Thinning partially offsets climate change-induced decline of Pinus koraiensis volume in broadleaf-Korean pine mixed forest in Northeast China based on transition matrix growth model simulations

Climate-driven deepwater deoxygenation is a growing global concern for lake ecosystems. We developed a simple 1-D deepwater oxygen profile model to understand the underlying physical mechanisms and to quantify the required climate-adaptive interventions. It was applied to Lake Erken, Sweden, using hydrodynamic forcing under three Representative Concentration Pathway (RCP) scenarios. From 2020 to 2099, the annual anoxic factor (the number of days when the anoxic sediment area equals the surface area) projections show non-significant trends under RCP2.6, while increasing by 0.4 and 0.6 days year -1 decade -1 under RCP6.0 and RCP8.5, respectively. This climate-driven future deoxygenation, consistent across multiple oxygen metrics, mainly stems from prolonged stratification. To mitigate climate impacts by 2100, oxygen depletion rates, as a proxy for eutrophication, would need to be reduced by approximately 9–13%, 20–24% and 26–35% under RCP2.6, RCP6.0 and RCP8.5, respectively. This data-efficient framework can be applied to physically-dominated, seasonally stratified lakes. • A simple mechanistic model was developed for simulating deepwater oxygen profiles • Oxygen dynamics were mechanistically studied under different climate scenarios • The estimated reduction in oxygen depletion rate, could guide long-term mitigation • Prolonged stratification plays a prominent role in future anoxia progression • 35% lower oxygen depletion rate may mitigate future deoxygenation in Lake Erken

Understanding the nuances of climate change on buildings in desert areas is a timely topic with several socioeconomic implications. This study quantifies the impacts of future climate variability and extreme events on the optimal design of renewable energy systems (RES) for buildings in Saudi Arabia. A comprehensive framework integrating multi-model climate projections, extreme event analysis, and stochastic optimization is developed to evaluate system reliability and economic performance under future climate uncertainty. Hourly downscaled climate data spanning 2025–2099 across multiple Shared Socioeconomic Pathways (SSP) are considered, and stochastic optimization is used to determine RES configurations that minimize life cycle cost (LCC) across all scenarios. Analyses are conducted at hourly resolution over three future 25-year periods: 2025–2049, 2050–2074, and 2075–2099. Results indicate that differences between climate scenarios become more pronounced in the late-century period (2075–2099), leading to increased sensitivity of optimal RES design to climate conditions. The stochastic design ensures higher operational reliability than systems optimized solely for a high-emission scenario, with a 3.1% increase in LCC. Relative to the sustainable scenario, the high-emission scenario requires larger RES capacities, particularly in battery storage, resulting in a 20% higher LCC. Accounting for extreme events from an additional 24 scenarios from multiple climate models increases the LCC by 31.7%, highlighting the cost of resilience in an uncertain future climate. • Climate change affects building energy demand and renewable supply in desert areas. • High-emission pathways increase life cycle costs by 20% compared to sustainable ones. • A stochastic-based design improves system reliability with only a 3.1% cost increase. • Extreme weather events across 24 scenarios increase system costs by 31.7% • The best renewable system design is highly sensitive to late-century climate shifts.

Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios

Increasing attention has focused on the capacity of current global food systems to provide accessible, affordable, and sustainable food to a growing human population, particularly amid ongoing climate and environmental changes. Concerns about the dysfunction of the global food system have led to the development of several initiatives to estimate current and predict future global nutrient supplies based on various climate, production, and demand scenarios. Yet none adequately accounts for differences in nutrient bioavailability across food groups. As nutrient bioavailability varies substantially between plant-source foods (PSFs) and animal-source foods (ASFs), accounting for these differences has important implications for global nutrient supplies and the environmental costs associated with their production. In this perspective, we highlight the variability in estimated bioavailabity across PSFs and ASFs for 27 key nutrients and the limited accounting for bioavailability in major studies and nutrition recommendations. We conclude with a discussion of current best practices, highlighting avenues for future research to account for bioavailability and to more accurately evaluate and propose nutritionally adequate diets. This perspective suggests that, although existing data limitations should not preclude food systems researchers from accounting for bioavailability, a concerted effort is needed to develop more consistent and representative estimates of bioavailability across a variety of nutrients.

