Climate Scenarios Research Articles

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.

Crop Coefficients and Irrigation Demand in Response to Climate-Change-Induced Alterations in Phenology and Growing Season of Vegetable Crops

This study investigates the effects of climate change on the irrigation demand of vegetable crops caused by alteration of climate parameters affecting evapotranspiration (ET), plant development, and growing periods in Central Europe. Utilizing a model framework comprising two varying climate scenarios (RCP 2.6 and RCP 8.5) and two regional climate models (COSMO C-CLM and WETTREG 2013), we calculate the daily crop water balance (CWBc) as a measure for irrigation demand based on reference ET and the temperature-driven duration of crop coefficients until 2100. Our findings for onion show that rising temperatures may shorten cultivation periods by 5 to 17 days; however, the irrigation demand may increase by 5 to 71 mm due to higher ET. By reaching the base temperatures for onion growth earlier in the year, cultivation start can be advanced by up to 30 days. Greater utilization of winter soil moisture reduces the irrigation demand by up to 21 mm, though earlier cultivation is restricted by frost risks. The cultivation of thermophilic crops, however, cannot be advanced to the same extent, as shown for bush beans, and plants will transpire more strongly due to longer dry periods simulated for summer. The results underscore the need for adaptive crop and water management strategies to counteract the simulated changes in phenology and irrigation demand of vegetable crops. Therefore, special consideration must be given to the regional-specific and model- and scenario-dependent simulation results.

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.

Population connectivity and size reductions in the Anthropocene: the consequence of landscapes and historical bottlenecks in white forsythia fragmented habitats

BackgroundWhite forsythia (Abeliophyllum distichum) is an endangered Korean Peninsula endemic that has been subjected to recent population genomics studies using SNPs via RAD sequencing. Here, we primarily employed the often underutilized haplotype information from RAD loci to further describe the species’ previously uninvestigated haplotype-based genomic variation and structure, and genetic-geographic characteristics and gene flow patterns among its five earlier identified genetic groups. We also inferred the time of past events that may have impacted the effective population size of these groups, as well as the species’ potential future distribution amidst the warming climate and anthropogenic threats.ResultsOur findings emphasized the recognition of the species’ regional patterns of genetic structure, and the role of topography and its associated gene flow patterns as some of the possible factors that may have influenced the species’ present-day fragmented population distribution. The inferred bottleneck events during the Anthropocene, some of which aligned with the time of historical catastrophic events on the Peninsula (e.g., the Korean War), were revealed to have contributed to the generally low effective population size of its five lineages, particularly those with marginal distributional range. Future distribution under both optimistic and pessimistic climatic scenarios suggests unlikely suitable habitats for these populations to expand from their current range limits, at least in the next 80 years.ConclusionsThe small effective population size and landscape-driven limited gene flow among white forsythia populations will remain a big challenge for the conservation management of the species’ already fragmented population distribution. To help mitigate these impacts, the merging of various research approaches and the use of genomic data to their full potential is recommended to provide the optimized knowledge-based tools for the conservation of this endangered species, and other similar plants under pressure.

Changes in Climatological Variables at Stations around Lake Erie and Lake Michigan

Climatological variables undergo changes over time, and it is important to understand such dynamic changes at global, regional, and local levels. While global and regional studies are common in the study of climate, such studies at a local level are not as common. The aim of this article is to study temporal changes in precipitation, snowfall, and temperature variables at specific stations located on the rims of Lake Erie and Lake Michigan. The identification of changes is carried out by applying change-point analysis to precipitation, snowfall, and temperature data from Buffalo, Erie, and Cleveland stations located on the rim of Lake Erie and at Chicago, Milwaukee, and Green Bay stations located on the rim of Lake Michigan. We adopt mainly the Bayesian information criterion (BIC) method to identify the number and locations of change points, and then we apply the generalized likelihood ratio statistic to test for the statistical significance of the identified change points. We follow this up by finding 95% confidence intervals for those change points that were found to be statistically significant. The results from the analysis show that there are significant changes in precipitation, snowfall, and temperature variables at all six rim stations. Changes in precipitation show consistently significant increases, whereas there is no similar consistency in snowfall increases. Temperature increases are generally quite sharp, and they occur consistently around 1985. Overall, upon combining the amounts of changes from all six stations, the average amount of change in annual average temperature is found to be 0.96 °C, the average percentage of change in precipitation is 16%, and the average percentage of change in snowfall is 17%. The changing local climatic conditions identified in the study are important for local city planners, as well as residents, so that they can be well prepared for changing climatic 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.

Potential Ecological Distribution of the Beetle Agrilus mali Matsumura (Coleoptera: Buprestidae) in China under Three Climate Change Scenarios, with Consequences for Commercial and Wild Apple Forests.

The apple jewel beetle (AJB), Agrilus mali Matsumura (Coleoptera: Buprestidae), is a dangerous pest of commercial apple orchards across China, the largest apple production country in the world, and has recently become invasive in the Xinjiang Uygur Autonomous Region (XUAR) of northwestern China, where wild apple forests also occur. This pest poses a serious threat to apple production and wild apple forests throughout the world. Global warming is expected to change the geographical distribution of A. mali in China, but the extent of this is unknown. Based on empirical data from 1951 to 2000, a MaxEnt model was used to forecast the ecological distribution of A. mali under three different climate scenarios projected in the fifth report of the Intergovernmental Panel on Climate Change. The results showed that the most important variables were the maximum temperature of November, precipitation in January, and minimum temperatures in April. Under all climate scenarios, the forecasted suitable regions for A. mali in China will expand northward in the 2050s and 2070s. The forecasted highly suitable regions will be 1.11-1.34 times larger than they are currently, and their central distributions will be 61.57-167.59 km further north. These findings suggest that the range and damage caused by A. mali in China will increase dramatically in the future. Monitoring and management measures should be implemented urgently to protect both the commercial apple industry and wild apple resources.

