Conservation Biology

Effective conservation planning increasingly relies on species distribution models (SDMs) to guide where actions deliver the greatest biodiversity benefits through spatial conservation prioritization. However, SDMs are inherently uncertain, and this uncertainty propagates through prioritization processes, affecting the identification of priority areas and influencing conservation decisions. Here, we evaluate whether correcting SDM overprediction reduces uncertainty propagation into spatial conservation prioritization. Using two large European datasets of vertebrates and invertebrates, we compared unconstrained SDMs with models corrected for overprediction through a Bayesian integration of occurrences, expert range maps, and habitat suitability. We found that overprediction correction reduced spatial and performance uncertainty, with uncertainty strongly structured by model and algorithm choice and amplified when overprediction was not corrected. Although no single modelling adjustment fully eliminates uncertainty propagation from SDMs into prioritization, we demonstrate that overprediction correction consistently reduces it across datasets, taxa, and modelling approaches, highlighting its importance for robust conservation planning.

Global conservation agreements emphasize protected area coverage targets, such as the Kunming-Montreal Global Biodiversity Frameworks 30x30 target, yet their effectiveness in safeguarding biodiversity remains uncertain. We measure the intersection between marine protected area (MPAs) coverage and the distribution of sharks and rays. Using global range maps and MPA boundaries within national Exclusive Economic Zones, we calculate the percent of species ranges within MPAs, focusing on no-take areas. We reveal significant shortfalls in species-level protection. Within national waters, no Critically Endangered species has more than 5% of its range in no-take MPAs, and 79% of threatened species have less than 1%. We also find the WDPA contains major gaps in take-status reporting, only one third of countries (34%) report take-status of any MPAs to the WDPA, further limiting estimates of meaningful protection. These results highlight the implementation gap between global coverage targets and biodiversity outcomes, reinforcing the need for species-focused protection.

Despite substantial global commitments to expand protected-area networks, the strategic allocation of limited resources remains challenging. Spatial conservation planning helps identify priority regions that maximise conservation benefits per unit area. Yet, they also tend to neglect two fundamental aspects of conservation: climate-driven range shifts and the representation of environmentally distinct populations within species. Here, we propose a continental-scale conservation planning framework that explicitly accounts for both processes through novel routines implemented in the conservation planning software CAPTAIN. We apply this framework to European crop wild relatives (CWR), for which niche coverage is a focal priority, as it underpins their potential to support agricultural adaptation to future environmental stressors through breeding programs. Comparative analyses on a subset of 186 CWR associated with five focal crops show that accounting for range shifts and niche coverage leads to substantially different conservation priorities from those obtained with a baseline model based on current distributions only. These additions reduced the number of non-protected species by 64%, increased the average protected distribution range by 43%, increased mean niche coverage from 75.8% to 84.5% and reduced the number of species with less than half of their niche protected from 35 to 10. Applied to a more comprehensive checklist of 1,140 European CWRs, the final framework identifies continental-scale priority areas representing 93.5% of these taxa and includes 94.4% of its critically endangered species. Our results highlight the importance of incorporating both temporal dynamics and within-species environmental representation when designing conservation strategies under climate change. RepositoryThe repository will be made publicly accessible after publication at doi: https://10.5281/zenodo.19855597

A central challenge in conservation is understanding how climate change interacts with other global-change drivers to shape future species extinction risk, threatened-species hotspots, and the effectiveness of protected areas. Here, we use an integrated over-the-horizon forecasting framework to jointly model changing species range dynamics and shifts in extinction risk for 1,914 Australian terrestrial vertebrates to 2100. Our approach links ensemble species distribution models with machine-learning-based automated threat assessment, incorporating species traits, changing distributions of invasive species, and projections of land use and human population density. Under a high-emissions scenario, up to 109 species are projected to lose all climatically accessible habitat by 2100 and the number of threatened species is predicted to increase, while under a moderate emissions scenario (SSP1.26) the number of threatened species remains relatively stable, and up to 19 lose all climatically accessible habitat. Spatially, threatened-species richness becomes increasingly concentrated in southeastern Australia. These shifts elevate the representation of threatened species within existing protected areas, largely because extinctions and range contractions occur disproportionately outside protected areas. Our results highlight that the identity of at-risk species and the occurrence of threatened-species hotspots will change dramatically, underscoring the need for forward-looking conservation strategies that anticipate future biodiversity patterns.