Wetland ecosystems, crucial for carbon sequestration and coastal hazard mitigation, have experienced tremendous losses in land surface area over the last century, primarily due to land reclamation. This has led to increased rates of land subsidence in regions with high levels of reclamation, causing heightened vulnerability in these areas under anticipated scenarios of climate induced sea-level rise. This study integrates multi-sensor satellite remote sensing (optical, thermal, and active microwave) with spatially explicit eddy covariance flux measurements to model gross primary productivity (GPP) in restored wetlands of California's Sacramento-San Joaquin Delta. GPP is a crucial process in this context, as it impacts the potential for wetlands to act as land carbon sinks and has been shown to reverse land subsidence in restored wetlands of previously reclaimed areas. Still, there remain gaps in understanding how vegetation vigor, wetland composition and structure, and environmental conditions individually and interactively impact carbon assimilation in these ecosystems. This research aims to understand how complementary remotely sensed signals from multiple satellite platforms across the electromagnetic spectrum combine to improve classical, optically based GPP models, while determining the relative importance of certain biotic and abiotic environmental conditions that regulate GPP. Using a Bayesian generalized additive modeling framework, we evaluated how vegetation vigor (NDVI), canopy structure and biomass density (microwave backscatter), and land surface temperature affect wetland GPP at 10-m spatial resolution over a five-year period. Our results reveal a strong hierarchical and complementary influence of these variables, with the highest GPP occurring in warm, well-watered, and densely vegetated conditions. The model explained on average 66% of GPP variability and provides a scalable, open-access framework for assessing carbon fluxes in wetland landscapes. These findings offer valuable insight into planning restoration, monitoring restoration outcomes, carbon accounting, and identifying coastal adaptation strategies for valuable blue carbon ecosystems. • We develop a wetland GPP model using eddy covariance and remote sensing data fusion. • Hierarchy of wetland GPP drivers revealed through remotely sensed observations. • Wetland GPP shows greatest sensitivity to interaction of drivers. • Hierarchical model enables informed upscaling of wetland GPP to landscape level.

WRF-based hybrid model for high-resolution prediction of urban thermal-wind environments under present and future climate scenarios

Extreme climate conditions increasingly threaten worldwide coastal biodiversity. We applied a trait-based approach to quantify the tolerance thresholds of 13 mollusk species from five tropical coastal habitats, using controlled experiments that simulate extreme temperature, salinity, submersion, and desiccation stress. Survival was analyzed in relation to functional traits, including isolation from ambient conditions (presence/absence of shell closure and/or operculum), respiration mode (branchial or pulmonate), shell thickness, and habitat type. Thermal safety margins (TSMs) were also projected under future climate scenarios. Species that possess isolation traits consistently survived across stress conditions. Bivalves generally tolerated higher temperatures and broader salinity ranges than gastropods, and pulmonate gastropods experienced high mortality under both prolonged submersion and desiccation. Desiccation tolerance was strongly associated with the ability to isolate. Thicker shells provided limited protection against extreme temperatures but did not protect taxa with narrow salinity tolerances. Projected TSMs indicated that species from mangrove and oyster-bank habitats will be subject to temperatures that exceed their upper thermal limits before the end of this century. Vulnerability to climate change arises from a combination of functional traits and habitat context. By addressing multiple climate-related variables, i.e., temperature, salinity, inundation, and desiccation, all of which are shifting with global climate change, this study identified combinations of traits that will confer tolerance to tropical mollusks under future conditions and identified taxa that will likely be at risk. This study fills critical data gaps for tropical habitats worldwide and provides a framework for predicting biotic responses to climate extremes.

The Ems Estuary faces existential challenges including flood risk, increasing turbidity, and biodiversity loss, all of which may intensify under future climate scenarios and require transboundary collaboration between the Netherlands and Germany. Addressing these challenges requires compliance with EU, national, and local regulations. Simultaneously each nation pursues socioeconomic benefits from the restoration through a holistic, system-based approach. This study synthesizes the key processes driving flood risk, hyper-turbidity, and salinization within the Ems Estuary. From this understanding the paper catalogues the planned and implemented pilot measures from both countries to advance their climate adaptation plans. Both nations share a common vision of leveraging the high turbidity of the estuary as an asset in climate adaption, e.g. for land raising, dyke reinforcement or habitat creation. Building on the pilot projects and shared visions, three transboundary upscaling strategies involving sediment management are proposed: (A) land elevation using dredged sediment; (B) multifunctional flood defences incorporating nature-based solutions; and (C) habitat creation and restoration to enhance ecological resilience. The Ems Estuary offers valuable insights for global transboundary estuarine management, illustrating how innovative sediment management and transboundary cooperation can be achieved to support climate adaptation and sustainable development. The study underscores the need for harmonized governance, standardized success metrics, and cross-border planning to enable effective upscaling.

