Environmental Research Letters

Nepal's rangelands provide multiple benefits, including support for pastoral livelihoods and alpine biodiversity, regulation of water and soil nutrients, and sequestering carbon. Climate change and anthropogenic pressures are altering these rangelands, leading to vegetation and biodiversity change. However, national-scale assessments of rangeland change are limited in Nepal. This study quantified rangeland changes at multiple spatial scales and assessed the climatic and non-climatic drivers of rangeland change. About 80.7% of Nepal's high-altitude rangeland (> 2,000m) outside protected areas showed no significant change. Among areas exhibiting significant annual maximum NDVI trends, 383,281 ha (18.6%) showed positive and 14,702 ha (0.7%) showed negative trends, corresponding the ratio of increase in vegetation greenness and decline in vegetation greenness to 26:1. Climate predicted positive trends covered 627,184 ha (30.5%), whereas residual trends caused by non-climatic drivers covered 94,656 ha (4.6%). Climate induced negative trends covered 47,609 ha (2.3%) while residual trends were observed in 6,260 ha (0.3%). Negative trend pixels were concentrated mainly within the 3,000 to 5,000 m elevation band, with Karnali Province recording the highest proportional climate predicted decline in vegetation greenness (3.4%). At the municipality scale, rangeland change showed no significant relationship with grazing pressure derived from gridded livestock data, suggesting that grazing pressure alone did not explain the non-climatic vegetation signal. These spatially explicit, nationally consistent results identify where rangeland change is occurring and help distinguish climatic and non-climatic drivers of rangeland vegetation change, providing evidence to support targeted rangeland management under Nepal's federal governance structure.

The integrity of forest-based climate solutions and carbon credits requires persistent carbon storage, but climate change is increasing the risk of natural disturbances that release carbon back into the atmosphere. Using global satellite data, disturbance modeling, and machine learning, we provide the first spatially explicit and scenario-based maps of long-term probability of carbon loss in global forests under different disturbance severities and climate scenarios. We find that North American conifer forests, tropical rainforests, and Asian (sub)tropical dry forests face the greatest risks, and that Eurasian temperate forests, African (sub)tropical dry forests face the lowest. Globally, the likelihood of reversals over 100 years is 31%-42% across all scenarios. Our work helps to maximize the benefits of forest-based climate solutions by informing more strategic project placement and more robust reversal-risk compensation mechanisms, such as buffer pools, and highlights critical additional science to better understand and manage risks of these essential climate solutions. Plain Language SummaryForests can help slow and lessen climate impacts. However, in places this benefit is becoming less reliable as climate change increases natural disturbances such as wildfires, drought, storms, and insect outbreaks, which can release stored carbon back into the atmosphere. In this study, we created the first scenario-based global maps of risks and found that the risk of carbon loss is widespread and highly variable across regions, with especially high vulnerability in North American conifer forests, tropical rainforests, and Asian tropical and subtropical dry forests. Our study highlights the importance of considering disturbance risks when siting forest projects for climate mitigation, and developing protocols for carbon markets, such as in voluntary programs and under the UNFCCC Paris Agreement. Key PointsO_LIA demographic model framework estimates the reversal risk from natural disturbances over 100 years in global forests C_LIO_LISpatially explicit maps under different severity scenarios show variation in the integrated 100-year risk of carbon reversal C_LIO_LISpatially explicit maps estimate the required buffer pool needed to compensate for disturbance-driven reversals in global forests C_LI

Tropical dryland ecosystems are highly biodiverse and fragmented and are experiencing significant anthropogenic and climatic changes. With increasing extremes in temperature and precipitation, coupled with significant alteration, these ecosystems are at greater risk of increased exposure and vulnerability to climatic change; however, little work has quantified the climatic shifts occurring within these ecosystems globally. Here, we aim to fill this gap by using the ERA-5 reanalysis and CHIRPS precipitation data to quantify changes in essential climatic variables in tropical drylands since 2000. Overall, we find that regional pressures differ, with tropical dry forests, savannas, and shrublands becoming hotter and drier in the Neotropics and parts of the Afrotropics and Australasia. By contrast, the tropical dry forests in the Indomalayan, Oceania, and Nearctic are experiencing hotter and wetter conditions. Globally, though, these ecosystems are experiencing more change than the global average, suggesting they may be approaching tipping points in their resilience, ultimately shrinking the area where they can survive.

