Diversity and Distributions

1. Species monitoring programmes regularly encounter missing data, which complicates tasks such as estimating population size or detecting temporal trends. Selecting an imputation method suited to the properties of the data is therefore an important practical challenge, particularly for species exhibiting overdispersion and zero-inflation. 2. We compare seventeen imputation methods, comprising thirteen Poisson-based statistical models (accounting for overdispersion and zero-inflation, with fixed, random and multivariate structures) and four contrast-based approaches (LORI, MICE, missForest, correspondence analysis). Using four complete monitoring datasets of waterbird species surveyed across France and Italy over 21 years, illustrating a variety of abundance distributions, we introduced missing observations under a realistic Missing At Random mechanism, at rates from 5% to 70%. We evaluated all methods on computational burden, prediction accuracy for positive and null counts, quantification of uncertainty, and accuracy of population-size estimates. 3. No single method dominates across all criteria. Statistical models and contrast-based approaches yield similar point predictions, but only statistical models provide a genuine measure of uncertainty. Models that jointly account for overdispersion and zero-inflation perform best at predicting zero counts and achieving reliable prediction intervals, and the most suitable method ultimately depends on the abundance distribution of the target species. 4. These results provide practical guidance for ecologists selecting an imputation strategy for incomplete count data, highlighting the trade-offs between predictive accuracy, computational cost, and the ability to propagate uncertainty through subsequent ecological analyses such as population-trend estimation.

Understanding the ecological determinants of species ranges is a central goal of ecology. Novel tools like global datasets and machine learning models allow us to describe species ranges with increasing scope and accuracy, and to develop and test ecological hypotheses about their determinants. Here, I focus on the widespread nuisance mosquito and dog heartworm vector Aedes sierrensis, a tree hole-breeding mosquito that is widespread and abundance within its native range in western North America, and develop species distribution models (SDMs) to characterize the species range and its environmental determinants. I find that the species range is highly predictable from long-term average bioclimatic variables. Temperature and precipitation were the primary determinants: suitability was highest at wet-season average temperatures of 0 - 10{degrees}C, minimum temperatures of -5 - 5{degrees}C, and summer temperatures of 8 - 22{degrees}C in environments with adequate seasonal rainfall concentrated in the winter. After accounting for climate, land cover variables showed minimal importance for prediction, but suitability was higher in forests and outside of urban areas. The results are consistent with ecological knowledge of the Ae. sierrensis life cycle from field observations and previous laboratory experiments, suggesting that individual physiological constraints scale up to determine species distributional limits.

Urban and rural woodlands have become important habitats for wildlife, providing opportunities for ecological and ethological adaptation to changing environmental conditions. Due to limited tree resources in anthropogenic woodlands, different species are often forced to interact more closely, and one possible outcome of such interactions is hybridisation. Recent studies have shown that hybrids between the uncommon Syrian Woodpecker (Dendrocopos major) and the often common Great Spotted Woodpecker (Dendrocopos syriacus) are relatively widespread, yet rare, in sympatric populations (Bakai et al. 2026). To understand the factors determining their occurrence in rural and urban landscapes, we conducted an intensive survey across Europe. We examined the effects of environmental factors, particularly woodland characteristics, as well as the co-occurrence of hybrids with the parental species and European Starlings (Sturnus vulgaris) (hereafter Starling). Spatial analyses revealed that the parental species generally have allotopic distributions, with Syrian Woodpeckers (hereafter SW) being more common in scattered woodlands in both rural and urban landscapes, while Great Spotted Woodpeckers (hereafter GW) are more commonly associated with denser woodlands and forests. Although in some areas the two species can co-occur, which provides opportunities for interspecific hybridisation, hybrid offspring showed no clear preference for any particular woodland type. However, they tended to occur in proximity to one of the parental species, usually the species that was locally rarer. Starlings were not identified as a factor influencing hybrid distribution. These results suggest that hybrids have a relatively broad ecological tolerance, potentially facilitated by heterosis. These findings contribute to our understanding of the factors influencing interspecific hybridisation and may have implications for the management of urban and rural woodlands to preserve habitats for the SW, an uncommon and strictly protected species in the west of the European Union.