Sensitivity-informed climate-responsive optimization: PCM-integrated office building envelopes assessed under future climate scenarios

Habitat-based BART models for cetaceans in the western South Atlantic: current and future distribution under climate change scenarios.

Lentil ( Lens culinaris M. ) and chickpea ( Cicer arietinum L. ) are valuable grain legumes that reduce dependency on synthetic fertilizers, enhance soil health, and diversify crop rotations, improving resilience to climate variability. However, their adoption in Europe remains limited due to irregular yields caused by biotic and abiotic stresses. Process-based soil-crop models can provide insights into the soil-plant-atmosphere dynamics of these legumes and support the development of innovative cropping systems. This study presents the first species-specific parameterization of the agro-ecosystem model MONICA for lentil and chickpea in Europe addressing a critical gap in simulating the growth of minor crops. A recently proposed generic calibration protocol was adapted and applied to real multi-environment field data. Following this structured protocol, 27 crop-related parameters were calibrated using literature-derived values, direct measurements, and mathematical optimization. Calibration utilized a unique multi-variable data from 28 site-year-management units (SYMU) for lentil and 24 for chickpea across diverse European climates along a latitudinal gradient. Evaluation utilized an independent dataset of grain yields encompassing numerous SYMU. After calibration, MONICA successfully simulated phenology, aboveground biomass, plant nitrogen concentration, grain yield, plant height, leaf area, and soil moisture, achieving acceptable model efficiency (0.32 to 0.98) and low relative bias (−7% to 3%) for most variables. However, it underpredicted yield in high-performing SYMU and showed greater errors for dynamic variables. Improvements in simulating biomass partitioning, physiological maturity, and detailed soil data are needed to simulate grain yield more accurately for future climate change adaption studies. This study provides a robust framework for parameterizing new crops and evaluating the agronomic performance of lentil and chickpea under diverse climatic scenarios, contributing to design sustainable cropping systems. • An agro-ecosystem model was calibrated for lentil and chickpea via a structured workflow. • Calibration: IA 0.32–0.98; relative bias − 34% to 28% across variables. • Protocol enables parameterizing new crops in generic crop models. • Yield underestimated in high-yielding site-years. • Correct maturity timing is crucial for end-season yield and other outputs.

ABSTRACT Climate and land‐use change threaten the persistence of Africa's cave‐roosting bats, yet their future distributions remain poorly understood. We present the first continental‐scale assessment of climate‐driven range shifts for cave‐dwelling bats in sub‐Saharan Africa, using Miniopteridae as an obligate cave‐dependent model taxon, by integrating species distribution models with a validated karst‐based roost suitability layer and a landscape‐stability index reflecting projected land‐cover vulnerability. Using 551 curated occurrence records, MaxEnt models under six CMIP6 climate scenarios projected substantial range contractions of 36%–64% by 2061–2080, with future suitability restricted mainly to southern Africa, the eastern highlands, and Madagascar. Geological constraints further limited persistence: only ~5% of roost‐suitable karst overlapped with future climatically suitable areas, and this overlap decreased to ~2% after weighting for land‐cover vulnerability. Similarity metrics revealed extremely low spatial congruence between roost availability and stable climatic refugia. Migration ecology suggests that fragmentation of seasonal roost networks may further hinder persistence by disrupting movement corridors and increasing energetic costs. Collectively, these results show that long‐term persistence of cave‐dwelling species like Miniopteridae will depend on conserving climate–karst refugia and maintaining connectivity between seasonal roosts.

Phortica okadai and Phortica variegata are the primary vectors of the zoonotic eyeworm Thelazia callipaeda, which infects humans and various mammals. Climate change and intensified human activities have altered the potential suitable habitats of these vectors, posing a risk of expanded T. callipaeda transmission. This study aims to predict the current potential suitable habitats and future distribution patterns of the two species, providing a scientific basis for vector-borne disease prevention and control. Species occurrence records were compiled from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/) and systematic literature reviews. The MaxEnt model was utilized to identify key environmental determinants influencing vector distribution. Climate data from WorldClim, future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), elevation data, and Human Footprint Index (HFP) were integrated to predict potential suitable habitats and future distributions (2041-2060) across China and Europe. The key environmental drivers for P. okadai are warmest quarter precipitation, HFP, and temperature seasonality, and for P. variegata they are HFP, coldest quarter precipitation, and temperature annual range. Currently, the suitable habitats of P. okadai are concentrated in central, eastern, and northeastern coastal China, with only sporadic low-suitability patches recorded in Europe. P. variegata exhibits a wide distribution across the UK, France, Belgium, and Italy, with nearly the entire Mediterranean coastal belt and its associated offshore islands falling within its suitable range. Under future climate scenarios, the suitable area of P. okadai is projected to expand significantly in Central/Western Europe (Italy, Austria, Switzerland, and western Russia). In contrast, the suitable habitats of P. variegata will shift significantly: The central-southern-eastern European transitional belt will lose almost all suitable habitat across scenarios, while the Mediterranean littoral and its offshore islands remain climatically suitable. The suitable area for P. okadai is projected to increase significantly, whereas that for P. variegata is expected to decline. Temperature and precipitation emerge as primary drivers of these contrasting distribution shifts. These findings underscore the need for enhanced vector surveillance and control strategies for T. callipaeda, particularly regarding the expanding P. okadai populations in Europe.