Environmental changes are reshaping the distribution and seasonal dynamics of vector-borne diseases, with important implications for public health. Tick-borne encephalitis virus (TBEV) and West Nile virus (WNV) cause growing concern in Europe, with rising case numbers and ever-expanding circulation areas. The transmission risk of TBEV and WNV follows characteristic seasonal patterns, driven largely by weather-dependent activity of their arthropod vectors. The relative roles of climate and land-use change on the seasonal dynamics and spread of these diseases and their vectors remain, however, poorly quantified. Here, we assess the spread and phenology of TBEV and WNV in response to historical and future climate and land-use changes across Europe. We developed spatiotemporal species distribution models (SDMs) for the viruses and their primary vector species, generating monthly environmental suitability predictions from the 1970s to 2050s. Virus models incorporated vector suitability as a nested predictor to capture the dependence of virus occurrence on vector presence. To disentangle drivers of observed changes, we applied counterfactual historical simulations, attributing shifts in seasonal transmission risk to climate or land-use changes. Historical attribution results show that land-use changes mainly affected absolute vector suitability, whereas climatic changes drove shifts in seasonal transmission risk. Transmission risk is projected to rise continent-wide for both TBEV and WNV over the coming decades. Further, TBEV is projected to undergo pronounced phenological shifts, with a dominant spring peak and a delayed autumn peak extending into October. Prolonged seasonal transmission windows are projected to create hotspots that both intensify and expand across large regions. Taken together, our findings underscore the need for coordinated transnational efforts to manage the projected health burden of TBEV and WNV across Europe, and support upstream prevention by providing climate-informed guidance on intervention timing and spatial prioritisation.

Rift Valley fever (RVF) is a mosquito-borne disease that can cause severe illness and death in both humans and livestock. Since 2016, Uganda has experienced recurrent but localized RVF outbreaks concentrated in the countrys southwestern region. The ecological drivers of this emergence remain unclear, as outbreaks have occurred throughout the year and show little association with meteorological patterns. We evaluated whether crop cultivation, particularly banana cultivation, is associated with RVF outbreak occurrence after controlling for likely confounders. We conducted a longitudinal study of human-inhabited 5 x 5 km grid cells across southwestern Uganda from 2016-2024. Annual Sentinel-2 satellite imagery composites were used to classify land cover into banana, coffee, ground crops, and non-crop categories, and the proportion of each land type was calculated for every grid-cell year. Because land cover proportions are compositional, isometric log-ratio transformations were used to estimate the independent effects of each land type. Confounding was addressed through propensity weighting, and crop substitution effects were estimated using g-computation. Banana land cover was the only land type consistently associated with increased RVF outbreak likelihood. In grid-cell years with low baseline banana cover, a 10-percentage point substitution from other land classes into banana was associated with a 1.64-fold increase in the odds of an RVF outbreak (95% CI: 1.17-2.29). In a simplified banana-only model, each 10-percentage point increase in banana cover was associated with a 1.21-fold increase in outbreak odds (95% CI: 1.02-1.43). Holding banana cover constant, substitutions among coffee, ground crop, and non-crop land showed weak or null associations. These findings suggest that banana cultivation may be an important ecological feature influencing RVF transmission dynamics and outbreak risk in southwestern Uganda. Author SummaryRift Valley fever (RVF) is a mosquito-borne disease that affects both humans and livestock and has caused repeated outbreaks in southwestern Uganda since 2016. While rainfall and flooding are often linked to RVF outbreaks elsewhere, Ugandas recent outbreaks have occurred across seasons and are not well explained by weather patterns alone. We investigated whether agricultural land use could help explain where outbreaks occur. Using satellite imagery from 2016-2024, we measured the amount of banana cultivation, coffee cultivation, ground crops, and non-crop land across southwestern Uganda and evaluated their association with RVF outbreak occurrence. We found that areas with greater banana cultivation were consistently more likely to experience RVF outbreaks, even after accounting for environmental and demographic factors. In contrast, coffee, ground crops, and non-crop land showed little evidence of an independent association with outbreak risk. These findings suggest that banana cultivation may create ecological conditions that favor RVF transmission. Rather than indicating that bananas themselves cause disease, the results point to banana-growing landscapes as potential environments where interactions among mosquitoes, livestock, and humans may increase transmission opportunities. Understanding these local ecological drivers could help improve surveillance, risk assessment, and prevention strategies for RVF in Uganda and other endemic regions.