Evidence is accumulating that global change is altering species distributions. Yet, detailed knowledge is missing about the relative and joint contribution of different drivers to observed species responses. Here, we implemented an impact attribution framework based on counterfactual simulations to assess the impact of climate and land use change on occupancy dynamics of North American breeding birds. We used a Bayesian framework to fit process-explicit dynamic occupancy models to long-term survey data for 159 species from 1995 to 2019, and quantified predictive performance using spatial and temporal cross-validation. We then assessed the relative importance and effect direction of climate and land use change while accounting for model predictive accuracy. Results indicate that climate change negatively affected 90 % of the species and land use change negatively impacted 96 %. Climate change emerged as more important than land use change for driving changes in occupancy across species. Remarkably, the effects of both drivers were mostly antagonistic rather than acting additively or synergistically. Climate was the most important driver for bird communities in the western USA, while land use change dominated in the southeast, and combined climate and land use change in the northeast. Our analysis demonstrates that recent changes in North American bird distributions are shaped by multiple global change drivers acting in concert. The effect of recent climate and land use change were mostly antagonistic, and thus trends in bird occupancy dynamics could not be understood by studying the impact of those drivers in isolation. By disentangling the effects of climate and land use change on biodiversity trends, impact attribution approaches can improve our understanding of global change impacts and can support conservation planning and more accurate and realistic projections of biodiversity response to global change.

Introduction and Aim: Future climate instability creates obstacles for organisms across taxa, especially in areas that experience higher stochasticity. Wetland wading bird populations are a prime example of this scenario, as the hydrologic cycles that drive population success in these habitats depend heavily on the predictability of wet and dry periods. When fluctuations in wet and dry periods shift excessively, populations may be forced to relocate or shift migration routes. Our goal is to predict if worsening environmental conditions could drive increases in facultative migration and subsequent residency shifts Location: State of Florida, USA. Methods: We developed an individual-based movement model motivated by wading birds in southern Florida to determine if this concept of climate-driven range or residency shift is likely to occur in the future. To further interpret the outputs of this individual-based model, we compared habitat suitability for the American wood stork across Southeast of the United States between 2010 and 2025, 2055 and 2085. Results: Our IBM model predicted that a northward residency shift is predicted to occur given the environmental scenario of harsher fluctuation via climate instability in the Everglades. Moreover, our findings suggest that wood storks with partial and/or facultative migration may be more likely to adapt to these fluctuations. Our suitability maps showed spatially heterogeneous responses, with notable increases in northern Georgia, and the Carolinas, and consistent declines in northern Florida, indicating a gradual northward shift in suitable breeding habitat. Main Conclusions: Conserving networks of suitable wetlands across broader spatial scales, may be critical for facilitating movement and persistence under hydrologic instability. Mechanistic models such as the one presented in this study can help anticipate these shifts and inform proactive conservation planning. This model can also be adapted to fit similar wetland systems, or other landscapes with various population and movement phenotypes present.

Arctic ecosystems are undergoing rapid climate warming, providing an opportunity to examine molecular evolution under environmental stress. Here, we used whole-genome resequencing to assess population structure, genetic diversity, and recent demographic history in the snowy owl (Bubo scandiacus), a highly mobile Arctic apex predator classified as Vulnerable by the IUCN. We analysed genome-wide data from individuals sampled across the species' circumpolar range using a chromosome-level reference genome. Population genomic analyses revealed an absence of detectable population structure, indicating extensive gene flow and near-panmixia across continents. Genome-wide nucleotide diversity was low ({pi}= 2.47 x 10^(-4)), while runs of homozygosity were dominated by short tracts, indicating limited standing genetic variation despite little evidence for recent close inbreeding. Our demographic inference revealed a sustained decline in effective population size over the past ~1,600 years, a pronounced reduction coinciding with the Medieval Warm Period followed by a partial recovery during the subsequent cooler interval. This direct, measurable effect of past climate warming on effective population size raises concerns for the long-term survival of the snowy owl. Our results demonstrate that high connectivity and low levels of recent inbreeding have not necessarily buffered this species against climate-driven demographic erosion of genetic diversity. This study highlights the value of using whole-genome population genomics to inform genomic monitoring and conservation decision-making in rapidly changing environments.

