Global Ecology and Conservation

Managing visitor-wildlife interactions in high-use protected areas requires identifying not only where visitors concentrate but also where visitor use overlaps with wildlife space use. We estimated relative human- macaque encounter potential in Kamikochi, Chubu Sangaku National Park, Japan, where Japanese macaques (Macaca fuscata) and visitors use the same frontcountry landscape. We integrated Mobile Spatial Statistics derived from mobile phone network data, trail-camera counts, and direct-follow location data from macaques along a frontcountry-backcountry gradient. Broad-scale 500-m grid estimates of daytime visitor presence for the 2023 and 2024 open seasons were allocated to 50-m trail sections and adjusted using counts from 10 trail cameras. Automated video counts based on YOLO11 and ByteTrack showed high agreement with manual counts of 100 validation videos (MAE = 0.10 persons/video, MAPE = 1.26%, exact agreement rate = 92.0%). Visitor-use and macaque-use data were converted into kernel density surfaces, and encounter potential was calculated as their normalized product. High encounter potential was concentrated in the central frontcountry: 87.1% of areas with index values [≥] 0.9 fell within the frontcountry and 12.9% within the backcountry. Areas with high visitor use or high macaque use alone did not necessarily show high encounter potential when the two distributions were spatially offset. These results show that spatial overlap provides management information not captured by visitor counts alone. Relative encounter potential can help prioritize patrols, visitor messaging, viewing management, and other interventions in protected areas where wildlife does not consistently avoid heavily used visitor spaces.

Effective conservation depends on demographic metrics that reliably reflect species status, particularly population abundance. For elusive species occurring at low densities, however, such metrics remain difficult to obtain. Spatial capture-recapture (SCR) models are the standardized approach for estimating density in marked populations, but their data requirements, especially the need for multiple spatial recaptures across individuals, often limit applicability in small or data-poor populations. This constraint has resulted in knowledge gaps for some of the most vulnerable species, undermining evidence-based conservation planning and management. Using camera-trap data and SCR-derived density estimates from data-rich populations, we evaluated alternative, less data-demanding metrics and tested the hypothesis: Space to Event (STE), Mean Local Abundance (MLA), and Relative Abundance Index (RAI) exhibit predictable relationships with SCR-derived density; if supported, these metrics can reliably estimate density in populations where SCR models cannot be implemented. We applied this framework to the European wildcat (Felis silvestris), an elusive small felid with highly fragmented populations across Europe, for which density estimates are largely lacking despite growing conservation concern. Across 21 study areas spanning most of the species' range, our results indicate that European wildcats generally occur at lower densities than previously reported. SCR-derived estimates (n=10) averaged 10.32 {+/-} 11.56 inds/100km2, while STE enabled density estimation in five additional data-poor areas (mean 5.52 {+/-} 5.33 inds/100km2). STE showed a strong linear relationship with SCR-derived density (R2=0.98), supporting its use as a viable alternative when SCR is infeasible, although it tended to underestimate compared to SCR, especially at higher densities. In contrast, MLA and RAI showed weaker and non-linear relationships with SCR-derived density (R2=0.65), indicating substantially lower explanatory power and suggesting their estimates are more strongly influenced by confounding processes. By explicitly calibrating alternative metrics across a wide density gradient throughout most of the species' distribution, this study provides a transferable methodological framework for estimating density in low-density wildlife populations and the first continent-wide, standardized density assessment of a carnivore species. From a management perspective, our findings identify populations that may be most vulnerable, particularly those with the lowest densities, and highlight the need to prioritize absolute abundance monitoring.

