Environmental Research Letters

It remains challenging to quantify recent changes in forest carbon due to lags in forest inventory measurements. The national U.S. forest inventory remeasures plots every five to ten years, so quantifying current carbon stocks using inventory data requires extrapolating from the last time plots were measured. We address this extrapolation challenge by fusing spatially explicit fire disturbance and canopy cover data from Landsat with forest inventory data using a statistical model. We produce annual estimates of live forest carbon across the Western U.S. from 2005 to 2022, and find that live forest biomass increased from 2005 to 2015, and then declined by 5% from 2015 to 2022 -- a signal missed by both official U.S. reporting and Earth system models. The trend reversal was driven primarily by increasing tree mortality from wildfire, and secondarily by slowing rates of carbon accumulation in undisturbed areas. Our results highlight the importance of accounting for rapidly changing disturbance regimes, and can help to improve jurisdictional carbon accounting and inform the extent to which federal and state climate mitigation strategies can rely on land to achieve net-zero emissions targets. Significance statementPolicy makers need to accurately and rapidly assess the status of the land carbon sink in order to make land management decisions and to assess progress towards climate commitments. However, lags in on-the-ground measurements make it challenging to do so, and it remains an open question whether Western U.S. forests are a net sink or a source of carbon. We fuse on-the-ground forest measurements with remote sensing data to show that live biomass is net declining in Western U.S. forests, and that this trend is driven primarily by increasing wildfire activity. This result challenges the idea that jurisdictions can rely on the land to offset fossil emissions, and supports tracking land carbon trends separately from fossil emissions inventories.

Statewide tracking of urban tree canopy change is essential for evaluating progress toward policy targets, but detecting real change requires both high-resolution mapping and rigorous uncertainty estimation. We produced a four-year canopy cover time series for all California census-designated places using 60-cm NAIP aerial imagery and a U-Net deep learning model trained with semi-automated LiDAR-derived labels and manually annotated tiles. Canopy cover and change were estimated using stratified, error-adjusted area estimation, enabling comparisons across years. Statewide canopy cover showed a modest negative trend from 2016 to 2022 (Sens slope: -0.60% per year), but confidence intervals included zero across all groups and climate zones, indicating that trends were not statistically distinguishable from no change. Urban canopy cover was consistently lower than non-urban canopy by approximately six percentage points, and canopy cover was highest in the Northern California Coast and lowest in the Southwest Desert. Residential parcels accounted for 55-56% of canopy within incorporated urban areas across all years, indicating that statewide canopy increase goals will require engagement with private landowners. Error adjustment substantially altered canopy estimates relative to raw pixel-count totals, with direct implications for AB 2251 canopy tracking where baselines and targets drawn from unadjusted maps may not reflect true canopy extent. This open-source workflow is transferable to future NAIP acquisition years and other U.S. states, providing a scalable framework for long-term urban forest monitoring.

National digital health platforms are scaling faster than the evidence on how to finance them. This paper develops a welfare-simulation framework that converts a published willingness-to-pay (WTP) distribution into a prescriptive pricing recommendation, applied to Thailands KhunLook maternal-and-child-health application. Predicted WTP values at the 25th, 50th and 75th unconditional quantiles and the OLS mean -- drawn from a survey of n = 680 Thai parents and relatives of young children previously reported in Lounkaew et al. (2025) -- enter the simulation as parametric inputs. Quintile-level WTP is imputed by monotone-cubic interpolation, a population of 250,000 caregivers is drawn from truncated-Normal distributions around the quintile means, and five financing scenarios are compared: full public provision (S1), a flat market-priced fee (S2), freemium (S3), fine-grained income-tiered pricing (S4), and a means-tested subsidy with a flat fee for the top 60% (S5). A thematic reading of Thai digital-health policy documents bounds the institutionally feasible scenario set and anchors the interpretation of the simulation numbers. Full public provision maximises total welfare at 437.4 million THB but runs a five-year fiscal deficit. The means-tested subsidy gives up about 15% of that welfare to recover 198.6 million THB in net producer surplus, distributes consumer surplus toward lower-income quintiles (concentration index -0.258), and plugs into the existing Thai state welfare card register at near-zero marginal administrative cost. The ranking holds across all twelve sensitivity specifications. Administrative simplicity in subsidy targeting, read against the Thai WTP distribution, dominates finer-grained tiering on both welfare and equity grounds. The framework transfers cleanly to other middle-income countries deciding how to price a national digital health platform. Author summaryMany middle-income-country governments now run free national smartphone apps for the health of mothers and young children, but the funding model is increasingly fragile as initial donor and research grants run out. The question this paper asks is simple: if such a platform had to start charging, what pricing structure would raise the most money without locking out the families who need the app most? Using a published Thai survey of 680 parents and relatives of young children, the paper simulates five alternative designs -- free, flat fee, freemium, fine-tiered by income quintile, and a means-tested subsidy -- and finds that offering the bottom 40% of households free access while charging the top 60% a flat 395 Thai baht per year (roughly USD 11) captures 85% of the welfare of the status-quo free model, generates 199 million baht of fiscal surplus over five years, and distributes benefits toward lower-income users rather than toward the well-off. The design works because Thailands state welfare card register already identifies the low-income target population, so means-testing is essentially free to administer. Other countries with comparable social registries can apply the same logic to their own digital health platforms.

