edge populations and how important these populations are for understanding range shifts and adaptive capacity under climate change. We used maximum entropy modeling to assess changes in the distribution of climatically suitable habitat under present and 2070 climate conditions for the representative concentration pathways 8.5 (RCP8.5). Our results revealed that ignoring rear‐edge populations leads to a significant underestimation of the future range (by about 14,700 km2). Rear‐edge populations were better adapted to warmer climates compared to core populations, and ignoring them, therefore, also underestimates adaptive capacity. Preventing the loss of rear‐edge populations should, therefore, be a priority for conservation planning in the face of climate change. Because the Caucasian grouse is associated with alpine mountain tops, conservation should focus on establishing connectivity between rear‐edge and core populations (e.g., via transboundary corridors or assisted colonizations). Our study reveals how species distribution modeling can highlight the importance of rear‐edge populations for mitigating climate change impacts on species of conservation concern.

Abstract As wild areas disappear and agricultural lands expand, understanding how people and wildlife can coexist becomes increasingly important. Human–wildlife conflicts (HWCs) are obstacles to coexistence and negatively affect both wildlife populations and the livelihood of people. To facilitate coexistence, a number of frameworks have been developed to both understand the drivers of conflict and then to find solutions that mitigate conflict. However, each framework has different foci and strengths in particular stages of analysis. Here, we propose an integrated framework that leverages the individual strengths of previously fairly isolated methodologies, allowing for holistic HWC analysis. The framework for participatory impact assessment (FoPIA) provides a toolset for developing wildlife scenarios, selecting assessment indicators and assessing the impact of different scenarios. The social‐ecological framework of ecosystem services and disservices (SEEDS) analyzes the ecosystem services trade‐offs related to scenarios, and the 3i stakeholder analysis approach, supports the identification of stakeholders and provides a mechanism to explore, in detail stakeholders' interests, relative influence, and how outcomes of research are likely to impact different stakeholders. We apply these approaches to eastern Germany, where the increase in several wildlife populations (i.e., wild boar, common crane, gray wolf, and European bison) has contributed to conflict with people. We demonstrate the complementarity of FoPIA, SEEDS, and 3i in identifying stakeholder needs and showing how wildlife dynamics may affect coexistence and create imbalanced ecosystem service and disservice distributions. The integrated framework introduced here provides guidelines for analyzing the multistage process of stakeholder participation and enables a comprehensive approach to the complex challenge of HWCs.

Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of the causes of disturbances. Here, we apply an approach for attributing agents of forest disturbance across large areas of tropical dry forests, based on the Landsat image time series. Focusing on the 489,000 km2 Argentine Dry Chaco, we derived metrics on the spectral characteristics and shape of disturbance patches. We then used these metrics in a random forests classification framework to estimate the area of logging, fire, partial clearing, riparian changes and drought. Our results highlight that partial clearing was the most widespread type of forest disturbance from 1990–to 2017, extending over 5520 km2 (±407 km2), followed by fire (4562 ± 388 km2) and logging (3891 ± 341 km2). Our analyses also reveal marked trends over time, with partial clearing generally becoming more prevalent, whereas fires declined. Comparing the spatial patterns of different disturbance types against accessibility indicators showed that fire and logging prevalence was higher closer to fields, while smallholder homesteads were associated with less burning. Roads were, surprisingly, not associated with clear trends in disturbance prevalence. To our knowledge, this is the first attribution of disturbance agents in tropical dry forests based on satellite-based indicators. While our study reveals remaining uncertainties in this attribution process, our framework has considerable potential for monitoring tropical dry forest disturbances at scale. Tropical dry forests in South America, Africa and Southeast Asia are some of the fastest disappearing ecosystems on the planet, and more robust monitoring of forest degradation in these regions is urgently needed.