Preventing extirpations requires timely detection of rapid declines and immediate conservation response. Egyptian fruit bats (Rousettus aegyptiacus) are globally listed as Least Concern, but their genetically isolated population in Cyprus is Critically Endangered. We document a severe ongoing decline of the Cypriot R. aegyptiacus population observed during standardized counts at major roost sites. April 2024 counts at four principal roosts recorded 4633 bats. Surveys in December 2025 documented 554 individuals across the same sites. April 2026 counts revealed a 90.1% decline since April 2024. Additionally, we found numerous dead bats at three roosts, and residents across the island reported dead bats, suggesting an acute mortality event beginning in early 2025. The cause remains unknown. Given the magnitude and pace of decline, this population potentially faces imminent extirpation. We recommend immediate action to protect roosting and foraging habitat, along with coordinated diagnostics, expedited research permits, standardized monitoring, and technical collaboration to identify and mitigate underlying causes of high mortality.

Understanding the spatial patterns of biodiversity and their environmental determinants is fundamental to ecology and conservation, particularly in mountainous transitional zones where species assemblages can shift rapidly. Amphibians, as highly sensitive organisms, are excellent indicators of environmental change, yet quantitative assessments of their diversity in the Huangshan Mountains-a biodiversity hotspot at the junction of the Palaearctic and Oriental realms in southern Anhui, China-remain limited. We conducted systematic line-transect surveys across 200 grids (5 km x 5 km) during the spring and autumn of 2023 and 2024, recording a total of 10,342 individuals belonging to 23 species, 20 genera, 9 families, and 2 orders. Three species (Fejervarya multistriata, Microhyla fissipes, and Bufo gargarizans) were identified as dominant, and two nationally protected species (Hoplobatrachus chinensis and Andrias davidianus) were detected. Inter-annual alpha diversity did not differ significantly, but pronounced seasonal variation was observed, with spring supporting higher Shannon-Wiener and Pielou evenness than autumn in both years. Using generalized additive models and model averaging, we found that annual precipitation and the normalized difference vegetation index (NDVI) were consistently the strongest positive predictors of Shannon-Wiener diversity, Simpson dominance, and species richness, while species richness also declined significantly with increasing human footprint. Total beta diversity (Soerensen dissimilarity) was very high (0.981) and overwhelmingly driven by species turnover (97.86%) rather than nestedness. Partial Mantel tests and distance-based redundancy analysis further revealed that environmental distances-particularly annual precipitation-significantly shaped overall beta diversity and its turnover component after accounting for geographic distance. These results highlight the predominant role of climatic and vegetation gradients in structuring amphibian assemblages in this subtropical mountainous region, providing baseline data to inform local conservation strategies.

AbstractIntegrating environmental history is essential for understanding present-day ecosystem dynamics and guiding modern conservation strategies, particularly under the EU Biodiversity Strategy for 2030. This data paper presents the nationwide digitisation and vectorisation of the French General Ordnance survey map (1818-1866), capturing the countrys forest cover at its historical minimum (a pivotal moment known as the "forest transition"). The original manuscript sheets surveyed at a 1:40,000 scale (976 sheets) were scanned, georeferenced and vectorised. They offer higher thematic accuracy than the older Cassini map and are far more feasible for nationwide vectorisation than the highly detailed Napoleonic cadastre. The area-weighted mean survey date is 1843, and the dataset covers 99.6% of modern mainland France. To correct for paper deformation, historical surveying errors and coordinate system transformations, a rigorous workflow was established. This shifted from a global 6-parameter affine transformation (root mean square error of 60 m) to a local elastic transformation based on thousands of control points, for 21% of the territory, which reduced the positioning error to 34 m. We assessed data quality by comparing the General Ordnance Survey maps with the Napoleonic cadastre--the standard reference for 19th-century land-use data--across more than 600 municipalities. The correlation between the two sources regarding forest cover percentages was exceptionally high (r>0.9). Because forests formed large, compact blocks of significant strategic interest to military engineers, they were mapped with high precision. Localised inaccuracies were primarily found in remote areas, most notably in the mountains. The resulting historical vector layer was intersected with contemporary forest data (BD Foret(R) v2, 2005-2019). Historical polygons smaller than 0.5 ha were filtered out to comply with modern FAO forest definitions. This spatial overlay generated a new dataset detailing four distinct land-use trajectories: . ancient forests (44.8% of present day forest): land classified as forest in both the 19th century and the present day (indicating maximum temporal continuity). . recent forests (55.2% of present day forest): land that was non-forested in the 19th century but has since undergone reforestation. . deforested areas (18.9% of 19th-century forest): land recorded as forest in the 19th century but subsequently converted to other land uses. . stable non-forest areas: land that has remained unforested across both periods. Our results suggest that the forest area of mainland France at its historical minimum should be revised upward to 9.9 million ha. A preliminary analysis further indicates that current spatial variation in forest cover is explained more by land-use dynamics occurring since the forest transition than by the initial extent of forest cover. These two open-access national datasets (the 19th-century forest layer and the land-use transition map) open the door to a better understanding of present-day forests : e.g. their biodiversity, soil quality, tree growth and belowground water quality. Furthermore, they provide decision-support tools for conservation planning. Key messageIn this data paper, we provide a map of 19th-century forests in France based on the digitisation of the military topographic map (1818-1866). By overlaying this historical source with the present-day forest map, we built a second map which allows the identification of ancient forests, recent forests, and deforestation. These maps offer avenues for historical ecology and the design of conservation strategies.

