Future Climate Scenarios Research Articles

Prediction of Potential Suitable Distribution Areas for Northeastern China Salamander (Hynobius leechii) in Northeastern China.

The Northeastern China Salamander (Hynobius leechii) is classified as a rare, nationally protected Class II wild animal in China. Its population is declining, and its habitat is deteriorating. This study aimed to predict the distribution of suitable habitats for the Northeastern China Salamander under both current and future climate scenarios, utilizing the MaxEnt model optimized through ENMeval parameters. Species distribution data were collected from field surveys, existing literature, amphibian records in China, and the Global Biodiversity Information Network. A total of 97 records were compiled, with duplicate records within the ENMTools grid unit removed, ensuring that only one record existed within every 5 km. Ultimately, 58 distinct distribution points for the Northeastern China Salamander were identified. The R software package 'ENMeval 2.0' was employed to optimize the feature complexity (FC) and regularization multiplier (RM), and the optimized model was applied to assess the suitable distribution regions for the Northeastern China Salamander under present and future climate conditions. The findings indicated that rainfall and temperature are the primary environmental factors influencing Hynobius. Currently, the suitable habitat for the Northeastern China Salamander constitutes 6.6% of the total area of Northeastern China. Projections for the periods of 2050 and 2070 suggest that suitable habitats for the Northeastern China Salamander will continue to expand towards higher latitudes across three climate scenarios. While this study focuses solely on climate change factors and acknowledges certain limitations, it serves as a reliable reference and provides essential information for the distribution and conservation of the Northeastern China Salamander.

Boron to salinity ratios in the Fram Strait entering the Central Arctic: The role of sea ice formation and future predictions

The Arctic Ocean's sea ice loss dynamically impacts carbon uptake potential, as assessed through measured carbonate parameters, such as total alkalinity. In the open ocean, boron (B) is the third largest contributor to alkalinity via borate and is usually accounted for through the conservative boron to salinity ratio (B/S), and not directly measured. Here, we present findings on non-conservative boron dynamics, that results in significant B/S deviations, observed in ice melt zone waters, snow, slush, brine, and annual sea ice (n = 169) in the Fram Strait entering the Central Arctic. These samples were collected during the onset of the melt season on the 2023 ARTofMELT expedition, covering a wide practical salinity range (2–59). Barring snow, the average B/S ratio across the study was 0.1321 ± 0.0032 mg kg−1 ‰−1, similar to the mean B/S ratio measured amongst several polar water masses near Iceland, as well as the accepted B/S for other ocean regions. Results indicate minor deviations from accepted B/S ratios (indicating conservative behavior) across the sample practical salinity range and reflect an uncertainty in the borate contribution to total alkalinity of less than 2.9 μmol kg−1 at in-situ temperatures. B fractionation appears to occur during sea ice formation, causing greater B in the sea ice reservoir whereas brine, slush, lead, and under-ice water reservoirs are depleted in B. As such, under-ice and lead, brine, and slush samples all had measured B/S ratios (0.1305 ± 0.0011, 0.1305 ± 0.0018, and 0.1304 ± 0.0017 mg kg−1 ‰−1, respectively) lower than the established ratio whereas the average sea ice B/S ratio (0.1331 ± 0.0035 mg kg−1 ‰−1) was closest to accepted values (0.1336 ± 0.0005 mg kg−1 ‰−1). Arctic open ocean samples also had a lower B/S ratio (0.1304 ± 0.0014 mg kg−1 ‰−1). Our findings, together with a previous Arctic B ice study, suggest that B (probably in the form of B(OH)4−) is incorporated into authigenic CaCO3 minerals, replacing CO32− within the mineral lattice during sea ice formation. This process consequentially lowers the B/S ratio in the open Arctic Ocean, compared to the established global ocean ratio. Nevertheless, the incorporation of B into the sea ice reservoir does not fully account for the deficit of B in the Arctic Ocean samples, suggesting further accounting of B Arctic pathways is necessary. In future climate scenarios involving increased sea ice melt, the transition from multiyear to annual sea ice, permafrost thaw, and increased riverine discharge, the behavior of B in the Arctic Ocean is expected to become more dynamic.

