Environmental Research Letters

Palm oil is the worlds leading vegetable oil, with production continuing to expand, particularly across Southeast Asia. Within established plantations, conserving and restoring riparian margins can reduce erosion, improve connectivity, and support biodiversity. Simultaneously, climate change is increasing thermal stress within tropical agricultural landscapes, raising the potential importance of riparian buffers as microclimatic refugia, although this has not previously been evaluated. The Riparian Ecosystem Restoration in Tropical Agriculture (RERTA) Project is a long-term Before-After Control-Impact experiment established within oil palm plantations in Riau, Indonesia, to evaluate alternative riparian restoration strategies following plantation replanting. Treatments included passive restoration (retaining mature oil palms and allowing natural regeneration), active restoration (enrichment planting with six native tree species), and a control (replanting immature oil palms to the river edge). Six years after restoration, we assessed vegetation structure using terrestrial laser scanning and thermal stability using temperature loggers and thermal imaging. We also evaluated the long-term temperature buffering capacity of each treatment under current and projected climates using CHELSA climate data and downscaled outputs from multiple General Circulation Models under Shared Socioeconomic Pathway scenarios for 2045 and 2085. Active restoration produced greater structural complexity, a more closed canopy, and cooler, more thermally stable conditions than passive restoration and control treatments. Restored margins also contained significantly more cool surface microhabitats, reducing thermal extremes under both current and future climate scenarios. Our findings demonstrate that active restoration of riparian margins can enhance thermal buffering and create microclimatic refugia for biodiversity within tropical oil palm landscapes. As agricultural intensification and climate change continue to reshape tropical ecosystems, restoring structurally complex riparian vegetation will be critical for sustaining biodiversity and the ecosystem services upon which agricultural production depends.

Despite the popularity of carbon credits as a tool to offset greenhouse gas emissions, there are growing concerns over whether their climate mitigation claims are robust. Most attention has focused on natural risks to forest carbon projects, such as wildfire, while social risks (e.g., those driven by fraud, mismanagement, and land conflicts) remain largely unquantified. Here we analyze 244 forest-based carbon projects to assess the vulnerability of the voluntary carbon market to social risks and find that projects systematically underestimate their risk of failure. Even under optimistic assumptions, projects are roughly twice as likely to fail within 25 years as their self-assessed risk implies over a 100-year horizon. Projecting these trends forward, we show that credits issued to failed projects could exceed the reserves registries hold to mitigate these risks within 1-2 decades. Overall, our results suggest stronger mechanisms and reserves are needed to ensure carbon credits deliver the promised climate benefits.

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.

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.

Mediterranean forests are emerging as one of the clearest hotspots of climate-change impacts, where rapid warming and increasingly severe droughts and heatwaves are pushing forest ecosystems toward their physiological and functional limits. These pressures threaten productivity, carbon dynamics, ecosystem stability, and the services forests provide. In this context, a process-based forest ecosystem model was used to assess climate-driven changes in carbon dynamics and productivity stability across Italian forests within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework. 5135 plots were simulated, corresponding to ~80% of the field plots included in the second Italian National Forest Inventory (INFI). Model simulations were first evaluated against independent observations of gross primary production (GPP) from satellite-based products and forest carbon variables from the third INFI. Forest dynamics from 2015 to 2100 were simulated under three CMIP6-based emission scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) using two climate forcings (GFDL-ESM4 and UKESM1-0-LL). Future responses were assessed for GPP, aboveground biomass, growing stock volume, and carbon stocks, while variability in GPP was quantified using the coefficient of variation as an indicator of forest stability. Results show that the model reproduced the main spatial and temporal patterns of forest productivity and carbon stocks. Future projections revealed strong spatial heterogeneity and increasing divergence among scenarios after mid-century. GPP showed the earliest and strongest response to climate change, with mountain forests generally maintaining or increasing productivity and exhibiting greater stability, whereas coastal forests showed pronounced productivity declines and increasing variability, particularly under higher-emission scenarios and more extreme climate forcing. In contrast, carbon stocks generally continued to increase throughout the century, although carbon accumulation progressively slowed toward the end of the century, particularly under higher-emission scenarios.

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.