This research investigates the impact of climate change on flood susceptibility assessment using four advanced deep learning models; Deep Learning Neural Network (DLNN), Artificial Neural Network (ANN), Deepboost, and XGBoost; across different climate projections for the year 2100. The study incorporates climate scenarios under three Shared Socioeconomic Pathways (SSPs); SSP 245 (moderate emissions), SSP 370 (high emissions), and SSP 585 (extreme emissions). Each model demonstrates distinctive strengths in flood risk prediction, with XGBoost offering a balanced and precise classification of flood-prone areas, while DLNN and ANN tend to highlight more extensive high-risk zones. Deepboost adopts a conservative approach, minimizing false positives but potentially underestimating the extent of flood susceptibility. Variables importance analysis shows that rainfall, slope, and land use/land cover (LULC) are critical factors influencing flood risk. The climate projections from the four models—ACCESS, CMCC-ESM2, MIROC6, and MRI-ESM2 show a clear trend: as emissions increase from SSP 245 to SSP 585, flood risks escalate significantly. Under SSP 585, regions considered moderate risk may face severe flood susceptibility by 2100. Under the SSP 370 scenario, flood susceptibility zones expand significantly, with many areas shifting from Moderate or Low to High or Very High risk, highlighting increased flood threats under intermediate climate change. In the more extreme SSP 585 scenario, widespread regions face elevated flood risks, indicating severe future impacts without strong emission reductions. The findings highlight the need for robust flood adaptation strategies, with XGBoost offering a balanced approach for urban planning and DLNN and ANN providing detailed high-risk zone identification for targeted mitigation efforts. This underscores the urgency of global emission reduction efforts to mitigate the worst effects of climate change.

Due to changes in temperature and precipitation patterns, aquatic and semi-aquatic plant species are seriously threatened by climate change. This study evaluated how Marsilea minuta L., a small aquatic fern found in tropical and subtropical wetlands, would be affected by climate change across geographic regions. Maximum Entropy (MaxEnt) was used to simulate species distributions using 963 spatially filtered occurrence records and five bioclimatic variables (BIO1, BIO2, BIO6, BIO12, and BIO13), selected after a thorough multicollinearity analysis. The BCC-CSM1.1 general circulation model was used to anticipate future climate scenarios for 2050 and 2070 under Representative Concentration Pathways (RCP) 2.6 and 8.5. The model showed outstanding prediction ability (AUC = 0.91, TSS = 0.71). According to current distribution modeling, M. minuta has a limited climatic niche that is focused between 30°N and 30°S, with South Asia, Southeast Asia, and equatorial Africa providing the best habitat. The most significant predictor was found to be the annual mean temperature, which was followed by precipitation variables and the lowest temperature of the coldest month. With net habitat losses ranging from 7.3% under RCP 2.6 (2050) to 17.2% under RCP 8.5 (2070), future predictions showed progressive range contractions across all scenarios. The gains were limited to isolated areas at higher latitudes, whereas habitat losses were concentrated at range edges. According to limiting factor analysis, the minimum temperature of the coldest month limited 28.3% of areas, mostly at higher latitudes, whereas annual precipitation limited dispersion throughout 34.7% of the investigated areas. The Congo Basin and South Asia were found to be possible climate refugia that might sustain stable, favorable conditions in a variety of scenarios. According to response curve analysis, ideal conditions include low diurnal temperature ranges, frost-free winters, high wet-season precipitation surpassing 1200mm, and an annual mean temperature of 20-25°C. These findings emphasize M. minuta susceptibility to climate change and the necessity of proactive conservation measures, such as safeguarding recognized refugia. Improvement of wetland connectivity and incorporation of climate factors into more comprehensive wetland management initiatives. Because losses under high-emission scenarios significantly outweighed those under strict mitigation paths, the projected range reductions highlight the crucial relevance of greenhouse gas mitigation in limiting biodiversity consequences.

