Diversity and Distributions

Conservation strategies and biodiversity assessments have long prioritized taxonomic metrics such as species richness and endemism, often neglecting intraspecific genetic diversity, a key driver of population adaptability and long-term resilience. Here, we present a scalable framework for quantifying and mapping multi-species genetic diversity using publicly available DNA sequence data. By calculating nucleotide diversity ({pi}) across taxa and aggregating values spatially, we define the Genetic Diversity Index (GDI): a site-level metric capturing geographic patterns of intraspecific genetic variation. Using simulations under different scenarios, we assessed the robustness of the GDI and implemented three corrective measures to address sampling bias and the additive effects of species richness. We queried over 25 million accessions from public databases representing 9,409 European vascular plant species and applied this approach to [~]630,000 georeferenced sequences across 1,860 species. Our results reveal distinct genetic diversity hotspots in the Anatolian Peninsula, Southern Iberia, and Eastern Alps regions corresponding to historical glacial refugia and ecological transition zones. GDI values were largely uncorrelated with species richness or phylogenetic diversity, confirming that the index captures a unique and independent dimension of biodiversity. Our indices performed extremely well and showed that fewer than 1% of studied sites exhibited a significant effect of sampling bias, validating the methods reliability under uneven data coverage. By integrating genetic diversity into spatial biodiversity analyses, the GDI detects large-scale patterns of evolutionary significance and fills a critical methodological gap, providing a reproducible tool to support conservation prioritization and policy at regional and global scales. SignificanceGenetic diversity is a fundamental component of biodiversity and critical to species resilience, yet it remains absent from most biodiversity assessments. We present a scalable framework for evaluating multi-species genetic diversity across landscapes, introducing three complementary Genetic Diversity Indices (GDI) designed for different scenarios in biodiversity assessment and conservation planning. Applying this approach to European vascular plants, we reveal that genetic diversity patterns are largely decoupled from taxonomic and phylogenetic diversity, highlighting a fundamental yet overlooked dimension of biodiversity. Our framework charts a clear path for global assessments of genetic diversity in vascular plants and beyond. Incorporating GDI into conservation strategies is critical to complement traditional metrics and better safeguard evolutionary potential of biodiversity in a rapidly changing world.

Understanding how geography and forest management shape genetic diversity is pivotal for the conservation of saproxylic beetles, the key indicators of forest ecosystem integrity. Using a multi-species Double Digest Restriction-site Associated DNA sequencing approach, we analyzed genome-scale SNP variation in 12 saproxylic beetle species (rare and common, incl. pests) differing in abundance, ecology, and mobility across central European forests, with respect to both geographic location and forest status. Analyses of distance-based redundancy test, molecular variance, and population structure consistently revealed that geography is the main determinant of genetic differentiation, with clear east-west clustering in several species. Forest status represented in four levels (commercial not protected, nature reserve, primeval not protected, and primeval protected) exerted a secondary but detectable effect in a subset of taxa, especially among rare and habitat-specialized species. For these species, populations from primeval protected sites showed distinct genetic structure compared to those from commercial stands. Our results highlight that regional connectivity, maintaining gene flow across a large spatial scale, should be prioritized in conservation planning, complemented by the protection of primeval forest refugia and the maintenance of structural complexity in commercial forests. By integrating high-resolution genomic data with geographical and management context, this study provides actionable insights into the processes shaping genetic structure in forest-dependent insects and underscores the importance of incorporating genetic monitoring into sustainable forest biodiversity management.

