Ecography最新文献

{"title":"Wild bees and landcover: bee species' body size does not predict the scale of effect, but bee phenology predicts association with landcover type","authors":"Dylan T. Simpson, Colleen Smith, Rachael Winfree","doi":"10.1002/ecog.07982","DOIUrl":"https://doi.org/10.1002/ecog.07982","url":null,"abstract":"Habitat is a key aspect of any species' niche and can affect populations at multiple spatial scales. Basic ecology and effective conservation thus require an understanding of which habitats matter and at what scales. Yet, habitat studies are rarely scale‐optimized, and what determines the scale(s) at which populations are affected by surrounding habitat (the ‘scale of effect') is poorly understood. In this study, we test the ‘mobility hypothesis,' which predicts that species with larger foraging ranges should have larger scales of effect. The mobility hypothesis is the most popular explanation of what determines species' scales of effect, but empirical support is mixed. We test the mobility hypothesis using wild bee species and, in doing so, also assess landscape‐scale habitat associations of 84 bee species. We collected 30 376 specimens of 84 bee species from 165 sites in the northeastern USA and used linear models to determine landcover associations and scales of effect for each species. To test the mobility hypothesis, we asked whether scales of effect varied with two mobility‐related traits – body size or sociality, which are the strongest known predictors of bee foraging ranges. Controlling the false discovery rate at 5%, we found 193 significant species–landcover associations across 60 (of 84) species. Scales of effect ranged from 100 to 8000 m (mode = 200 m; median = 1000 m) and, counter to the mobility hypothesis, were not associated with body size or sociality. As a result, we argue that ecologists should reconsider making assumptions about species' scales of effect and should instead explicitly measure scales of effect for their particular study organism and system. Considering the landcover associations themselves, we found these were broadly explained by phenology, with spring‐flying bees being associated with forests and summer‐flying bees being associated with more open, non‐forested habitats.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"54 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Twenty years of dynamic occupancy models: a review of applications and look to the future","authors":"Saoirse Kelleher, Gurutzeta Guillera-Arroita, Jane Elith, Natalie J. Briscoe","doi":"10.1002/ecog.07757","DOIUrl":"https://doi.org/10.1002/ecog.07757","url":null,"abstract":"Since their introduction over 20 years ago, dynamic occupancy models (DOMs) have become a powerful and flexible framework for estimating species occupancy across space and time while accounting for imperfect detection. As their popularity has increased and extensions have further expanded their capabilities, DOMs have been applied to increasingly diverse datasets and research objectives in applied ecology. At the same time, technological advancements have resulted in massive increases in available data, offering both new opportunities and challenges for users of DOMs. Given these developments, it is timely to examine common practices in building these models to understand the breadth of modelling approaches, determine potential vulnerabilities, and identify priorities for future research. We reviewed a sample of articles that have fit DOMs in the past 20 years, examining the contexts of their application and the approaches taken to the model‐building process. We find that these models have been used to pursue diverse objectives, based on datasets with wide‐ranging spatial and temporal scales collected using a variety of survey methods. Our comparisons of modelling approaches indicate that many applications of DOMs considered relatively few covariates on key model parameters, as well as a tendency towards linear responses over more complex non‐linear or interactive forms. Model selection techniques were largely idiosyncratic with little consensus on the best approaches, and model evaluation was rare across reviewed applications. Based on these findings we highlight aspects of the modelling process that merit closer attention, such as the possible impacts of low complexity and missing drivers of heterogeneity on model performance, the uncertainties around robust and appropriate model selection techniques for different contexts, and the need for trusted and reliable tools for model assessment and evaluation.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"115 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in marine species distribution models: a review of methodological advances and future challenges","authors":"Moritz Klaassen, Tiago A. Marques, Filipe Alves, Marc Fernandez","doi":"10.1002/ecog.07702","DOIUrl":"https://doi.org/10.1002/ecog.07702","url":null,"abstract":"Correlative species distribution models (SDMs) are quantitative tools in biogeography and macroecology. Building upon the ecological niche concept, they correlate environmental covariates to species presence to model habitat suitability and predict species distributions. Since their development, SDMs have undergone substantial advances in their predictive accuracy, benefiting from increased data availability, advanced machine learning algorithms, novel data integration procedures, refined model validation techniques, and incorporation of biotic predictors. Although initially applied in terrestrial systems, these models are now also widely used in the marine environment, recognized for their value in conservation planning, fisheries management, and understanding species responses to climate variability and change. Despite their increased application, SDMs face unique challenges when applied in the marine environment. These challenges include the three‐dimensional complexity of marine ecosystems, the availability of environmental covariates across suitable spatial and temporal scales, the dynamic properties of these covariates, and unique dispersal patterns and mobility traits of marine species. Here, we review recent methodological advances and emerging trends in marine SDMs. We highlight three‐dimensional modelling approaches that capture species distributions below the sea surface and assess the importance of temporal resolution, particularly for modelling highly mobile marine species in dynamic marine environments. Further, we discuss the expansion in the types of occurrence data being used, including fishery‐dependent and fishery‐independent sources, citizen science contributions, and satellite tracking data, along with the methods used to address their associated biases. We also explore and discuss novel methodologies for environmental data collection, such as remote‐sensing technologies and numeric ocean models, considering the existing limitations in spatial and temporal resolution. Together, our review synthesizes methodological innovations, highlights ongoing challenges, and discusses emerging trends within the extensive literature on marine SDMs.