Conservation Science and Practice

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.

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.

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.

The accelerated loss of biodiversity in sub-Saharan Africa threatens the functioning of tropical ecosystems. In Cote dIvoire, the Comoe National Park (PNCOMOE), a Sudano-Guinean savannah, and the Tai National Park (PNTAI), a dense rainforest, both UNESCO World Heritage Sites, are home to fauna assemblages of global importance, whose long-term resilience remains insufficiently quantified. This study assesses and compares, over a decade (2014-2025), the functional stability of vertebrate communities in these two contrasting ecosystems, using nine metrics covering resistance, invariance, persistence, interspecific synchrony, Tilmans stability, Jacobian resilience and a Composite Stability Index (CSI). Abundance data for 107 vertebrate species were collected via foot transects at PNTAI and aerial surveys at PNCOMOE. The stability metrics were calculated using the R package estar, integrated with an alpha diversity analysis (Shannon H', species richness S, Pielous evenness J') and a Jacobian spectral analysis within a multidimensional ecological assessment. PNTAI (0.708) exhibits significantly higher alpha diversity (H' = 2.82; S = 55.7 taxa) and community resilience 4.6 times higher than in the PNCOMOE (0.153). Its interspecific asynchrony index (0.504) reveals a strong portfolio effect, absent in the PNCOMOE (0.232). In contrast, PNCOMOE exhibits higher temporal invariance (0.382 versus 0.116) and Tilman stability (0.276 versus 0.152), reflecting more predictable dynamics. The overall ICS favours the PNTAI (0.484) and (0.370). The Jacobian analysis detects local instability in both parks (Re({lambda}max) = 5.58 at the PNTAI; 3.73 at the PNCOMOE). The two parks exhibit distinct yet complementary stability architectures: PNTAI relies on dynamic stability based on resilience and interspecific compensation, whilst PNCOMOE demonstrates conservative stability through temporal regularity. The absence of calculable resilience at PNCOMOE suggests a potential crossing of a functional degradation threshold, arguing for urgent restoration interventions and differentiated conservation strategies, tailored to the resilience mechanisms specific to each ecosystem.

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.

Small-scale fisheries provide essential coastal livelihoods in developing regions, yet they often confront severe institutional voids within fragile-state contexts. Adopting a social-ecological systems framework, this study addresses three sequential objectives: characterizing the contemporary institutional and demographic status of fishery cooperatives across the five South-Central Federal Member States in comparison to their late-1980s historical peak; ranking the socio-economic, infrastructural, regulatory, and environmental impediments to cooperative efficacy; and evaluating the contributions of these cooperatives to sustainable fisheries development. This investigation analyzes how fishery cooperatives act as institutional intermediaries to bolster economic resilience within the Somali coastal economy--a sector central to the National Transformation Plan, despite persistent regulatory fragmentation. Through semi-structured qualitative interviews with key informants--analyzed via reflexive thematic analysis and chi-square testing of age-cohort structure--the study finds that illegal, unreported, and unregulated fishing and inadequate post-harvest infrastructure are the principal constraints, whereas training, education, and community collaboration constitute the primary contributions. The results characterize these cooperatives as hybrid institutions that compensate for limited central-state capacity in remote coastal settlements while serving as intermediaries for the Federal Ministry of Fisheries and Blue Economy where co-management mandates are implemented. By extending the actor-community dimension of the SES framework to include age-cohort structure, this research demonstrates that clan-based reciprocal arrangements can serve as functional substitutes for Ostroms first design principle in contexts of persistent fragility. The study recommends multi-stakeholder partnerships to advance targeted capacity-building, financial literacy, and technological integration, and suggests future research into the post-harvest and retail sectors to examine the role of women in the Somali blue economy.

Arthropods represent the most species-rich animal group on the planet, yet they remain largely overlooked in formal conservation planning. New Mexico is a US state with exceptional biodiversity, including numerous endemic, rare, and threatened arthropod species. Recent attention to global insect declines has highlighted the need to document possible threats to and declines of New Mexico insects and other arthropods. All terrestrial arthropods in New Mexico lacked state-level regulatory protection until 2025, and baseline data on the taxonomy, ecology, geographic distribution and conservation needs for most New Mexico arthropod species remained absent. To address this gap, we developed the New Mexico Rare Arthropod Resource (NM-RARe), a publicly accessible regional database for arthropod species and subspecies of conservation concern in New Mexico. Drawing from multiple vetted sources, we compiled an initial working list of 7,018 taxa and conducted 261 preliminary IUCN Red List assessments and 185 NatureServe state rank assessments, covering 285 total taxa. Of those assessed by IUCN Red List standards, 52 were subspecies and 209 were species level assessments, 165 of which met the criteria for inclusion and were incorporated into the NM-RARe database (https://nmrare.org/). Ninety-three of the pollinating insect species from NM-RARe were then formally incorporated into New Mexico's 2025 State Wildlife Action Plan (SWAP), representing a significant conservation milestone. Beyond its immediate regional impact, NM-RARe presents a standardized, scalable methodology adaptable to any region or taxonomic group, offering a replicable framework for arthropod conservation assessment worldwide.