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.

Nepal's rangelands provide multiple benefits, including support for pastoral livelihoods and alpine biodiversity, regulation of water and soil nutrients, and sequestering carbon. Climate change and anthropogenic pressures are altering these rangelands, leading to vegetation and biodiversity change. However, national-scale assessments of rangeland change are limited in Nepal. This study quantified rangeland changes at multiple spatial scales and assessed the climatic and non-climatic drivers of rangeland change. About 80.7% of Nepal's high-altitude rangeland (> 2,000m) outside protected areas showed no significant change. Among areas exhibiting significant annual maximum NDVI trends, 383,281 ha (18.6%) showed positive and 14,702 ha (0.7%) showed negative trends, corresponding the ratio of increase in vegetation greenness and decline in vegetation greenness to 26:1. Climate predicted positive trends covered 627,184 ha (30.5%), whereas residual trends caused by non-climatic drivers covered 94,656 ha (4.6%). Climate induced negative trends covered 47,609 ha (2.3%) while residual trends were observed in 6,260 ha (0.3%). Negative trend pixels were concentrated mainly within the 3,000 to 5,000 m elevation band, with Karnali Province recording the highest proportional climate predicted decline in vegetation greenness (3.4%). At the municipality scale, rangeland change showed no significant relationship with grazing pressure derived from gridded livestock data, suggesting that grazing pressure alone did not explain the non-climatic vegetation signal. These spatially explicit, nationally consistent results identify where rangeland change is occurring and help distinguish climatic and non-climatic drivers of rangeland vegetation change, providing evidence to support targeted rangeland management under Nepal's federal governance structure.

Waterbirds serve as important indicators of both aquatic and terrestrial ecosystem health, making effective monitoring essential for tracking population health and identifying potential causes of decline. Drones have provided opportunities to overcome historic waterbird monitoring challenges, but the expertise and time required for manual image analysis creates a major bottleneck. Recent advances in deep learning-based object detection have enabled rapid, automatic detection of features in complex ecological imagery, though applications have largely been limited to single-species colonies, and practitioners lack quantitative comparisons of annotation time and accuracy across different levels of automation. We systematically compared four waterbird monitoring approaches using identical survey areas from Chester Island, a mixed-species colony in Matagorda Bay, Texas, in 2025: (1) traditional ground-based counts, (2) manual drone imagery-based counts, (3) computer-assisted counts using pre-annotations from an object detector with manual human verification (Human+ML), and (4) fully automated counts using object detector annotations (ML-only). We trained a YOLOv10 object detection model on manually annotated imagery of Chester Island in 2021 and applied it to the 2025 imagery. Manual drone annotation detected 6,530 birds in 40.5 hr and served as the primary reference standard. Human+ML detected 5,826 birds (89% of manual) in 7.7 hr, an 81% reduction in annotation time. ML-only detected 5,679 birds (87% of manual) in approximately 46 min, a 98% reduction. Ground counts recorded 5,868 birds (90% of manual). Detection generalized well across species while classification depended heavily on training data and morphological distinctiveness. The Human+ML workflow emerged as a practical middle ground, providing practitioners with empirical data to evaluate partial versus full automation strategies based on monitoring objectives.