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"52 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of phytoplankton species distribution on particulate organic carbon dynamics along a coastal gradient","authors":"Catharina Uth, Joris Wiethase, Tjardo Stoffers, Eero Asmala, Aleksandra Lewandowska","doi":"10.1002/ecog.08008","DOIUrl":"https://doi.org/10.1002/ecog.08008","url":null,"abstract":"Phytoplankton communities affect carbon dynamics worldwide, strongly influencing the quality and quantity of organic carbon in coastal ecosystems. Yet, we still know little about the impacts of changing phytoplankton community composition on the potential carbon pathways in estuaries and coasts. Here, we sampled 25 sites along a coastal salinity and nutrient gradient, collecting water for water chemistry and phytoplankton for community composition analyses. For each site, we determined phytoplankton taxonomic diversity and used Bayesian joint species distribution models considering species interactions, taxonomic relatedness and traits to identify key environmental factors driving phytoplankton community composition. Subsequently, we used structural equation modelling to establish direct and indirect links between the identified key environmental factors, taxonomic diversity (richness and evenness) and particulate organic carbon (POC). We found that the phytoplankton distribution along the estuarine gradient was mainly driven by changes in salinity. Increasing salinity (ranging between 0.8–6.4) benefited motile species and reduced the phytoplankton richness, which resulted in a decrease in POC concentration. This indirect effect of salinity on POC was stronger than a direct one, highlighting the mediating role of phytoplankton richness. This emphasizes the importance of diversity regulating coastal biogeochemical processes and suggests that future changes in salinity might shift coastal carbon dynamics due to changes in phytoplankton community composition.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"25 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correcting environmental sampling bias improves transferability of species distribution models","authors":"Arman Pili, Boris Leroy, Damaris Zurell","doi":"10.1002/ecog.08002","DOIUrl":"https://doi.org/10.1002/ecog.08002","url":null,"abstract":"Sampling bias is an inherent problem in widely available biodiversity data, undermining the robustness of correlative species distribution models (SDMs). To some extent, subsampling occurrence data can account for uneven sampling efforts; yet, conventional approaches subsample in geographical space, while subsampling in environmental space remains underexplored. Here, we compared the effectiveness of subsampling methods that correct sampling bias either in geographical space (spatial gridding, spatial distance thinning) or directly in environmental space (environmental gridding), including two novel approaches introduced here: environmental clustering and environmental distance thinning. We hypothesised that environmental subsampling methods would be more effective in improving SDM performance across its three primary uses: explaining, predicting, and projecting. Using a virtual ecologist framework, we assessed SDM performance against four evaluation tests: replicating true species–environment response curves, predicting within the sampling region via internal cross‐validation and evaluation against independent data, and projecting outside the sampling region. Our findings demonstrate that environmental subsampling methods, especially environmental clustering and environmental distance thinning, outperformed other methods in yielding robust SDMs in almost all evaluation tests. Interestingly, cross‐validation favoured SDMs with no sampling bias correction, highlighting the inability of cross‐validation to identify unbiased models. Our findings emphasise a critical conceptual disconnect: SDMs appearing to perform well in predicting species' distributions may not reliably estimate species–environment relationships, nor transfer predictions onto novel environments. Environmental subsampling methods are reliable approaches for all uses, but are particularly suited for explaining species' niches and transferring predictions across space and/or time, such as when anticipating species' responses to climate change or assessing the risk of biological invasions. Conversely, geographic subsampling methods may suffice for predicting species' distributions within their current environmental context, as required in conservation planning. Our study firmly establishes the critical importance of correcting environmental sampling bias, while also providing reliable solutions for supporting biodiversity conservation in an ever‐changing world.