As global change accelerates, accurate predictions of species distributions and biodiversity patterns are critical to prevent population declines and biodiversity loss. However, at continental and global scales, these predictions are often derived from species distribution models (SDMs) fit at coarse spatial grains uninformed by ecological processes. Coarse-grain models may systematically bias predictions of distributions and biodiversity if they are consistently over- or under-estimating area with suitable habitat, and this bias may intensify in regions with heterogenous landscapes or with poor data coverage. To test this, we fit presence-absence SDMs characterizing both the summer and winter distributions of 572 North American bird species - nearly the entire avian diversity of the US and Canada - across five spatial grains from 1 to 50 km, using observations from the eBird citizen science initiative. We find that across both seasons, models fit at 1 km performed better under cross-validation than those at coarser scales and more accurately predicted species presences and absences at local sites. Coarser-grain models, including models fit at 3 km, consistently under-predicted range area relative to 1 km models, suggesting that coarse-grain estimates of distributions could be missing important habitat. This bias intensified during summer (83% of species) when many birds have smaller operational scales via localized home ranges and greater habitat specificity while breeding. Biases were greatest in heterogenous desert and scrubland regions and lowest in more homogenous boreal forest and taiga-dominated regions. When aggregating distributions to produce continental biodiversity predictions, coarse-grain models overpredicted diversity in the west and underpredicted it in the great plains, prairie pothole region and boreal/taiga zones. The modern availability of high-performance computing and high-resolution observational and environmental data provides opportunities to improve continental predictions of species distributions and biodiversity.

Connectivity is critical to sustaining endangered carnivores in speciose yet fragmented landscapes such as in the global south. Corridors to mitigate fragmentation are designed based on charismatic species or habitat-based approaches, but their multispecies effectiveness for maintaining functional connectivity remains poorly tested. We combined landscape genetic analyses across five sympatric carnivores - Panthera tigris, Panthera pardus, Prionailurus viverrinus, Felis chaus, and Melursus ursinus, to evaluate how landscape features shape functional connectivity in a globally important felid landscape. We then assessed the efficiency of existing tiger corridors and single-species surrogates for maintaining multispecies functional connectivity. Species exhibited contrasting responses to landscape variables, producing distinct resistance surfaces and connectivity corridors. Spatial similarity of connectivity between species pairs was highly variable (r = 0.14-0.93), but no single species effectively captured connectivity patterns of the broader carnivore community (maximum mean overlap of <0.7 across species). Moreover, genetically optimized corridors were at least 70% more efficient in capturing connectivity compared to existing tiger corridors, demonstrating mismatches between structural and functional connectivity. Our results highlight limitations of surrogate-based corridor planning and demonstrate that integrating multispecies functional connectivity can substantially improve conservation planning in human-dominated landscapes.

There are several approaches to understand how a landscape, with its several components, affects the genetic population structure by imposing resistance to gene flow. Here we propose the creation of resistance surfaces using a Pattern-Oriented Modeling approach to explain genetic differentiation, estimated by pairwise FST, among "Baruzeiro" populations (Dipteryx alata), a tree species widely distributed in Brazilian Cerrado. To establish the resistance surface, we used land use layers from the area in which the 25 "Baruzeiro" populations were sampled, generating 10000 resistance surfaces. To establish the resistance surface, we used land use layers from the area in which the 25 "Baru" populations were sampled, generating 10000 resistance surfaces. We randomized the cost values for each landscape component between 0 and 100. We use these surfaces to calculate pairwise matrices of the effective resistance among populations. Mantel test revealed a correlation of pairwise FST with a geographical distance equal to r = 0.48 (P < 0.001), whereas the Mantel correlations between pairwise FST and the generated resistance matrices ranged between r = -0.2019 and r= 0.6736. Partial regression on distance matrices was used to select the resistance matrix that provided the highest correlation with pairwise FST, based on the AIC criterion. The selected models suggest that the areas with lower resistance are characterized as natural savanna habitats of different forms, mainly arboreal dense savannas. In contrast, roads, big rivers, and agricultural lands cause higher resistance to gene flow.

The scale at which diversity is observed shapes the patterns we find. While spatial scale is known to influence biodiversity patterns, the effects of temporal scale, namely the average duration of sampling (known as temporal span), have been mostly overlooked. Here, we investigate how temporal span affects species richness patterns, their environmental drivers, and species richness hotspots. We used species richness data from several large bird datasets from Czechia, with over 7000 observations, a spatial grain ranging from 0.03 to 100 km2, and a temporal span ranging from 1 to 36 years (1985-2017). Using Random Forests, we modelled species richness as a response to temporal span, while also including area, geographic location, time, and environmental and land-cover predictors. We found that the temporal span is consistently among the most important predictors of bird species richness. Moreover, temporal span interacts with key environmental conditions, particularly precipitation and water bodies, modulating their effects on species richness and revealing processes that differ from those traditionally attributed solely to spatial grain. We also found that using different time spans can shift the predicted locations of biodiversity hotspots. Our results provide empirical evidence that temporal span should be included in studies about biodiversity and conservation planning, given the urgent challenges arising from ongoing biodiversity change and the complexity of its drivers.