Hunting is a widespread but often overlooked land-use activity, providing major benefits to society. Hunting takes place in most landscapes, yet it remains unclear which types of landscapes foster or dampen hunting-related services, and how hunting relates to other land uses. A better understanding of these relationships is key for sustainable land-use planning that integrates wildlife management. This is particularly urgent for Europe, where wildlife populations are increasing. Focusing on Sweden, we explored the spatial associations among hunting, agriculture, and forestry to identify archetypical combinations of these land uses. Specifically, we combined indicators on the extent and intensity of agriculture and forestry, with data on hunting bags for 63 game species using self-organizing maps, a non-parametric clustering approach. We identified 15 typical bundles of co-occurring land uses at the municipality level across Sweden. The harvest of forest grouse, bears, and moose co-occurred with forestry in northern Sweden, whereas the harvest of small game, different deer species, and wild boar co-occurred with agriculture across southern Sweden, reflecting species' biology, environmental factors, and management. Our findings also highlight the strength of associations among hunting and other land uses. Importantly, we identified large areas in central Sweden where harvest of game was below average, possibly indicating that intensity of hunting is out of balance with that of agriculture or forestry, potentially fostering conflict between wildlife and land use. Collectively, our results suggest that (1) hunting should be considered a major land use that, in Sweden, is more widespread than agriculture and forestry; (2) land-use planning must therefore integrate wildlife management; and (3) such an integration should occur in a regionalized manner that considers social-ecological context. Our approach identifies a first spatial template within which such context-specific land-use planning, aiming at aligning wildlife and diverse land uses, can take place.

Abstract Mountain ungulates around the world have been decimated to small, fragmented populations. Restoring these species often is limited by inadequate information on where suitable habitat is found, and which restoration measures would help to increase and link existing populations. We developed an approach to spatially target threat‐specific restoration actions and demonstrate it for bezoar goats (Capra aegagrus) in the Caucasus. Using a large occurrence dataset, we identified suitable habitat patches and evaluate them in terms of connectivity, protection status, and competition with other mountain ungulates. We found extant bezoar goat populations to be highly isolated, yet with widespread areas of suitable, unoccupied habitat between them. Many unoccupied habitat patches were well‐connected to extant populations, were at least partly protected, and have low potential for competition with other Capra species. This signals substantial pressure on bezoar goats, likely due to poaching, which currently prevents natural recolonization. Our study shows how restoration planning is possible in the face of multiple threats and scarce data. For bezoar goats in the Caucasus, we pinpoint priority patches for specific restoration measures, including reintroductions and anti‐poaching action. We highlight that many patches would benefit from multiple interventions and that transboundary restoration planning is needed, a situation likely similar for many mountain ungulates around the world.

Agricultural expansion into tropical and subtropical forests often leads to major social-ecological trade-offs. Yet, despite ever-more detailed information on where deforestation occurs, how agriculture expands into forests remains unclear, which is hampered by a lack of spatially and temporally detailed reconstruction of agricultural expansion. Here, we developed and mapped a novel set of metrics that quantify agricultural frontier processes at unprecedented spatial and temporal detail. Specifically, we first derived consistent annual time series of land-use/cover to, second, describe archetypical patterns of frontier expansion, pertaining to the speed, the diffusion and activity of deforestation, as well as post-deforestation land use. We exemplify this approach for understanding agricultural frontier expansion across the entire South American Chaco (1.1 million km ^2 ), a global deforestation hotspot. Our study provides three major insights. First, agricultural expansion has been rampant in the Chaco, with more than 19.3 million ha of woodlands converted between 1985 and 2020, including a surge in deforestation after 2019. Second, land-use trajectories connected to frontier processes have changed in major ways over the 35 year study period we studied, including substantial regional variations. For instance, while ranching expansion drove most of the deforestation in the 1980s and 1990s, cropland expansion dominated during the mid-2000s in Argentina, but not in Paraguay. Similarly, 40% of all areas deforested were initially used for ranching, but later on converted to cropping. Accounting for post-deforestation land-use change is thus needed to properly attribute deforestation and associated environmental impacts, such as carbon emissions or biodiversity loss, to commodities. Finally, we identified major, recurrent frontier types that may be a useful spatial template for land governance to match policies to specific frontier situations. Collectively, our study reveals the diversity of frontier processes and how frontier metrics can capture and structure this diversity to uncover major patterns of human–nature interactions, which can be used to guide spatially-targeted policies.