Coral bleaching is among the most infamous climate impacts. Yet the extent of bleaching due to human-caused climate change, as opposed to natural El Nino variability, and the contributions of individual countries and carbon majors (fossil fuel and cement producers) remains unquantified. Here, we leverage coral monitoring globally across 4,479 sites from 1985-2024 to link specific emitters to marine heat stress and bleaching. For events with >10% of corals bleached at a site, we find that 99% (95% CI: 88-100%) were attributable to human-caused climate change. Even during major El Ninos, 97-100% of bleaching events were attributable to human-caused climate change and the extent of damage increases over time from 23% of sites bleached (95% CI: 3-35%) during 1998 to 71% of sites (95% CI: 37-85%) during 2023-2024. Emissions of the United States, the largest historical emitter, contributed to 51% (95% CI: 39-65%) of bleaching events. Emissions linked to Chevron, Saudi Aramco, Gazprom, and ExxonMobil each contributed to 14% (95% CI: 11-19%) of bleaching events -- roughly double the contribution of the 39 countries of the Alliance for Small Island States. It is virtually certain emissions of China, the United States, and Indonesia caused bleaching in their coastal waters. Strong inequalities exist where countries with high socioeconomic dependence on coral reefs experienced 54% of bleaching yet contributed only 5% to bleaching. As global warming rapidly nears levels catastrophic for warm-water corals, our results show the massive contribution from anthropogenic emissions to historical coral beaching and help fill an important evidentiary gap on accountability for non-economic loss and damage from climate change.

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.

Background. Naegleria fowleri, the causative agent of primary amoebic meningoencephalitis (PAM), is a thermophilic free-living amoeba historically endemic to warm freshwater environments, particularly in the southern United States. While human infections remain exceedingly rare, they are almost uniformly fatal. Global climate change is hypothesized to expand the geographic range of thermotolerant pathogens, potentially introducing them into previously unaffected regions. Objective. This study aims to quantify the current and future habitat suitability for N. fowleri within the contiguous United States (US) and, critically, to project these models onto Europe, a continent traditionally considered low-risk, to identify regions of potential emergence under climate change scenarios. Methods. We employed a modeling approach using the machine learning algorithm Maxent, implemented via the `flexsdm` R package. We utilized 204 occurrence records from the contiguous US. We evaluated three environmental datasets (ENVIREM, Global Cloud Dynamics, Worldclim v.2), selecting Worldclim v.2 for its superior predictive performance. Future projections were generated for the 2041-2060 period under the Shared Socioeconomic Pathway (SSP) 2-4.5 (intermediate scenario). Variable importance was assessed using Shapley additive explanations (SHAP) to decouple the contribution of each Bioclimatic predictor and identify the key drivers of range expansion. Results. The Worldclim v.2 dataset provided the most robust model. The model accurately identified the southeastern US as the core current habitat, consistent with historical PAM case distribution. Crucially, when projected onto Europe, the model reveals extensive regions of currently low suitability that transition to moderate or high suitability by 2050, particularly across the Mediterranean basin, parts of Central Europe, and the Atlantic coastal fringes. SHAP analysis identified temperature-related variables, specifically Annual Mean Temperature, Mean Temperature of the Coldest Quarter, and Mean Diurnal Range, as the most influential predictors, followed closely by precipitation variables (Precipitation of Driest Quarter/Month and Precipitation Seasonality). Under the SSP2-4.5 scenario, the model projects a distinct northward and eastward expansion of suitable habitat, with Ukraine emerging as a region of particular concern, especially in light of recent anthropogenic hydrological changes to the Dnipro River basin. Conclusion. Our findings support the hypothesis that global warming will facilitate the geographic spread of N. fowleri, carrying this pathogen into uncharted European waters. The projections for Europe, and for Ukraine, in particular, suggest a non-negligible risk of future autochthonous PAM cases. These results warrant the proactive inclusion of this pathogen in public health surveillance programs and climate change adaptation strategies across the continent.

Plant phenology influences biotic interactions and the cycling of water, carbon, and nutrients, ultimately affecting global climate. Accurately quantifying phenological dynamics is therefore essential for ecological forecasting, yet estimates vary widely depending on data sources, smoothing approaches, and date-extraction techniques. For this reason, the selection of an appropriate processing pipeline requires benchmarking under controlled conditions. In this study, we use a Land Surface Model (LSM) to simulate ecosystem dynamics at four temperate forest sites under five lesser studied climate perturbations. The resulting synthetic dataset allows us to control for environmental conditions while comparing commonly used phenological detection methods (relative thresholds at 10%, 20% and 50%, highest rate of change, detection of inflection points in the curvature angle, detection of recovery and senescence points). Amongst tested methods, we find general agreement in responses to climate alterations, where inflection and low thresholds (10%) methods provide the closest match to the model predicted start of season, with a maximum discrepancy of 4 days. Higher thresholds (20%, 50%) are closer to the model predicted end of season, though discrepancies reach up to 10 days. High thresholds and rate-of-change methods also reliably detect the peak of season. We further show that some methods detect phenological responses missed by temperature-only type growing season, supporting the need for more realistic phenological representations in LSMs. Beyond evaluating extraction methods within a common framework, our study expands on the potential of using LSMs as sandboxes for ecological testing rather than solely predictive tools.