Monitoring populations is fundamental for conserving threatened carnivores, but can be difficult to sustain in the long term across large landscapes. We present a collaborative framework for monitoring dholes (Cuon alpinus) in India's Western Ghats, combining fecal DNA-based individual identification, Spatially Explicit Capture-Recapture (SECR) models, state Forest Department participation, and trained citizen-volunteers. We surveyed across six sites between 2019-2023, encompassing three Protected Areas (Wayanad, Parambikulam, Periyar) and their adjacent Territorial Divisions. Fecal samples were genotyped using a panel of 96 Single Nucleotide Polymorphisms, and detections were analyzed using SECR models to estimate densities. In Wayanad Sanctuary, densities remained stable across 2019, 2022 and 2023, increasing marginally from 15.67 to 19.01 individuals per 100 sq.km. In 2023, densities varied substantially among six sites, from 9.73/100 sq.km in Parambikulam to 29.23 in Nemmara Territorial Division. Field implementation involved an average of 46 Forest Department personnel co-collecting data per site-year. Among former citizen-volunteers who were surveyed for project feedback (N=29), 83% went on to either work or pursue higher education in wildlife-related or -adjacent fields. We demonstrate that molecular individual identification coupled with SECR can provide a practical basis for dhole population assessments while enabling multi-stakeholder participation in field monitoring. Sustained engagement with wildlife managers and locally recruited volunteers can strengthen institutional capacity, facilitate knowledge exchange, and improve continuity of monitoring programs. We argue that integrating molecular ecology with collaborative governance and democratizing field-science offers a scalable pathway for landscape-level monitoring required to guide dhole conservation.

This study examined the spatial distribution of potential human encounters with dangerous large mammals in and adjacent to the Ifakara-Lupiro-Mang'ula Wildlife Management Area (ILUMA WMA) and Nyerere National Park (NNP) in southern Tanzania. Using published survey data collected over one year, this secondary analysis focused on four potentially dangerous large mammals: elephant (Loxodonta africana), buffalo (Syncerus caffer), hippopotamus (Hippopotamus amphibius) and hyena (Crocuta crocuta). Potential encounter risk was estimated using overlap indices based on co-occurrence of signs of wildlife and human activities. Potential encounter risk was found to be concentrated around readily identifiable foci within the study area and differed between species and distinguishable human livelihood activities. Potential for encountering the two most apparently important dangerous animals, elephant and buffalo, was closely associated with authorized fishing camps within the ILUMA WMA and the ecotone between substantially disturbed and largely intact natural land cover. The greatest potential for encountering hippopotamus occurred largely around two river beds, while hyena encounter potential was more closely associated with established villages and converted lands close to the western boundary of the ILUMA WMA. These new maps of hazard and risk suggest that effective human-wildlife conflict (HWC) mitigation in this WMA is unlikely to arise from any stand-alone intervention, but from targeted actions that reduce spatiotemporal overlap between people and wildlife by keeping them apart and/or protecting them against each other. Overall, this study demonstrates that mapping potential encounter risk based on spatiotemporal overlap of indirect activity indicators like tracks, spoor and signs provides a proactive and statistically powerful way to identify where hazardous encounters are most likely to occur, offering considerable advantages over retrospective assessments of much sparser observations of actual realised conflict events after they have already occurred. This new spatial risk mapping approach provides a framework for shifting management from reactive responses after conflict occurs towards proactive prevention.

