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.

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.

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.

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.

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.

Background Italy experiences a substantial heat-related mortality burden, but evidence on cause-specific mortality across its heterogeneous climatic settings remains limited. We aimed to quantify cause-specific mortality attributable to summer heat in Italy at national and ecological region levels and to map its distribution across municipalities. Methods We analysed daily deaths classified by underlying cause in eight ICD-10 groups among individuals aged 18-64 years and 65 years or older across 7,864 municipalities during June-August, 2004-2023. Mortality data were linked to area-weighted ERA5-Land daily maximum temperature. Nonlinear temperature-mortality associations were estimated with Bayesian hierarchical Poisson models, fitted separately for each cause, age group, and ecological region, with municipality-level attributable fractions. Findings High temperatures were associated with mortality from a broad range of causes, particularly among individuals aged 65 years or older. Among the study population, heat-attributable fractions were highest for nervous system diseases (14.93%, 95% credible interval 14.22-15.69) and mental and behavioural disorders (14.47%, 13.55-15.39). Circulatory diseases accounted for the largest absolute burden, with 85,524 heat-attributable deaths (82,762-88,271) across the summers of the 20 years considered. Interpretation The mortality burden associated with summer heat in Italy extended well beyond circulatory and respiratory diseases, highlighting the relevance of a broader range of health outcomes. These findings support heat-health strategies that account for cause-specific and geographical differences in heat-related risk, with greater consideration of neurological and mental health outcomes alongside established cardiorespiratory risks. Funding NextGenerationEU (CUP: B53C23002660006); Imperial College Research Fellowship; NIHR Imperial Biomedical Research Center (BRC: NIHR203323).

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

Background Short-term weather conditions can trigger paediatric hospital admissions, but evidence remains limited on the health relevance of intraday temperature variability, humidity, and precipitation, and generally on which subgroups are most susceptible. Methods We conducted a nationwide case time-series study of Swedish children and adolescents aged 6-18 years using linked register data. Associations between daily mean temperature, intraday temperature variability, relative humidity, and precipitation and inpatient hospitalisations were estimated with Poisson regression (individual fixed effects) combined with distributed lag non-linear models over lags 0-21 days. Outcomes included all-cause hospitalisation and cause-specific admissions. Effect modification was examined by sex, age group, socioeconomic indicators, region, and calendar periods. Findings Among 4,638,247 children and adolescents in the cohort, 1,070,702 (23.1%) experienced at least one inpatient hospitalisation, contributing 1,736,169 hospitalisation events. All-cause hospitalisation increased with higher mean temperature, with risk highest at hot extremes. Intraday temperature variability showed a U-shaped association, with higher rates at both lowest and highest exposure levels. Relative humidity showed no association with hospitalisation risk. Higher precipitation was associated with increased hospitalisation risk, but estimates at high rainfall levels were imprecise. Stratified analyses indicated modest differences by sex and socioeconomic indicators, but clearer heterogeneity by age and region, with the highest risks among younger children and in East Sweden; associations varied across calendar periods and were imprecise for most cause-specific outcomes. Sensitivity analyses mostly showed that the main curve shapes were robust. Interpretation Short-term exposure to hot temperatures, both low and high intraday temperature variability, and higher levels of precipitation were associated with an increased risk of all-cause hospitalisation among children in Sweden, with clearer variation by age group and region than by sex or socioeconomic position. These findings support targeting paediatric climate-health preparedness and early-warning systems toward younger children and higher-risk regions such as eastern Sweden and extending such systems beyond mean temperature to include intraday temperature variability, a distinct, currently overlooked driver of risk that heat-focused systems are not designed to capture. Fundings This research was funded by Formas the Swedish Research Council for Sustainable Development (grant no. 2023-01774 and 2022-01845) and the Swedish Research Council (2025-02804 and 2022-06599).