AimExpert-based regional Red Lists (RL) carry conservation legislation globally. Yet, they are often difficult to reproduce and their regular compilation by a dwindling number of expert assessors burdens many regional conservation authorities. Here we batch-estimate RL indicators and extinction risk for >1,100 plant species and test the potential of this automated approach to support the expert-based RL process. LocationState of Hesse, Central Germany MethodsFirst, we estimated current population status, short-term population trend, long-term population trend, and extinction risk by binning existing occurrence probabilities modelled at three time slices with cut-off values derived from RL methodology. Subsequently we compared the results with the latest version of the Hessian expert-based RL using summary statistics and selected example species. ResultsWe find the assessments of extinction risk to agree in c. 60% of the cases, mostly for species not threatened with extinction. Existing mismatch was by one category in most cases, but up to 6 categories in some cases (mean: 1.6 categories). Furthermore, agreement was highest for extreme categories and very abundant species. Main conclusionsAutomated assessments were simplistic for many rare and taxonomically challenging species, but we considered them more accurate than the expert assessments for species with intermediate population size and for species of anthropogenic habitats. Furthermore, the automated assessments are particularly informative for the estimation of long-term and short-term population trends, for which experts are often left to guesstimate based on little data.

Understanding genetic responses to forest dynamics is essential for predicting the long-term viability of understory plant populations and for developing effective conservation strategies. This study investigates genetic extinction debt and colonization credit in Circaea lutetiana, a clonal forest understory species, across its European range. Using pooled genotype-by-sequencing data from 40 forest edge and core populations, we examined to what extent population size, latitude and historical changes in forest configuration predict genetic diversity. Our findings reveal that the historical forest configuration profoundly shapes present-day genetic diversity. Long-established forest edge populations exhibit significantly reduced allelic richness (-9%) compared to core populations, indicating the partial pay-off of a genetic extinction debt. In contrast, populations from recently established forest edges maintain comparable allelic richness to core populations, suggesting delayed population genetic responses to land use changes. Finally, populations established in areas that were afforested during the past 250 years exhibit lower genetic diversity than historical forest core populations, indicating a delay in genetic recovery and thus a potential genetic colonization credit. Our results highlight that C. lutetiana populations are not at equilibrium with the current forest configuration, underscoring the role of lagged genetic responses across very long time scales. Connectivity and population size further moderate genetic diversity, with smaller, isolated populations particularly vulnerable to genetic erosion. Given the limited research on delayed evolution in forest understory species, our results improve the understanding of extinction risk dynamics and underscore the need for history-informed restoration efforts.

AimSpecies distribution models (SDMs) are an important tool for predicting species occurrences in geographic space and for understanding the drivers of these occurrences. An effect of environmental variable selection on SDM outcomes has been noted, but how the treatment of variables influences models, including model performance and predicted range area, remains largely unclear. For example, although landcover variables included in SDMs in the form of proportions, or relative cover, recent findings suggest that for species associated with uncommon habitats the simple presence or absence of a landcover feature is most informative. Here we investigate the generality of this hypothesis and determine which representation of environmental features produces the best-performing models and how this affects range area estimates. Finally, we document how outcomes are modulated by spatial grain size, which is known to influence model performance and estimated range area. LocationNorth America MethodsWe fit species distribution models (via Random Forest) for 57 water bird species using proportional and binary estimates of water cover in a grid cell using occurrence data from the eBird citizen science initiative. We evaluated four different thresholds of feature prevalence (land cover representations) within the cell (1%, 10%, 20% or 50%) and fit models across both breeding and non-breeding seasons and multiple grain sizes (1, 5, 10, and 50 km cell lengths). ResultsModel performance was not significantly affected by the type of land cover representation. However, when the models were fitted using binary variables, the model-assessed importance of water bodies significantly decreased, especially at coarse grain sizes. In this binary variable-case, models relied more on other land cover variables, and over-or under-predicted the species range by 5-30%. In some cases, differences up to 70% in predicted species ranges were observed. Main conclusionsMethods for summarizing landcover features are often an afterthought in species distribution modelling. Inaccurate range areas resulting from treatment of landcover features as binary or proportional could lead to the prioritization of conservation efforts in areas where the species do not occur or cause the importance of crucial habitats to be missed. Importantly, our results suggest that at finer grain sizes, binary variables might be more useful for accurately measuring species distributions. For studies using relatively coarse grain sizes, we recommend fitting models with proportional land cover variables.