Prediction of the potential distribution of a raspberry (Rubus idaeus) in China based on MaxEnt model

Rubus idaeus is a pivotal cultivated species of raspberry known for its attractive color, distinct flavor, and numerous health benefits. It can be used in pharmaceutical, cosmetics, agriculture and food industries not only as fresh but also as a processed product. Nowadays due to climatic changes, genetic diversity of cultivars has decreased dramatically. However, until now, the status of wild R. idaeus resources in China have not been exploited. In this study, we investigated the resources of wild R. idaeus in China to secure its future potential and sustainability. The MaxEnt model was used to predict R. idaeus suitable habitats and spatial distribution patterns for current and future climate scenarios, based on wild domestic geographic distribution data, current and future climate variables, and topographic variables. The results showed that, mean temperature of the coldest quarter (bio11), precipitation of the coldest quarter (bio19), precipitation of the warmest quarter (bio18), and temperature seasonality (bio4) were crucial factors affecting the distribution of R. idaeus. Presently, the suitable habitats were mainly distributed in the north of China including Xinjiang, Inner Mongolia, Gansu, Ningxia, Shaanxi, Shanxi, Hebei, Beijing, Liaoning, Jilin, Heilongjiang. According to our results, in 2050s, the total suitable habitat area of R. idaeus will increase under SSP1-2.6 and then will be decreased with climate change, while in the 2090s, the total suitable habitat area will continue to decrease. From the present to the 2090s, the centroid distribution of R. idaeus in China will shift towards the east and the species will always be present in Inner Mongolia. Our results provide wild resource information and theoretical reference for the protection and rational utilization of R. idaeus.

Changes of blue and green water in arid inland dissipation area based on coupled surface water and groundwater model

Study regionThe Mainstream in the Tarim River Basin, Southern Xinjiang, China Study focusClimate change and human activities have significantly altered the water cycle, and water security evaluation and management are urgent. In arid and semi-arid areas, the assessment of blue and green water is particularly important. In this study, the MIKE SHE model is used to simulate the spatial and temporal changes of blue and green water resources in the mainstream of the Tarim River Basin (TRB) from 1990 to 2050 under land use and climate change conditions. The scarcity and vulnerability of blue-green water are introduced to evaluate the water security of the basin. The impacts of different land uses on blue-green water resources were also calculated according to the model zoning. New hydrological insights for the regionThe results indicate that from 1990 to 2050, blue water resources show a spatial pattern of gradual decrease from upstream to downstream. Compared to blue water, green water is dispersed more evenly over space. Future climatic scenarios will impact water security, as will changes in blue and green water security in terms of time and space. By comparing the influence of ecological water transport on the change of blue and green water before and after 2000, it was found that environmental water transport plays a certain role in improving the blue water scarcity in downstream of the TRB. The study is significant in maintaining regional water security and ecosystem stability.

Optimized Methods for Applying and Assessing Heat, Drought, and Nutrient Stress of Maize Seedlings in Controlled Environment Experiments.

Maize (Zea mays), also known as corn, is an important crop that plays a crucial role in global agriculture. The economic uses of maize are numerous, including for food, feed, fiber, and fuel. It has had a significant historical importance in research as well, with important discoveries made in maize regarding plant domestication, transposons, heterosis, genomics, and epigenetics. Unfortunately, environmental stresses cause substantial yield loss to maize crops each year. Yield losses are predicted to increase in future climate scenarios, posing a threat to food security and other sectors of the global economy. Developing efficient methods to study maize abiotic stress responses is a crucial step toward a more resilient and productive agricultural system. This review describes the importance of and methods for studying the effects of heat, drought, and nutrient deficiency on early developmental stages of maize grown in controlled environments. Studying the early effects of environmental stressors in controlled environments allows researchers to work with a variety of environmental conditions with low environmental variance, which can inform future field-based research. We highlight the current knowledge of physiological responses of maize to heat, drought, and nutrient stress; remaining knowledge gaps and challenges; and information on how standardized protocols can address these issues.