Soil degradation is a widespread challenge that requires a broad response at the individual farm level. To ensure effectivity, the practices should be tailored to the farm context: land manager objectives and farm specific challenges. These have however been difficult to quantify. Here we demonstrate that a workable farm context can be created based on a value survey, open satellite and soil data, and published models for vegetation gross primary productivity and soil erosion. Based on the findings, despite individual differences, farmers value profitability and operational efficiency, but also biodiversity and soil health. At least the regenerative farmers surveyed also value working for the greater good more than maintaining tradition or power. In spite of wide differences in farm production orientation, we also found that each farm also had a broad variation in individual fields GPP. Most fields have a stable GPP level, which is either high or low, and that there is a 2-3-fold difference between the weakest and best producing fields indicating the potential for improving GPP by improving the growing conditions on currently weak fields. In addition, soil loss was found to be highly concentrated in critical source areas, where 10% of the field area contributed to 50% of the soil loss. Overall, open data can be linked to modelling workflows to rapidly produce a decision-making context for farmers. This facilitates benchmarking and co-learning as well as enables land managers and advisors to identify the farm context for planning effective responses to soil degradation.

Globally, contaminated soils pose serious risks to agriculture, biodiversity of ecosystems, and human health. Although a variety of monitoring and remediation methods have been developed over the years, there remains a lack of theoretical modelling to better understand the impact of contamination on soil carbon in a microbially-mediated system. In this study, we develop a model which incorporates contaminated soil organic carbon, uncontaminated soil organic carbon, dissolved organic carbon, microbes, extracellular enzymes, and CO2, to explore how different amounts of contamination and modes of contamination (pulse and press) affect microbially-mediated carbon flux within both vegetated and barren environments. The model represents contamination as a pool of contaminated soil organic carbon that is more resistant to microbial degradation than uncontaminated soil organic carbon. The results show that, in barren environments, the amount and type of contamination has no notable impact on the soil system. In contrast to this, in vegetated environments, plants mitigate the impact of large contamination inputs with differences in outcome depending on the amount and type of contamination.

Brazil has one of the highest dengue burdens globally, with transmission influenced by climate events. Yet, the effects of compound climate extremes on dengue risk and prediction have not been quantified to date. Using a Bayesian spatiotemporal model of monthly dengue cases across 435 health regions from 2010 to 2025, we estimated relative risk associated with interacting long-lag and short-lag drought indicators and temperature anomalies across nine Koppen-Geiger climatic zones, adjusting for absolute temperature and El Nino-Southern Oscillation. Long-lag dry, mid-lag warm and short-lag wet conditions were associated with the highest relative risk in five climatic zones, with temperature anomalies modifying hydrometeorological dynamics more strongly in temperate than tropical areas. Climate covariates improved the prediction of case counts and outbreaks compared to baseline models at lead times of 1, 3, 6 and 12 months, and for the upcoming season 5 months ahead. Improvements were greatest in the Northeast, Southeast, and Midwest. Recent dengue incidence provided the greatest improvement at the 1-month lead, whereas combining incidence with climate information performed best at 3- and 6-month leads. We extended our framework to weekly state-level predictions of the season ahead to align with national public health planning. We proposed a multi-staged dengue forecasting approach to enhance early warning and response across Brazil.

1. Grassland ecosystems are pivotal for global biodiversity, carbon (C) storage and food security. This biome, however, together with the ecosystem services it provides, is threatened by global change perturbations. These perturbations rarely occur in isolation and most often affect environments simultaneously, which is a pressing issue in ecology. Nevertheless, most of the research conducted so far has focussed on single simultaneous perturbations or pairs thereof. The few examples considering the effect of more than two simultaneous perturbations focussed only on plants or soil separately, which ignores the potential buffering or feedback effects on ecosystem functioning induced by plant-soils interactions. 2. To tackle this knowledge gap, we used an experimental setup involving mesocosms (soil and plants intact) collected in an extensively managed permanent grassland. Mesocosms were arranged in a greenhouse experiment where we applied individual and combined global change perturbations (up to ten in total) including application of fertilisers, pesticides and antibiotics, simulated trampling and grazing, heat stress and drought. Measured ecosystem functions and properties included a large set of biological, physical and chemical soil and plant associated variables. 3. Results showed that multiple perturbations in most cases had negative effects irrespective of the direction found for individual perturbations. However, in contrast to previous bare soil experiments, we only found scarce evidence for substantial synergistic effects of simultaneous perturbations in our experiment conducted with mesocosms that contained vegetation. This suggests a mitigating impact of plants on soil responses to global change. Moreover, we found a strong dominative effect of drought on ecosystem functioning, driving most of the effects observed. 4. Synthesis: Our results highlight the importance of evaluating global change impacts under more realistic settings, i.e., applying multiple perturbations simultaneously and including plant-soil interactions. Mitigation of global change in terrestrial ecosystems will only be possible once we holistically understand how ecosystems respond to altered conditions.