Traditional agricultural safety assessments often rely on raw incident counts that emphasize exposure but underrepresent outcome severity. This study presents a multi-criteria impact framework to distinguish frequency-driven activity patterns from severity-driven risk across the U.S. Midwest. Agricultural incident records from 2012 to 2023 across seven states were analyzed using descriptive statistics, county-level mapping, and quartic kernel density estimation. Comparative impact indices were constructed using Analytic Hierarchy Process (AHP) and Geometric-Fuzzy AHP weighting schemes to integrate incident frequency, outcome severity, and post-incident survivability. Results indicate that while overall incident frequency is strongly concentrated in northwestern Iowa, reflecting intensive agricultural activity, fatal outcomes exhibit a broader spatial footprint extending across central and northern Iowa and into central-southern Minnesota. Severity-weighted mapping further consolidates northwestern Iowa and the Minnesota-Iowa corridor as dominant high-impact zones. At the regional scale, Geometric-Fuzzy AHP produced consistently lower mean scores and reduced dispersion than AHP, yielding smoother spatial gradients while preserving the primary hotspot structure. These findings demonstrate that frequency-based mapping alone fails to capture the multi-dimensional nature of agricultural risk. By explicitly linking incident locations with survival infrastructure, this research provides an evidence-based framework for targeting safety interventions and improving rural emergency medical service planning.

O_LIAnimal-mediated seed dispersal is key for the maintenance and functioning of tropical ecosystems. Specifically, in the Cerrado, the largest Neotropical savanna and a global biodiversity hotspot, nearly 60% of plant species rely on animals for dispersal. C_LIO_LIClimate change threatens these interactions by affecting species distributions, reshaping communities, and potentially decoupling plants from their dispersers. Anticipating how such disruptions may alter seed dispersal networks is particularly relevant for understanding the resilience of future tropical ecosystems. C_LIO_LIHere, we combined empirical data on 139 pairwise plant-frugivore interactions with species distribution forecasts to build probabilistic interaction matrices under present and future climate scenarios, which were then used to construct 6,221 local seed dispersal networks. Using ecological niche modelling, we tested how climate change influences species range size and centroid displacement. Then, we evaluated whether such changes translate into losses of pairwise plant-frugivore co-occurrence. Finally, we investigated how these changes in occurrence overlap may affect key structural properties of future local seed dispersal networks. C_LIO_LIWe forecast that by the 2070s, under a business-as-usual climate scenario, species are likely to contract their ranges by 56 {+/-} 33% and shift their distribution centroids by 88 {+/-} 57 km within the Cerrado, leading to a 27 {+/-} 29% loss in plant-frugivore co-occurrence mainly driven by reductions in plant species distributions. At the community level, these losses will lead to smaller and more nested networks and specialized, indicating a structural simplification of seed dispersal systems in the Cerrado. C_LIO_LISynthesis: By combining empirical data on animal-mediated seed dispersal with forecasts of species distributions, we found that climate change may simplify frugivore-plant interaction networks in the Cerrado by decreasing species ranges and co-occurrence of partners. Our study demonstrates that future climate may pose a threat not only to species distributions but also to ecological interactions, such as seed dispersal, that are key to enabling climate-tracking by plants. Thus, preventing the simplification of interaction networks will be essential to conserve biodiversity in species-rich regions. C_LI

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

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.