Information on Western bongo (Tragelaphus eurycerus eurycerus) across its range remains scarce and fragmented, leaving the status of populations poorly understood, limiting effective conservation planning. We conducted three camera-trap surveys across Semuliki National Park, Uganda, between October 2019 and August 2020 (90 camera locations; 11,640 camera days), during which we detected the only currently known population of Western bongo in East Africa. We assessed the status of this population using data from the final survey (March-August 2020), when bongos were detected, applying an individual identification system adapted from Eastern bongo (Tragelaphus eurycerus isaaci) and spatially explicit capture-recapture (SECR) modelling to estimate density and abundance. To reflect known aspects of bongo ecology, we incorporated sex and habitat covariates derived from remotely sensed data as predictors of density. Bongos were detected on 17 occasions, and we identified 18 individuals (11 females and 7 males), of which seven immature individuals (5 females and 2 males). The top-ranking SECR model included the tasseled cap greenness index and sex as predictors of density. The model estimated a population size of 56.49 individuals (SE = 7.69; 95% CI = 41.43-71.56), with an estimated density of 0.58 individuals km-2 (SE = 0.171; 95% CI = 0.32-1.09) within Semuliki National Park. These results provide the first quantitative baseline for this population and suggest the presence of an established breeding population, highlighting the need for structured monitoring to support its long-term conservation.

Madagascar hosts exceptional biodiversity and endemism, yet its native bee fauna remains poorly characterised. We assembled and cleaned the first comprehensive occurrence dataset for Malagasy bees, reducing 10,721 raw records to 4,071 validated occurrences covering 218 georeferenced species of the 224 checklist species across six families (89.3% endemic). Despite near-complete checklist coverage, the dataset remains critically sparse for Madagascar's size, with most species documented by only a few records. Sampling was highly uneven across taxa: most genera were underrepresented while a few were disproportionately sampled due to ecological prevalence, detectability, and collector specialisation. Spatial concentration within limited grid cells amplified these biases. Temporally, effort varied markedly, with historical peaks driven by individual collectors and a post-2010 shift toward Apidae-dominated records. Spatially, 79.9% of 25x25 km grid cells intersecting Madagascar held no bee records, and 77.1% of records fell within 2.5 km of roads, mirroring global sampling patterns. Sampling clustered near major cities, with common species consistently found near roads and rare species spread across wider distance ranges. Among 231 Key Biodiversity Areas (KBAs), 68.4% were entirely unsampled; sampled KBAs held only 26.7% of all records, and sampling remained uneven even there, leaving substantial undetected diversity across most sites. These results reveal pervasive temporal, spatial, taxonomic, and collector-driven biases, underscoring the need for targeted surveys within KBAs and beyond roadsides to improve coverage of data-deficient species and strengthen conservation assessments.

Over the past two decades, significant population recoveries of tigers and leopards have driven a substantial rise in human-carnivore conflict across South Asia. Sustaining the long-term conservation of both species demands effective, adaptive conflict management. However, existing research is largely confined to isolated landscapes or protected areas, hindering the identification of spatial conflict drivers across broader, heterogeneous regions. To address this gap, this study compiled 4,457 human leopard conflict (HLC) and 2,563 human tiger conflict (HTC) records spanning 2000 to 2024 across Bangladesh, Bhutan, India, and Nepal. We evaluated conflict locations against 33 landscape, anthropogenic, climatic, topographic, and hydrological predictors across three spatial scales using binomial generalized linear regression models and three alternative background sampling methods. Model performance peaked at the largest spatial scale for both species and remained consistent across sampling strategies. HTC correlated positively with forest fragmentation and proximity to human settlements, but decreased with higher overall anthropogenic disturbance. Conversely, HLC concentrated in highly productive, high-precipitation, human-modified landscapes. These contrasting spatial patterns highlight distinct ecological responses to habitat modification and carry unique conservation implications. Ultimately, this study underscores the need to integrate carnivore abundance, wild prey densities, and socioeconomic factors to achieve a holistic understanding of conflict dynamics.

Incorporating the complexities of climate change into conservation planning can be challenging. Climate change is projected to change the mean and variability of temperature and precipitation, and change dynamics of extreme events of many climate variables globally. These changes will all have compounding effects on ecosystems worldwide, affecting species distributions, seasonal timings, and population dynamics. However, most recent climate-informed conservation planning frameworks only focus on changes in mean conditions, leaving out ecologically important information about seasonality and extreme events. Using a case study of estuaries in the Pacific Northwest, this research seeks to answer the question "How can the effects of climate change on estuaries best be modelled and described for practical use in ecological management?" To answer this question, we used downscaled climate projection data to estimate changes in the mean and variability of temperature and precipitation. Applying extreme value theory to these projection data, we also projected changes in the magnitude of extremes in temperature and precipitation for estuaries in the region. Using descriptive statistics and open source data, our results present a novel, holistic method of understanding climate risk, including estimating extreme events, highlighting a key research gap in conservation planning. These results also highlight that trends in the mean, variability, and magnitude of climate extremes are not consistent with each other, further underscoring the importance of considering multiple dimensions of climate change together. Despite uncertainty given by climate models, the methods presented here provide a reasonable approach to plan for conservation management in the face of climate uncertainty.