Future Derecho Potential in the United States

Abstract This study uses high-resolution, convection-permitting, dynamically downscaled regional climate simulation output to assess how long-lived, convectively induced, extratropical windstorms known as derechos may change across the CONUS during the 21st century. Three 15-year epochs including a historical period (1990 – 2005), and two, separate late-21st century periods (2085 – 2100) employing intermediate (RCP 4.5) and pessimistic (RCP 8.5) greenhouse gas concentration scenarios are evaluated. A mesoscale convective system (MCS) identification and tracking tool catalogs derecho candidates across epochs using simulated radar reflectivity and maximum 10-meter wind speed as a proxy for near-surface severe wind gusts. Results indicate that MCS-based windstorms, including derechos, are more frequent, widespread, and intense in both future climate scenarios examined for most regions of the central and eastern CONUS. Increases are suggested across all parts of the year, with significant changes in populations concentrated during the early spring and summer months, suggesting the potential for a longer, more extreme MCS windstorm season. This research provides insights for forecasters, emergency managers, and wind-vulnerable stakeholders on how these events may change across the 21st century so that they may mitigate, adapt to, and become resilient against severe convective storm perils.

Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios

Flash floods are highly destructive, and their frequency and intensity are expected to escalate due to climatic changes. This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. To predict FFS, we evaluated twelve flood-influencing variables: elevation (EL), slope (SL), aspect (AS), drainage density (DD), distance to stream (DS), topography roughness index (TRI), stream power index (SPI), topographic wetness index (TWI), soil permeability (SP), precipitation (PR), land use and land cover (LULC) and normalized difference vegetation index (NDVI). Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. Our findings indicated that the areas at high to very high risk of flooding are projected to increase significantly under both the SSP1-2.6 and SSP5-8.5 scenarios. Initially, around 38 % of the studied region had high to very high flood susceptibility, but this is expected to rise to 40–42 % over the projected time periods. These spatial delineations of flood-prone areas can provide guidance for developing effective mitigation and adaptation strategies to address the adverse impacts of flash flooding in the hilly river basins of Bangladesh.

Phytoclimatological study in the high plains of Sétif: application of the bioclimatic classification system (Algeria)

The Global Bioclimatic Classification System (WBCS) by Rivas-Martínez is an effective approach to reflect and study the role of climate in determining the potential distribution of natural vegetation at various temporal and spatial scales. In particular, the diversity of physical characteristics in the Setifian high plains leads to the complexity of large-scale phytogeographic belts. Isobioclimates, unique combinations of bioclimatic indices (continentality, ombrotype, and thermotype), have been created for the study area based on Rivas-Martínez's global bioclimatic classification system for current and future climate scenarios, using the parallel climate model from CMIP5 (Coupled Model Intercomparison Project) under the intermediate greenhouse gas emission scenario (RCP4.5). Precipitation and temperature data from the "WorldClim" dataset were used as the data source. These climatic variables were reduced to a spatial resolution of 1 km. The objective is to understand how the climate is evolving in this region, what the consequences are, and what the future holds for these natural ecosystems. A phytosociological study complemented by satellite image analysis reveals a high diversity of pre-forestry formations. The digitally mapped results were used to 1) assess the relative redistribution of isobioclimates and the magnitude of change, 2) identify and locate the new projected isobioclimates, and 3) explore the compositional changes in vegetation types among analogous isobioclimatic zones. The ordination of the relationships between vegetation and bioclimates shows a strong correlation between Rivas-Martinez indices and the distribution and composition of vegetation. Changes in ombrotype, thermotype, bioclimate, ombrotype, and isobioclimate differed between the current and future series. The resulting map presents 9 isobioclimatic units, which are advantageous for confirming the diagnosis of bioclimatic characteristics and describing the relationships between climatic variables and the corresponding distributions of natural vegetation. We identified eight different isobioclimates for the current period (1979-2013) and seven for the future period (2050), three thermotype horizons for both the current and future periods, and finally, four ombrotype horizons for the period (1979-2013), while two ombrohorizons represent the period (2050). This approach is useful for explaining territorial diversity in environmental applications related to ecological adaptation assessment, nature conservation, landscape regionalization, and planning.