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.

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.

Subalpine forests in the Alps are fragile ecosystems that play a crucial role in regional water resources and the local climate. These ecosystems are ecologically significant due to their unique biodiversity and vulnerability to climate change. While several components of the hydrological balance have been studied, the interplay between catchment-scale processes and plot-scale drivers such as fog presence and forest age remains insufficiently understood. To address this, we investigated the hydrological balance of a subalpine coniferous forest catchment at the Renon site in the Italian Alps, integrating observations across spatial scales. The study area includes a mosaic of mature and younger regrowth forest, where both interannual and seasonal variability in precipitation and fog presence are pronounced. At the catchment scale, we quantified above-canopy precipitation, evapotranspiration (ET, measured via eddy covariance at the ICOS tower), stream discharge, and soil moisture dynamics. Within the catchment, we characterised water partitioning using sap flow sensors for tree transpiration, throughfall and stemflow collectors with rain gauges above and below the canopy and epiphyte sampling. Mixed fog-rain events frequently coincided with higher throughfall. However, these changes had a minor effect on soil water storage and catchment discharge in the annual water balance, which was nearly closed. At the plot scale, our results show that tree transpiration was higher in the younger forest structure, while canopy interception is a dominant process in water partitioning in the older forest structure, where lichen abundance likely enhances interception. This study highlights the importance of multi-scale monitoring in temperate mountain forests, where forest age influences water partitioning, and fog presence, though not directly quantified, can still contribute to reducing evaporative processes. Such contributions may gain importance under changing climate conditions, albeit less prominently than in tropical or subtropical cloud forests.

Background Increasingly accessible satellite imagery provides scalable measures of the built and natural environment relevant to population health. However, whether such imagery can capture subnational variation in mortality and life expectancy remains unclear. We therefore assessed its predictive value for regional mortality and life expectancy across OECD regions. Methods We conducted an ecological, cross-sectional prediction study using 2023 data from OECD Territorial Level 3 (TL3) regions. Annual cloud-masked composites from the Harmonized Landsat and Sentinel-2 collection were processed in the Google Earth Engine, tiled at 224 x 224 pixels, and encoded with the pretrained Prithvi foundation model to derive region-level satellite embeddings. For each outcome, we trained LightGBM regressors for a country-only baseline, a satellite-only model, a combined model (country + satellite), and a final contextual model that additionally included prespecified socioeconomic and environmental covariates. Performance was evaluated using 10-fold outer cross-validation with held-out test folds; R2 was the primary metric. Results The analytic sample comprised 2,414 OECD TL3 regions across 38 countries, for which 939,959 satellite image tiles were processed. In paired bootstrap comparisons, adding satellite features to country indicators improved predictive performance for all outcomes, with incremental R2 ranging from 0.097 to 0.233. The final contextual model achieved R2 values of 0.78 (95% CI, 0.74-0.81) for crude mortality, 0.87 (0.84-0.89) for age-adjusted mortality, 0.86 (0.82-0.88) for infant mortality, and 0.76 (0.69-0.84) for life expectancy. In SHAP analyses, the aggregated satellite image effect consistently ranked among the top predictors across outcomes. Conclusion Satellite imagery captures subnational environmental heterogeneity relevant to regional mortality and life expectancy beyond country identity alone. Earth observation may therefore provide a scalable, complementary data source for characterizing geographic disparities in population health.

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.

Forests play a crucial role in mitigating climate change as primary terrestrial carbon sinks. While some studies suggest that global warming enhances forest productivity, a growing body of evidence highlights detrimental impact primarily driven by increased water stress. Yet the extent to which positive effects of climate change offset its negative impacts on tree species productivity remains unclear at large spatial extents. We assessed forest growth and mortality for the 21 most abundant tree species in Europe using National Forest Inventory data from more than 50,000 plots and 700,000 trees to disentangle the relative importance of climate and forest structure. Specifically, we examined how vapor pressure deficit (VPD) anomalies across species climatic edges and stand developmental stages affect forest growth and mortality occurrence and intensity (i.e. whether mortality occurred and the amount of basal area lost). Then, we aggregated the responses across species and separately for broad-leaved and needle-leaved species to assess whether forest growth and mortality differed between major functional groups. Although the importance of forest growth and mortality drivers varied markedly among species, climate had a stronger influence on mortality than on growth, particularly in needle-leaved species. Forest growth declined and mortality increased along VPD anomaly in most species and forests studied. Responses were most pronounced at arid species edges in early-stage broad-leaved forests and at wet edges in late-stage needle-leaved forests, where differences between functional groups were also highest. We evidence the need to parametrise species-specific models of forest growth and mortality across large spatial extents to better understand and predict effects of climate change on forest productivity. In addition, our results emphasize the importance of improving the understanding of forest mortality processes given the strong influence of climate on mortality, while also further studying vulnerable populations to climate change in arid edges of species distributions.