Citizen science observations are abundant, but conservation requires turning uneven records into reliable predictions and directing new surveys to where information is missing. We developed a biodiversity platform for Japan that is updated monthly and integrates 2.32 million records to predict 8,297 species across seven taxonomic groups. Shared representation models outperformed species-specific models in four groups and extended predictions to species with few records. Five independent datasets, including structured monitoring, environmental DNA and complete forest inventories, confirmed that the models ranked observed species and occupied sites above alternatives, with median AUCs of 0.724 to 0.894 across sites and 0.650 to 0.841 across species. For any user-selected area, the platform returns candidate species, distribution predictions, a biodiversity map corrected for uneven observation effort, a conservation priority map for native species and a map recommending where to survey next. This map highlights places where species with few records are predicted to occur despite limited sampling. Independent observations showed that areas ranked highly by this predicted potential contained many such species, indicating that model predictions can help direct surveys toward knowledge gaps. New observations are incorporated into monthly updates, creating a national feedback system connecting citizen science, local conservation decisions and future surveys.

Businesses are increasingly encouraged to disclose their nature-related dependencies, impacts, risks and opportunities. A common component of sustainability reporting is screening operational sites for ecologically sensitivity to identify locations for further evaluation and action. However, with 600+ biodiversity metrics available, selecting and interpreting appropriate metrics remains challenging for business. We developed a simple screening framework informed by the Taskforce for Nature-Related Financial Disclosures guidance, grouping eleven widely used global biodiversity metrics into four complementary [&prime]baskets[&prime], representing different aspects of biodiversity. We created hypothetical but realistic mining, onshore wind energy and agricultural companies, to assess how metric choice, buffer size, scoring approach and sensitivity thresholds influence screening outcomes. Our basket framework consistently identified similar high-priority sites across metric combinations, but site rankings varied with methodological choices. We recommend clearer guidance on metric selection and application, alongside greater transparency from business regarding assumptions, methods and limitations when screening sites for ecological sensitivity.

Social wasps (family: Vespidae) are increasingly concerning invaders and have been subject to increased detections and a growing number of invasive populations in the last few decades. As established invasive populations are challenging to eradicate, preventing introductions and prioritizing early interventions are the most cost-effective management solutions to mitigate these effects. A current challenge to this approach is that species distribution data is limited for many social wasp species, hindering our ability to accurately predict novel habitats with high suitability. To address this gap, we used MAXENT to create species distribution models (SDM) for 299 species of social vespid. We identified existing invasive populations of social wasps and incorporated their current invasive ranges to improve the transferability of our models in predicting habitat suitability in new environments. Current range sizes and habitat suitability varied widely among species and genera. We identified new species of high invasive concern, particularly in the genus Vespa. We also identified previously unrecognized regions that may be at high risk of future invasion primarily in Central Africa and the Indo-Australian Archipelago. Combining current and suitable ranges, we calculated an "Invasion Risk Score" to compare the relative likelihood of each species establishing a new invasive population based upon habitat suitability. To assess invasion risk in the future, we projected habitat suitability under four Shared Socioeconomic Pathway (SSP) climate change scenarios. Under all scenarios, species faced significant changes in habitat suitability for current native ranges. Habitat suitability generally shrank and shifted towards the poles, leaving equatorial species at highest risk of habitat loss. Notably, Vespa was the only genus whose suitable habitat expanded under these climate scenarios. Our framework demonstrates how multi-species SDMs can be applied to risk management of invasive populations.

1. Assessing the conservation value of secondary forests and reforestation is important for conservation planning. Butterflies are widely used as indicators, and fruit-feeding butterflies are often used in biodiversity assessments in tropical landscapes. 2. We examined fruit-feeding butterfly diversity, abundance, and community composition across a primary forest, a secondary forest, and a plantation forest in northern Vietnam. We sampled butterflies nearly monthly during one year using baited traps. 3. Primary and secondary forest supported higher species richness than plantation forest. Moreover, while secondary forest had mainly a subset of species found in primary forest, the plantation forest had a distinct butterfly fauna. 4. Butterfly abundance and community composition varied across months, without showing a clear seasonal pattern. 5. Our results suggest that secondary and plantation forests do not fully replace primary forests, but they contribute meaningfully to butterfly conservation in human-modified tropical landscapes.