Background: Non-optimal air temperature increases the risk of preterm birth, but how temperature-related risks may translate into future population burden remains uncertain. We aimed to estimate the association between temperature and preterm birth in Sweden and assess how preterm birth burden may change under plausible future temperature conditions. Methods: We conducted a nationwide population-based cohort study of singleton births in Sweden from 1973 to 2018. Weekly mean temperature was assigned at the parish level according to maternal residence. Cox proportional hazards models with a distributed lag nonlinear model were used to estimate nonlinear and delayed associations between temperature and preterm birth. The estimated exposure response relationship and baseline hazards were then incorporated into an individual-based microsimulation of gestational progression under projected temperatures for 2025 to 2055. Future projections were examined across multiple climate models under SSP1-2.6 and SSP3-7.0 scenarios. Outcomes included the annual number and cumulative incidence of preterm births and the temperature-attributable fraction. Results: Among 4,338,825 singleton births, 211,397 (4.9%) were preterm. Temperature preterm birth associations were modest overall. The clearest increase in hazard was observed following exposure to very low temperatures, particularly -10C at short lags. Higher temperatures showed no clear increase in risk. From 2025 to 2055, the annual cumulative incidence of preterm birth and the temperature-attributable fraction was expected to remain broadly stable, with limited differences between emissions scenarios. In contrast, the absolute number of preterm births was expected to over the projection period, primarily reflecting increases in the projected number of pregnancies rather than substantial changes in temperature-associated risk. Conclusions: Very low temperatures were associated with a modest short-term increase in preterm birth risk in Sweden, hot temperatures showed little evidence of increased risk. Applying these associations to future climate conditions suggested limited changes in preterm birth incidence or the proportion attributable to temperature through 2055. However, the absolute burden of preterm birth may increase as the population at risk grows. This highlights the importance of considering demographic change alongside climate projections when assessing future maternal and neonatal health burden.

Introduction: With global warming, heatwaves are increasing in severity, affecting species worldwide. Recent studies show associations between heat exposure in pregnancy and adverse birth outcomes such as preterm birth, low birthweight and stillbirth across multiple species. This suggests potentially conserved mechanisms such as maternal inflammation, dehydration and changes in placental physiology in response to heat exposure. Moreover, the gestational timing of antenatal heat likely impacts the type and severity of outcome, yet this is not fully understood. Methods: We conducted a scoping review of studies on heat exposure and pregnancy outcomes in humans and other animals, to identify common mechanisms and temporal dynamics of effects. Temperature-, trimester- and species- specific patterns were reviewed across 197 studies and ten species (human, cow, sheep, pig, goat, rat, mouse, guinea pig, lizard and rabbit). Results: Qualitative trends indicate that high temperatures are associated with adverse birth outcomes. In particular, third trimester exposure increases risk of preterm birth and low birthweight. Many studies emphasise placental involvement in adverse outcomes and point to potential aberrations in placental physiology or cortisol signaling pathways. Conclusions: Overall, literature presents that heat exposure increases risk of adverse birth outcomes across studies, and taxa. Understanding the relationship between heat exposure and adverse birth outcomes, as well as the mechanisms and timing, is vital for informing public health policies and creating mitigating strategies for expecting mothers.