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"147 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The division of food space among mammalian species on biomes","authors":"Sara Gamboa, Sofía Galván, Mar Sobral, Manuel Hernández Fernández, Sara Varela","doi":"10.1002/ecog.07660","DOIUrl":"https://doi.org/10.1002/ecog.07660","url":null,"abstract":"Understanding how species' ecological partitioning functions across biomes is fundamental to macroecology and conservation biology. Here, we examine the global distribution of dietary strategies in terrestrial mammals, focusing on how biome specialization modulates trophic diversity and structure at a broad geographical scale. Using species‐level data from over 3600 terrestrial mammal species, we constructed a multivariate dietary space and quantified the area, redundancy, dispersion, uniqueness, and turnover of trophic strategies across ten major biomes. Species were classified as biome specialists, moderate generalists, or extreme generalists based on their biome breadth. By analysing biome specialists and generalists separately, we show that biome specialists tend to exhibit more constrained and compositionally distinct dietary niches in less productive biomes, while generalists, particularly moderate generalists, dominate functional space occupancy in all biomes, even the harsher ones such as tundra and taiga. This highlights how environmental constraints and ecological roles shape trophic strategies at a global scale. Notably, extreme generalists tended to exhibit more carnivorous or insectivorous diets, suggesting a strategy based on mobile predation or opportunism rather than a highly diversified omnivory. Despite these general patterns, highly productive biomes supported the greatest diversity of dietary strategies, with higher functional redundancy and niche packing. Nestedness and turnover analyses revealed that biome specialists diets are often subsets of generalists diets, but with significant compositional shifts across biomes. These findings underscore the dual role of biome generalists as both functional stabilizers and potential limiters of ecological diversity, and highlight the vulnerability of specialist species to global change. Our study offers a mechanistic framework for understanding how dietary strategies interact with environmental filtering, and for identifying functional risks in changing ecosystems.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"694 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climatic resilience after extreme drought in Mediterranean shrubland plant communities","authors":"Raquel Díaz‐Borrego, Francisco Lloret, Luciana Jaime, Jordi Margalef‐Marrase, José Miguel Moya‐Pérez, María Ángeles Pérez‐Navarro","doi":"10.1002/ecog.07835","DOIUrl":"https://doi.org/10.1002/ecog.07835","url":null,"abstract":"Extreme climatic events are increasing with climate change, producing changes in communities' climatic characterization. So, mismatches (climatic disequilibrium, CD) between climatic conditions inferred from species' requirements (community inferred climate, CIC) and macroclimate may undergo changes with extreme climatic events. Climatic resilience is defined as the ability to maintain or recover community climatic characteristics, regardless of species' identity, after disturbance or stress.We evaluated the dynamics of plant community climatic characterization in Mediterranean shrublands that experienced a drought event, considering CIC and CD. CIC was calculated by averaging species' climatic niche centroids, weighted by species' relative abundances, in the multivariate environmental space obtained from the climate of the species' geographical occurrence. CD was estimated as Euclidean distance in this space between the observed historic macroclimate and CIC. Climatic resistance was inferred by the distance between pre‐drought and drought CIC, climatic resilience by the distance between pre‐drought and post‐drought CIC, and relative climatic resilience by the same distance weighted by the climatic displacement suffered during the drought. We found a significant reduction in community CD after drought, with CIC becoming more arid, likely due to environmental filtering of those species with wetter distribution. Communities with less pre‐drought CD showed higher climatic resistance but pre‐drought CD did not explain climatic resilience. Communities with more arid CIC exhibited high climatic resilience regardless of drought impact (high relative climatic resilience), except for certain communities exhibiting highly arid CICs. Communities with less arid CIC showed low relative climatic resilience, as their resilience was associated with high resistance.The study highlights community impacts by extreme droughts through filtering of species distributed in more humid climates. This produces changes in the CD of communities, whose resilience is determined by CIC, pre‐drought CD, and drought impact in terms of CIC change.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"19 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal variation in community structure and elevational distributions of mammals and birds in the Central Himalayas","authors":"Yiming Hu, Yuping Xu, Huijian Hu, Brett R. Scheffers","doi":"10.1002/ecog.07973","DOIUrl":"https://doi.org/10.1002/ecog.07973","url":null,"abstract":"Montane species utilize various habitats along elevations to adapt to seasonality, providing an ideal opportunity to study how species respond to shifting environments. This study characterizes seasonal changes in community structure and elevational distributions across multiple taxa in the Central Himalayas. We compared species richness, community co‐occurrence network, and composition of mammals and birds across twelve 300‐m elevational bands during the warm and cold seasons. We calculated seasonal shifts in the species elevational ranges, assessing how species traits influenced these shifts and testing the most widely accepted hypotheses for seasonal shifts in elevation distribution: the ‘food‐limitation hypothesis' and the ‘climatic constraint hypothesis'. Simpler community network structures emerge during cold seasons, with distinct patterns observed across taxa. Mammals and birds exhibit a hump‐shaped elevational pattern in species richness, with peak richness shifting to lower elevations in the cold season as high‐elevation species moved downslope. Temporal beta diversity from warm to cold seasons were primarily caused by species losses rather than species gains in high‐ and middle‐elevation communities for both taxa. High‐ and middle‐elevation mammals and birds, as well as insectivorous birds, significantly