AimSpecies in mountain ecosystems often experience upslope distribution shifts in response to climate change. However, elevational range dynamics exhibit substantial complexity around this general trend, with varying magnitude and direction of shifts observed among studies. We tested whether factors other than temperature, such as habitat size and land use, are also responsible for observed elevational shifts in a cold adapted species. LocationHokkaido, Japan MethodsWe resurveyed 61 historical (1963-2007) occurrence sites covering a wide elevational range (60-2,210 m) within the distribution range of the northern pika (Ochotona hyperborea). We assessed the existence of elevational shifts using quantile regression and the relative importance of temperature, habitat size, and land use on the shift using occupancy analysis. ResultsWe detected presence of the northern pika at 41 sites, suggesting extirpations at 20 sites (32.8%) across a wide elevational range of 60-1,550 m. The concentration of extirpation sites at low to mid elevations resulted in a significant upslope shift of the distribution centroid, although the shift was nonsignificant at lower and upper portions of the range. The occupancy analysis revealed a negative effect of long-term mean of summer maximum temperature and a stronger positive effect of habitat size. ConclusionsThis highlights the susceptibility of the northern pika to heat stress and the importance of larger habitats and thus habitat heterogeneity for persistence of local populations. Given the possibility that the observed shift represents a precursor of elevational contraction, continuous monitoring of the local populations is highly needed to evaluate their long-term viability.

A remnant Atlantic Forest fragment within Sao Paulos urban environment supports six mammal species, with Didelphis being most abundant, while maintaining natural temporal activity patterns and successful reproduction, demonstrating the conservation value of small protected areas within megacities for preserving native wildlife despite intense anthropic pressure. The Forest Reserve of the Institute of Biosciences (FRIB) is a remnant of the Atlantic Forest located at the University of Sao Paulo, city of Sao Paulo campus, in Brazil. Despite urban pressures such as altered food resources, pollution, pathogens, and interactions with invasive and domestic species, several wildlife species persist, demonstrating adaptability to urban environments. This study aimed to sample the mammal species diversity of FRIB using camera traps. A total of 367 records of six mammal species were obtained over 107 days of sampling, between June and November 2024, resulting in 7,872 hours of sampling effort. The species recorded were Callithrix jacchus, Callithrix penicillata, Didelphis aurita, Didelphis albiventris, and unidentified species of the orders Chiroptera and Rodentia. Didelphis sp. was the most recorded genera with 201 records. These results highlight the significance of this forest fragment in protecting wild animals in an environment facing intense anthropic pressure. ResumoA Reserva Florestal do Instituto de Biociencias (FRIB) e um remanescente da Mata Atlantica situado na Universidade de Sao Paulo, em Sao Paulo, Brasil. Apesar das pressoes urbanas, como mudancas nos recursos alimentares, poluicao, patogenos e interacoes com especies invasoras ou domesticas, diversas especies de animais selvagens continuam existindo, evidenciando sua capacidade de adaptacao aos ambientes urbanos. O objetivo deste estudo foi amostrar a diversidade de especies de mamiferos da FRIB atraves de armadilhas fotograficas. Durante um periodo de 107 dias de amostragem, de junho e novembro de 2024, foram obtidos 367 registros de seis especies diferentes de mamiferos, totalizando 7.872 horas de esforco. As especies registradas foram Callithrix jacchus, Callithrix penicillata, Didelphis aurita, Didelphis albiventris, alem de especies nao identificadas das ordens Chiroptera e Rodentia. Didelphis sp. foi o genero com maior numero de registros (n=201). Esses resultados ressaltam a importancia desse fragmento florestal na protecao da fauna selvagem em um ambiente que enfrenta intensa pressao antropica.