AbstractOngoing declines in insect populations have led to substantial concern and calls for conservation action. However, even for relatively well-studied groups, like butterflies, information relevant to species-specific status and risk is scattered across field guides, the scientific literature, and agency reports. Consequently, attention and resources have been spent on a miniscule fraction of insect diversity, including a few well-studied butterflies. Here we bring together heterogenous sources of information for 396 butterfly species to provide the first regional assessment of butterflies for the 11 western US states. For 184 species, we use monitoring data to characterize historical and projected trends in population abundance. For another 212 species (for which monitoring data are not available, but other types of information can be collected), we use exposure to climate change, development, geographic range, number of host plants, and other factors to rank species for conservation concern. A phylogenetic signal is apparent, with concentrations of declining and at-risk species in the families Lycaenidae and Hesperiidae. A geographic bias exists in that many species that lack monitoring data occur in more southern states where we expect that impacts of warming and drying trends will be most severe. Legal protection is rare among the taxa with the highest risk values: of the top 100 species, one is listed as threatened under the US Endangered Species Act and one is a candidate for listing. Among the many taxa not currently protected, we highlight a short list of species in decline, including Vanessa annabella, Thorybes mexicanus, Euchloe ausonides, and Pholisora catullus. Notably, many of these species have broad geographic ranges, which perhaps highlights a new era of insect conservation in which small or fragmented ranges will not be the only red flags that attract conservation attention.

A growing awareness, now enshrined in the Kunming-Montreal Global Biodiversity Framework, of the need to monitor biodiversity effectively at scale has led to a proliferation of novel solutions for doing so. Although global biodiversity encompasses all life, from tiny nitrogen-fixing bacteria to emergent rainforest trees, birds have several characteristics that make them a frequent focus of such monitoring efforts. In particular, birds frequently give diagnostic, species-specific vocalizations that simplify monitoring, they perform a number of critical ecosystem services, they are widely distributed in most ecosystems with strong representation on all continents, and the basic ecology, conservation status, populations, and distributions of many species is well known; birds thus provide a window into the underlying health and habitats of the systems under study. How best to summarize biodiversity monitoring results is a research question that has led to the development of approaches that incorporate species IUCN Red List threat assessments into site-level biodiversity scores. Notably, birds vocal behavior means that they can be effectively surveyed at scale with passive acoustic monitoring, and the potential to link such monitoring with automated identification and therefore quickly generate site-level biodiversity scores is an appealing approach to implement rigorous evaluations of global biodiversity. Yet, while many of the worlds birds are suffering worrisome population declines, the vast majority of species (78%) are still ranked "Least Concern" by the Red List. In an effort to develop a species scoring system that would be more conducive to such site-level valuations, we integrated key databases of species population status assessments, exposure to known vulnerability factors, and their functional and phylogenetic uniqueness to provide quantitative summaries of their conservation significance. We augmented these databases with two novel data sets available for most of the worlds birds: quantitative measurements of migration distances, and species-level phylogenetic and functional uniqueness values comparing each species to those it co-occurs with throughout its range. While the resulting BirdsPlus species scores also inherently reflect our own scientific expertise and judgement, our approach is transparent, dynamic, easily updated, and readily modified by users with different goals or values.