Target 4 of the Kunming-Montreal Global Biodiversity Framework (KMGBF) calls for urgent management actions to halt humaninduced extinctions and enable species recovery. However, most Parties face substantial challenges in determining which species require urgent management actions. Here, we present a transparent, standardised protocol that identifies and ranks species most likely to need urgent management actions at the national level, using globally available data from the IUCN Red List of Threatened Species. The protocol integrates four criteria aligned with Target 4: global extinction risk, rate of decline, population or range restriction, and endemism, to generate a national ranked list of species. Species scoring highly on these four criteria, and therefore most in need of urgent management action, are ranked most highly. We applied this method to all 250 countries and territories listed in the IUCN Red List and pilottested national rankings with participants from eight diverse countries. Across pilots, participants reported that the ranked lists were scientifically robust, timesaving, and valuable starting points for national prioritysetting, while stating the importance of national context, and the need for additional technical and financial support for implementation. Our results demonstrate that a sciencebased approach can meaningfully support Parties in identifying species requiring urgent action under Target 4, in a standardised way. With 2030 approaching rapidly, this protocol provides an immediate, practical tool to accelerate progress toward halting extinctions and advancing species recovery.

Enhanced AbstractO_ST_ABSBackgroundC_ST_ABSThere is overwhelming evidence that global change is having widespread, detrimental impacts on biodiversity. Population declines and local disappearances have been recorded with increasing frequency across all taxa, resulting in a steady rise in the number of threatened species. However, the number of documented extinctions remains counterintuitively low ([~] 1000 species across all kingdoms) compared to the sense of emergency pervading the scientific community. In isolation, that figure might fuel scepticism about the biodiversity crisis, but when put into context, it reveals that current extinction rates might be comparable to those that occurred during past mass extinction events estimated from the fossil record ([&ge;] 75% extinctions within < 2 million years). Although this is an important clue supporting the claim that we might now be witnessing a new ( sixth) mass extinction, it falls short of definitive proof. The claim bears such high importance that it requires exceptionally solid foundations. However, our main aim was not to ascertain whether current extinction rates qualify as a new mass extinction event in progress. Instead, we examined the intersection of potential future loss scenarios and species discovery rates to address the fundamental question of whether and when we will be able to confirm a mass extinction is under way. AdvancesOur extrapolations suggest that the timing for a mass extinction to materialise (2,604-34,808 years from now at 75% diversity loss) is consistent with past mass extinctions (e.g., 12,000-108,000 years estimated for the Permian-Triassic extinction to unfold) under modern extinction rates (loss of 0.004%-0.053% of global species richness per year). We identify the minimum necessary conditions in which we could confirm a mass extinction under the full range of assumptions related to total species diversity (ranging from < 1.8 million to 163.2 million animal species) and discovery rates (e.g., [~] 13,110 new animal species described per year as of 2026, with the number growing by [~]77 species per year), and the associated timeframe required. We show that there are many realistic future scenarios where we would fail to detect a mass extinction in progress. OutlookBased on available evidence, the rate of global biodiversity loss might already be consistent with the standard definition of a mass extinction. But even if true, current extinction rate estimates (20-8343 times background rates) would not necessarily imply a mass extinction is currently unfolding, because this claim can only be verified a posteriori. Our projections instead indicate that there is a high risk of not recognising a mass extinction as it unfolds -- 49% across all parametrisations we explored. Furthermore, the temporal scale required for a mass extinction to materialise is orders of magnitude longer than relevant policy and legislative horizons, a mismatch that might appear to absolve todays society of responsibility. In reality, the opposite is true -- underestimating the likelihood of already being on a trajectory toward a mass extinction could have catastrophic consequences for future generations and historical accountability. Future generations will be forced to confront a world they perceive as normal, unaware of how much better off humanity could have been.