Extensive deforestation and disturbance have reshaped forest landscapes in West Africa, including areas surrounding Tai National Park, the largest remaining block of Upper Guinean rainforest. Although many degraded areas are currently undergoing regeneration, the capacity of these secondary forests to recover their biodiversity attributes remains insufficiently assessed within this park. This study quantifies the multidimensional restoration of biodiversity across 125 secondary forest plots and 15 primary forest plots representing 14,731 inventoried individuals in four sectors of the park. Using a Bayesian modelling framework, we estimate recovery trajectories of Shannon diversity, floristic composition, functional traits (wood density, leaf mass per area, seed mass), and conservation-relevant species. The different biodiversity attributes recovered at contrasting rates. Diversity was restored more rapidly ({lambda} = 0.06) than floristic composition ({lambda} = 0.03), and these recovery varied according to environmental variables. Among these, the presence of remnant trees showed the highest median on diversity (0.53 {+/-} 0.61) and floristic composition (0.37 {+/-} 0.17), promoting the rapid recovery of both attributes, followed by prior land use, particularly cocoa farming which also positively influenced the recovery of alpha diversity ({lambda} = 0.03) and floristic composition ({lambda} = 0.02). Regarding functional traits, they displayed contrasting dynamics: specific leaf area and seed mass recovered rapidly along successional gradients, whereas wood density followed a more gradual recovery trajectory. From a conservation perspective, although the proportion of IUCN Red List species remained stable along the successional gradient, old-growth indicator species were significantly more abundant in primary forests. To ensure the long-term conservation of biodiversity, protecting both primary and regenerating forests is essential to preserve the ecological resilience of this park.

iNaturalist has rapidly grown to become one of the largest contributors of global biodiversity data, being widely used in academic research, to support and inform applied conservation, and to help develop policy indicators for decision makers. However, the availability of iNaturalist data varies based on the digital platform it is accessed from. Here, we assess whether the pathway used to access iNaturalist data from three commonly available biodiversity data sources: iNaturalist, the Global Biodiversity Information Facility (GBIF) and ESRIs ArcGIS Online, alters occurrence record availability and downstream analyses for ecological inference. First, we investigate iNaturalist data availability on ArcGIS Online and find that the reduced field metadata for record location and obscuration information can lead to biased assumptions in the spatial ranges of sensitive species. Second, when assessing iNaturalist records available through GBIF, we find that restrictive Creative Commons observation licenses prevent an average of 26.1% of iNaturalist Research Grade records from being exported to GBIF, and this can lead to differences in environmental niche analysis when compared to datasets including all available research grade records. Understanding differences in platform licensing when integrating across biodiversity data repositories is another consideration for researchers and practitioners when conducting biodiversity assessments. Our results show that the pathway through which iNaturalist data are accessed can function as a methodological filter, potentially altering spatial coverage, the climatic conditions represented by occurrence datasets, and downstream ecological analysis.

The tidal wetlands of Louisiana comprise about 25% of those found throughout the conterminous United States yet estimates of wetland loss rates in the region between 1932 and 2016 have exceeded 60 km2 yr-1. To mitigate further degradation and wetland loss in the region, a globally unprecedented $50B, 50-year plan for coastal Louisiana is driving restoration efforts, and demand exists from multiple stakeholders for regularly updated, regional-scale, accurate land cover information. We used machine learning (random forests; RF) and cloud computing to develop a new Landsat-based, marsh vegetation community geospatial dataset. The dataset depicts wetland vegetation community types defined in a previous study at annual (1985-2025) time steps at 30-m resolution. An RF algorithm was used to integrate training samples with feature variables derived from Landsat imagery, and the resulting geospatial data product achieved an overall correct classification rate of 78%. The approach for development of the land cover dataset presented here has potential for application in other coastal wetland habitats throughout the world.