Understanding the habitat requirements of imperiled flora is critical for informing ex situ conservation practices, designing effective reintroduction strategies, and understanding how climate change will impact such species, especially in montane regions with high levels of environmental heterogeneity. In southern Appalachia, USA, the mountain sweet pitcher plant (Sarracenia rubra subsp. jonesii) and mountain purple pitcher plant (Sarracenia purpurea var. montana) inhabit overlapping ranges. These taxa rarely co-occur in the same mountain bogs but frequently hybridize at sites where they do co-occur. We assessed patterns of climatic niche differentiation in these imperiled taxa to explore whether they naturally co-occur or may have been brought into secondary contact through human translocations. In addition, we constructed ecological niche models to evaluate the comparative availability of suitable habitat for each taxon under present and future climates. Sarracenia purpurea var. montana inhabits higher elevation habitats than Sarracenia rubra subsp. jonesii, and these differences in elevation correspond with differences in climatic niche. Under the most likely future climate scenario, models predicted that habitat matching the climatic niche of Sarracenia rubra subsp. jonesii will decline in area by 95% by 2080, while habitat matching the climatic niche of Sarracenia purpurea var. montana will increase in area by 13%. Despite high spatial overlap, these two related taxa exhibit divergent climatic niches, resulting in highly different management needs and conservation approaches. We raise concerns about the future of mountain bog plant assemblages, and the rare species they include, under climate change.

Climate change will impact the distribution of daily deaths in Austria until the end of the century. This study examines the net effects of fewer cold and more-frequent hot days on daily mortality under different climate and demographic scenarios. Projected district-level mortality data and daily temperatures based on Representative Concentration Pathways (RCP4.5 and RCP8.5) are analyzed to estimate the number of attributable deaths for every fifth year due to heat and cold using district-wise temperature–effect estimates from a previous analysis. While the overall shape of the time course of temperature-attributable deaths depends mostly on the demographic developments (with the highest numbers of daily mortality mid-century), under all climate scenarios investigated, the increase in heat-attributable deaths will be more pronounced than the decrease in cold-attributable deaths. Contrary to common claims, shift in temperatures due to climate change already has a net negative effect on population health in Austria now.

Forests are complex socio-ecological systems that deliver key ecosystem services such as biodiversity conservation, water regulation, carbon storage and timber production. Increasingly, traditional timber-focused management is giving way to ecosystem-based approaches that incorporate plant traits into forest management plans. This shift supports biodiversity conservation, improves ecosystem resilience and promotes long-term forest health. Plant functional traits such as wood density, leaf size, root depth and specific leaf area, play a key role in regulating ecosystem processes and elucidating interactions between tree species and their environment. This review summarises current evidence that trait-based frameworks improve forest management, support climate change efforts and enhance ecosystem services. A structured literature search identified studies linking functional diversity to ecosystem stability, carbon sequestration, nutrient cycling and adaptability. By focusing on functional traits, this approach provides insight into how these traits influence ecosystem functions like carbon storage, nutrient recycling and habitat availability. Furthermore, practitioners can predict forest responses to climate change and other biotic and abiotic stresses by incorporating functional traits into forest management strategies, thereby fostering ecosystem services. Overall, integrating functional traits provides a robust framework for improving forest resilience, conserving biodiversity and balancing ecological health with economic goals in changing climate scenarios.

Abstract Carbon dioxide removal (CDR) plays an important role in climate scenarios, and multiple CDR research and development efforts are ongoing. For instrumental, substantive and normative reasons, the public should be involved in decision-making related to CDR. We simulate a consultation process using online deliberation groups based on random draws from the Norwegian population, bookended by surveys. Using a multi-mode approach, we find that deliberation substantially affects enhances participants’ assessment of land-based bioenergy carbon capture and storage by .29 steps on a 1-4 scaleonly one of the five marine and terrestrial CDR options presented, compared to a control group, while causing no significant changes in the assessments of four other CDR options presented. At the same time, the deliberation treatment reduces the incidence of “don’t know” and “no opinion” responses by 71% for the main questions about the five technologies. However, it broadly increases participants’ confidence in their assessments and their views on climate change. Analysis of deliberation transcripts shows that participants emphasize the effectiveness, feasibility, and potential for unintended consequences of CDR, whereas questions of scale and relations with climate targets receive little attention. We conclude with observations on how deliberative formats may be used and enhanced as a research approach and procedure for involving the public in formulation of net-zero policy.