Elevational migration is short-distance migration through which species track seasonal changes in weather and resources along elevational gradients. Key identified drivers of elevational migration include climatic variability, resource availability, and predation. Although the spatiotemporal coverage of participatory science platforms like eBird can be used to quantify elevational migration patterns, such documentation is missing for most of the worlds ecozones. We investigate the extent of seasonal elevational migration for birds present year-round in the tallest mountains of the Western Ghats biodiversity hotspot - the Nilgiris. We combined rigorously curated participatory science data with systematic field surveys. We estimated the elevational shifts of 70 species of birds between the hottest and coldest quarters in the eastern Nilgiris. We ran regression models to quantify the associations between these shifts and their suspected drivers. Around 70% of the species in the region shifted in their elevational ranges, and the frequency of shifts was higher at lower and mid elevations. Downslope shifts were positively associated with a narrow thermal range and species lower temperature limit, while the opposite associations were seen for upslope shifts. We suspect species might be tracking their thermal regimes, and the temperature limits of species can be a major driver of elevational migration in the Nilgiris. The predominance of partial elevational migration in the region provides opportunities for testing theories on biotic drivers of these movements in the future. We highlight the combined usage of field and participatory science in examining ecological patterns in understudied parts of the world.

MotivationHigh-quality biodiversity data with temporal replicates, produced using standardized fieldwork protocols, are rare yet essential for studying long-term biodiversity dynamics. Most available large-scale temporal data only date back one or two decades and/or originate from spatially discrete local observations. Here, we release spatially contiguous, systematically collected, and gridded occurrence data for breeding birds in Czechia, covering the periods 1973--1977, 1985--1989, 2001--2003, and 2014--2017. This database represents the monitoring of ca. 41% of European bird species over 40 years, and it is one of the longest-running nationwide bird-monitoring efforts in the world. We also complement the original data with geospatial metrics to characterize the sampling polygons and provide proxies of sampling effort. By making this dataset openly accessible, we aim to strengthen biodiversity change studies, citizen science, and ornithological research with long-term, highly curated records, backed by well-documented methods, and ready for integration with other datasets. Main Types of Variables ContainedA total of 286302 breeding bird detections/non-detections per-grid-cell from 247 species (ca. 41% of the 596 species breeding in Europe). The fourth atlas also contains 9,471 timed species lists totaling 276076 additional records collected with standardized effort and partially random spatial sampling on smaller squares dividing the original grid cells. Spatial Location and GrainCzechia (total area of 78,871 km2) covered by a grid of 887 grid cells of 10 by 10 km for the period 1973--77, and 678 cells of 6 minutes latitude and 10 minutes longitude ([~]11.2 x 12 kilometers) from 1985 onwards. The timed species lists were collected across 4,851 of 9,844 small squares ([~]2.8 x 3 km) that subdivide each original grid-cell into 16 smaller polygons. Time Period and GrainThe sampling years were 1973--1977 (5 breeding seasons), 1985--1989 (5 breeding seasons), 2001--2003 (3 breeding seasons), and 2014--2017 (4 breeding seasons). Major Taxa and Level of MeasurementBirds (Aves). The breeding evidence per species and grid cell was classified following the European Breeding Birds Atlas 2. We provide species-level records matched to the HBW/BirdLife version 9 (2024). FormatThe dataset is available for download from Zenodo and is provided as CSV files with fields standardized to Darwin Core, and a GeoPackage file containing all of the spatial grids used. The data are organized into separate files for records and sampling events, corresponding to each atlas. All data are licensed under CC-BY 4.0.

Ensuring that species of conservation concern achieve favorable conservation status (FCS) is central to European Union (EU) biodiversity conservation targets. A key criterion for FCS is exceeding the favorable reference range (FRR)--the range extent needed for long-term species stability and full ecological variation. However, due to data limitations, FRRs are often unknown, undermining their applicability. We developed a machine-learning approach to estimate and standardize FRRs across the EU. Applied to amphibians, mammals, and reptiles, our method provided FRRs for 99.5% of species of conservation concern, compared to 17.5% previously available (with satisfactory modelling performance: R2 0.75). We reassessed conservation status using the estimated FRRs, finding that species in FCS (34.8%) are notably fewer than reported in official documentation (69.1%). The average proportional distance to FRR for species in unfavorable conservation status is -64.4%. Our approach may support periodic FCS reassessments and help refine the targets of EU conservation policies.