For decades, the "population bomb" has dominated environmental discourse, arguing that high fertility rates -especially in the low income countries- drive global environmental problems. However, current trajectories show global declines in population growth rates, especially in higher development index (HDI) nations, which have the highest consumption. Here we showcase evidence for a paradigm shift from the "population bomb" to a "consumption bomb" narrative of the Anthropocene emphasizing the central role of increases in per capita energy use and CO2 production, modulated by current standard metrics for development and affluence, in transforming the Earth system. Defusing and manoaging the consumption bomb requires rethinking economic growth and wellbeing metrics, reallocating resources toward global change retribution and mitigation, especially in low HDI countries, and transitioning from continually-increasing energy expenditures, especially from fossil fuels, toward more equitable and ecologically resilient ways of living. A new sustainability science must move beyond population counts to confront the biophysical and energetic consequences of the changing cultural, economic, and technological systems that sustain ever-growing demands on Earths life-support systems.

Background: The impact of the Inflation Reduction Act (IRA) upon late-stage developments has been assumed to be limited. The Congressional Budget Office's IRA analysis excluded post-approval innovation, potentially overlooking substantial economic risks to drug developers and declines in the availability of treatments in areas of high unmet medical need such as oncology. Methods: A total of 1148 secondary trials from 364 FDA-approved medicines, published from 2018 to 2025, were obtained from Biomedtracker and clinicaltrials.gov. Using fractional multinomial logit, we model the share distribution of secondary indication studies across 19 disease groups and assess the change in this distribution post-IRA. We also assessed the number of secondary treatment studies pre- vs. post-IRA using multiple linear regression. Results: After the IRA's introduction, small molecule follow-on studies in oncology exhibited a statistically significant 35% decline (R2 = .48, p < 0.014) and lead indication small molecule oncology approvals exhibited a statistically significant 27% decline (R2 = .70, p < 0.002). We also find a statistically significant 14% decline in the share of orphan oncology studies pre- vs. post-IRA (p<0.001). Research Conclusions: This study's results refute claims that the IRA would have minimal negative effects on patient access or late-stage biopharmaceutical R&D. We hope this study reinvigorates debate about the law's unintended consequences and encourages thoughtful policy solutions, as the IRA manifestly creates disincentives that negatively impact patients seeking needed new medicines, particularly those requiring cures addressing metastatic late-stage cancers.

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.

Giraffes, unlike other large mammals, typically pose minimal risk to humans, their animals, and crops, so they are traditionally not involved in human-wildlife conflict. Tree crops, however, are expanding across Africa, resulting in crop raiding by giraffes and retaliatory snaring, poaching, and killing of giraffes in response. The dynamics of giraffe crop raiding, however, are poorly understood, making effective interventions difficult to implement. To better understand key factors for humans and giraffes that mediate crop raiding, we used a multi-method approach to estimate giraffe abundance and activity, understand farmers perceptions and decisions, and test countermeasures around Garissa Giraffe Sanctuary in eastern Kenya. We hypothesized that 1) giraffe farm invasion would occur in dry seasons, 2) farms growing mangoes would be more likely to be invaded, 3) reducing invasion with only physical barriers would be less effective than adding behavior-based countermeasures, 4) perceptions would match giraffe activity and 5) countermeasure adoption would be driven by cost. We found that invasion and crop raiding primarily occur during the dry season and are associated with mangoes. Farmers are using many countermeasures. Effective countermeasures target giraffe behavior combined with physical barriers. Countermeasures are most effective when negative associations with humans are reinforced. Floodlights and speakers that play predator calls both reduce invasion, but only if used consistently. Overall, farmers perceptions matched giraffe dynamics. Availability was the most important factor in farmers willingness to try a countermeasure. Our results suggest that conflict can be reduced and there is interest from farmers in doing so, but use of countermeasures should be consistently applied and supported by making necessary equipment and instructions available.