Further cropland expansion might be unavoidable to satisfy the growing demand for land- based products and ecosystem services. A crucial issue is thus to assess the trade-offs between social and ecological impacts and the benefits of converting additional land to cropland. In the former Soviet Union countries, where the transition from state-command to market-driven economies resulted in widespread agricultural land abandonment, cropland expansion may incur relatively low costs, especially compared with tropical regions. Our objectives were to quantify the drivers, constraints and trade-offs associated with recultivating abandoned cropland to assess the potentially available cropland in European Russia, western Siberia, Ukraine and Kazakhstan - the region where the vast majority of post- Soviet cropland abandonment took place. Using spatial panel regressions, we characterized the socio-economic determinants of cropland abandonment and recultivation. We then used recent maps of changes in cropland to 1) spatially characterize the socio-economic, accessibility and soil constraints associated with the recultivation of abandoned croplands and 2) investigate the environmental trade-offs regarding carbon stocks and habitat for biodiversity. Less cropland abandonment and more recultivation after 2000 occurred in areas with an increasing rural population and a younger labor force, but also improved yields. Synergies were observed between cropland recultivation and intensification over the 2000s. From 47.3 million hectares (Mha) of cropland abandoned in 2009, we identified only 8.5 (7.1-17.4) Mha of potentially available cropland with low environmental tradeoffs and low to moderate socio-economic or accessibility constraints that were located on high-quality soils (Chernozems). These areas represented an annual wheat production potential of 14.3 (9.6-19.5) million tons (Mt). Conversely, 8.5 (4.2-12.4) Mha had high carbon or biodiversity trade-offs, of which 10% might be attractive for cropland expansion and thus would require protection from recultivation. Agroenvironmental, accessibility, and socio-economic constraints suggested that the remaining 30.6 (25.7-30.6) Mha of abandoned croplands were unlikely to provide important contributions to future crop production at current wheat prices but could provide various ecosystem services, and some could support extensive livestock production. Political and institutional support could foster recultivation by supporting investments in agriculture and rural demographic revitalization. Reclaiming potentially available cropland in the study region could provide a notable contribution to global grain production, with relatively low environmental trade-offs compared with tropical frontiers, but is not a panacea to address global issues of food security or reduce land-use pressure on tropical ecosystems

The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because agricultural expansion entails substantial environmental trade-offs, intensification and recultivation are currently gaining increasing attention. Assessing where these strategies may be pursued, however, requires improved spatial information on land use intensity, including where farmland is active and fallow. We developed a framework to integrate optical and radar data in order to advance the mapping of three farmland management regimes: (1) large-scale, mechanized agriculture; (2) small-scale, subsistence agriculture; and (3) fallow or abandoned farmland. We applied this framework to our study area in western Ukraine, a region characterized by marked spatial heterogeneity in management intensity due to the legacies from Soviet land management, the breakdown of the Soviet Union in 1991, and the recent integration of this region into world markets. We mapped land management regimes using a hierarchical, object-based framework. Image segmentation for delineating objects was performed by using the Superpixel Contour algorithm. We then applied Random Forest classification to map land management regimes and validated our map using randomly sampled in-situ data, obtained during an extensive field campaign. Our results showed that farmland management regimes were mapped reliably, resulting in a final map with an overall accuracy of 83.4%. Comparing our land management regimes map with a soil map revealed that most fallow land occurred on soils marginally suited for agriculture, but some areas within our study region contained considerable potential for recultivation. Overall, our study highlights the potential for an improved, more nuanced mapping of agricultural land use by combining imagery of different sensors.

As the global food system contributes significantly to global greenhouse gas (GHG) emissions, understanding the sources of GHG emissions embodied in different components of food systems is important. The collapse of the Soviet Union triggered a massive restructuring of the domestic food systems, namely declining consumption of animal products, cropland abandonment, and a major restructuring of agricultural trade. However, how these complex changes have affected global GHG emissions is uncertain. Here, we quantified the net GHG emissions associated with changes in the former Soviet Union’s food systems. Changes in food production, consumption, and trade together resulted in a net emissions reduction of 7.61 Gt carbon dioxide equivalents from 1992 to 2011. For comparison, this corresponds to one quarter of the CO _2 emissions from deforestation in Latin America from 1991 to 2011. The key drivers of the emissions reductions were the decreasing beef consumption in the 1990s, increasing beef imports after 2000, mainly from South America, and carbon sequestration in soils on abandoned cropland. Ongoing transformations of the food systems in the former Soviet Union, however, suggest emissions will likely rebound. The results highlight the importance of considering agricultural production, land-use change, trade, and consumption when assessing countries emissions portfolios. Moreover, we demonstrated how emissions reductions that originate from a reduction in the extent and intensity of agricultural production can be compromised by increasing emissions embodied in rising imports of agricultural commodities.