1. Occurrence records underpin biodiversity analyses, but their coordinates are often erroneous or missing. Misplaced coordinates distort environmental predictors and range estimates used in conservation assessment. Existing tools flag suspect records or georeference locality text, but rarely correct coordinates, provide calibrated uncertainty, or quantify downstream consequences. 2. We present GeoMend, a deterministic, rule-based method that flags and corrects suspect coordinates and georeferences records lacking coordinates from locality text, assigning each a precision tier or uncertainty radius. It uses no machine learning or artificial intelligence. We evaluate it against expert coordinates for 16,913 records and 736 species on Hispaniola, compare georeferencers using 5,000 localities from 72 countries, and measure changes in environmental predictors and the extent of occurrence. 3. On held-out Hispaniola records, GeoMend flags 92% of coordinates more than 5 km from expert truth, versus 27% for CoordinateCleaner. Among the accepted corrections with paired coordinates, the median error falls from 2.19 to 0.82 km, and 68% of the complete held-out pairs improve. On records placed by both GeoMend and GEOLocate that also have original coordinates, the median error is 0.67 km for GeoMend versus 10.69 km for GEOLocate. For records lacking coordinates, evaluated uncertainty radii contain expert truth about 92% of the time in well-supported gazetteer classes. Correction reduces the mean standardized environmental distance from expert truth by 38% and moves the extent of occurrence toward expert truth by a median of 8% (mean of 79%). Corrections and newly georeferenced records give 17 Hispaniola species their first range estimate. 4. On the global benchmark, GeoMend is more accurate than GPT-4o for two-thirds of the records placed by all four comparators, with median errors of 4.4 and 10.8 km, respectively. Applied to Global Biodiversity Information Facility (GBIF) records lacking coordinates, it places records from 82,180 previously unmapped species across 233 countries, ranging from precise locality interpretations to administrative centroids. Automated placement makes the global backlog more approachable, while explicit tiers distinguish uses supported by precise and coarse coordinates. Further georeferencing requires regional expertise to combine automated placement with local knowledge.

Information on the long-term dynamics of species distributions is essential for assessing global biodiversity change and its causes, and for informed conservation decisions. A valuable source of such historical information are atlases. They provide spatial standardisation, extensive geographic coverage, temporal replication, documented sampling protocols, and span multiple species. Here we present model-based occupancy probabilities for 1,055 bird and 58 butterfly species, derived from nine gridded atlas datasets spanning several editions over the past 50 years. These are: Japan, New Zealand, New York state (US), the states of Alberta, Quebec, and The Maritimes (Canada), Czech Republic, United Kingdom, and Europe. Despite standardisation and coordination during data collection, the raw atlas data still contain some variation in sampling effort. We accounted for this using a two-step process: (1) we estimated a proxy for sampling effort in each grid cell within each atlas period using the Frescalo Algorithm, and (2) we ran a Bayesian spatial occupancy model for each species and atlas period, using the estimated sampling effort as the single predictor of detection in the observation process of the model. As the main product, we provide occupancy probabilities for species across grid cells and atlas periods. We also report uncertainty around each probability estimate and flag ~21% (rare) species per atlas for which the model did not fit well. The probability distributions are fit for purpose and served in a user-friendly database with standardised geographic projection, metadata, and taxonomy.

Reliable estimates of population size are fundamental to understanding wildlife population status and informing conservation. Obtaining such estimates remains challenging for unmarked species, particularly in tropical countries where logistical and financial constraints limit intensive monitoring. The endangered dhole (Cuon alpinus), a highly social canid without natural markings, is one such species for which robust population estimates remain scarce. We estimated dhole density in the Valparai plateau, a human-dominated agroforest-forest mosaic in India's Western Ghats. We compared Spatially Explicit Capture-Recapture (SECR) models based on individual identification from fecal DNA and Space-to-Event (STE) models based on camera-trap detections. We additionally applied both approaches to individually identifiable leopards (Panthera pardus) to cross-validate STE versus SECR estimates. SECR estimated dhole density at 22 individuals per 100 sq.km, with STE estimates varying substantially depending on how the camera viewshed area was defined. STE estimates based on post hoc viewshed measurements, particularly after reducing the viewshed by 10%, were comparable to SECR estimates. In contrast, STE underestimated leopard density relative to SECR (7 versus 12 per 100 sq.km), although uncertainty intervals overlapped. Ecologically, our results indicate that human-use agroforest mosaics can support dhole densities comparable to, or exceeding, those reported from several Protected Areas, highlighting their potential importance for dhole conservation. More broadly, we argue that STE models may offer a practical and relatively inexpensive approach for baseline monitoring of dholes from camera-trap surveys, but their reliability depends strongly on accurate characterization of the effective viewshed and may vary across species and landscapes.