Climate change favors expansion of three Eucalyptus species in China.

Eucalyptus has become one of the most widely planted species in tropical and subtropical regions in China, with important economic, ecological, and social values. However, it is currently unclear how climate change will affect different Eucalyptus species. Therefore, it is urgent to investigate the potential distribution and dynamics of Eucalyptus under current and future climate scenarios. In this study, we analyzed the potential distribution patterns of the three main Eucalyptus species (Eucalyptus grandis, Eucalyptus urophylla, and Eucalyptus tereticornis) under current and future climatic conditions (2041-2060 and 2061-2080) using the optimized MaxEnt model, which integrates a variety of environmental data including climate, topography, soil, and human influence. We also identified the main factors affecting the potential distributions of the three main Eucalyptus species. The model indicated that E. grandis exhibited heightened sensitivity to the mean temperature of the coldest quarter (7.0-20.0 °C) and annual mean temperature (11.9-24.2 °C), whereas E. urophylla displayed heightened sensitivity to precipitation of the warmest quarter (272-1694mm) and annual precipitation (812-2624mm). Conversely, E. tereticornis demonstrated heightened sensitivity to annual mean temperature (12.7-24.5°C) and temperature seasonality (63.8-598.9). Under the current climate, E. tereticornis had the widest suitable distribution area (124.91 × 104 km²), followed by E. grandis (124.89 × 104 km²) and E. urophylla (119.81 × 104 km²). Under future climate change scenarios, the suitable ranges of E. grandis, E. urophylla and E. tereticornis will continue to expand. This study highlights the importance of climate change in Eucalyptus distribution and provides quantified potential distribution maps for three Eucalyptus species under current and future climate conditions in China. This research offers valuable scientific insights pertinent to the management and rational site selection for Eucalyptus plantations.

Predicting the Invasion Risk of the Highly Invasive Acacia mearnsii in Asia under Global Climate Change.

Acacia mearnsii, among the 100 worst invasive weeds worldwide, negatively impacts native biodiversity, agriculture, and natural ecosystems. Global climate change, characterized by rising temperatures and altered precipitation patterns, enhances the risk of A. mearnsii invasion in Asia, making it crucial to identify high-risk areas for effective management. This study performed species distribution modeling using the maximum entropy (MaxEnt) algorithm to predict the potential introduction and spread of A. mearnsii under various climate scenarios based on shared socio-economic pathways (SSP2-4.5 and SSP5-8.5). Currently, only 4.35% of Asia is invaded, with a high invasion risk identified in six countries, including Bhutan, Lebanon, and Taiwan, where more than 75% of their areas are threatened. Under future climate scenarios, 21 countries face invasion risk, among which 14 countries, such as Georgia, Laos, Republic of Korea, and Turkey, are at moderate to very high risk, potentially encompassing up to 87.89% of their territories. Conversely, Northern Asian countries exhibit minimal changes in invasion risk and are considered relatively safe from invasion. These findings underscore that climate change will exacerbate invasion risks across Asia, emphasizing the urgent need for robust management strategies, including stringent quarantine measures and control efforts, to mitigate the threat of A. mearnsii expansion.

Forecasting malaria dynamics based on causal relations between control interventions, climatic factors, and disease incidence in western Kenya.

