Forest ecosystems are increasingly exposed to climatic and anthropogenic disturbances under accelerating global change, making their resilience crucial for sustaining ecological functions. While resilience has become a key concept in forest management, its associations under combined pressures of climate stressors and fragmentation remain insufficiently understood, especially in metropolitan forested landscapes. We quantified and mapped forest resilience in the Core area of the Wuhan Metropolitan Region (CWMR), a rapidly urbanizing region in subtropical China, from 2004 to 2023, and investigated the associations between climatic factors, landscape fragmentation, and spatial patterns of forest resilience. Forest resilience was quantified using the lag-1 temporal autocorrelation (TAC) of normalized difference vegetation index (NDVI) time series, based on critical slowing down theory. Key climatic factors associated with forest resilience were first identified using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Structural equation modeling (SEM) was then used to examine the direct and indirect associations among landscape fragmentation, climatic factors, and forest resilience. We found that 60.54
Understanding the relationship between Exotic Annual Grass (EAG) expansion, drought, and fire frequency across western US rangelands is complex, but critical for maintaining ecological integrity. We quantified the impacts of wildfire on EAGs, with emphasis on understanding the factors driving the spatial and temporal variability of those impacts, highlighting the role of drought. We investigate two study periods using 1985–2024 and 2016–2024 EAG cover datasets to better understand EAG dynamics in relation to fire. We, 1) leverage mapped EAG cover responses to fire in the context of various biophysical variables to model expected change in EAG cover (ΔG) given a fire in a specific location, even if no fire has occurred in that location in the period of record; 2) evaluate the influence of drought and other biophysical variables on the ΔG; 3) combine our ΔG values with burn probability and ecological integrity data to evaluate the profile of EAG risk due to fire in sagebrush habitats. Our results show that fire impacts on EAG are often delayed, with limited effects in the first post-fire year followed by increasing cover over subsequent years. Importantly, areas with low pre-fire EAG cover exhibited the greatest potential for post-fire increases, whereas heavily invaded sites showed lower incremental responses, suggesting saturation or carrying-capacity constraints. Understanding the connections among EAGs, drought, and wildfire is critical for maintaining the ecological integrity of western rangelands. Identifying the magnitude of fire impacts to EAG cover provides critical insight for land managers.
Understanding how habitat configuration influences biodiversity is central to landscape and seascape ecology, yet experimental tests that isolate fragmentation per se from habitat area remain rare. In an earlier experiment that disentangled the effects of habitat area and spatial configuration, we found a unimodal species–fragmentation relationship, where species richness peaked at intermediate fragmentation. Although the mechanisms were not identified then, dispersal limitation was proposed as a key driver underlying this pattern. Here, our goal was to test whether intermediate fragmentation generates the strongest dispersal limitation by reducing gastropod movement, using two complementary studies conducted in the same experimental landscapes. We created experimental intertidal landscapes by attaching artificial habitat tiles to seawalls. The design crossed three levels of tile cover (7
Urban green spaces play a vital role in sustaining biodiversity, particularly bird communities, in rapidly urbanizing regions. Two contrasting land-use strategies—land sparing (LSP) and land sharing (LSH)—have been proposed to reconcile urban development with biodiversity conservation, yet their relative benefits remain poorly understood across gradients of green-space cover, seasonal dynamics, and species groups. This study aims to compare the effects of LSP and LSH on urban bird diversity and explore how green-cover gradients, bird residency status (resident vs. migratory), and seasonal periods (breeding vs. non-breeding) influence the biodiversity outcomes of the two land-use strategies. Using Nanjing, China, a highly urbanized city along the East Asian-Australasian Flyway, as a case study, we collected 116,387 citizen-science bird records from 2022 to 2024. Avian diversity was quantified using species richness, Shannon–Wiener, and Simpson indices, which were subsequently integrated through principal component analysis. Diversity was compared across three spatial scales (500, 1000, and 1500 m), considering green-cover gradients, bird residency status (resident vs. migratory), and seasonal periods (breeding vs. non-breeding). Generalized additive models were applied to identify the key environmental drivers of bird diversity under each strategy. Our study found that LSP tended to be associated with higher avian diversity, especially for resident birds in areas with green cover below 50