Dengue fever risk and burden has increased globally in the past decade, with record-breaking outbreaks driving high case numbers, outbreaks increasing in existing transmission suitable regions, and occurring in new locations. A combination of global change processes, including climate change, have provided the environmental backdrop for introductions and resurgences of mosquito-transmitted dengue virus. Understanding shifts in exposure risk is integral to public health preparedness. This study provides global mapping of the thermal suitability of dengue transmission for CMIP6 climate scenarios, across a range of general circulation models (GCMs), and we created spatially explicit demographic projections of transmission risk using year-matched RCP-SSP frameworks for demographic and emissions scenarios. Globally, poleward shifts in projected distributions of suitability for transmission for both Ae. aegypti and Ae. albopictus suitability are shown in both the near term (2030s) and longer term (2050). Under a 'middle of the road' climate scenario (CMIP6 SSP2-4.5), regions in Africa and Asia are the major areas driving increases in year-round (12 months) population at risk (PAR) through 2050, with an anticipated net gain in 932 million people at risk for Ae. aegypti transmission and 24 million for Ae. albopictus, which includes multiple regions losing areas of year-round suitability as temperatures exceed the higher thermal boundary for transmission. In contrast, the estimated net increase in PAR for one or more months of transmission suitability at a global scale by 2050 is 3.29 billion people for Ae. aegypti transmission and 3.30 billion for Ae. albopictus transmission. This snapshot of a 'middle-of-the-road' combination of climate and demographic driven increases in potential dengue transmission exposure emphasizes the importance of both expanding suitability in new areas, and growing populations in areas approaching and becoming exposed year-round. Globalization, urbanization, and shipping will continue to provide the potential for introductions into newly suitable areas as season lengths increase, sparking outbreaks in unexposed populations. This is compounded and becomes ever more probable as the number of people and places at year-round risk also increases. This project provides all global gridded outputs for onward mapping and reuse, to add to the toolkit to anticipate and prepare for prevention and response to dengue in a changing world.

AimHere we aim to disentangle species-specific bioclimatic drivers of forest site productivity and project their future dynamics, providing a spatially explicit basis for anticipating climate-driven shifts in productivity and their implications for forest carbon sequestration. LocationIberian Peninsula. Time period1985-2014 (calibration); 2071-2100 (projected under CMIP6 scenarios). Major taxa studied21 Iberian tree species. MethodsWe used Site Form (SF) maps derived from the Third Spanish National Forest Inventory, spatially interpolating plot-level SF estimates as a continuous productivity index and relating them to 25 bioclimatic variables. Multiple linear regression models were selected via complementary stepwise and subset regression and validated on independent hold-out data (80%/20% split). ResultsValidated [Formula] ranged from 0.46 (Quercus faginea) to 0.97 (Pinus pinaster); 17 of 21 species reached [Formula]. BI013 precipitation of the wettest month), not BI014, was the most frequently retained predictor (15/17); BI014 was retained in only (11/17 models with a near-even sign split. Combining projected changes in mean productivity and habitat extent under SSP5-8.5, fifteen of sixteen applicable species lose total productivity by 2071-2100, six -- including Fagus sylvatica and Betula alba -- collapsing to below 1% of their reference-period value; only Pinus pinaster gains, and only under the lowest-emission pathway (up to 175%) -- under SSP5-8.5 it too loses productivity, albeit less than any other species (35% of its reference-period value retained). Limiting warming to SSP1-2.6 spares Mediterranean pine and oak species but not Euro-Siberian and montane ones. Main conclusionsThese validated, extrapolation-aware models reveal a near-universal, climate-driven collapse in Iberian forest site productivity, with direct implications for the carbon-sink potential currently attributed to these forest types, and provide a route to dynamic, climate-aware carbon-uptake estimates for the region.