To prevent species extinctions, targeted action must focus on areas of threatened biodiversity facing intense human pressures. This objective is even more important in the run-up to 2030, the target date to conserve 30% of lands and waters globally. Conservation Imperatives (unprotected terrestrial sites that harbour rare, range-restricted, and threatened species) are critical to preventing imminent species losses. To prioritize among the 16,825 Conservation Imperatives Sites spanning 1.64 million km2, we ranked each site using a prioritization framework based on four criteria: number of threatened species per site; irreplaceability of the site; the proportion of an ecoregions remaining habitat contained in the site; and conversion pressure. Our approach prioritizes 1,667 sites representing 501,426 km2, or 0.37% of Earths terrestrial surface, most in need of urgent protection, with 87.34% of these sites occurring in 20 countries and in 250 ecoregions. This prioritization directly addresses the concern that protected areas must be targeted to protect endangered species, habitats and populations: 33.46% of the prioritized Conservation Imperatives Sites scored higher in irreplaceability than 90% of existing protected areas. Additionally, 51.53% are within 2.5 km2 of an existing protected area, making extending protection or restoring connectivity more feasible. Targeting conservation actions, especially in this small set of countries and ecoregions identified here, would contribute "high quality" areas for biodiversity as part of reaching the 30% coverage target by 2030.

Effective biodiversity conservation requires tools that can identify priority areas under growing human pressures. Building on the concept of global biodiversity hotspots, we present a transparent and repeatable approach to mapping conservation priorities using data for 33,604 species of terrestrial vertebrates from the IUCN Red List. This framework expands the taxonomic scope of previous efforts and integrates updated information on key human-driven threats to biodiversity. We identify that around 13% of Earths terrestrial surface qualifies as vertebrate conservation hotspots, often shaped by distinct combinations of species groups and threats. These results highlight the need for tailored, context-specific conservation strategies. By providing a robust method to guide spatial prioritization, our work supports more effective implementation of conservation targets in a rapidly changing world.

Accelerating environmental change in the Antarctic and Southern Ocean (ASO) necessitates robust extinction risk assessments to inform conservation priorities and track progress towards global biodiversity targets. Nevertheless, no systematic, region-wide baseline of extinction risk currently exists for tracking ASO biodiversity responses to ongoing change, a significant barrier to global biodiversity monitoring. Here, we present the first comprehensive synthesis of extinction risk knowledge spanning plants, animals, and fungi across the ASO, examining biases in current assessments, the distribution of Threatened species and their associated threats. In the absence of a complete regional species checklist, species were compiled from >6,800,000 occurrences and existing checklists, yielding 5,403 assessments representing 2,806 species using a data-inclusive workflow that increased available assessments by over three-fold. Assessments are heavily biased towards vertebrates (56% assessed), while invertebrates, despite their ecological prevalence, are markedly underrepresented (4% assessed). Among vertebrates, mammals have the highest proportion of Threatened species (35%), while ASO birds are disproportionately Threatened (27%) compared to the global average (12%) with the greatest threat for ASO species being Biological Resource Use. Despite more Threatened species in the sub-Antarctic islands and the Antarctic Peninsula, relative to assessment effort, these regions had fewer Threatened species than expected, indicating these areas may function as refugia. These pronounced assessment biases highlight the need for more balanced, representative, and data-inclusive extinction risk assessments to be able to effectively detect conservation status change. This work represents an important step in ensuring ASO representation in global biodiversity monitoring frameworks strengthening the capacity of these frameworks to detect, attribute, and respond to future biodiversity changes.