Rising sea levels are causing lasting alterations to low-lying coastal landscapes, with the southeastern United States being particularly susceptible. The inundation of low-lying land by rising sea levels disrupts terrestrial linkages and intensifies erosion, leading to significant habitat fragmentation. Consequently, the movement patterns of wildlife and overall ecosystem functionality are negatively impacted. Understanding changes in landscape connectivity for forest and wetland ecosystems is crucial for assessing ecological impacts and guiding effective conservation efforts. This study aims to create an analytical methodological approach that assesses the impacts of sea level rise (SLR) on omni-directional landscape connectivity, while simultaneously exploring the factors that may drive these changes. This analysis, which includes the influence of core area (key habitat for recolonization/restoration) size and perimeter, is designed to serve as a direct reference for regional decision-makers. This study assessed landscape connectivity changes in two southeastern U.S. coastal counties (Chatham and St. Johns) using omnidirectional circuit theory under sea level rise scenarios through 2100. Ecological connectivity was modeled based on how different land cover types impede or facilitate movement. The resulting connectivity maps were then analyzed to identify changes and find correlations with core area metrics. Sea level rise will greatly affect forests and wetlands connectivity in Chatham and St. Johns counties. Current connectivity patterns are not uniform across the study areas. In Chatham County, inland areas have higher ecological flow than its more vulnerable coastal regions. Projections indicate that Chatham County faces a more severe overall decline in connectivity (17
Ongoing climate change has intensified compound climate extremes, increasing the long-term exposure of ecosystems to compound climatic stress and posing growing risks to ecosystem services (ESs) through complex and spatially heterogeneous associations. Understanding these associations, and whether CES-level relationships exhibit nonlinear features, is essential for formulating effective landscape management and adaptation strategies under increasing climatic pressure. This study aims to (1) characterize the spatiotemporal dynamics of four types of compound climate extreme exposure, six key ESs, and comprehensive ecosystem services (CESs) from 2000 to 2020, and (2) compare the nonlinear associations and spatial explanatory effects of different compound climate extremes on CESs. Daily data were used to identify compound extreme days, while five-year aggregated indicators were used to characterize recent cumulative exposure corresponding to CESs observation years. We integrated long-term high-resolution datasets with random forest (RF) and the optimal parameter geographic detector (OPGD). RF was used to assess the relative national-scale contribution of compound climate extremes to CESs prediction and to identify CES-level nonlinear association patterns, whereas OPGD was used to quantify spatial explanatory power and interaction effects associated with CESs heterogeneity. The spatial patterns of the six ESs and CESs remained relatively stable during 2000–2020, whereas the total amount of CESs showed an initial increase followed by a decline. In contrast, compound climate extremes showed clear regional divergence. RF-based analyses suggested apparent nonlinear association patterns at the equal-weighted CES level. Among the four extreme events, WD showed the most pronounced nonlinear pattern in the PDPs, with a curve-based transition from a weak positive association at lower exposure levels to a stronger negative association at higher exposure levels, most apparent around 10 days of recent cumulative WD exposure under the aggregation framework used in this study. Across both the national-scale contribution and spatial analyses, dry-related compound climate extremes showed stronger associations with CESs than CW and WW. These findings indicate that recent cumulative exposure to compound climate extremes is associated with CES-level nonlinear and spatially heterogeneous patterns across contrasting regional climate–landscape contexts in China. This study provides an empirical basis for ecosystem management and region-specific adaptation under escalating climate risk.