Understanding the spatial distribution of species and their patterns of endemism is necessary for establishing effective conservation priorities. Despite the vital pollination services bees provide, Californias native bee distribution patterns remain largely unexplored relative to plants and butterflies. We analyzed bee species richness and endemism across California and their concordance with plant distributions. Richness was high across areas of the California Floristic Province, including the Sierra Nevada, San Francisco Bay Area and Central Coast, South Coast Ranges, and the Transverse and Peninsular ranges. Bee endemism was more localized, concentrated in the San Joaquin Valley, eastern Sierra Nevada and adjacent Great Basin, Sierra Nevada foothills, and California deserts. Because richness and endemism appear to operate at different spatial scales and likely respond to different environmental drivers, effective conservation strategies must address both. Additionally, conservation plans that incorporate both plant and bee diversity are needed to achieve more comprehensive biodiversity protection.

Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.

Resource scarcity prompts animals to adjust their space use in ways that enhance their survival. In wild herbivores, seasonal habitat shifts are well studied; however, little is known about how large herbivores navigate human-dominated landscapes under fluctuating resource conditions. In Hong Kong, feral water buffalo (Bubalus bubalis; henceforth buffalo) experience declines in body condition score during the dry season. While buffalo expand spatial ranges in the dry season, whether this expansion reflects access to improved ecological conditions, and how key anthropogenic factors (like distance to roads and human density) further influence space use, remains understudied. We observed six buffalo herds (n=92 known individuals) across one wet (July-September 2023) and one dry (January-March 2024) season. We recorded herd locations and extracted remotely sensed habitat and environmental data. We used Normalized Difference Vegetation Index (NDVI) as a proxy of vegetation productivity and included elevation, distance to roads, and human density as additional predictors. We hypothesized that dry-season space use would shift toward higher vegetation productivity, and that anthropogenic factors would have weak influence on space use due to the adaption of buffalo in human-dominated landscapes. We found that buffalo used areas with higher vegetation productivity and higher elevation in the dry season than in the wet. Distance to roads and human density had no detectable effect. Our findings reveal that space use by a large herbivore like buffalo in human-dominated landscapes is strategic and resource driven, and that these seasonal shifts may have important implications for local biodiversity and human-animal interactions.

1.AimThe Species Threat Abatement and Restoration (STAR) metric was developed to support setting and measuring progress towards science-based targets for species conservation, in alignment with the Kunming-Montreal Global Biodiversity Framework. The STAR metric quantifies the potential reduction in species global extinction risk achievable through actions to abate threats (START) and restore habitat (STARR). The STAR metric is used across multiple sectors to assess contributions to nature-positive species outcomes and implement action for biodiversity. Here we present a substantially enhanced global estimate of START for terrestrial vertebrates. Developed in response to user demand, this work integrates advancements in methodology and data quality, increased spatial resolution, and addition of reptile species. LocationGlobal Time PeriodCurrent Major taxa studiedTerrestrial vertebrates MethodsSTART was estimated at 1km2 resolution for 9,100 species of threatened and Near Threatened birds, mammals, amphibians and reptiles, using IUCN Red List assessments (version 2025-1) and area of habitat (AOH) maps generated using an advanced data-driven methodology and satellite-derived land-cover data. AOH maps were validated using a two-stage protocol using species observation data. ResultsThe six countries with the highest estimated START scores, and therefore the largest individual potential to reduce global extinction risk via tackling threats nationally were Brazil, Madagascar, Indonesia, Mexico, Ecuador and Colombia (each contributing over 5% of global estimated START). The threat with the greatest individual potential to reduce extinction risk was annual and perennial non-timber crops (21.2% of global START). Main conclusionsTargeted actions to tackle a few high-impact threats in a few discrete locations, and cumulative effort across multiple areas with lower individual potential, are both required to meaningfully reduce species extinction risk through threat abatement. This global scale estimation of START enables consistent scoping of conservation opportunity over large areas and provides the critical initial data to support planning and action.