Rainfall and vapor pressure deficit (VPD) are well-studied hydrological variables that largely determine aboveground net primary production (ANPP) in most ecosystems. Meanwhile, the impacts of another important part of the hydrologic cycle, non-rainfall water from fog and dew, remain poorly understood at the ecosystem level. To fill this gap, we used meteorological variables measured at weather stations along with satellite-derived vegetation greenness data from surrounding areas to examine how fog and dew frequency affect summer plant growth across the contiguous United States. Our analysis shows that, even after accounting for precipitation, VPD, and land-cover type, fog and, more so, dew enhanced vegetation productivity in water-limited regions. In contrast, non-rainfall water had a neutral or negative impact on plant growth in humid regions, with fog showing the strongest and most widespread negative effects. Taken together, our findings reveal that summertime non-rainfall water has differential effects on vegetation that are largely determined by ecosystem-level water availability. These aridity-dependent effects of fog and dew should be considered in future ecological and agricultural studies and in assessments of projected climate impacts on vegetation.

ContextArtificial light at night (ALAN) has profound impacts on individual organisms and entire communities. Still, humans tend to underestimate the true biological (spatial) footprint of ALAN, in part because of our limited sensitivity to light compared to other organisms. ObjectivesWe sought to demonstrate how far ALAN can reach into dark spaces at levels that can impact organismal behavior and physiology using a fundamental physical law, the inverse square law. MethodsWe created a spatially explicit model of light spread on real landscapes, parameterized using increasingly available landscape-scale vegetation data to account for attenuation through forests and blocking by topographic relief. ResultsLighting types common in rural areas can produce biologically important effects more than 1 kilometer from the source, and effects of large lights might stretch 3 kilometers or more. The footprint of a light is determined by the complex and multidimensional interaction between characteristics of the light itself and the environment. For example, attenuation through a dense forest might decrease the footprint of a light more than 90% compared to the same light on a grassland. In complex environments, even small changes in light placement and characteristics can lead to large changes in the biological footprint of the light. ConclusionsDesigners and land stewards must account for lighting type, brightness, directionality, and reflected light to create ecologically responsible lighting. Vertical vegetation and topography strongly influence the propagation of biologically detrimental light, and environmental context is vital when planning and installing lights to minimize the biological impacts.

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

Forests are essential to the global carbon cycle with light use efficiency (LUE) as a key parameter for assessing carbon sequestration capacity. However, the variations and drivers of LUE remain inadequately understood. Using remote sensing data, we analyzed global LUE patterns across five forest types and identified the main drivers. The global average annual LUE of forests is 0.93 {+/-} 0.36 g C MJ-1 during the period 2001-2022, with an increasing trend of 0.0034 g C MJ-1 yr-1. Among forest types, evergreen broadleaf forests exhibited the highest LUE, followed by evergreen needleleaf forests. Deciduous broadleaf forests and mixed forests showed similar levels, while deciduous needleleaf forests exhibiting the lowest LUE. Variations in LUE were jointly driven by plant traits and climatic conditions, with generalized linear models explaining 86% and 98% of spatial and temporal LUE variations, respectively. These findings highlight the critical role of plant traits and climate in shaping forest LUE, providing insights for enhancing carbon cycle models and informing forest management strategies in the context of global change.

Commodity maps and the ability to monitor commodity-driven deforestation are essential for sustainability risk assessment and due diligence. Natural rubber (Hevea brasiliensis), used primarily in tyres, remains challenging to map because of its similarity to other tree cover. Integrating optical and radar satellite data, we produced a 10 m map of rubber distribution in Southeast Asia for around 2020 and assessed this map and similar products for deforestation due diligence. In continental Southeast Asia, estimated users accuracy was 0.95 and area-adjusted producers accuracy 0.78. Rubber-associated clearances of natural and semi-natural tree cover during 2011-2016 were estimated at 0.3-0.4 Mha, predominantly associated with industrial-scale plantations and concentrated in Cambodia. Historical clearances were less certain, with plausible bounds of 1.3-3.0 Mha since 1990. Evaluation of rubber and forest maps highlighted persistent limitations, particularly in insular Southeast Asia and smallholder systems. Accounting for these limitations is critical for fair and effective due diligence.