Heat-related mortality is increasingly attributed to human-caused climate change, but most estimates are aggregated across time and population groups, masking who carries the burden and how that distribution shifts. We estimated heat-related deaths attributable to climate change in the Netherlands from 1971 to 2019 disaggregated by age and sex using a model in which the temperature-mortality relationship changes continuously from year to year. Among adults aged 65 and over, heat caused an average of 644 deaths per warm season, of which 381 (59%) would not have occurred without global warming. This fraction rose from 46% in 1971-1994 to 79% in 2010-2019. The burden falls overwhelmingly on the oldest: women aged 80 and over account for 55% of climate-change-attributable deaths and men of the same age for 24%, leaving 21% for the 65-79 groups. Per-capita rates at these ages are now similar in both sexes, so the female burden reflects women outliving men rather than greater susceptibility. Over the study period the standardized heat-mortality rate fell by more than half, because vulnerability declined faster than exposure rose. That decline began decades before the 2007 Dutch Heat Plan, which argues against the plan as the main driver, and it followed different paths in men and women. The decline in heat vulnerability must be better understood if it is to be sustained and extended against the unprecedented heat ahead. At the same time, we must urgently limit climate change to prevent further loss of life.

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.

Climate change will alter habitat suitability for plant communities in mountain ecosystems of the Western Himalaya. There will be contrasting magnitudes and spatial configurations of these changes for different plant species, and it is an important question how to translate these contrasts into effective climate-proof multi-species conservation planning. This study assessed current habitat associations and projected changes in suitable habitat for two contrasting species, Meconopsis aculeata and Rhododendron campanulatum, in Himachal Pradesh, India. Field surveys and spatially filtered occurrence records were combined with MaxEnt modelling using 10 non-collinear environmental predictors. Habitat suitability was projected to 2050 and 2070 under Shared Socioeconomic Pathways; SSP1-2.6, SSP2-4.5 and SSP5-8.5 using HadGEM3-GC31-LL. Field observations showed contrasting habitat associations, with M. aculeata occurring mainly in open alpine herbaceous communities and R. campanulatum in subalpine woody communities. Elevation was an important predictor for both species while climatic predictors contributed differently to their relative importance and response patterns. Future projections indicated reductions in suitable habitat of approximately 25% for M. aculeata and 27-28% for R. campanulatum. Habitat-change maps showed heterogeneous patterns of loss, gain and persistence rather than uniform contraction, and centroid displacement remained small (7.7-8.5 km). We performed proximity analyses to show which currently suitable areas are most close to areas of future gain, in order to support the design of climate-proof protected areas. We discuss how interspecific differences in response are relevant to multi-species conservation planning that considers the spatial relationship between current high-suitability areas and the gain, loss and redistribution of future suitable habitat.

Forests are known to burn and desiccate under a changing climate; less appreciated is that they also drown. Across much of the globe, a warming atmosphere is amplifying the length and intensity of both droughts and floods. Whereas massive die-offs from drought and fire are widely studied as consequences of climate change on forests, inundation-induced mortality remains poorly quantified. Using approximately 1 m resolution aerial imagery and deep learning, we tracked over 260 million dead trees across the United States' Great Lakes and ocean coastlines from 2012 to 2023. Within 10 km of the coasts, more than half of mapped mortality was concentrated in just 10% of forests, primarily in high-elevation inland wetlands and low-lying forests prone to inundation. Mortality rates doubled within a decade around expanding freshwater lakes and in inland wetlands experiencing intensifying regional-scale precipitation, while tripling in low-lying coastal forests where salinization compounded inundation in sea-level rise hotspots. Our findings identify rising waters as a widespread, underappreciated driver of forest die-off that can extend far inland, underscoring the need to reassess the impacts of climate change on the functioning, demography, and carbon-methane budgets amongst the world's most carbon-rich forests as seas rise and hydroclimate shifts globally.

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.

Abstract Background: Rising global temperatures are intensifying exposure to extreme heat, yet access to air conditioning (AC) remains unequal which leaves vulnerable populations without protection against the heat. Household survey data on AC ownership is scarce in low- and middle-income countries limiting the ability to identify areas of cooling gaps. Methods: In this study, we develop a geospatial machine learning framework that integrates household data from the Demographic and Health Surveys (DHS) program with high resolution climatic, socioeconomic and demographic covariates to generate a 5km resolution prediction of AC ownership across low- and middle-income countries in Africa and Asia for the year 2023. Results: The resulting map reveals strong spatial inequalities with high predicted prevalence in wealthier and urban regions, and near zero prevalence across much of Central and West Africa and rural areas. These patterns reflect the adaptation cooling gap where those most exposed to extreme heat are least able to adapt through access to cooling. Conclusion: By producing the first continental-scale, high resolution map of AC ownership, this study lays the foundation for designing and targeting equitable cooling strategies, support heat-health planning particularly in communities most affected and informing sustainable energy policy.

