Environmental Research Letters

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.

Peatlands are an important terrestrial carbon sink which, when drained, can produce substantial CO2 efflux. Low productivity forestry planted on drained peatlands can become a net carbon source if losses from drained soils exceed sequestration by the trees. Decision support tools which assist resource allocation and intervention planning in forest-to-bog restoration are needed to mediate this substantial environmental harm. Predicting carbon mitigation benefits associated with forest-to-bog restoration is a major challenge, however, due to the lack of long-term monitoring programs and the fact that mitigation times depend on processes distant from the intervention. Here we introduce the PEATREST life cycle assessment (LCA) which predicts carbon fluxes associated with forest-to-bog restoration, including due to processes far from restored sites. The LCA estimates mitigation timescales defined as the time following intervention at which the restored peatland is predicted to sequester or store more carbon than the forestry would have if retained. HighlightsO_LIHere we develop a novel forest-to-bog Life cycle assessment (LCA) tool C_LIO_LIThe LCA predicts carbon mitigation times following peatland restoration C_LIO_LIThe model combines a variety of process-based and empirical sub-models C_LIO_LIExample implementations for two different restoration scenarios are explored C_LIO_LISensitivity analysis highlights the model inputs that most impact outcomes C_LI Graphical abstract(A single, concise figure that serves as a visual summary of the main research findings described in your manuscript.) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/715261v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@f243f5org.highwire.dtl.DTLVardef@14bc4c7org.highwire.dtl.DTLVardef@164261borg.highwire.dtl.DTLVardef@1db3b_HPS_FORMAT_FIGEXP M_FIG The PEATREST Life cycle assessment (LCA) generates compound time series of carbon sequestration and carbon storage for two scenarios: the forest-to-bog peatland restoration (PR) and a counterfactual (CF) of forestry retention. By comparing the two scenarios, the LCA predicts the carbon mitigation timescales (vertical dashed lines). These are defined as the time following harvesting at which the peatland is predicted to sequester more (emit less), or to have stored more (lost less) carbon, than the forestry would have if retained. C_FIG

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.

Mountain ecosystems face unprecedented pressures from anthropogenic activities and climate change, challenging the productivity of these vital habitats. In the Tien Shan mountains, understanding localized responses to these pressures is often hindered by the coarse spatiotemporal resolutions of available data. To address this, we combined high-resolution satellite imagery (1997-2021) to map land-cover dynamics in the Naryn oblast, Kyrgyzstan across a gradient of grazing intensities. We classified and quantified land-cover distribution over 24 years, investigating the roles of topography, elevation, and anthropogenic disturbances as drivers of change. Our results identify intermediate elevations, high degrees of disturbance, and the interaction between the two as the primary contributors to recent transitions in grassland, forest, and barren habitats. By integrating Landsat analysis-ready data, European Space Agency WorldCover dataset and digital elevation models at fine spatial scales, we provide valuable contemporary and historical landscape and habitat-level insights and a high-resolution framework for disentangling climate-driven shifts from land-use impacts. These findings highlight the urgency of localized management in remote, data-poor regions where rapid environmental change threatens both biodiversity and pastoral livelihoods. Our work serves as a critical baseline for characterizing the adaptability of semi-arid mountain rangelands under escalating global and regional pressures.

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.

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.

Invasive insect species pose serious threats to agriculture and ecosystems, with their spread increasingly accelerated by global trade and climate change. To support prevention and mitigation efforts, it is essential to map the regions where these pests can survive and thrive. Here, we apply MaxEnt, a leading species distribution modeling framework, to estimate current (2020) and future (2040-2060) suitable habitats for five major invasive insects across the contiguous United States: brown marmorated stink bug, corn earworm, spongy moth, root weevil, and spotted lanternfly. To account for an uncertain climatic future, these projections are generated under four shared socioeconomic pathways, which reflect a range of plausible climate change scenarios. Beyond forecasting distributions, we examine several key modeling decisions, especially those often overlooked in practice. In particular, we find that background sampling strategies play a critical role in model calibration and that a hybrid sampling approach with a moderate buffer bias provides better predictive accuracy. We also show that permutation importance scores, commonly used to rank environmental variables, are highly sensitive to small changes in the background data and should be interpreted with caution. Finally, to bridge the gap between ecological modeling and applied machine learning, we provide a self-contained, math-focused background to MaxEnt aimed at practitioners outside of traditional ecological fields. Overall, this work delivers reproducible modeling workflows and critical insights into building robust, transparent, and ecologically meaningful MaxEnt models for climate-informed species distribution analysis.