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

For over a decade, the Shared Socioeconomic Pathways (SSPs) have served as the principal framework for quantitative modeling of the socioeconomic dimensions of global environmental change. The SSP scenarios describe many of the ecological and social processes thought to shape pandemic risk, including the emergence of novel pathogens (accelerated by processes such as deforestation, livestock intensification, and land-use change) and their subsequent spread (mediated by factors such as inequality, human mobility, and health system capacity). However, the SSP framework has not been widely incorporated into pandemic risk assessment. Here, we assess how pandemic risk is embedded in the SSP framework, and find that the framework captures most of the social-environmental drivers of pathogen spillover, and many of the social-economic drivers of pandemic spread and impacts. Because climate change and pandemics share many drivers and risk factors-- including ecosystem degradation, animal agriculture, and weak governance--SSP scenarios characterized by higher barriers to climate adaptation also generally imply lower chances of outbreak containment, and greater pandemic impacts on vulnerable populations. Pandemic risk is therefore lowest in SSP1 and highest in SSP3, but SSP5 shows that frequent spillover and effective containment can coexist. These findings suggest that pandemic risk can be understood as part of a broader polycrisis, linking climate change, biodiversity loss, and global health. We suggest that new scenario extensions, or entirely novel frameworks, will ultimately be needed to capture possible shifts in the global health landscape; however, in the meantime, scenario frameworks from the environmental sciences could be valuable tools for initiatives to quantify future pandemic risks.

Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.

Offshore wind farms are rapidly expanding to meet growing demands for renewable energy, with development expected to extend further offshore into deeper waters. This expansion requires a robust understanding of the long-term ecological consequences of offshore wind farms (OWFs) and how these may interact with ongoing climate change. We used the coupled hydrodynamic-ecosystem-biogeochemical water-column model (GOTM-ERSEM-BFM) to investigate ecosystem-wide responses to environmental changes associated with OWFs and climate warming. Specifically, we examined OWF-related scenarios of reduced benthic suspension-feeding activity, representing potential effects of contaminant emissions from OWFs, and reduced wind forcing, together with increased sea surface temperature. The scenarios were simulated individually and in combination to explore potential interactive effects. These scenarios were simulated at two contrasting locations in the North Sea, representing a well-mixed coastal site and a seasonally stratified offshore site. The coastal site exhibited comparatively modest ecosystem responses across the scenarios, whereas responses were generally stronger at the deeper offshore site. At the offshore site, changes in stratification altered vertical nutrient dynamics and contributed to pronounced differences in ecosystem responses between the surface and bottom layers. Our results demonstrate that ecosystem responses to OWF-related and climate-driven environmental changes are strongly dependent on local environmental conditions, suggesting that ecological consequences may differ substantially as wind farm development expands into deeper offshore environments.

Invasive plants pose a major environmental problem, threatening biodiversity, altering ecosystem functions, and causing economic loss. Climate change is altering environmental conditions, potentially facilitating the spread of invasive plant species, posing challenges for ecosystem management and biodiversity conservation. Accurate predictions of invasive species distributions are therefore essential for effective monitoring and early intervention. Species distribution models (SDMs) have become an important tool for predicting species habitats, but many studies rely on traditional machine learning approaches, focus on single-species predictions and overlook uncertainty associated with future climate scenarios. This study aims to evaluate the performance of a deep learning-based SDM framework, Deepbiosphere, for predicting both native and invasive plant species distributions on a regional scale, the US state of Michigan, and to assess how climate scenario uncertainty influences spatial predictions of invasive species risk particularly on two focal invasive species. Results show that Deepbiosphere outcompeted other baseline models by on average of 10.98% with a mean AUC-ROC of 0.79 across 1553 vascular plant species. For two invasive species Rhamnus cathartica and Ailanthus altissima, Deepbiosphere respectively improved modeling accuracy by an average of 56.41% and 74.99%, suggesting its enhanced predictive capability for invasive species. Current predictions indicated that R. cathartica is already broadly suitable across much of Michigan, whereas A. altissima is currently more restricted to southern regions. Under future climate scenarios, both species were projected to expand northward, with a particularly strong expansion signal for A. altissima. Prediction uncertainty was spatially heterogeneous, where general circulation models (GCMs) were the dominant source of uncertainty across most of the state. By integrating citizen science, remote sensing, and deep learning, we produced high-resolution risk-uncertainty maps for key invasive species and highlighted the importance of explicitly mapping uncertainty to support more informed invasive species management under climate change.