Land-system change reshapes landscapes, but its ecological consequences depend on land-use type, management, and disturbance, not vegetation cover alone. Conventional land-cover classes obscure ecologically distinct uses and cannot consistently link past and projected change. We developed an integrated land-use reclassification and scenario-simulation framework for China and used it to identify the dominant transition pathways from 2000 to 2020 and their divergence among scenarios by 2030 and 2050. Using the China Land Cover Dataset as the base map, we combined remote sensing with human-activity and disturbance indicators to reclassify China into seven land-use types, separating primary vegetation, secondary vegetation, plantation, and pasture. Land-use patterns were then simulated for 2030 and 2050 under four SSP-RCP scenarios. From 2000 to 2020, the cumulative transition area reached 39.99
Inconsistent findings regarding the impact of urban blue-green space patterns on regional thermal environments hinder the practical application of research in landscape ecology. This gap largely stems from a predominant focus on the global effects of landscape metrics, while their nonlinear inflection points, pairwise interactions, and underlying causal pathways have been insufficiently explored. This study aimed to (1) quantify the nonlinear and threshold effects of blue-green pattern metrics on land surface temperature (LST), (2) identify critical interactions between these pattern metrics, and (3) assess the direct and indirect pathways through which key drivers influence LST. The study was conducted in Suzhou, a humid subtropical canal city in China. We employed XGBoost-SHAP to decipher nonlinear effects and interactions, and further constructed a structural causal model (SCM) to quantify the direct and indirect pathways influencing LST. Blue-green pattern metrics effectively predicted seasonal land surface temperature (R2 = 0.667–0.830). Water coverage (PLANDW) was the dominant cooling factor across all seasons, followed by grassland coverage (PLANDG) except in winter. Both water and forest coverage exhibited activation thresholds (2
Global road network expansion has exacerbated wildlife-vehicle collisions. However, traditional risk assessments predominantly assume a linear relationship between landscape features and observed collision quantities, overlooking the non-linear threshold characteristics of ecological responses. This study aimed to quantify the non-linear response thresholds of terrestrial vertebrates to diverse landscape conditions and to characterize national spatial patterns of threshold-based cumulative scores. We further examined how positive- and negative-response taxa were associated with different landscape-cover intervals. Using extensive citizen science survey data across mainland China, we employed Threshold Indicator Taxa Analysis at an optimal spatial scale of 500 m. We then integrated community-level abrupt change points into a threshold-based cumulative scoring system. Positive-response taxa experienced abrupt increases in observed roadkill quantities within specific landscape-cover intervals. Negative-response taxa exhibited declines in reported records under relatively low levels of impervious surface coverage or specific landscape-cover conditions. High-scoring road segments were concentrated in the agricultural and urban landscapes of southeastern China, reflecting the geographic co-occurrence of multiple landscape thresholds associated with increases in reported roadkill quantities. Low-scoring road segments contained fewer reported collisions, but this did not necessarily indicate ecological safety. These contrasting patterns could arise from multiple ecological and observation-related processes, including differences in wildlife occurrence, movement behavior, road-crossing probability, reporting effort, and detectability. The identified landscape thresholds provide a hypothesis-generating framework for exploring spatial variation in reported WVC patterns. To transition from broad-scale screening to targeted mitigation prioritization, future research should validate these patterns by incorporating localized reporting effort, road exposure, traffic intensity, and biogeographic turnover.
Species distribution models combine environmental and occurrence data to map and predict habitat suitability for a species. Predicting species distributions in agricultural regions can be challenging due to historical under-sampling in those areas and the presence of small landscape elements or non-productive land adjacent to the agricultural fields. Here we model bees and hoverflies in Dutch agricultural regions at a 10 m resolution to measure the improvement in habitat suitability predictions when landscape elements are included as model parameters. We compared models excluding landscape elements with those incorporating them in two ways, as distance to the nearest element and as percentage local cover, and further assessed the latter against null models with randomized landscape variables. We evaluated the models using both withheld training data and independent field-collected data from three distinct agricultural regions. Our results demonstrate that integrating linear landscape elements into species distribution models significantly improves model performance by correcting the overprediction in agricultural fields and under prediction in field edges. Herbaceous landscape elements were especially important for modelling the distribution of pollinators, showing statistically significant model improvements for the withheld test data and independently collected data. Integrating landscape elements into distribution models is becoming both increasingly relevant and more feasible. It is gaining relevance as European policies promote the expansion of semi-natural habitats within agricultural landscapes, while advances in the availability of high-resolution spatial data are making such integration more achievable. We recommend that modelers integrate these elements to more accurately predict biodiversity in agricultural habitats. The resulting maps provide a more robust estimate of habitat suitability, underpinning conservation and management strategies.