1. Agriculture is the most significant driver of global land-use change, placing increasing pressure on biodiversity. Balancing rising food demand with the need to conserve biodiversity is therefore an urgent challenge, especially as biodiversity can underpin key ecosystem functions that support agricultural systems. A greater general understanding of how biodiversity influences agroecosystem function is critical for developing agricultural practices that benefit both agricultural production and wider ecosystem processes. 2. Using a global dataset of 184 biodiversity-agroecosystem function relationships covering agricultural and silvicultural systems, we developed a classification system for different biodiversity-enhancing approaches, ranging from the planting of multiple crop species to creating tree islands or flower strips, providing the first comprehensive assessment of how these approaches affect agroecosystem function. 3. We found biodiversity enhancement generally had a positive, or at least neutral, association with multiple agroecosystem functions, indicating that it contributes to agricultural sustainability. Crop diversification had the greatest impact on key agroecosystem functions compared to other practices, likely due to the low heterogeneity already present in agricultural systems and the scale at which crop diversification is applied and measured. Overall, biodiversity's largest effect was on multifunctionality, highlighting its role in supporting the simultaneous delivery of multiple agroecosystem functions. 4. Variability in relationships among systems and agroecosystem function categories suggests that no single biodiversity-enhancing approach will universally benefit all functions simultaneously. Instead, a combination of approaches, assessed through their contribution to overall multifunctionality, is likely to be most effective, ensuring that agricultural sustainability claims reflect benefits across systems. 5. Policies should incentivise biodiversity-enhancing management approaches as effective strategies to improve agroecosystem multifunctionality, benefiting both agricultural productivity and the wider ecosystem.

BackgroundVietnams reported dengue burden has risen roughly five-fold since 1990. Multi-decadal studies linking climate indices to dengue rarely separate genuine year-to-year coupling from a long-term trend the two share. MethodsWe assembled a provenance-preserving national annual dengue series (1990-2025; OpenDengue plus Ministry of Health figures) and correlated it with annual and March-May means of eight tropical sea surface temperature (SST) indices at lags of zero and one year under five trend-correction lenses: raw, linear detrending, first-differencing, socio-demographic-index residualisation and AR(1) prewhitening. FindingsCases rose 3{middle dot}5 percent annually. Seven predictors were significant at zero lag, led by the annual Indian Ocean Basin-Wide index (IOBW; Spearman +0{middle dot}534), but linear detrending removed all. First-differencing preserved six, led by spring IOBW (+0{middle dot}468), the annual Atlantic Multidecadal Oscillation (AMO; +0{middle dot}462) and annual IOBW (+0{middle dot}423); three survived AR(1) prewhitening - annual AMO and annual and spring IOBW. Spring AMO and a lag-1 Tropical North Atlantic signal (-0{middle dot}452) did not, and are hypothesis-generating. El Nino-Southern Oscillation indices failed throughout. InterpretationMost of the apparent association reflects a trend shared by warming oceans and expanding surveillance; we could not demonstrate that climate is the primary driver at this scale. Trend is not the whole story: IOBW and annual AMO persist under trend- and persistence-removing transformations. Because transmission responds to climate over weeks to months, annual averaging smooths the lags through which El Nino acts; these nulls reflect temporal scale, not climate insensitivity; usable predictors will require monthly, province-level models. FundingCenter for Environmental Intelligence, VinUniversity (project VUNI.CEI.FS_0001). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, Web of Science and Google Scholar for studies published up to May 2026 linking large-scale climate indices or sea surface temperature to dengue incidence, combining dengue, climate, sea surface temperature, ENSO, teleconnection and time-series terms with Vietnam, without language restriction. Many studies covering two or more decades reported strong correlations between basin-scale indices and national dengue counts. Most, however, relied on raw correlations or a single detrending choice, and rarely tested whether an apparent association reflected genuine year-to-year coupling or merely a shared long-term trend. Added value of this studyMost long-term studies remove the shared upward trend in only one way, or not at all. To our knowledge this is the first study to compare five trend-correction methods on a multi-decadal national dengue record and to read their agreement or disagreement as a diagnostic of which climate signals are real. A signal that appears only before the trend is removed is probably following it; one that persists is more likely real. Applied to a record spanning more than three decades, this comparison separates the two: several widely reported raw correlations weakened once the shared trend was accounted for. Implications of all the available evidenceClimate-informed analyses of multi-decadal data should report at least two trend-correction approaches alongside the raw correlation and treat their disagreement as evidence about where a signal sits, rather than operationalising raw long-span correlations. For Vietnam, the apparent national-scale association is dominated by a shared long-term trend but retains a smaller, robust inter-annual component led by the Indian Ocean and AMO signals; genuine coupling is more likely detectable at monthly resolution and provincial scale, where statistical power and physical mechanism are jointly available. Surveillance systems should retain explicit source provenance, so trend-corrected re-analysis remains possible as records grow.