BackgroundA structural governance failure sits at the intersection of international biodiversity law and the digital genomics revolution. The Convention on Biological Diversity (CBD) and the Nagoya Protocol on Access and Benefit-Sharing (ABS) were designed to ensure that countries of biological origin share equitably in commercial benefits from their genetic resources. Critically, these instruments apply exclusively to non-human genetic resources: plants, animals, fungi, and microbiota. Human genetic resources are deliberately excluded from the CBD and Nagoya ABS framework and are governed separately through bioethics instruments, including the World Health Organization (WHO) framework and the Declaration of Helsinki. This study focuses on non-human digital sequence information (DSI), nucleotide and protein sequence data derived from non-human organisms deposited in open-access databases, which underpins industries generating over USD 1.56 trillion in annual revenue. Africa, hosting approximately 25% of global terrestrial species and nine of the worlds 36 biodiversity hotspots, provides a disproportionate share of the genetic resources from which non-human DSI is derived, yet receives negligible monetary returns because digitisation severs the traceability chain that ABS governance requires. Human genomic data is presented here solely as a secondary indicator of Africas broader infrastructure; it does not constitute the legal basis for Africas modelled allocation share under the Cali Fund. ObjectivesThis study systematically characterises (i) Africas non-human biodiversity endowment as the basis for Cali Fund claims; (ii) ABS governance readiness across 54 African Union (AU) member states; (iii) the commercial trajectories of non-human DSI-dependent industries and projected Cali Fund benefit-sharing flows; and (iv) Africas human genomic representation as a secondary infrastructure indicator, explicitly distinguished from the non-human DSI benefit-sharing argument. MethodsA structured evidence synthesis was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting elements, where applicable to a secondary data analysis design. Literature was searched across PubMed, Scopus, Web of Science, Google Scholar, and official repositories of the CBD, Food and Agriculture Organization of the United Nations (FAO), International Union for Conservation of Nature (IUCN), and United Nations Environment Programme (UNEP). The search was restricted to January 2022 - April 2026 to capture post-Kunming-Montreal Global Biodiversity Framework (KMGBF) literature. A total of 412 records were identified before screening; 34 peer-reviewed articles and 19 institutional documents met all inclusion criteria. Quantitative Cali Fund scenario modelling used the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) and KPMG (2024) non-human DSI sector revenue baseline (CBD/WGDSI/2/2/Add.2). The 12.5% net profit margin is a cross-sector proxy from that study; actual margins vary by sector. Africas modelled allocation share (20-25%) is the authors analytical construct based on Africas non-human species richness and hotspot share; it is not an internationally agreed formula. ResultsAfricas non-human biodiversity endowment is exceptional: 25% of terrestrial species, nine of 36 biodiversity hotspots, and the worlds second-largest tropical forest system. Non-human DSI from African genetic resources is a critical input to industries generating USD 1.56 trillion annually, yet Africa contributes a marginal and unmeasured fraction of International Nucleotide Sequence Database Collaboration (INSDC) sequences. As a secondary indicator, 94.48% of genome-wide association study (GWAS) participants as of 2024 were of European ancestry (Corpas et al., 2025); this human genomic data is presented for contextual illustration only and is not the basis for Africas Cali Fund modelled allocation share. Zero African Union member states have enacted legislation explicitly covering non-human DSI in their ABS framework. Africas modelled allocation share ranges from USD 312 million (Scenario A, 20% weight) to USD 5.83 billion (Scenario C, 25% weight) annually. ConclusionsAfrica is among the most biologically rich continents on Earth for non-human life, yet structurally excluded from the benefit-sharing framework the CBD intended to create. The Cali Fund represents the first mechanism capable of correcting this at scale. Realising Africas modelled allocation share requires urgent legislative reform, institutional capacity investment, sequencing infrastructure development, and a coordinated African position at COP17 scheduled in Yerevan, October 2026.

Rapid climate change is reducing the capacity of protected areas (PAs) to conserve biodiversity, but exposure metrics alone do not show whether species can reach areas where suitable climates persist. We developed a climate-informed connectivity framework integrating climate velocity, PA climatic residence time, PA size, and functional connectivity based on energetics-informed resistance surfaces from species distribution models. Using high-resolution climate projections and omnidirectional connectivity modelling across Europe, we show that climate-tracking opportunities are more limited and spatially uneven than structural connectivity alone suggests. Small, climate-exposed PAs are especially vulnerable because they provide little internal climatic buffering and are often embedded in landscapes with low movement feasibility, whereas larger and more climatically stable PAs are more often situated in landscapes that can support redistribution. These findings provide a spatially explicit basis for restoration and conservation planning to maintain the functionality of PA networks under future climate change. TeaserMany European protected areas are too isolated, climate-exposed, and energy-constrained to support climate-tracking connectivity.