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.

Marine protected areas (MPAs) are increasingly promoted as climate mitigation tools, yet guidance on their placement to maximize resilience against climate stressors like marine heatwaves remains limited. Here, we develop MPA placement guidelines that explicitly consider a mechanistic pathway through which MPAs could enhance kelp forest resilience to heatwaves: protecting fishery-targeted urchin predators to prevent kelp overgrazing. Using a spatially explicit, tri-trophic model of California kelp forests, we evaluate alternative MPA configurations across a hypothetical coastline where half the habitat experiences an increased probability of experiencing heatwaves. We found that effective MPA placement depends on whether MPAs are being newly established or reconfigured within an existing network, and that among-patch connectivity and spillover played vital roles in the relative effectiveness of different MPA configurations. Changes in resilience occurred primarily at the patch scale, with trade-offs between increased within-MPA resilience and decreased resilience in some fished areas, resulting in minimal coastwide population effects. For example, for new MPAs, large single MPAs within heatwave-prone areas maximized within-MPA resilience gains, while multiple small MPAs in heatwave refugia best supported whole-coast resilience. When reconfiguring established networks, expanding existing MPAs in refugia areas was most effective. We also demonstrate the importance of considering MPA recovery timescales: for example, relocating old MPAs to heatwave refugia yielded minimal short-term benefits due to the loss of rebuilt, previously fished, predator biomass. Our findings demonstrate that climate-adaptive marine planning should explicitly consider the spatiotemporal implications of trophic cascades, connectivity, and transient population dynamics to support ecosystem resilience.

Estimates of the marginal cost per disability-adjusted life year (DALY) averted from government health expenditure (GHE) provide an empirical basis for allocating scarce health resources to maximise population health. Existing cross-country estimates have informed priority setting in several countries and international policy discussions but are based on data that are now more than a decade old. Since then, patterns of health expenditure, disease burden, and global health financing have changed substantially. This paper provides updated estimates of the marginal cost per DALY averted for 92 low- and middle-income countries (LMIC) by applying previously estimated elasticities of the effect of GHE on health outcomes from Ochalek et al. (2018) to recent data on mortality, morbidity, population structure, and GHE. Two policy options for improving health in LMIC are assessed: (1) the implications of countries allocating 15% of general government expenditure to health consistent with the Abuja Declaration; and (2) reallocating development assistance for health (DAH) to maximise health across countries. Scenario analyses use the estimated elasticities to reflect diminishing marginal returns to health expenditure when calculating the health gains associated with additional resources. Updated estimates of the marginal costs per DALY averted range from approximately $78 to $15,789 across countries. In most countries (72%), estimates are higher than in the previous analysis, largely reflecting increases in GHE. Increasing domestic expenditure to achieve the Abuja Declaration objective would avert 234 million DALYs but require $563 billion across countries. Reallocating $39.1 billion in existing DAH could avert 133.6 million DALYs. Updated estimates provide an empirical basis for informing both domestic priority setting and the allocation of international health financing. Aligning donor funding with country-specific opportunity costs could substantially increase the global health gains achieved with limited resources.