Habitat loss is a major driver of biodiversity decline, particularly for wide-ranging carnivores that depend on connected landscapes. For species such as the jaguar (Panthera onca), changes in forest configuration and landscape connectivity can rapidly compromise corridor functionality and long-term population persistence. This study aims to (1) evaluate recent changes in structural connectivity within the jaguar corridor system in the Central Pacific region of Mexico and (2) identify and prioritize areas for ecological restoration to maintain or enhance landscape connectivity. The study was conducted in the jaguar corridor system in the Central Pacific region of Mexico, a key stronghold for the jaguar. We assessed recent landscape changes by integrating high-resolution land cover maps from 2019 and 2023 with structural connectivity analysis and functional connectivity modeling across jaguar corridors. Land cover analysis revealed a net forest loss of 216 km2 (0.53
Natural and anthropogenic stressors of global change are restructuring distributions of flora and fauna worldwide. Species distribution models allow ecologists to understand and predict changes in species distributions based on environmental conditions. To date, species distribution models for migratory birds are based on conditions from only the breeding season, overlooking conditions from the rest of their annual cycles. Here, with data from the well-studied American Redstart (Setophaga ruticilla), a long-distance migratory bird, we examine the importance of nonbreeding conditions in models of breeding abundance. We compared the accuracy of estimated breeding redstart abundance from models based on: (1) breeding conditions only, (2) breeding and nonbreeding conditions, and (3) breeding and nonbreeding conditions with an interaction between nonbreeding rainfall and migration distance. We then assessed whether conditioning on counts of other migratory species improved predictions of current redstart breeding abundance by capturing additional variation in nonbreeding conditions. Finally, we forecasted future breeding abundance based on the full annual cycle model that incorporates the seasonal interaction. Overall, all three models performed similarly in predicting current breeding redstart abundance. Conditioning predictions on counts of other migratory species improved predictions of current breeding redstart abundance. The full annual cycle model with an interaction forecasts range-wide population change that differs from predictions from the breeding-only model in key breeding redstart regions. Although the three models performed similarly well, the models that incorporated nonbreeding conditions identified relationships between nonbreeding conditions and breeding abundance. Continued, iterative revisions to these quantitative tools are critical for reconstructing past, estimating current, and forecasting future species distributions under global change.
The rapid expansion of renewable energy infrastructures—including solar, wind, storage, and transmission networks—is reshaping landscapes worldwide. While essential for decarbonization, their growing spatial footprints intensify conflicts over land use, biodiversity, and place-based values, signaling that energy development is fundamentally a landscape challenge. This perspective proposes a spatial framework for renewable energy planning grounded in the Integrated Landscape Approach (ILA) and structured through the landscape transect and energyshed. Drawing on literature in Landscape Sustainability Science, ILA, energy landscapes, and landscape planning and design, the paper examines how five ILA principles—multifunctionality, transdisciplinarity, stakeholder participation, complexity, and sustainability—can inform renewable energy planning and evaluation. The landscape transect differentiates renewable energy opportunities and constraints across urban, suburban, rural, and coastal landscapes, while the energyshed situates these places within broader systems of energy production, transmission, and consumption. They link place-based design with regional energy dynamics and support spatially explicit scenario- development, integrating ecosystem services, cultural landscapes, and participatory visualization. Applying ILA to spatial planning redefines renewable energy systems as multifunctional landscapes. This perspective positions ecologists, planners, designers, and communities as collaborative stewards of shared energy landscapes and supports transitions that are spatially grounded, socially legitimate, and ecologically resilient.