There is an urgent need to gather data on harvest rates of waterbirds in Europe to assess the sustainability of hunting. Estimates of total waterbird harvest in the United Kingdom (UK) and the relative harvest of different huntable species come from two separate surveys, the Value of Shooting (PACEC 2014) and National Gamebag Census (NGC, Aebischer 2019), and these have been recently used to explore the likelihood of unsustainable harvests of wild waterbirds by UK hunters (Ellis and Cameron 2022; Madden et al., 2025). The reliability of these sustainability estimates depends on how representative the original surveys are of hunter behaviour and success. There are also 1-3 million released game-farm mallard (Anas platyrhynchos) that takes up considerable and unquantified proportions of the UK waterbird harvest. Here we explore uncertainties in the UK winter harvest of wild waterfowl by comparing estimates from the NGC dataset with those from the Crown Estate coastal hunting clubs, and a novel approach using analysis of social-media images (2019/20 to 2023/24). We explore the difference in species-specific harvest with and without the uncertainties in the number of released mallard and the total number of duck harvested in the UK. Waterbird harvest estimates differ markedly depending on the input dataset and whether released mallard are included in the analysis. Confidence intervals of each estimate are inflated by uncertainties in the number of released game-farm mallard contributing to, and the size of that national bag. Estimates extrapolated from social media suggest the national harvest of several species may be considerably larger than the corresponding NGC estimates (e.g. Teal *2.07 and gadwall *11.2), while mallard harvests away from formal shoots represented by NGC are significantly lower (*0.71). Excluding released mallard reduces the statistical estimate of total wild duck harvest by 56-63%, which would have biologically significant effects if realised.

The 36 global biodiversity hotspots harbor a disproportionate share of the worlds endemic species, making their conservation critical for planetary health. Traditionally hotspots were defined as ecoregions with [&ge;]1,500 endemic vascular plant species and >70% natural habitat loss; this relied heavily on expert judgment, with subjective assessments of endemism and habitat loss applied. Challenges in defining endemism, quantifying habitat loss, and the global unevenness in available vascular plant data have hindered hotspot identification over the past decades. Here, we built a global dataset of 150,487 rare vascular plants, identified from 88.1% of the worlds known vascular species, and recognize hotspots based on their richness using three complementary conservation targets and algorithms. We then quantified natural habitat loss and habitat fragmentation using high-resolution remote sensing data and assessed the diversity and distribution of terrestrial vertebrates within these newly identified hotspots. Our data-driven method recovered all the 36 established global biodiversity hotspots, revised 17, and identified 11 new hotspots spanning diverse ecosystems across six continents. These 47 hotspots cover 26.63% of global land area, yet contain 83.8% of rare vascular plants, 92.4% of mammals, 96.1% of birds, 87.8% of reptiles, and 95.0% of amphibians. Collectively, they encompass >89% of terrestrial vertebrates classified as IUCN threatened species. Only 10 hotspots have undergone [&ge;]70% habitat loss, and the lack of a consistent relationship with habitat fragmentation suggests that this criterion is not globally applicable. Effectively protecting [~]27% of Earths land could theoretically safeguard >89.8% of threatened terrestrial species and >67% of threatened terrestrial species hotspot area, assuming effective protection of the identified biodiversity hotspots. Targeted conservation efforts within these global biodiversity hotspots can meet the established biodiversity targets of the Kunming-Montreal Global Biodiversity Framework as well as post-2030 biodiversity targets. Most importantly, our framework enables conservation scientists to iteratively identify and update global biodiversity hotspots in step with growing global biodiversity data.

In an era of severe global biodiversity threats, understanding the link between species traits and their endangerment helps uncover causes of risk and infer threats to understudied species. Most animals have complex life cycles with distinct stages that may face stage-specific threats. Current conservation frameworks rely heavily on adult traits, potentially misjudging extinction risk. Using Chinese anurans as a model, we integrated functional traits from both adult and tadpole stages to examine their association with extinction risk. We found that body size positively correlates with risk in both stages. Microhabitat use related with extinction risk in tadpoles but shows no significant link in adults. Adult relative tympanum diameter and head length also correlate with extinction risk. These results indicate that species vulnerability is shaped by multi-stage traits, with both shared and stage-specific threats. Conservation based solely on adult traits may fail to accurately assess species threats. We call for integrating a whole-life-history perspective into biodiversity assessment and conservation to more effectively address the global biodiversity crisis.