Agricultural expansion threatens biodiversity due to habitat loss and fragmentation. In the Gran Chaco, a global deforestation hotspot, rampant cropland and pasture expansion raise concerns about the sustainability of these land-use changes. Zoning policies were recently enacted in the Argentine Chaco to balance agriculture and conservation, yet the environmental outcomes of implementing these policies remain unclear. Here, we focused on the province of Formosa (Argentina) to evaluate how fully implementing zoning there would affect forest loss and connectivity, and how multiscale landscape planning could enhance environmental outcomes. Specifically, we simulated potential future forest cover for different spatial planning scenarios to assess the effect of (a) implementing regional corridors and (b) enacting additional policies to minimize forest fragmentation at the plot level, under both high and low deforestation rates. We then quantified forest connectivity and fragmentation using morphological image segmentation and landscape indices. Our results show that implementing regional corridors reduced the extent of potential deforestation by 650,000 ha (43%), and this alone strongly increased forest connectivity compared with scenarios without corridors. However, how deforestation would be carried out at the plot level was critically important. Plot-level spatial planning could have a strong and positive effect on mitigating fragmentation and on maintaining connectivity, even in scenarios with high deforestation rates (i.e., reducing the number of forest fragments by up to 35%, increasing the core forest by up to 6%). Moreover, under high deforestation rates, implementing regional corridors and plot-level design had a strong complementary effect on mitigating forest fragmentation (17% less forest fragments than when implementing either of the two strategies alone). Our analyses clearly highlight the opportunities of multiscale spatial planning and the need to complement broad-scale zoning with plot-level landscape design in order to mitigate the negative impacts of deforestation in the Chaco and other active agricultural frontiers.

The world’s grasslands, both natural and managed, provide food and many non-provisioning ecosystem services. Although most grasslands today are used for livestock grazing or fodder production, little is known about the spatial patterns of grassland management intensity, especially at broad geographic scales. Using the European Union as a case study, we mapped mowing frequency as a key indicator of grassland management intensity. We used MODIS NDVI time series from 2000–2012 to map mowing frequency using a spline-fitting algorithm that detects up to five mowing events within a single growing season. We combined mowing frequency maps with existing maps of livestock distribution and grassland management frequency to identify clusters of similar grassland management intensity across Europe. Our results highlight generally high mowing frequency in areas of high grassland productivity, especially in Ireland, northern and central France, and the Netherlands. Our analyses also show distinct clusters of similar grassland management, representing different grassland-management intensity regimes. High intensity clusters occurred particularly in western and southern Europe, especially in Ireland, in the northern and central parts of France and Spain, and the Netherlands but also in northern and southern Germany and eastern Poland. Low intensity clusters were found mainly in central and eastern Europe and in mountainous regions but also in Extremadura in Spain, Wales and western England (UK). Generally, our analyses emphasize the usefulness of jointly using satellite time series and agricultural statistics to monitor grassland intensity across broad geographic extents. Our maps allow for a new, spatially-detailed view of management intensity in grassland systems and may help to improve regionally targeted land-use and conservation policies.

Scattered trees support high levels of farmland biodiversity and ecosystem services in agricultural landscapes, but they are threatened by agricultural intensification, urbanization, and land abandonment. This study aimed to map and quantify the decline of orchard meadows (scattered fruit trees of high nature conservation value) for a region in Southwestern Germany for the 1968 2009 period and to identify the driving forces of this decline. We derived orchard meadow loss from 1968 and 2009 aerial images and used a boosted regression trees modelling framework to assess the relative importance of 18 environmental, demographic, and socio-economic variables to test five alternative hypothesis explaining orchard meadow loss. We found that orchard meadow loss occurred in flatter areas, in areas where smaller plot sizes and fragmented orchard meadows prevailed, and in areas near settlements and infrastructure. The analysis did not confirm that orchard meadow loss was higher in areas where agricultural intensification was stronger and in areas of lower implementation levels of conservation policies. Our results demonstrated that the influential drivers of orchard meadow loss were those that reduce economic profitability and increase opportunity costs for orchards, providing incentives for converting orchard meadows to other, more profitable land uses. These insights could be taken up by local- and regional-level conservation policies to identify the sites of persistent orchard meadows in agricultural landscapes that would be prioritized in conservation efforts.