Understanding the demographic processes shaping population connectivity is essential for conservation. Yet disentangling the effects of dispersal and local effective density remains challenging because traditional genetic approaches integrate signals over evolutionary timescales. Identity-by-descent (IBD) segments provide opportunities to reconstruct recent demographic connectivity over timescales relevant to contemporary conservation challenges, but their reliability under realistic ecological conditions remains poorly evaluated. Here, we assess the ability of two promising spatiall IBD-based methods, IBD-Analysis and MAPS, to estimate effective density and dispersal variance. Using a simulation-inference framework combining spatially explicit simulations, IBD segment extraction, and demographic inference, we evaluated both methods across a wide range of demographic, ecological and methodological scenarios. Both approaches reliably recovered demographic parameters under homogeneous landscapes, with IBD-Analysis providing accurate estimates of global effective density and dispersal across a broad range of conditions. Spatial heterogeneity emerged as the main driver of inference performance. When demographic parameters varied across space, IBD-Analysis integrated local variation into biased global estimates, whereas MAPS captured spatial patterns but showed substantial variability and generated spurious spatial structure in homogeneous landscapes. Methodological factors had comparatively weaker effects than ecological complexity, highlighting the importance of accounting for landscape structure when applying IBD-based demographic inference. These results highlight the potential of IBD-based approaches to move beyond descriptive patterns of genetic connectivity toward mechanistic inference of demographic processes. By distinguishing the contributions of dispersal and density, these methods can improve conservation decisions by linking genomic patterns to key demographic parameters relevant for conservation.

Rosa gallica L., the French rose, is a perennial, tetraploid, heterozygous species that naturally propagates by seed and sucker. It occurs in the wild, primarily in Europe, and also exists as cultivated varieties. R. gallica cultivars were extensively bred and cultivated in France at the beginning of the 19th century. Although several hypotheses have been proposed regarding the species expansion based on historical records, none have been assessed using molecular data. Indeed, its genetic diversity has so far been investigated only at local or regional scales, hindering the identification of the evolutionary factors shaping its present-day distribution. Using 29 sequenced microsatellites, we genotyped a comprehensive sample of 1618 individuals, including wild R. gallica from 219 sites across the species range, rose cultivars, and specimens from other Rosa species. We then detected clonal lineages and characterized the range-wide genetic diversity and structure, aiming to disentangle the roles of natural and human factors in shaping the distribution of R. gallica, with particular focus on France. French diversity appears particularly structured compared to the rest of the range, suggesting multiple origins within France. Populations in South Alps, Central Eastern Europe, and Eastern France appear to have recolonized naturally from a single southern glacial refugium. In contrast, populations in the western part of France likely resulted from more recent natural or human-mediated dispersal. Finally, clonal lineages containing both wild and cultivated individuals were predominantly found in France, highlighting the role of human-mediated dispersal in 28 of the 98 French sites studied. These findings show that the present-day natural range of R. gallica was shaped primarily by post-glacial recolonization, but also reveal a contribution of human activities to its recent dispersal, particularly in France, where cultivated varieties were intensively bred and exchanged.

Achieving global conservation and restoration targets requires knowing not only how much land to conserve and restore, but also where such actions will have the greatest ecological effect. Conservation and, in particular, restoration priorities are often inferred from habitat condition, opportunity, or broad ecological value, yet these criteria do not necessarily identify where local changes generate the largest landscape-scale consequences. Building on sensitivity-based conservation prioritization, we extend the approach to derive ecological loss and gain potential by differentiating a persistence-relevant connectivity metric with respect to local habitat condition. Sensitivity identifies areas where local degradation or improvement would most strongly affect connected habitat at the landscape scale. Combining sensitivity with current condition distinguishes high loss potential, where degradation would have large ecological consequences, from high gain potential, where ecological improvement could generate large benefits. Applying the framework to national forest-naturalness data in Norway, we show that loss and gain potential are spatially related but far from redundant, and that gain potential is not simply concentrated in the most degraded forests. Both high-loss and high-gain areas were poorly represented within current protected areas, although loss potential was consistently better represented than gain potential. Extending sensitivity-based prioritization in this way provides a general and scalable means of linking local habitat-condition change to persistence-relevant landscape outcomes, while distinguishing the ecological potentials that can inform conservation and restoration decisions.