Spatially explicit indicators that quantify to which extent landscapes support biodiversity are essential for guiding evidence-based conservation planning. Here, we present a 25-meter resolution dataset for Switzerland, encompassing three key biodiversity indicators-- Complementarity, Extinction Risk, and Ecological Connectivity--developed across 17 major taxonomic groups, using habitat suitability maps for approximately 7,500 individual species. These three indicators capture (1) the contribution of landscapes to taxonomic, functional, and phylogenetic diversity, (2) species vulnerability to extinction and (3) ecological connectivity. Outputs are provided for both terrestrial and aquatic realms, including versions adjusted for species richness, and integrated into composite indices. In total, 272 spatial layers were produced and made openly accessible. Relationships among the three indicators were analyzed to assess their distinct contributions, and expert-based validations were performed to evaluate their ecological plausibility and relevance for conservation applications. This dataset provides a robust foundation to support spatial planning and conservation decision-making in Switzerland and can be used as a blueprint for analogue integration in other countries.

Protecting habitat of species-at-risk is critical to their recovery, but can be contentious. For example, protecting species that are locally imperilled but globally common (e.g. species that only occur in a jurisdiction at the edge of their geographic range) is often thought to distract from protecting globally-imperilled species. However, such perceived trade-offs are based on the assumption that threatened groups have little spatial overlap, which is rarely quantified. Here, we compile range maps of terrestrial species-at-risk in Canada to assess the geographic overlap of nationally and globally at-risk species with each other, among taxonomic groups, and with protected areas. While many nationally-at-risk taxa only occurred in Canada at their northern range edge (median=4% of range in Canada), nationally-at-risk species were not significantly more peripheral in Canada than globally-at-risk species. Further, 56% of hotspots of nationally-at-risk taxa were also hotspots of globally-at-risk taxa in Canada, undercutting the perceived trade-off in their protection. Hotspots of nationally-at-risk taxa also strongly overlapped with hotspots of individual taxonomic groups, though less so for mammals. While strong spatial overlap across threat levels and taxa should facilitate efficient habitat protection, <7% of the area in Canadas at-risk hotspots is protected, and more than 70% of nationally and globally-at-risk species in Canada have <10% of their Canadian range protected. Our results counter the perception that protecting nationally vs. globally at-risk species are at odds, and identify critical areas to target as Canada strives to increase its protected areas and promote species-at-risk recovery.

AimOur study provides foundational resources for future SDMing: methods for generating fine-scale, equal-area predictor datasets and best-practice SDM guidelines. We also provide reproducible code to streamline their implementation. LocationSouthwestern North America MethodsUsing over 215,000 research-grade iNaturalist occurrence records for 127 species of conservation concern or scientific interest in California and surrounding area, we quantified and compared SDM performance between two predictor datasets that differ in their source of bioclimatic data, spatial resolution, and coordinate reference system: one generated using ClimateNA software (resolution = 300 x 300 m; NAD83/California Albers) and the other using existing WorldClim data (varying resolution = [~]669-797 x 926 m; WGS84). We also compared two modeling algorithms (MaxEnt vs Random Forests), and two background point selection strategies (random points vs weighted points accounting for sampling effort). As an example application, we used SDM predictions to evaluate the conservation value of different protected area types within California. ResultsClimateNA outperformed WorldClim for 94% of species, Random Forests outperformed MaxEnt for 87%, and random background points outperformed weighted background points for 100%. All differences were statistically significant. Together, the ClimateNA dataset, Random Forests, and random background points achieved highest performance for 86% of species. Using this best-performing set of models, we found that regional parks, county parks, state beaches, and open spaces in California were highest in multi-species suitability, while larger protected areas, such as national parks and national forests, generally exhibited surprisingly low suitability. Substantial spatial biases intrinsic to SDMing with unprojected predictor datasets (e.g., WGS84) are described, along with clear solutions using equal-area predictor datasets. Main conclusionsConsiderable disparity was observed among the performance of common SDM methods. This study highlights the importance of fine-scale, equal-area predictor datasets and best-practice guidelines, and demonstrates how SDMs can provide critical insights into protected area planning.