The persistent decline of farmland bird populations across European agricultural landscapes has been linked to agricultural intensification and landscape homogenisation, making cropping system diversification an important conservation strategy. However, assessing the combined effects of multiple diversification measures at the field scale remains challenging because conventional experimental designs with independent replicates are often infeasible. This study aimed to introduce Spatial Point Pattern Analysis (SPPA) to analyse data from a high-factorial experimental design without real replicates to evaluate the effects of diversified, small-scale patch cropping on bird habitat use in arable landscapes in north-east Germany. Additionally, the influence of field structure and agricultural management intensity on spatial patterns of bird occurrence is analysed. Using three years (2021–2023) of georeferenced bird observations, we compared diversified patch-cropping fields with adjacent sole-cropped large-sized reference fields and related bird occurrence to field structure and management intensity by applying SPPA. SPPA identified higher bird occurrence and habitat use frequency in patch-cropping fields than in reference fields. Bird observations were more strongly clustered in diversified fields, while lower pesticide use and higher weed cover were positively associated with bird occurrence. Crop type showed no clear effect. This study showcases SPPA as a promising tool for agroecological field research under on-farm conditions, capable of detecting spatial habitat use structures in non-replicated, real-world landscapes. In particular, the findings illustrate how SPPA can identify spatial associations between diversified cropping systems, management practices and farmland bird occurrence in European arable landscapes.
Habitat fragmentation is a major ecological concern across intensively cultivated landscapes of the U.S. Midwest. Within the corn–soybean production landscape of the U.S. Midwest, natural ecosystems have largely been replaced by monoculture cropping systems and minimal contiguous tree cover, or vegetative buffer areas remain, leading to severe habitat isolation and loss of biodiversity. This study aims to (1) quantify the spatial extent and connectivity of vegetated buffer zones embedded within dominant corn–soybean agricultural systems across the U.S. Midwest using satellite-based remote sensing with deep learning classification methods, and (2) assess their ecological integrity through widely-recognized landscape metrics relevant to biodiversity and ecosystem functionality. We process Sentinel-2 NDVI time series observations and train a Long Short-Term Memory (LSTM) neural network to classify corn-soybean area under cultivation. Landscape metrics, including Effective Mesh Size and Contagion, are evaluated across ten states. The LSTM-based classification proved highly effective, achieving overall accuracy ranging from 0.88 to 0.97. We found that vegetated and natural buffer zones are unevenly distributed across the region. Natural areas embedded within corn–soybean landscapes in Iowa, Illinois, and Minnesota exhibited the lowest connectivity, here the corn–soybean landscape is characterized by dense, continuous crop cover, indicating limited ecological functionality. In contrast, Ohio, Missouri, and Indiana formed an intermediate cluster, where some natural patches persisted within the dominant agricultural matrix, reflecting moderate spatial variability. Meanwhile, Michigan, Wisconsin, Kentucky, and Tennessee displayed greater natural connectivity, resulting in a more fragmented agricultural landscape and enhanced potential for ecosystem service provision. This study provides a spatially explicit assessment of buffer zone integrity across the corn–soybean landscapes of the U.S. Midwest. The classified high-resolution layer represents a valuable resource for evaluating landscape functionality at both macro and micro scales, supporting sustainable land use and guiding targeted interventions. Dually, when combined with additional data, it can be used to explore dynamics related to management and variability in the agronomic context.