Large-scale annual releases of pheasants Phasianus colchicus and their subsequent management for recreational shooting create various ecological impacts in the UK. While effects at release sites are fairly well understood, dispersing birds may influence areas farther away. If they enter ecologically important but sensitive protected areas (PAs), any negative impacts could be especially harmful. Using tracking data, from 766 birds across 10 sites, we estimated survival and dispersal of released pheasants and applied these patterns to gamebird release records near English PAs to gauge intrusion risk. Of 2,885 registered release sites, just over half lay within 2 km of a PA. A large number of shoots release relatively few birds while a small number release many birds. Thus, numbers expected to enter a particular PA likely depend both on the size of releases and proximity to the PA. We estimate that, at a national level, a maximum of between 525,000 and 784,000 pheasants might be found within PAs very soon after release, representing around 1.7% of all the pheasants released annually. This number declines over the months after release until in February, we estimate that there are between 131,000 and 196,000 pheasants (0.4% of the total release) might be found within PAs. The critical metric by which ecological damage might occur is their density within PAs. Mean densities soon after release averaged 12.0 birds/ha in PAs within 250 m of release sites. This density declined markedly both in time (as birds died) and space (as they moved further from the pen as potential areas increased). By November, densities in PAs 500-1000m from release sites peaked at 0.5 birds/ha, falling to 0.16 birds/ha in February. These estimated densities are around two orders of magnitude lower than those known to cause strong, lasting impacts within release pens. The results are subject to assumptions about movement behaviour, game management and bias in registration. Despite these constraints, considerable local variation exists, with a minority of high-volume release sites very near PAs posing the greatest potential ecological risk.

Systematic conservation planning provides a science-based framework for defining conservation goals and supporting transparent spatial decision-making under limited resources. Within this framework, spatial conservation prioritisation tools are widely used to identify areas of high biodiversity value by integrating information on species distributions, connectivity, costs, and other factors into spatially explicit recommendations. Zonation is one of the leading software tools in this field, producing hierarchical priority rankings of landscapes based on conservation value. However, its standard workflow typically relies on manual steps for data preparation, execution, and post-processing, which can become inefficient and difficult to reproduce when multiple scenarios are analysed, limiting accessibility and broader uptake. We introduce ZonationR, an R package that provides a streamlined interface to the Zonation software, enabling fully reproducible and automated spatial prioritisation workflows. The package integrates the entire analysis pipeline, encompassing input preparation, execution of Zonation, and post-processing, while supporting both single-variant and multi-variant workflows. ZonationR also provides tools to explore and interpret outputs, including priority maps, feature performance curves, cost summaries, feature representation metrics, and similarity assessments between prioritisation solutions. By linking directly to the original Zonation engine, the package enables users to benefit from ongoing methodological developments in Zonation and access its functionality through a transparent, script-based workflow, thereby reducing technical barriers to running and understanding spatial prioritisation analyses. Beyond these advantages, its integration within the R environment supports iterative testing of conservation scenarios and more rigorous assessment of methodological decisions, while facilitating seamless connections with wider ecological workflows (e.g., species distribution modelling). As conservation planning increasingly relies on large, complex, multi-source datasets and integrative approaches, such integration is essential for enabling robust, transparent, and reproducible decision-making across spatial scales.

Identifying what makes species vulnerable to extinction requires accounting for complex biological and environmental interactions. Due to their high predictive accuracy, machine learning methods have been widely used for these assessments; however, relying on black-box models offers limited interpretability. Here, using a comprehensive dataset of anthropogenic, ecological, morphological, demographic, and biogeographical variables from 9,053 species (81% of birds worldwide), we applied Inductive Logic Programming (ILP), an explainable artificial intelligence framework, to generate explicit and quantitative IF-THEN rules with confidence scores for bird extinction risk. Our approach revealed that extinction vulnerability follows a hierarchical structure, shaped by interactions among range size, morphological traits, and human pressures. The framework recovered well-established knowledge, while also revealing previously undescribed extinction patterns. For example, consistent with prior evidence, species with geographic ranges below [~]13,500 km{superscript 2} were identified as higher risk (88% confidence). Nevertheless, this threshold shifted to [~]3,270 km{superscript 2} when human impacts were removed, revealing quantitatively how anthropogenic activities expand the pool of vulnerable species beyond those at risk due to biological and biogeographical traits alone. Beyond established patterns, species with tail length >304 mm were identified as higher risk (82% confidence), a pattern not previously documented. ILP models achieved 91% overall accuracy, slightly lower than Random Forest (93%), but notably better than Neural Networks (83%). These results show that ILP can offer high accuracy results with full interpretability, also providing quantitative transition thresholds that clarify the structural architecture of extinction risk, and translate complex ecological interactions into actionable tools for conservation.