We compared the effectiveness of environmental variables, and in particular of land-use indicators, to explain species richness patterns across taxonomic groups and biogeographical scales (i.e. overall pan-Europe and ecoregions within pan-Europe). Using boosted regression trees that handle non-linear relationships, we compared the relative influence (as a measure of effectiveness) of environmental variables related to climate, landscape (or habitat heterogeneity), land-use intensity or energy availability to explain European vertebrate species richness (birds, amphibians, and mammals) at the continental and ecoregion scales. We found that dominant land cover and actual evapotranspiration that relate to energy availability were the main correlates of vertebrate species richness over Europe. At the ecoregion scale, we identified four distinct groups of ecoregions where species richness was essentially associated to (i) seasonality of temperature, (ii) actual evapotranspiration and/or mean annual temperature, (iii) seasonality of precipitation, actual evapotranspiration and land cover) and (iv) and an even combination of the environmental variables. This typology of ecoregions remained valid for total vertebrate richness and the three vertebrate groups taken separately. Despite the overwhelming influence of land cover and actual evapotranspiration to explain vertebrate species richness patterns at European scale, the ranking of the main correlates of species richness varied between regions. Interestingly, landscape and land-use indicators did not stand out at the continental scale but their influence greatly increased in southern ecoregions, revealing the long-lasting human footprint on land-use-land-cover changes. Our study provides one of the first multi-scale descriptions of the variability in the ranking of correlates across several taxa.

Rising global demand for agricultural products results in agricultural expansion and intensification, with substantial environmental trade-offs. The South American Dry Chaco contains some of the fastest expanding agricultural frontiers worldwide, and includes diverse forms of land management, mainly associated with different land tenure regimes; which in turn are segregated along environmental gradients (mostly rainfall). Yet, how these regimes impact the environment and how trade-offs between production and environmental outcomes varies remains poorly understood. Here, we assessed how biodiversity, biomass stocks, and agricultural production, measured in meat-equivalents, differ among land tenure regimes in the Dry Chaco. We calculated a land-use outcome index (LUO) that combines indices comparing actual vs. potential values of ‘preservation of biodiversity’ (PI), ‘standing biomass’ (BI) and ‘meat production’ (MI). We found land-use outcomes to vary substantially among land-tenure regimes. Protected areas showed a biodiversity index of 0.75, similar to that of large and medium-sized farms (0.72 in both farming systems), and higher than in the other tenure regimes. Biomass index was similar among land tenure regimes, whereas we found the highest median meat production index on indigenous lands (MI = 0.35). Land-use outcomes, however, varied more across different environmental conditions than across land tenure regimes. Our results suggest that in the Argentine Dry Chaco, there is no single land tenure regime that better minimizes the trade-offs between production and environmental outcomes. A useful approach to manage these trade-offs would be to develop geographically explicit guidelines for land-use zoning, identifying the land tenure regimes more appropriate for each zone.

Global agricultural production will likely need to increase in the future due to population growth, changing diets, and the rising importance of bioenergy. Intensifying already existing cropland is often considered more sustainable than converting more natural areas. Unfortunately, our understanding of cropping patterns and intensity is weak, especially at broad geographic scales. We characterized and mapped cropping systems in Europe, a region containing diverse cropping systems, using four indicators: (a) cropping frequency (number of cropped years), (b) multi-cropping (number of harvests per year), (c) fallow cycles, and (d) crop duration ratio (actual time under crops) based on the MODIS Normalized Difference Vegetation Index (NDVI) time series from 2000 to 2012. Second, we used these cropping indicators and self-organizing maps to identify typical cropping systems. The resulting six clusters correspond well with other indicators of agricultural intensity (e.g., nitrogen input, yields) and reveal substantial differences in cropping intensity across Europe. Cropping intensity was highest in Germany, Poland, and the eastern European Black Earth regions, characterized by high cropping frequency, multi-cropping and a high crop duration ratio. Contrarily, we found lowest cropping intensity in eastern Europe outside the Black Earth region, characterized by longer fallow cycles. Our approach highlights how satellite image time series can help to characterize spatial patterns in cropping intensity—information that is rarely surveyed on the ground and commonly not included in agricultural statistics: our clustering approach also shows a way forward to reduce complexity when measuring multiple indicators. The four cropping indicators we used could become part of continental-scale agricultural monitoring in order to identify target regions for sustainable intensification, where trade-offs between intensification and the environmental should be explored.