Malaria remains one of the deadliest diseases worldwide, especially among young children in sub-Saharan Africa. Predictive models are necessary for effective planning and resource allocation; however, statistical models suffer from association pitfalls. In this study, we used empirical dynamic modelling (EDM) to investigate causal links between climatic factors and intervention coverage with malaria for short-term forecasting. Based on data spanning the period from 2008 to 2022, we used convergent cross-mapping (CCM) to identify suitable lags for climatic drivers and investigate their effects, interaction strength, and suitability ranges on malaria incidence. Monthly malaria cases were collected at St. Elizabeth Lwak Mission Hospital. Intervention coverage and population movement data were obtained from household surveys in Asembo, western Kenya. Daytime land surface temperature (LSTD), rainfall, relative humidity (RH), wind speed, solar radiation, crop cover, and surface water coverage were extracted from remote sensing sources. Short-term forecasting of malaria incidence was performed using state-space reconstruction. We observed causal links between climatic drivers, bed net use, and malaria incidence. LSTD lagged over the previous month; rainfall and RH lagged over the previous two months; and wind speed in the current month had the highest predictive skills. Increases in LSTD, wind speed, and bed net use negatively affected incidence, while increases in rainfall and humidity had positive effects. Interaction strengths were more pronounced at temperature, rainfall, RH, wind speed, and bed net coverage ranges of 30-35°C, 30-120 mm, 67-80%, 0.5-0.7 m/s, and above 90%, respectively. Temperature and rainfall exceeding 35°C and 180 mm, respectively, had a greater negative effect. We also observed good short-term predictive performance using the multivariable forecasting model (Pearson correlation coefficient = 0.85, root mean square error = 0.15). Our findings demonstrate the utility of CCM in establishing causal linkages between malaria incidence and both climatic and non-climatic drivers. By identifying these causal links and suitability ranges, we provide valuable information for modelling the impact of future climate scenarios.

Ectomycorrhizal fungal community response to warming and rainfall reduction differs between co-occurring temperate-boreal ecotonal Pinus saplings.

Understanding the responses of ectomycorrhizal (ECM) fungi and their tree hosts to warming and reduced soil water availability under realistic future climate scenarios is essential, yet few studies have investigated how combined global change stressors impact ECM fungal community richness and composition as well as host performance. In this study, we leveraged a long-term factorial warming (ambient, + 1.7 ºC, + 3.2 ºC) and rainfall reduction (ambient, 30% reduced rainfall) experiment in northern Minnesota, USA to investigate the responses of two congeneric hosts with varying drought tolerances and their associated ECM fungal communities to a gradient of soil moisture induced by a combination of warming and rainfall reduction. Soil drying had host-specific effects; the less drought tolerant Pinus strobus had decreased stem growth and lower ECM fungal community richness (fewer ECM fungal Operational Taxonomic Units, OTUs), while the more drought tolerant Pinus banksiana experienced no decline in stem growth but had an altered ECM fungal community composition under drier, warmer soils. Taken together, the results of this study suggest that the combined effects of warming and decreased precipitation will largely be additive in terms of their impact on host performance and ECM fungal community richness, but that drier and warmer soil conditions may also differentially impact specific ECM fungal genera independently of host performance.

Climate Change and Building Renovation: The Impact of Historical, Current, and Future Climatic Files on a School in Central Italy

Climate change significantly affects the operating environment of buildings. These changes impact both energy efficiency and occupants’ comfort and remain crucial even in building restoration, where design decisions typically rely on historical data, yet performance depends on anticipated future scenarios. The present work evaluates the impact of different climate datasets on dynamic energy simulations for an educational building in Central Italy, focusing on estimating heating demands across historical, current, and future climatic scenarios. The assessment considers both the building’s current state and potential energy-efficient retrofits. Initially, various meteorological datasets, including measured and model-generated data, are selected to predict key weather parameters. The analysis reveals the potential and limitations of regional climate models (RCMs) in estimating these variables, with the MM5 dataset emerging as the most reliable. Subsequently, the energy performance of the reference building and its vulnerability to climate change are assessed. Our results show significant differences in energy demand based on construction periods, with the oldest section consuming 29% to 54% more energy monthly than the newer sections. Moreover, using non-representative climatic files can lead to prediction errors of up to 199%. Finally, the building’s energy behaviour is analysed under future climate conditions by generating typical meteorological years (TMYs) for 2030, 2050, and 2070. This analysis evaluates the energy requirements for both existing and retrofitted building configurations. The findings confirm that retrofit interventions with high-performance insulation and upgraded windows significantly enhance the building’s energy efficiency and resilience to future climate conditions, leading to annual energy savings of 50% to 57%.