Global species observations from community science platforms offer an unprecedented opportunity to analyze large-scale biodiversity patterns. This holds great promise for developing data-driven metrics to measure species responses to landscape-level environmental changes, essential for ameliorating the ongoing biodiversity crisis. A primary cause of declines in local, native species is the expansion and intensification of urbanized landscapes. A species response to urbanization can remain consistent across spatial and temporal scales. However, city-specific factors, such as land-use dynamics or local biotic interactions, can profoundly influence species and cause shifts in responses. This underscores the importance of measuring responses to urbanized landscapes at the local population level in a way that facilitates spatial and temporal comparisons. I propose utilizing the receiver operating characteristic (ROC) and its area under the curve (AUC), a statistical classifier, in a novel way for this purpose. Specifically, the disparity in suitable habitat between urban and adjacent non-urban areas is summarized by a single metric, HDURBAN, which facilitates population-level comparisons across time and space. I apply it to geographic predictions of the realized niche of 1,023 species from a highly urbanized landscape in California, USA. Additionally, modeling virtual species for each of the urban response types introduced by Blairs seminal 1996 study provides place-based, expected HDURBAN scores for urban avoider, urban utilizer, and urban dweller species. Validation efforts demonstrate accurate predictions of expected urban responses of real species in three scenarios. First, following a route-of-introduction hypothesis, non-native species exhibit significantly higher HDURBAN than native species. Second, assessing the robustness of urban responses in space and time, HDURBAN is significantly correlated to the urban response of the same 38 bird species from Blairs study, conducted 450 km apart and 25 years earlier. Third, testing for sensitivity to temporal shifts within species, HDURBAN decreases significantly in a species displaced from urban areas due to negative interactions with a recently introduced, congeneric species. In summary, HDURBAN is a versatile metric that provides a data-driven way to compare species responses, supporting future research into the patterns and processes underlying spatial and temporal variation in responses to urbanized landscapes.

AimWe aim to quantify population trends of Common Toad in Flanders and assess to which extent population trends depend on general landscape variables (land use identity, structure and change). LocationFlanders, Belgium MethodsUsing standardized time series obtained from citizen science and spanning over four decades, we used an end-begin contrast to get a trend value for 234 populations. Next, we developed a testing strategy to find associations between these trend values and the surrounding landscape ResultsAcross the study region, 40% of the populations have declined significantly, while only 10% show an increase. Declines were not associated with landscape characteristics in our study area, which is one of the most fragmented regions in Europe. Main conclusionsWe discuss how the research region, spatial and temporal resolution, as well as the generalist nature of the species, may explain these findings. Our study suggests limited effects of general landscape characteristics on the decline of Common Toad populations, indicating that other major drivers are likely responsible.

Land-use and land-cover (LULC) change is a dominant driver of biodiversity loss, yet its long-term role in restructuring species distribution remains poorly understood due to limited historical baselines and the scarcity of long-term analyses across multiple ecological transition zones. Here, we investigate nearly two centuries of land-use change across a biogeographically diverse region to evaluate how landscape transformation has reshaped vertebrate distribution range dynamics, and whether ecoregional context, habitat specialization, and taxonomic identity better explain these patterns than recent landscape change alone. Using Texas, a biogeographic crossroads with diverse ecoregional zones and a long history of intensive land-use, we quantify distributional changes in 100 native vertebrate species (mammals, birds, reptiles, and amphibians), using Vernon Baileys late nineteenth-century surveys as a historical benchmark. We integrated multi-temporal LULC datasets with spatial and multivariate analyses to assess how habitat change, species traits, and regional context relate to patterns of range expansion, contraction, and redistribution. We show that Texas has undergone non-linear landscape transformations characterized by early agricultural expansion followed by cropland abandonment, shrubland encroachment, and rapid urbanization. Range contractions were most pronounced among amphibians, reptiles, and habitat specialists, whereas generalist species and many birds exhibited greater range stability or expansion. Across taxa, primary habitat, vertebrate class, and ecoregional context emerged as the strongest predictors of distributional change, underscoring the importance of historical and environmental context over simple measures of habitat loss and fragmentation. These results indicate that while long-term LULC change establishes the underlying landscape template, species responses are structured by ecological context, including habitat affinity, taxonomic traits, and regional environmental gradients. By linking historical landscape trajectories to contemporary biodiversity patterns, this study provides a transferable framework for investigating how land-use legacies influence species distributions and community reassembly in heterogeneous human-dominated landscapes, highlighting the importance of adaptive capacity and connectivity for conservation planning.