Assessing changes in the extent and management intensity of land use is crucial to understanding land-system dynamics and their environmental and social outcomes. Yet, changes in the spatial patterns of land management intensity, and thus how they might relate to changes in the extent of land uses, remains unclear for many world regions. We compiled and analyzed high-resolution, spatially-explicit land-use change indicators capturing changes in both the extent and management intensity of cropland, grazing land, forests, and urban areas for all of Europe for the period 1990–2006. Based on these indicators, we identified hotspots of change and explored the spatial concordance of area versus intensity changes. We found a clear East–West divide with regard to agriculture, with stronger cropland declines and lower management intensity in the East compared to the West. Yet, these patterns were not uniform and diverging patterns of intensification in areas highly suitable for farming, and disintensification and cropland contraction in more marginal areas emerged. Despite the moderate overall rates of change, many regions in Europe fell into at least one land-use change hotspot during 1990–2006, often related to a spatial reorganization of land use (i.e., co-occurring area decline and intensification or co-occurring area increase and disintensification). Our analyses highlighted the diverse spatial patterns and heterogeneity of land-use changes in Europe, and the importance of jointly considering changes in the extent and management intensity of land use, as well as feedbacks among land-use sectors. Given this spatial differentiation of land-use change, and thus its environmental impacts, spatially-explicit assessments of land-use dynamics are important for context-specific, regionalized land-use policy making.

The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300–3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales.

Landscapes are closely linked to human well-being, but they are undergoing rapid and fundamental change. Understanding the societal transformation underlying these landscape changes, as well as the ecological and societal outcomes of landscape transformations across scales are prime areas for landscape research. We review and synthesize findings from six important areas of landscape research in Europe and discuss how these findings may advance the study of ecosystem change and society and its thematic key priorities. These six areas are: (1) linkages between people and the environment in landscapes, (2) landscape structure and land-use intensity, (3) long-term landscape history, (4) driving forces, processes, and actors of landscape change, (5) landscape values and meanings, and (6) landscape stewardship. We propose that these knowledge areas can contribute to the study of ecosystem change and society, considering nested multiscale dynamics of social-ecological systems; the stewardship of these systems and their ecosystem services; and the relationships between ecosystem services, human well-being, wealth, and poverty. Our synthesis highlights that knowledge about past and current landscape patterns, processes, and dynamics provides guidance for developing visions to support the sustainable stewardship of social-ecological systems under future conditions.

During the Soviet Virgin Lands Campaign, approximately 23 million hectares (Mha) of Eurasian steppe grassland were converted into cropland in Northern Kazakhstan from 1954 to 1963. As a result Kazakhstan became an important breadbasket of the former Soviet Union. However, the collapse of the Soviet Union in 1991 triggered widespread agricultural abandonment, and much cropland reverted to grasslands. Our goal in this study was to reconstruct and analyze agricultural land-cover change since the eve of the Virgin Lands Campaign, from 1953 to 2010 in Kostanay Province, a region that is representative of Northern Kazakhstan. Further, we assessed the potential of currently idle cropland for re-cultivation. We reconstructed the cropland extent before and after the Virgin Lands Campaign using archival maps, and we mapped the agricultural land cover in the late Soviet and post-Soviet period using multi-seasonal Landsat TM/ETM+ images from circa 1990, 2000 and 2010. Cropland extent peaked at approximately 3.1 Mha in our study area in 1990, 38% of which had been converted from grasslands from 1954 to 1961. After the collapse of the Soviet Union, 45% of the Soviet cropland was abandoned and had reverted to grassland by 2000. After 2000, cropland contraction and re-cultivation were balanced. Using spatial logistic regressions we found that cropland expansion during the Virgin Lands Campaign was significantly associated with favorable agro-environmental conditions. In contrast, cropland expansion after the Campaign until 1990, as well as cropland contraction after 1990, occurred mainly in areas that were less favorable for agriculture. Cropland re-cultivation after 2000 was occurring on lands with relatively favorable agro-environmental conditions in comparison to remaining idle croplands, albeit with much lower agro-environmental endowment compared to stable croplands from 1990 to 2010. In sum, we found that cropland production potentials of the currently uncultivated areas are much lower than commonly believed, and further cropland expansion is only possible at the expense of marginal lands. Our results suggest if increasing production is a goal, improving crop yields in currently cultivated lands should be a focus, whereas extensive livestock grazing as well as the conservation of non-provisioning ecosystem services and biodiversity should be priority on more marginal lands.