Unraveling Lake Geneva's hypoxia crisis in the Anthropocene

AbstractDespite global evidence of lake deoxygenation, its duration, timing, and impacts over decadal to centennial timescales remain uncertain. This study introduces a novel model approach using 150 yr of limnological and paleolimnological data to evaluate the anthropogenic impacts on deep oxygen in Lake Geneva. Results highlight an increase in oxygen consumption rates due to cultural eutrophication, initially triggering historical hypoxia, subsequently exacerbated by reduced winter mixing induced by climate change. Simulations of pre‐eutrophication conditions and future climate scenarios define safe operating spaces for the lake to thrive without severe hypoxia risk. Complete winter mixing and O2 recharge once every 3 yr can compensate the oxygen demand in Lake Geneva, even when exceeding 1.5 g O2 m−2 d−1. However, when complete winter mixing becomes less frequent, even consumption rates similar to those observed before eutrophication can cause persistent hypoxia, posing a significant threat to the survival of hypolimnetic aquatic life.

Climate change may make pine wilt disease more prevalent

Abstract Pine wilt disease is one of the most severe and devastating diseases affecting pine forests worldwide, resulting in huge economic losses in many countries. The pinewood nematode (PWN), Bursaphelenchus xylophilus, is the causal agent of pine wilt disease and is obligately vectored by pine sawyer beetles, of the genus Monochamus. For the disease to be present, the habitat must be suitable for the PWN, and include at least one vector species, and at least one host species. To predict its potential distribution, a model must consider all three components. However, no comprehensive study has examined the influence of climatic suitability on the distribution of this “biological complex”. This study addresses this gap by incorporating biotic interactions, specifically involving 13 vectors and 61 host plants, into projections based on the PWN model. We predicted the global potential distribution of pine wilt disease and compared it with the PWN model to highlight the importance of including biotic interactions in species distribution models under climate change. We found that the model revealed an overall trend of increasing suitability scores for both the PWN and pine wilt disease models under future climate scenarios. Furthermore, compared to the PWN model, the biotic model results in an apparent increase in suitability worldwide in the future as the climate will be more suitable to vector and host complexes, suggesting that pine wilt disease could potentially spread to other places via available hosts and vectors. Synthesis and applications. By incorporating biotic interactions, we projected a more accurate suitable area for pine wilt disease, offering valuable insights into regions at high risk for future invasions by the disease and its vectors. This information supports the development of management and early detection strategies in areas of high suitability, helping to mitigate potential economic and ecological losses. Additionally, this study introduces a novel approach for integrating biotic factors into species distribution models.

Predicting Gross Primary Productivity under Future Climate Change for the Tibetan Plateau Based on Convolutional Neural Networks

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO2 concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3–7.0 and SSP5–8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management.

Spatial distribution projections of suitable environmental conditions for key Baltic Sea zooplankton species

AbstractEnvironmental changes reshape biological communities, inducing cascading effects throughout the food webs. These changes pressure species either to adapt or to track favorable habitats. Estuaries represent an interesting case study to investigate such responses as species will rapidly reach physical boundaries if they cannot adapt fast enough and need to track suitable conditions. One such estuary is the Baltic Sea, characterized by a salinity and temperature gradient that shapes species distribution and imposes physiological stress on organisms. The Baltic Sea is projected to be affected by substantial modifications in environmental conditions by the end of the 21st century, which could have major consequences for species distribution and community composition. However, despite the impending changes and their potential impact, there is a gap in understanding the potential consequences on pelagic species of the Baltic Sea. This study employs long‐term observations of primary zooplankton species in the pelagic food web to model changes in their distribution under future climate projections. We found that the parameters having the largest influence on habitat suitability varied across species, although maximal temperature was the most important for six out of seven species. In addition, there was a shrinkage of suitable area for several key species driven by a decrease in salinity and a rise in water temperature. We discuss the complex interplay between environmental changes and the spatial distribution of pelagic species in the Baltic Sea, highlighting the need for proactive management strategies to mitigate potential ecological impacts in the face of future climate scenarios.