Recovery debt (RD) quantifies the interim deficit of biodiversity and function during the recovery process after disturbance. Unlike typical recovery indices derived from data on experimental-control comparisons, RD further considers the target (reference) biodiversity level, modelling the rate at which it is approached over time. However, the application of the RD approach to active restoration has not been explicitly implemented to date. Here, we extend the RD framework to evaluate active ecological restoration in agricultural systems, defining the onset of recovery as the shift from intensive to wildlife-friendly management. We applied this approach to assess short-term pollinator recovery in 14 olive groves across a gradient of farming intensification and landscape complexity in southern Spain. Restoration actions included adopting low-intensity ground cover management and actively restoring field margins. At one, three, and five years post-restoration, we assessed community responses by quantifying bee abundance, species richness, plant-bee network properties, and flower visitation rates. Reference systems were defined by olive groves in complex landscapes with low-intensity herb cover management and organic farming practices. Following restoration, the RD of bee abundance decreased from 71% to 55%. We found no significant effects of pre-intervention agricultural management on RD. Instead, across sites, the reduction of the RD (i.e., recovery) of bee abundance, richness, network connectance and flower visitation rate was strongly mediated by the availability of high-quality semi-natural areas in the surrounding landscape and by the ecological contrast created by restoration interventions at both the farm and floral patch levels. RD for other network metrics showed no significant pattern of variation. Our study demonstrates that wildlife-friendly management and targeted habitat restoration can rapidly reduce recovery debt for bee abundance and function in permanent agroecosystems. However, the recovery of more complex interaction-network properties likely requires longer timescales.

Aedes albopictus is an invasive mosquito species transmitting dengue, chikungunya, Zika and other arboviruses. Its ongoing geographical expansion across Europe, driven by climate change, trade, human mobility and urbanisation, is increasing the risk of vector-borne disease transmission in previously unaffected regions. Accurate mapping of mosquito distribution and abundance is essential for timely and targeted vector control and public health preparedness. Here we present AIedes, a neural network framework that predicts both the presence and weekly abundance of Ae. albopictus across Europe using climate variables alone. The model reproduces the large-scale spatial distribution of the species and captures fine-grained spatiotemporal variation in egg-laying activity. To support reproducible evaluation, the framework is trained and validated on newly harmonised, large-scale surveillance and climate datasets, which we release together with the model and code. These resources provide a consistent benchmark for the comparison of vector distribution and abundance models and enable extension to other regions and species where comparable data are available.

AimHuman land-use change contributes to biodiversity declines, but also creates new niches that facilitate novel biotic interactions. These interactions can reshape ecological communities and ecosystem function, yet remain poorly understood. Swiftlets and swallows in Southeast Asia present a classic example: coexistence is facilitated by fine-scale diet partitioning, with population sizes historically limited by available nesting substrates. However, several species now nest on manmade structures, particularly "nest farms" built to harvest edible swiftlet nests. We evaluated whether land-use change, especially the spread of nest farms, is leading to breakdowns in niche partitioning and increased competition among six sympatric swiftlets and swallows. LocationNorthern Borneo MethodsWe calculated geographic niche overlap using species distribution models (SDMs) with different environmental predictors, hypothesizing greater overlap when land-use variables were included. We then implemented joint species distribution models (JSDMs) to partition shared environmental responses from potential biotic interactions, predicting that competition would emerge as negative residual correlations. We used sightings from citizen-science datasets and structured surveys to evaluate the influence of climate, land-use, nest farms, morphology, and foraging behavior on species occurrences. ResultsSDMs that included land-use variables showed high niche overlap, suggesting that human activity homogenizes niches. The optimal JSDM, based on structured survey data, identified distance to nest farms as the strongest predictor of occurrence for all species, with species showing both positive and negative responses. Morphology and behavior had small effects, and residual correlations were weak, indicating limited unexplained biotic interactions. Main conclusionsHuman activity, through the creation of artificial nesting sites, broadly drives co-occurrence of swallows and swiftlets across our study region. These effects appear to operate primarily through environmental filtering rather than direct competition. Our findings reveal substantial and complex impacts of land-use change and anthropogenic nest sites on the distribution and composition of aerial insectivore communities.