shifted their upper elevational boundaries downslope in the cold season. Innovatively, we analyzed the seasonal distribution shifts of congeneric competing species to understand the interplay between abiotic and biotic factors in driving species' adaptive responses. From warm to cold season, high‐elevation genera increased spatial separation while low‐elevation genera decreased it, indicating that interspecies relationships adjust to environmental fluctuations and vary across contexts. This study provides empirical evidence of seasonal variations in community structure and species distributions across Himalayan taxa, highlighting how seasonality drives shift in species distributions across elevations and emphasizing the dynamic nature of elevational ranges over time. This variability underscores the need to incorporate elevational range flexibility into climate change research to understand climate‐driven distribution shifts.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"103 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144568533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns and drivers of biotic disturbance hotspots in western United States coniferous forests","authors":"Michele S. Buonanduci, Sarah J. Hart, Patrick C. Tobin, Brian J. Harvey","doi":"10.1002/ecog.07680","DOIUrl":"https://doi.org/10.1002/ecog.07680","url":null,"abstract":"Globally, forest disturbances caused by herbivorous insects and plant pathogens (i.e. biotic disturbances) have increased since the 1990s, a trend linked in part to climate warming. With increases in biotic disturbance activity, an emerging ecological phenomenon has been documented: biotic disturbance ‘hotspots', or areas where two or more biotic disturbance agents co‐occur in space and time. Biotic disturbance hotspots may have important implications for forest resilience, particularly if they erode mechanisms of post‐disturbance forest recovery. The factors leading to hotspot occurrence, however, remain poorly understood. We characterized the patterns and drivers of biotic disturbance hotspots occurring from 2000 to 2020 across three broad forested regions in the western United States (US; the Southern Rockies, Middle Rockies, and Cascades). Using Bayesian spatio‐temporal models, we evaluated whether hotspots can be predicted from predisposing factors expected to increase forest susceptibility to biotic disturbance (i.e. forest composition, topography, and average climate), as well as inciting factors known to trigger individual bark beetle and pathogen outbreaks (i.e. annual weather). Biotic disturbance hotspots exhibited distinct spatio‐temporal patterns and trends within each region. Forest structure and composition were the strongest and most consistent drivers of hotspots. Other factors varied in their importance by region, reflecting regional differences in biophysical context. Relative to the predictor variables included in our models, estimated spatio‐temporal random effects were more closely correlated with model predictions, suggesting that dynamic factors such as outbreak spread strongly shape patterns of biotic disturbance hotspots. Our results illustrate the widespread nature of biotic disturbance hotspots across western US coniferous forests and demonstrate the importance of forest structure and regional outbreak dynamics in anticipating hotspots at regional scales. These findings provide a deeper understanding of interacting forest disturbances and have important implications for the resilience of forests during a period marked by continued increases in disturbance activity.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"7 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reviewing and benchmarking ecological modelling practices in the context of land use","authors":"Elie Gaget, Martin Jung, Matthew Lewis, Florian Hofhansl, Laura Jane Graham, Eleanor Warren‐Thomas, Piero Visconti","doi":"10.1002/ecog.07745","DOIUrl":"https://doi.org/10.1002/ecog.07745","url":null,"abstract":"Despite habitat loss and degradation are the primary drivers of biodiversity loss, different conclusions have been drawn about the importance of land‐use or land‐cover (LULC) change for biodiversity. Differences may be due to the difficulty of framing a coherent model design to assess LULC effects. Recommendations have previously been identified for the design of statistical models and failing to follow them can risk misidentification of drivers, misinterpretation of predictions, overconfidence, high uncertainty, and incorrect management recommendations. We review modelling practices in statistical models assessing biodiversity responses to LULC, and investigated relationships between modelling practices and citations by scientific articles and policy documents. We benchmarked practices across model approaches, political extents, and objectives. From 346 model applications, we found that more than half of the model applications have justified ecologically‐relevant predictors, have used 1 km² or lower LULC spatial resolution, have used fine LULC thematic resolutions, performed validation or communicated uncertainty. However, we found that the model approach and political extent were strong determinants of the misuse of modelling recommendations. Top–down models followed less frequently three recommendations out of six, compared to other model approaches. Global studies used coarser LULC thematic and spatial resolution than studies at other extents, and thus potentially underestimated the relationships between LULC and biodiversity. Global studies were however more frequently cited by both scientific studies and policy documents. Modelling recommendations are not universally applied, especially because of methodological tradeoff, technical difficulties in their applications and data requirements. However, the multiples risks associated with the misuse of modelling recommendations, particularly in large‐scale modelling exercises, raise concerns on model interpretation and policy support from science, regarding the impacts of LULC on biodiversity.","PeriodicalId":51026,"journal":{"name":"Ecography","volume":"67 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}