Maxent modelling reveals suitable habitat remains unchanged for Carcinus maenas in Australia

ABSTRACT This study assesses the habitat suitability of the invasive green crab, Carcinus maenas, in Australia using MaxEnt modelling under current and future climate scenarios. The species has significant ecological impacts, displacing native species and altering habitats. Using 90 occurrence points from the Global Biodiversity Information Facility, the MaxEnt model shows high performance (Area Under the Curve value of 0.99), indicating reliable predictions. Currently, the green crab's habitat suitability along the Australian coast ranges from high to medium in Tasmania, Victoria, New South Wales and South Australia and low in Western Australia and South Queensland. Future projections suggest a decrease in suitable habitats, confined to Tasmania, Victoria, Adelaide and New South Wales. These findings can guide the development of long-term management and conservation plans for C. maenas, with an emphasis on areas with consistent habitat suitability. Additionally, further field and laboratory experiments are recommended to understand the species’ tolerance to environmental changes. Recognising the ecological impact of C. maenas is crucial for informed decision-making to preserve Australia's ecosystems and ensure the sustainable coexistence of native species amidst invasive threats.

Optimising building performance for a resilient Future: A Multi-Objective approach to Net Zero energy strategies

The escalating impacts of climate change on urban environments underscore the imperative for energy-efficient buildings. As the building sector constitutes approximately 40% of global energy consumption, the implementation of NZE designs is critical for emission reduction. This study utilised Building Performance Optimisation methodologies alongside a multi-stage optimisation framework to evaluate energy consumption, Life cycle cost and carbon emissions under various climate scenarios (RCP 2.6, 4.5, and 8.5) in three Algerian cities—Constantine (semi-arid), Algiers (Mediterranean), and Ghardaïa (arid).The results revealed that the integration of passive, active, and renewable energy strategies significantly curtailed energy consumption and CO2 emissions across different climatic contexts. Specifically, passive measures achieved reductions in energy use ranging from 39% in Ghardaïa to 15% in Constantine. Active systems, including high-efficiency HVAC technologies, resulted in energy savings of 59% in Ghardaïa, 52% in Algiers, and 47% in Constantine. Furthermore, renewable energy sources, with a focus on solar photovoltaics, contributed additional reductions: 44% in Algiers, 26% in Ghardaïa, and 20% in Constantine. The multi-stage optimisation approach also enhanced computational efficiency, significantly reducing simulation time and facilitating the rapid identification of optimal strategies.These findings highlight the efficacy of integrating a range of energy strategies to achieve NZE. Nevertheless, while these systems demonstrate robust performance under current conditions, their effectiveness diminishes under future climate scenarios, underscoring the necessity for adaptive and resilient design approaches. Ongoing optimisation and innovation will be essential to maintaining the efficacy of NZE strategies in the face of extreme future climate conditions.

Influence of isolation on the resilience of tank bromeliad ecosystems to drought in a Neotropical rainforest

AbstractLittle is known of how Neotropical freshwater ecosystems will respond to future climate scenarios. In Neotropical rainforests, a substantial fraction of the freshwater available to the aquatic fauna is found within phytotelmata, plant-held waters that form aquatic islands in a terrestrial matrix. We hypothesized that phytotelmata in close proximity have higher resilience capacity to severe drought than the isolated ones, under the assumption that immigration from nearby sources promotes faster recovery. We used rainshelters to emulate an extreme drought (67 days without rainfall) in tank-forming bromeliads arranged in patches of 1, 3 or 6 plants in a primary forest of French Guiana. Habitat size was a stronger determinant of invertebrate species richness and biomass per bromeliad than patch size. Larger bromeliad patches attenuated the adverse effect of drought on the biomass of predators, probably because short-range migration within dense patches allowed individuals to find moist refuges. However, the recovery of aquatic communities and ecosystem functions was mostly supported by in situ resistance, and a rescue effect of immigration was weak. Whilst environmental management plans tend to focus on dense networks of connected water bodies, our study shows that efforts should not omit the isolated ones.