Retrospective ecological niche modeling (rENM) combines historical species occurrence records with historical environmental data to reconstruct the spatio-temporal dynamics of species-environment relationships under changing conditions. Despite growing recognition that those relationships can be nonstationary, time-series approaches to ecological niche modeling remain uncommon, and the tools to support them at scale are limited. Here, we describe the rENM Framework, an experimental, open-source suite of R packages that automates a complete rENM workflow spanning data preparation, ensemble time-series construction, trend analysis, AI interpretation, and report generation. The framework integrates eBird occurrence records with environmental variables derived from NASAs MERRA-2 reanalysis across a 45-year study period (1980-2024) and executes a complete analysis for any species with eBird data through a single function call. By treating climatic suitability as a dynamic ecological response surface rather than a static baseline, the framework produces the following analytical products that complement conventional ecological niche modeling approaches: suitability time series, long-term trend and acceleration maps, centroid displacement estimates, bioclimatic velocity metrics, variable contribution trajectories, and hotspot analyses identifying areas of accelerating suitability decline. We illustrate the frameworks outputs with a representative run for Cassins Sparrow (Peucaea cassinii), a grassland species of conservation concern in the arid southwestern United States and the focal species throughout our development work. The frameworks automated, unsupervised pipeline makes systematic application across large numbers of species tractable, with direct implications for conservation assessments, such as State Wildlife Action Plans, where species-specific analytical capacity is often limited by available resources. The rENM Framework is openly available on GitHub and archived on Zenodo.

Here we describe substantial range shifts of historically differentiated wolf populations and formation of novel sympatric zones during the recent decade in Central Europe. This region provides a natural laboratory for testing alternative scenarios of population interactions from continued isolation or restricted gene flow to progressive population fusion, while prompting a reassessment of their geographic ranges. Based on sampling across the Czech Republic and Slovakia obtained from large-scale monitoring programmes over five wolf years (2020/21_2024/25), complemented by comparative material from neighbouring regions, we analysed mitochondrial haplotypes, autosomal microsatellite genotypes and sex-linked loci. The Central European population with Baltic ancestry predominated across large parts of Central Europe including the Bohemian Massif with enclaves in the Western Carpathians. The Carpathian population was predominant in Slovakia, with a smaller satellite occurrence in the northern part of the Bohemian Massif. Alpine population was centred in the Alps but extended into southern parts of Bohemian Massif and Central German Uplands. Following the sporadic occurrence of admixed individuals, broad mosaic and dynamic sympatric zones have formed in the Czech Republic and Slovakia in the last decade. These scenarios could be facilitated by the presence of intermediate habitats and isolation of the Bohemian Massif structural basin, framed by a massive ring fault system. Recent-immigration estimates are asymmetric, with the largest mean contributions from the Alpine to the Central European population and from the Central European to the Carpathian population, with the second case potentially linked to source-sink dynamics driven by the hunting pressure within the Carpathian population (whereas the others are protected year-round). Whether increasing admixture will enhance viability of populations (that currently have small effective sizes) through genetic rescue or carry risks of outbreeding depression remains uncertain, highlighting the need for continued transboundary monitoring within conservation biology framework.

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.

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.

Protected areas are vital to the recovery of endangered species. Of the 24 remaining endemic passerines in Hawaii, 16 species are endangered or critically endangered. Yet protected areas in the archipelago are changing as a result of climate change and biological invasions. For example, the non-native southern house mosquito (Culex quinquefasciatus), the primary vector of avian malaria (Plasmodium relictum), has been encroaching upward as higher elevations warm. How the ranges of endemic birds have also shifted their range upward in the Ka[u] Rainforest, the largest native forest on the Island of Hawaii, is not well understood. We used hierarchical distance sampling to characterize how population density has changed from 2002 to 2024 for eight endemic bird species in the Ka[u] Rainforest. For five species, the most parsimonious model included an interaction between year and a quadratic elevation effect. Species that were most common at lower elevations in 2002, e.g., Apapane (Himatione sanguinea), tended to be most common at mid-elevations by 2024. Species that were already most common at higher elevations, such as Iiwi (Drepanis coccinea) and Hawaii [A]kepa (Loxops coccineus), tended to decline in density. For example, Iiwi density at 1,530 m declined from 3.31 birds per ha (95% confidence interval [CI]: 2.67-4.11) to 1.34 birds per ha (95% CI: 1.05-1.71), and Hawaii [A]kepa were completely extirpated from elevations below 1,530 m by 2024. Our results demonstrate how the elevational ranges of endemic species have shifted in response to climate change, biological invasions, and habitat degradation. Translating these results into conservation measures may require a more thorough investigation of the causal mechanisms of these range shifts with finer scale habitat data, under the same hierarchical modeling framework.