The global market concentrates extractive pressure on lands held by Indigenous peoples, including peoples living in isolation, for whom free, prior and informed consent cannot be obtained and protection must therefore rest on territorial instruments. Halmahera, Indonesia, holds some of the worlds largest lateritic nickel reserves beneath a lowland rainforest inhabited by the Hongana Manyawa, yet the trajectory of land use and cover change across the island has not been quantified. We characterised land use and cover change over the 17,437 km2 island between 2014 and 2024 using MapBiomas time series, and projected a business-as-usual scenario to 2054 with a stochastic cellular-automata model implemented in Dinamica EGO, calibrated with weights of evidence on eight variables describing mining and logging concessions, transport infrastructure, settlements and previous clearing. Forest covered 83.0% of the island in 2014 and 82.1% in 2024; under unchanged policy it falls to 73.7% by 2054, a net loss of 162 thousand ha, or 11.2% of the 2014 baseline, at gross rates of 47,000-51,000 ha per decade. Deforestation probability is highest within 500 m of previous clearing and declines with distance from settlements, cities and mining sites, while proximity to national parks carries a negative weight of evidence. The frontier is self-propagating and spatially predictable, and legally designated territory retains forest within it. Protecting the Hongana Manyawa consequently depends on excluding extractive licensing from the interior forest ahead of the frontier rather than behind it.

Wolbachia-mediated incompatible-insect technique (IIT) via wAlbB, wMel or wPip/wAlbA/wAlbB strains are promising approaches for suppressing wildtype Aedes mosquitoes and therefore Aedes-borne diseases. Yet, the effectiveness of this technique under climate change remains uncertain. Here, we evaluate the long-term robustness of male Wolbachia-infected mosquito releases to suppress wildtype Aedes aegypti and Ae. albopictus populations across future climate scenarios across diverse geographical regions. We compiled large publicly available datasets on Aedes abundance across Singapore, China, the European Union and the United States, historical and projected climatic conditions in these regions and conducted experiments to test the thermal stability of cytoplasmic incompatibility in Wolbachia-infected male Aedes aegypti and albopictus. A climatically-driven entomological model was developed and calibrated using a Bayesian approach to model observed Aedes population dynamics and infer area-specific climate-driven variation in mosquito life-history traits. We back-inferred historical mosquito abundance and projected mosquito abundance in future climate change scenarios incorporating experimental and locally inferred entomological parameters and then simulated the counterfactual implementation of IIT in these regions. We find that Aedes populations are projected to increase in most regions across all climate change scenarios from 2050-2100 even under high heat conditions in the absence of interventions. While we found that IIT can suppress wild-type populations effectively across all future scenarios and in high heat conditions, effectiveness was found to depend heavily on mosquito emigration rates, overflooding ratios, release intervals and release strategies Extensive robustness checks confirmed that the model reproduced historical temporal trends, captured the influence of individual parameters on outcome and was sensitive to changes in values of inferred parameters and implement policy. These findings demonstrate that IIT may be a robust vector control tool under future climate conditions.

Southwestern Europe faced an extreme wildfire season in 2025, with nearly 700,000 hectares burned in the Iberian Peninsula (IP) alone. Here, we analyze the drivers and impacts of the 2025 wildfire season in the IP and its significance within the ongoing global pyrocrisis. Decades-long declines in burned area, driven by increased fire suppression, ceased after an inflection point in 2022. Fire intensity has escalated over the last two decades, and the energy emitted in 2025 approached that produced annually by a 1,000MW nuclear reactor. Despite a historically wet spring, an extreme summer heatwave triggered a flash drought, dehydrating fuels below critical thresholds. Remarkably, 29-42% of all wildfires spread faster at night than during the day, a seldom-reported phenomenon likely arising from interactions between surface weather, atmospheric instability, and pyroconvective processes. Global change-induced increases in fire intensity facilitated the overwhelming of suppression efforts during simultaneous fire events that may have been manageable decades ago. Fire activity expanded into previously fire-free high-altitude regions, and there was a marked change in fire-size distributions, with the largest wildfire in record and the largest proportion of burned area by megafires (those burning over 5,000ha). Impacts included over 2,000 premature deaths from smoke exposure and significant effects on protected areas. These results indicate shifts in key components of anthropogenic fire regimes, including unprecedented nocturnal fire acceleration and increased burned area and fire intensity, with escalating impacts on human health and ecosystems.