Subsurface tile drainage rapidly transports nutrients and biological contaminants to surface waters, yet multi-year evaluations of drainage water management (DWM) effects on nutrients, Escherichia coli, and antibiotic resistance genes (ARGs) remain limited. A paired-field design with high-frequency soil moisture monitoring was used to quantify water yield, dissolved nitrogen, phosphorus, and Escherichia coli (E. coli) over seven and eight water years, and ARGs (tetM, tetW, ermB, sul1) over two years in southern Michigan. Soil moisture at tile depth (~75 cm) was higher under controlled drainage, confirming that DWM increases field water-holding capacity. Compared to the free drainage field, DWM reduced cumulative tile discharge by 43% and annual nutrient yields by 46-83% for nitrate and 12-82% for soluble reactive phosphorus, but effects on ammonium were variable. Average daily nutrient yields declined by ~50% over the study period, driven primarily by managed periods. Nutrient concentrations did not differ between treatments, indicating that yield reductions resulted from decreased water loss. Mean daily E. coli yields declined by 71% over the study period, and concentrations were 46% lower under controlled drainage. Controlled drainage reduced tetM and sul1 ARG concentrations and yields during managed periods and throughout the study; however, sensitivity analyses showed that these effects were strongly influenced by a high-precipitation event. After excluding the event, differences in ARG concentrations were no longer detected, although ARG yields remained 43% lower during managed periods. These findings highlight the potential for DWM to mitigate nutrient, microbial, and antibiotic resistance transport from agricultural drainage systems.

Forest canopy height is sensitive to shifts in demography (which affect tree size distributions and abundances of short-vs. tall-statured species) and allometry. Demographic and allometric effects on canopy height have not previously been studied across a large region. We analyzed more than two million individual tree measurements from about 60,000 remeasured inventory plots in non-plantation forests across 19 ecoregions of the conterminous United States to quantify changes in aboveground live tree biomass (AGB), canopy height, and its demographic and allometric components from ~2003-2025. We tested the hydraulic limitation hypothesis, which predicts decreasing tree height with increasing hydraulic stress. AGB was strongly correlated with canopy height across ecoregions, and AGB change was strongly correlated with canopy height change over time. Canopy height change was positive in nearly all eastern ecoregions but varied in sign across western ecoregions. Demographic shifts led to increased canopy height in most eastern ecoregions but decreased canopy height in most western ecoregions. In contrast, allometric shifts, averaged across species, caused either positive or no significant change in ecoregion-mean canopy height. Our results were consistent with several predictions of the hydraulic limitation hypothesis: (i) intraspecific allometric shifts were more positive for understory than for canopy trees, (ii) across ecoregions, canopy height was positively correlated with precipitation and negatively correlated with vapor pressure deficit, and (iii) canopy height change over time was positively correlated with precipitation change. However, our results suggest modifications to the hydraulic limitation hypothesis, because allometric shifts tended to be positive, even in ecoregions with decreasing precipitation and increasing vapor pressure deficits. Such allometric shifts, which seem counter to the hydraulic limitation hypothesis, may be caused by CO2-driven increases in water-use efficiency. Whatever their cause, allometric shifts should be accounted for to avoid biased estimates of canopy height change and biomass change.

Forests regulate climate and sustain biodiversity, but their resilience is increasingly threatened by drought-induced hydraulic failure, when xylem water transport is impaired by embolism. A critical but poorly constrained determinant of this risk largely due to limited observations of deep root water uptake is the amount of root-accessible (sub)soil water storage (SR), which governs land-atmosphere exchanges during prolonged dry periods. While ecohydrological theory has long suggested a balance between soil water storage in the rooting zone, vegetation growth and drought tolerance, this concept has not yet been integrated into predictive models of hydraulic failure. Here we propose a novel, process-based inversion framework that integrates ecohydrological optimality theory with principles of plant hydraulic to infer SR. Using the SurEau plant hydraulic model, we estimate the SR value that balances the costs of soil exploration with the avoidance of drought-induced hydraulic damage. Applied first to a well-instrumented Mediterranean Quercus ilex forest, the inferred SR closely matched independent neutron probe and eddy covariance measurements and accurately reproduced observed drought responses, including leaf water potential and sap flow dynamics. We then scaled the approach across European forests remotely-sensed data, to assess spatially explicit SR estimates and associated hydraulic failure risk. Across more than 20 species and sites, inferred SR and drought stress metrics aligned with field observations and outperformed estimates based on conventional soil databases or remote-sensing-only approaches. By linking plant physiology and ecohydrological theory within a scalable inversion framework, this approach improves predictions of forest drought risk under climate change.