Small populations are increasingly common on the landscape, and many face rising probabilities of extinction. Demographic factors alone can drive a small population into an extinction vortex, but genetic factors such as inbreeding and genetic drift lead to reductions in genetic diversity and expression of deleterious alleles, further impacting fitness. Populations on islands are also impacted by founder effects, minimal gene flow, and habitat constraints, yet naturally small, island populations have persisted through many generations despite all these factors. This suggests that island biota may operate at smaller viable population sizes than their mainland counterparts while avoiding extinction vortices. Using 26 microsatellites, we describe the genetic diversity and population structure of 13 subpopulations of a large island vertebrate, the endangered Turks and Caicos rock iguana (Cyclura carinata), found on a range of small cays (0.151-374ha). Most subpopulations had decreased genetic variation but limited evidence of inbreeding despite small island size. Even with water barriers limiting gene flow, some subpopulations in geographical proximity are functioning as a metapopulation while others are not. These subpopulations are not in danger of extirpation, even though census size could be as low as 10 individuals. We believe this study shows that C. carinata has a low population size threshold for long-term persistence that can inform the species' future management, such as with conservation translocations. Furthermore, this project anecdotally adds more evidence to how island populations persist through selection despite genetic drift.

1. Plant populations at high elevation persist near their physiological limits, and treeline position is one of the clearest expressions of this constraint. In many mountain regions, however, this limit reflects historical land use as much as climate. Treeline shift is most often tracked as the displacement of a boundary line (1D) overlooking structural changes only visible when the ecotone is analysed as a surface (2D). 2. We reconstructed treeline ecotone dynamics at nine sites in the Italian Alps using aerial imagery from 1954 and 2017. At each site we detected the timberline and tree species line, quantified elevational displacement, and measured ecotonal surface change, relating them to climatic, land-use, and topographic predictors. 3. Both boundaries advanced upslope at all nine sites, with timberlines shifting faster (1.6 m/yr) than tree species lines (1.1 m/yr). This decoupled velocity caused the ecotonal surface to contract by ~17%. Land-use variables consistently explained more variance than climatic ones. 4. Land-use legacy and abandonment have a major role in shaping treeline dynamics. Boundary displacement reveals vegetation advance at both margins, while the surface view reveals concurrent narrowing of the habitat available for tree establishment. Both perspectives are needed to fully characterize how plant populations respond to shifting limiting factors at the treeline ecotone.

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.

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

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.

O_LIUnderstanding the processes underlying successful eradication of invasive species is essential for achieving global island conservation goals. Despite the widespread availability of capture records from eradication programs, modeling frameworks that utilize these datasets to elucidate spatio-temporal population dynamics remain underdeveloped. C_LIO_LIIn this study, we reconstructed the spatio-temporal population dynamics of the small Indian mongoose on Amami-Oshima Island (712 km{superscript 2}), Japan, where the species was introduced in 1979 and officially declared eradicated in 2024 after more than 30 years of systematic removal. We integrated introduction records, capture data, and monitoring data using a hierarchical harvest-based model (HBM). To evaluate the models capacity to support management decisions and assess eradication success, we conducted retrospective analyses and compared estimated eradication probabilities with those obtained from a rapid eradication assessment (REA; Samaniego-Herrera et al., 2013). C_LIO_LIThe estimated population size (before reproduction) peaked at 5,449 individuals (95% CI: 4,703, 6,175) in 2000 and subsequently declined almost monotonically. The maximum invaded area was 547.78 km{superscript 2} (posterior median, 95% CI: 496.47, 566.04) in 2009, indicating that the removal program successfully prevented island-wide expansion. Retrospective analyses showed that population estimates remained within the 95% credible intervals of the full dataset estimates, demonstrating temporal consistency. Eradication probabilities estimated by the HBM were substantially higher than those from the REA, highlighting the sensitivity of estimates to fine-scale heterogeneity in detection processes. C_LIO_LISynthesis and applications: Hierarchical HBMs provide a powerful framework for reconstructing, predicting, and evaluating invasive species eradication dynamics. Being aware of the limitations for application to eradication evaluations, HBMs can support adaptive management in long-term eradication programs and improve our understanding of the mechanisms underlying successful eradication. C_LI