Conserving migratory birds effectively requires full annual cycle strategies that identify where on-the-ground efforts can have the greatest impact. Here, we present a hemispheric spatial framework to identify priority areas for 112 migratory bird species across the Americas. Building on full annual cycle prioritizations, we defined finer-scale spatial planning units that reflect differences in migratory and congregational behaviors between shorebirds and landbirds. We compiled population data for each planning unit and focal species and applied conservation planning tools to design area-efficient portfolios of sites and landscapes that secure 10% of each species population within the Americas flyways. The resulting minimum area portfolios include 175 shorebird sites and 80 landbird landscapes optimized to meet the species-specific 10% representation targets across breeding, non-breeding, and passage seasons. We also identified a broader set of complementary solutions, ranked by an importance score, to provide decision-makers with flexible options for strategic resource allocation. This framework provides the scientific foundation for the Americas Flyways Initiative (AFI), which aims to catalyze investment in nature-based solutions and bird-friendly infrastructure to enhance the conservation of migratory birds and strengthen the resilience of the Americas flyways by 2050.

Assessing the availability of protected habitats for at-risk species is needed to determine how the spatial distribution of land cover and land use shapes conservation outcomes. The Loammi skipper (Atrytonopsis loammi) is a nonmigratory, prairie-associated butterfly that has experienced a significant reduction in its formerly widespread southeastern United States distribution over the past several decades. Observations in recent years have been limited to a small number of isolated Florida populations, and it is unclear how much suitable habitat remains and what proportion of that habitat is protected. Here we used publicly available community science data and collected specimens to identify the predominant land cover types occupied by A. loammi. We then quantified the extent to which each of these land cover types overlap with protected areas to estimate the proportion of the butterflys current distribution that occurs on managed conservation lands. Our findings show that A. loammi relies heavily on protected lands, with over half of the total area of the most strongly associated habitats located within protected areas. Given the continued expansion of human-modified landscapes in Florida and the reliance of A. loammi on protected lands, the long-term persistence of A. loammi will likely depend on the conservation and effective management of its remaining suitable habitat. Implications for insect conservationOur results highlight the critical role of protected areas in sustaining at-risk insect populations, particularly for species with limited dispersal and shrinking ranges. More broadly, they suggest that conserving and restoring protected habitats while maintaining connectivity are essential strategies for effective insect conservation in rapidly developing regions.

Understanding how scientific effort is distributed is essential for evaluating the evidence available to guide species conservation. We assessed the temporal, thematic, methodological, and spatial organization of jaguar (Panthera onca) research across the species range. Using 1,028 bibliographic records, we applied a metadata-based scope filter, defining a primary analytical corpus of 857 articles and an inclusive sensitivity corpus of 989. We analyzed publication growth from 1946-2024, author-keyword networks, high-specificity research-domain and methodological indicators, article-based spatial concentration across nine regions, and anthropogenic context using the Human Impact Index. Publication output increased strongly and nonlinearly, with the negative-binomial generalized additive model explaining 96% of deviance. Population and abundance, human-wildlife conflict and coexistence, and movement and connectivity were the most frequent research domains, whereas camera trapping was the most frequent methodological approach. Spatial analyses included 237 georeferenced terrestrial articles. Research was strongly concentrated in the Pantanal, Mesoamerica, and Atlantic Forest, but was underrepresented relative to range area in the Andes and Choco-Darien, Amazon, and Guiana Shield. These patterns were stable to the broader scope definition. Research concentration showed no strong association with region-wide anthropogenic pressure, although studies within several extensive regions tended to occur in more human-influenced portions than the regional average. Jaguar research therefore shows substantial growth and partial alignment with applied conservation challenges but remains geographically uneven. Expanding representative research in underrepresented regions, while maintaining work in threatened landscapes, would strengthen the evidence base for range-wide conservation planning and improve coordination among countries, institutions, and regional research traditions.