We present a spatially explicit, global-scale index to assess the effects of the five direct anthropogenic drivers of biodiversity loss identified by the IPBES: land use change, natural resource extraction, climate change, pollution, and invasive alien species. The Biodiversity Pressure Index (BPI) covers 30 years (1990-2020) with an annual time-step and a spatial resolution of 0.1{degrees}. We find that the coverage of drivers in available data varies and we highlight the key uncertainties that result from this. Using the best available data, we show that large parts of the terrestrial biosphere (approximately 89%, including Antarctica and Greenland) are under medium or high human pressure and that almost all areas (approximately 96%) have experienced an increase in pressure over the past three decades. The BPI shows varied spatial and temporal patterns across world regions and biomes, but many of these areas are dominated by pressures associated with rising temperatures and trade flows. Tropical and subtropical areas are subject to particularly rapidly-growing pressures, while wetlands consistently show the highest pressure levels across biomes. In revealing these and other patterns, the BPI provides a basis for improved understanding and management of biodiversity impacts in the future.

Reliable freshwater access drives terrestrial wildlife movements and habitat use globally. The small, rain-fed seasonal pools critical for dryland wildlife persistence are vulnerable to rising temperatures and unstable precipitation regimes projected under climate change. In southern Africa, which is expected to warm rapidly by 2100, the drying and disappearance of surface water may cause a breakdown in seasonal migrations of large, area-sensitive, and water-dependent wildlife species. Furthermore, the disappearance of ephemeral water may concentrate wildlife around remaining surface water, increasing resource competition and human-wildlife conflict. An accurate understanding of the dynamics and drivers of seasonal surface water will therefore be critical to wildlife and human health as climate change intensifies. Here, we present a framework and empirical analysis of fine-scale surface water mapping in the 520,000km2 Kavango Zambezi Transfrontier Conservation Area (KAZA), the worlds largest terrestrial conservation area. From 2019-2025, we implemented Otsu thresholding on median Automated Water Extraction Index imagery from 10m Sentinel-2 MSI, leveraging high wet season contrast between vegetation and water as a dry season positive mask. We created >35 quasi-monthly KAZA-wide Ephemeral Surface Water (ESW) rasters (mean classification accuracy 87%, compared to 50% accuracy for existing water products), and found wet season precipitation drivers of non-riparian water fill levels did not extend into the dry season. Then, using GPS data from 27 African savanna elephants (Loxodonta africana), which typically visit water every 48 hours, we compared elephant water visitation rates based on ESW to existing 30m Global Surface Water (GSW) maps. Models using ESW estimated 99% of elephant data came within a 48-hour window, compared to 42% for GSW, suggesting that ESW is a better proxy for actual wildlife water use in animal movement modeling. As aridification threatens to diminish surface water resources, we must model the drivers of wildlife movements at the scale of wildlife needs. With ESW, we provide fine scale accessible surface water data and a straightforward coding architecture for applications beyond KAZA.

Animal population control is widely used to mitigate conflicts between wildlife and agriculture worldwide. Structured, monitored removals are rare in South America, however, and their consequences for wildlife populations as well as their effectiveness in reducing crop damage are little understood. Using eight years of data from an experimental white-lipped peccary management program in an agricultural mosaic in the Brazilian Cerrado biome, we assess how structured, non-lethal removals affect both peccary demography and second-crop corn damage. Leslie removal models based on 6,619 captured individuals indicated that cumulative removals to approximately 85% of the initial population strongly reduced peccary abundance, with limited demographic compensation despite fluctuations in reproductive output. Corn crop damage, quantified with satellite imagery, declined over time and was correlated with peccary population size. Interannual variation in population growth and juvenile recruitment was poorly explained by climate, fire, or landscape composition. Source-sink dynamics likely play a role in maintaining healthy populations at the regional scale. Together, these results demonstrate that sustained and monitored ungulate removals can reliably reduce population size and agricultural damage, supporting coexistence between wildlife and food crop production in human-dominated tropical landscapes.