Farmland bird populations have experienced widespread declines across Europe, raising urgent questions about how agricultural landscapes should be structured to reconcile biodiversity conservation and food production. Land sparing and land sharing represent two contrasting strategies, yet empirical evidence remains mixed. We aim to quantify responses of bird communities along a land sharing–sparing gradient and identify which landscape management strategies best support different ecological groups. Using the French Breeding Bird Survey, we analyzed 810 locations monitored between 2001–2024. A land sharing–sparing gradient was quantified using forest area, agricultural heterogeneity, and landscape diversity indices. Bird counts were modeled with negative binomial spatio-temporal Bayesian models (INLA), incorporating climate, spatial random fields, and temporal autocorrelation. Forest specialists responded positively to greater forest area, supporting land sparing. Generalists benefited from higher agricultural heterogeneity, consistent with land sharing. Unexpectedly, farmland specialists did not benefit from increased heterogeneity, and conservation-priority farmland species showed negative associations with natural elements. No single land-use strategy maximizes abundance across all bird groups. Land sparing favors forest specialists and conservation-priority species, while land sharing benefits generalists. Context-dependent landscape planning and customized agricultural practices are essential.
Understanding how landscape composition influences biodiversity is a central objective of landscape ecology. Because species respond to environmental conditions across different spatial scales, landscape effects are inherently scale-dependent, making the identification of ecologically relevant spatial scales essential for robust analyses of patterns in insect communities. We investigated the spatial scale at which landscape variables across habitats best explain insect biomass and diversity of (I) whole insect communities, (II) different taxonomic and functional groups and (III) tested whether empirically identified optimum scales of effect support common assumptions regarding the mobility and the trophic level of taxonomic groups. We quantified insect communities in gradients of land-use intensity and climate using data from 1293 Malaise trap samples from 179 plots in southern Germany to analyze the variance explained by environmental factors at different radii around sampling sites. We estimate the respective scale of effect for total insect biomass and diversity, as well as for different taxa and functional groups, using sample coverage standardized measures for diversity and a novel approach to estimate biomass for subgroups via sequencing reads. We find that the scale of effect of landscape variables differed between insect biomass and diversity. Overall diversity, as well as the diversity of most subgroups, was best explained by local habitat conditions (100—500 m). In contrast, although local conditions also contributed to explaining variation in overall biomass, larger scales (1500—2000 m) provided the strongest explanatory power. However, patterns differed between rare and dominant species. In addition, we could not confirm common assumptions with respect to species mobility or trophic level. Our findings highlight the importance of a thorough selection of the landscape scale when assessing diversity and biomass variables or taxon-specific groups and provide suggestions for the most suitable scale to be selected depending on the target variable under study in insect community research.
Loss of woody plants due to intensive agricultural expansion is particularly critical because of their role in ecosystem functions and services, as well as in ecosystem resilience. Therefore, understanding the effects of landscape changes on multiple dimensions of biodiversity (i.e., taxonomic, phylogenetic, and functional) in intensive agricultural landscapes is of utmost importance for the conservation agenda aimed at maintaining ecosystem functions and services. Here, we address the effects of spatial and temporal changes over 37 years in intensive farming landscapes on taxonomic (species richness and abundance), and phylogenetic and functional diversity (Mean Phylogenetic Distance, MPD, and Mean Nearest Taxon Distance, MNTD). We analyzed woody plants’ diversity in 20 savanna and 21 forest intensive agriculture farming landscapes encompassing a Long-Term Ecological Research (LTER) project in the Brazilian Cerrado. We mapped landscape changes from 1985 to 2022 (the year of fieldwork) and calculated metrics of landscape composition and configuration. The best models explaining taxonomic, phylogenetic, and functional diversity were selected with the Akaike Information Criterion. Overall, changes in both savanna and forest amounts were the main variables explaining species richness, abundance, and functional diversity of vegetative, pollination, and seed dispersal traits, together with the amount of agroecosystem. However, the direction of the relationship (positive or negative) depended on the vegetation type (savanna or forest) and the diversity dimension. Changes in the landscape are already selecting clades adapted to more open forests with higher edge effects, which can compromise ecosystem resilience and resistance to ongoing and predicted climate change. Savanna and forest showed complementation effects, increasing species richness and functional diversity in each other. Taken together, our results point to the need for conservation and restoration of forests and savannas to increase habitat amount, patch density, and landscape heterogeneity of natural vegetation.