Mesic resources, the late-season herbaceous vegetation found in riparian areas and wet meadows, provide disproportionately important forage and habitat across western U.S. rangelands, yet their response to climatic variability and anthropogenic influences remains poorly understood. Using a 40-year Landsat time series (1984-2024), we quantified trends in late-season productivity (NDVI) across 4.5 million hectares of the sagebrush biome and applied random forest models to distinguish between temporal and spatial predictors of mesic resource productivity. We identified a fundamental shift in how mesic resources respond to drought: from 1984 to 2004, mesic productivity was strongly correlated with drought severity (Palmer Drought Severity Index, R{superscript 2} = 0.92), but this relationship weakened substantially in the next two decades (2005-2024; R{superscript 2} = 0.28), during which time productivity increased despite persistent aridity. Temporal modeling identified rising atmospheric CO2 concentrations as the strongest predictor of this shift, consistent with enhanced plant water-use efficiency under CO2 fertilization. Spatially, large agricultural valley floodplains act as anthropogenic refugia, sustaining productive mesic resources through flood irrigation and subsequent groundwater recharge into late summer. These findings suggest that human water management and physiological shifts in vegetation are currently buffering mesic systems against meteorological drought throughout U.S. rangelands. However, this apparent buffering is spatially heterogeneous and may mask vulnerability to groundwater depletion, shifts in precipitation regimes, and woody encroachment. Sustaining these vital ecosystems will require conservation approaches that go beyond climate monitoring to include balanced management considering both agricultural and ecological water needs and constraints.

We urgently need to restore degraded tropical forests to mitigate the climate and biodiversity crises, but how to do so rapidly and cost-effectively remains an open question. Here we provide a long-term, landscape-scale assessment of the effectiveness of enrichment tree planting and liana cutting, the two most common restoration interventions used across many tropical regions. Leveraging one of the worlds largest and longest running forest restoration experiments, we used repeat airborne laser scanning to track the 3D structural recovery of 500 ha of selectively logged rainforest in Borneo. Over an 18-year period, enrichment planting increased mean canopy height by 1.6 m relative to unplanted controls. Remarkably, liana cutting increased canopy height more than four times faster (3.7 m over just 9 years). This recovery was jointly driven by accelerated canopy gap closure, enhanced tree growth, and a 50% reduction in tree mortality. Given that liana cutting is around 10 times cheaper to implement than enrichment planting, our results suggest it provides a cost-effective, scalable solution to accelerate the structural recovery of logged tropical forests.

Climate change can affect salmon and steelhead (Oncorhynchus spp.) throughout their anadromous life cycles, yet there have been no assessments of which Canadian populations face the greatest exposure. We developed a framework to quantify relative climate change exposure of salmon and steelhead populations based on the spatial and temporal distribution of different life stages. Exposure was calculated from climate model projections for freshwater and marine climate variables considering unique impact thresholds for each population and life stage. We applied this framework to 60 Conservation Units of Pacific salmon and steelhead in the Fraser River basin, British Columbia. Lake-type sockeye had the highest exposure, driven by elevated stream temperatures during adult freshwater migration and spawning stages and relatively low thermal tolerance of marine stages. Chinook salmon were the next most exposed, while coho, pink, and chum salmon had relatively low exposure. Uniquely, steelhead exposure was driven by high stream temperatures during incubation. Our framework is broadly applicable, and our findings provide critical input for climate change vulnerability assessments and forward-looking resilience planning for Pacific salmon.

Groundwater-dependent ecosystems are biodiversity hotspots that provide habitat for specialised species. The EU Water Framework Directive (WFD) stresses the importance of identifying and protecting these ecosystems. However, they remain poorly mapped in temperate regions, as most studies have focused on (semi-) arid regions, where groundwater use by vegetation is both more prevalent and easier to detect from remote sensing. In this study, we transfer mapping approaches for groundwater-dependent vegetation (GDV) from dry climates into a novel framework for humid climates. To do so, we integrated, ECOSTRESS evapotranspiration data, together with high-resolution remote sensing data, regional geospatial data and field data to identify GDV. To test our framework, we trained and validated Random Forest models with eight predictor variables using 166 ground-truth vegetation plots to map GDV in Saxony-Anhalt (Germany). The final model achieved an overall accuracy of 0.97, identifying 2,067 km2 (41%) of GDV. Currently, only 19% are protected under the EU WFD. The proposed mapping framework offers a new solution for identifying GDV in temperate regions. The new GDV maps can contribute to managing groundwater resources and preserving biodiversity hotspots in regions facing increasing droughts, ultimately supporting implementation of the EU WFD.