Abstract Background Estimates of the healthcare costs associated with influenza treatment are essential for cost-effectiveness analyses that inform preventative and treatment strategies such as vaccination and non-pharmaceutical interventions. Existing evidence varies across treatment settings, age groups, and countries and there is little data from low- and middle-income settings. Methods We conducted an umbrella review of systematic reviews and meta-analyses reporting direct admitted and non-admitted hospitalisation costs of seasonal influenza treatment in children, adults, and older people. We supplemented an evidence gap with a systematised review of studies reporting adult non-admitted treatment costs. Costs were extracted and adjusted to 2023 USD. We fitted a Bayesian mixed-effects model to estimate influenza treatment costs using national economic indicators, age group, and treatment setting as a priori predictors. We predicted admitted and non-admitted treatment costs for children, adults, and older people in 195 countries and territories. Results Seven reviews were included in the umbrella review, supplemented by 17 primary studies for adult non-admitted care. Extracted data points most frequently described admitted treatment costs and came from high-income countries. The best-fitting model used healthcare expenditure per capita to predict treatment costs, with country-level random effects. Estimated costs increased with healthcare expenditure per capita, were higher for admitted than non-admitted care, and were highest for adults and lowest for children. Conclusions This study provides novel estimates of influenza-associated healthcare costs across age groups and treatment settings by synthesising the available global evidence. By extrapolating to settings without data, our estimates can inform future economic evaluation studies of influenza interventions worldwide.

Background: Previous studies indicated that regional fluctuations in COVID-19 impact were associated with socioeconomic, demographic and environmental factors early in the pandemic. It is unclear whether factors associated with regional COVID-19 mortality and SARS-CoV-2 infections changed over the course of the pandemic. Methods: We evaluated the association of ecological factors with municipality-specific COVID-19 mortality in the national population of Austria between January 2020 and June 2023. Analyses were conducted separately per calendar year and pooled across years via random-effects meta-analysis. Outcomes were analysed using univariable and multivariable zero-inflated negative binomial (ZINB) regression, including annually averaged environmental exposome exposure (including air pollution and UV radiation), socioeconomic, demographic and infection dissemination predictors with significance threshold set to p<0.005. Secondary analysis modelled SARS-CoV-2 infections using of negative-binomial model (NB). Two additional approaches for smaller predictor set selection were applied as sensitivity analyses. Results: Within the entire observation period 18,430 COVID-19 deaths were reported. In univariable analyses (pooled RR (99.5% CI)), COVID-19 mortality was significantly associated with the proportion of commuters (0.79 (0.73-0.86)), residents aged 75-84 (1.18 (1.01-1.37)) and [&ge;]85 years (1.29 (1.04-1.60)), residents with at most compulsory education (1.15 (1.02-1.29)), employed residents aged 15-64 (0.89 (0.85-0.93)), and homeowners (0.91 (0.83-0.99)), as well as with precipitation (0.94 (0.89-0.99)) and the average time with a mean temperature of 5-15 C (0.95 (0.90-0.99)). In multivariable analyses the share of population aged 85 or older (1.30 (1.09-1.54)) and home ownership (0.92 (0.87-0.98)) were independently significant. Mortality predictor estimates were relatively stable across pandemic years. Across 5,963,882 documented SARS-CoV-2 infections share of employed in 15- to 64-year-olds (1.01 (1.01-1.02)), part-time employment rate (1.01 (1.00-1.02)) and district level nursing care allowance recipiency (0.97 (0.95-0.98)) were associated with detected infections. Overnight stays, population 85 or older, income, compulsory education or less and air pollution all had multiple significant estimates with opposite directionality, leading to non-significant overall associations. Sensitivity analyses outcomes mirrored main analyses outcomes. Conclusions: Throughout the pandemic in Austria, demographic and socioeconomic factors were associated with COVID-19 mortality and infections, but many associations, especially for infections, were unstable over time. These findings underscore the importance of structural vulnerabilities in epidemic impact.