Global change presents risks and opportunities for social-ecological systems worldwide. Key challenges for sustainability science are to identify plausible future changes in social-ecological systems and find ways to reach socially and environmentally desirable conditions. In this context, regional-scale studies are important, but to date, many such studies have focused on a narrow set of issues or applied a narrow set of tools. Here, we present a holistic approach to work through the complexity posed by cross-scale interactions, spatial heterogeneity, and multiple uncertainties facing regional social-ecological systems. Our approach is spatially explicit and involves assessments of social conditions and natural capital bundles, social-ecological system dynamics, and current development trends. The resulting understanding is used in combination with scenario planning to map how current development trends might be amplified or dampened in the future. We illustrate this approach via a detailed case study in southern Transylvania, Romania, one of Europe's most significant biocultural refugia. Our goal was to understand current social-ecological dynamics and assess risks and opportunities for sustainable development. Our findings show that historical events have strongly shaped current conditions and current development trends in southern Transylvania. Moreover, although external drivers (including EU policies) set the general direction of regional development trajectories, local factors, including education, leadership, and the presence of bridging organizations, can enhance or counteract their effects. Our holistic approach was useful for generating an in-depth understanding of a regional social-ecological system and could be transferred to other parts of the world.

The demand for agricultural products continues to grow rapidly, but further agricultural expansion entails substantial environmental costs, making recultivating currently unused farmland an interesting alternative. The collapse of the Soviet Union in 1991 led to widespread abandonment of agricultural lands, but the extent and spatial patterns of abandonment are unclear. We quantified the extent of abandoned farmland, both croplands and pastures, across the region using MODIS NDVI satellite image time series from 2004 to 2006 and support vector machine classifications. Abandoned farmland was widespread, totaling 52.5 Mha, particularly in temperate European Russia (32 Mha), northern and western Ukraine, and Belarus. Differences in abandonment rates among countries were striking, suggesting that institutional and socio-economic factors were more important in determining the amount of abandonment than biophysical conditions. Indeed, much abandoned farmland occurred in areas without major constraints for agriculture. Our map provides a basis for assessing the potential of Central and Eastern Europe’s abandoned agricultural lands to contribute to food or bioenergy production, or carbon storage, as well as the environmental trade-offs and social constraints of recultivation.

Widespread changes of agricultural land use occurred in Eastern Europe since the collapse of socialism and the European Union’s eastward expansion, but the rates and patterns of recent land changes remain unclear. Here we assess agricultural land change for the entire Carpathian ecoregion in Eastern Europe at 30 m spatial resolution with Landsat data and for two change periods, between 1985–2000 and 2000–2010. The early period is characterized by post-socialist transition processes, the late period by an increasing influence of EU politics in the region. For mapping and change detection, we use a machine learning approach (random forests) on image composites and variance metrics which were derived from the full decadal archive of Landsat imagery. Our results suggest that cropland abandonment was the most prevalent change process, but we also detected considerable areas of grassland conversion and forest expansion on non-forest land. Cropland abandonment was most extensive during the transition period and predominantly occurred in marginal areas with low suitability for agriculture. Conversely, we observed substantial recultivation of formerly abandoned cropland in high-value agricultural areas since 2000. Hence, market forces increasingly adjust socialist legacies of land expansive production and agricultural land use clusters in favorable areas while marginal lands revert to forest.