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.

Canadas ambition for mineral security and its responsibilities to protect at-risk species and uphold Indigenous rights clash in the case of the Grays Bay Road and Port (GBRP) in Nunavut, an infrastructure project intended to unlock critical mineral deposits. We compiled Indigenous and Western science through a density analysis of caribou harvesting data near the proposed project site. We identified three consistently used harvesting hotspots, with the most significant hotspot lying directly in the path of the proposed GBRP project. These results indicate that the GBRP project will have significant and unmitigable negative effects on caribou conservation, food security, and Inuit harvesting rights. Prime Minister Carney claims that middle power countries must act consistently in this era of geopolitical rupture; this commitment must transfer to natural resource development reviews so that decision-making may be consistent and rooted in cross-legislation responsibilities and values, including the land claims agreements between Indigenous groups and the Government of Canada.

Climate change is expected to alter forest community composition and functioning, with consequences for the ecosystem services forests provide. However, most macroecological projections focus on individual species distributions and offer limited insight into whether entire communities will remain functionally compatible with future climatic conditions. Here we quantify the risk that present-day forest communities will become functionally misaligned with projected climates using a trait-based approach. We analysed forest inventory data from more than 42,000 mature plots across the United States and Canada. For each plot we estimated community-weighted means for 24 functional traits describing leaf economics, hydraulic function, wood structure, abiotic tolerances and symbiotic strategies. We modelled relationships between community functional composition and environmental conditions, and used these relationships to estimate the trait profiles most compatible with projected late-century climates (2080-2100). Trait-environment misalignment (TEM) risk was quantified as the multivariate distance between current community trait composition and the trait profile associated with the projected future climate at each location, accounting for covariance among traits and intraspecific trait variation. Projected climatic conditions favour trait combinations associated with greater hydraulic capacity and reduced cold and shade tolerance. However, the magnitude of functional misalignment varies strongly across space. The highest TEM risk occurs in high-latitude and montane conifer forests across western and central North America, whereas many mid-latitude broadleaf and mixed forests show lower risk because projected climatic changes reinforce existing drought-adapted functional strategies. Critically, high species richness was the strongest predictor of reduced risk, reinforcing the importance of biodiversity in buffering against adverse outcomes. Our results suggest that many forests are projected to experience climatic conditions associated with functional strategies that differ from those characterising the current community. By identifying where the largest functional adjustments are implied, this trait-based framework provides a scalable way to pinpoint forests most likely to experience suboptimal climate conditions and to prioritise monitoring and climate-adapted management.

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.

The mosquito species Aedes aegypti and Aedes albopictus are the primary vectors of the arboviruses dengue, Zika, and chikungunya. Expansion of these vectors into previously non-endemic regions due to climate and environmental changes has accelerated global burden from arboviral diseases. To combat this, predictive models accurately mapping Aedes habitats are essential for epidemiological modelling, effective vector control, and disease prevention. We introduce the Climademic Suitability Model, a machine learning model that delivers monthly global predictions of Aedes habitat suitability at 0.25{degrees} spatial resolution between 1975--2024. The model leverages integrated climate, land use, human population, and mosquito surveillance data to provide an explainable view of the factors governing habitat dynamics. SHAP-based explainability analysis identified temperature and dew point temperature as dominant features driving habitat suitability. Long-term analysis reveals a complex global redistribution of expanding and contracting vector habitats. Suitable areas for both species now encompass regions home to over 5 billion people, coinciding with the worlds most pronounced population growth and surpassing projections previously placing this threshold at 2050. The Climademic Suitability Model serves as a framework for near-real-time vector surveillance, climate scenario projection, and integration into transmission models to advance epidemic preparedness in an era of accelerating environmental change.