Climate change is likely to favour polyvoltine and invasive insect species, leading to more damage in the mid-latitude vineyards of Neuchatel

Climate change has major impacts on viticultural ecosystems worldwide, affecting wine production. Apart from direct impacts, the increase in temperature during the development season is likely to favour insect species, including polyvoltine ones, leading potentially to more damage in the vineyards. In this study, we examined the extent to which changes in mean temperature over the development season (March through September) can potentially increase the voltinism change the phenology and influence the reproduction of pest species in the vineyard area of Neuchatel (Switzerland). We first analysed long-term daily mean temperature data from 1970 to 2022 at the meteorological station of Neuchâtel. Then we used two climate scenarios (RCP4.5 and RCP8.5) to analyse daily mean temperature during the period 2023–2099. For both of these periods, we computed the number of growing degree days (GDDs) above 10 °C, as it is the base development temperature for many polyvoltine pest species. We then used specific bioclimatic models for two major pest species, namely the European grapevine moth (Lobesia botrana) and the American grapevine leafhopper (Scaphoideus titanus) to compare the current and future suitability of these two species to the air temperature conditions in Neuchâtel. Our results show an increase of about 425 GDDs since 1970 (+85 GDDs per decade). According to our models, values will continue to rise during the next decades, with a trend ranging from +28 GDDs per decade (RCP4.5) to +100 GDDs per decade (RCP8.5). This could lead to additional generations of polyvoltine pest species. A third annual generation can be expected in one year out of four for the European grapevine moth by the middle of the 21st century. While temperature conditions are currently moderately favourable for the American grapevine leafhopper, they are predicted to become highly favourable by the middle and the end of the century under both scenarios. These trends mean that climate change is likely to increase pest damage risks in the vineyards of Neuchatel in the future. Pest regulation will remain crucial in limiting major impacts on wine production.

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%.

Designing nature to be a solution for climate change in cities: A meta-analysis

Nature-based solutions (NbS) are designed as a win-win strategy to address societal challenges while providing biodiversity, social, and economic benefits. However, uncertainties and gaps persist, particularly regarding the criteria that define a NbS measure and the specific requirements for a solution to be fully recognized as such, which limit the full potential of these strategies in practice. Another persistent issue is the lack of data on strategy responses across different implementation scales (local, city, regional) and climatic zones (temperate, arid, tropical). This article provides an overview of the potential of NbS to promote climate adaptation in cities. Our meta-analysis, which compiled 7163 records from 89 articles worldwide, indicates that integrating NbS strategies with traditional approaches (gray infrastructure and sustainable technologies) is the most effective response to concurrently address multiple climate-related hazards. Flooding had the highest impact at 35.7 %, followed by increased runoff at 30.5 %. Peak flow and water pollution both had an impact of 10.3 %, while temperature increase accounted for 5.7 %, and decreases in thermal comfort made up 5.0 %. We concluded that all evaluated strategies reduced the impact of climate-related hazards, but this reduction was twice as large when incorporating NbS (18.6 % vs 8.1 %). We also demonstrate that this effect is observed under projected climate scenarios, reinforcing the role of NbS in making cities more resilient and sustainable.

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.

Next‐Gen Crop Monitoring: MTEG‐RTU Algorithm and UAV Synergy for Precise Disease Diagnosis

ABSTRACTThe rapidly changing climatic scenarios are highly favorable for the rising diseases that lead to increasing threats to food production and supply. Various scholars and scientists make long steps to hasten the process of making innovations in farming for managing these issues. In this context, UAV is applied for the purpose of managing and monitoring plant health. The abiotic stresses available in plant diagnosis through traditional strategies are highly labor‐intensive and unfit for large‐scale deployment. Conversely, UAVs designed with mobile sensors, multispectral, radar, and so on make them flexible, affordable, and more effective. Thus, this study proposes a novel meta ensemble transfer extreme gradient‐based random tactical unit (MTEG‐RTU) algorithm for diagnosing crop illnesses precisely. The proposed MTEG‐RTU methodology entails three methods such as transfer learning, adaptive boost, and meta‐ensemble, and the hyper parameters are tuned using random tactical unit algorithm. Healthier and disordered crop images gained from the crop disease dataset comprise 8000 images and are preprocessed. The more optimal features from the preprocessed images are learned through the ResNet method, and these features enter into the classification phase. Random tactical unit algorithm enhanced the performance by optimizing the hyperparameters of MTEG classifier. The experimental results conducted based on the various assessment components and validation dataset indicate that the developed method outperformed the other chosen models, achieving precision, recall, and accuracy of 98.5%, 97.9%, and 98.6%, respectively. The other achievements made by the model are offering technical guidance for conducting the precise diagnosis and treatment of plant pathologies with less time of 9 s.

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.

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.

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.

Uncovering hazards and adaptive capacity: A comprehensive risk assessment study in three conservation areas in Spain

The increase in the frequency, duration and severity of disturbances can disrupt forest composition and structure, posing threats to ecosystem services. Adaptive forest management has the potential to adjust forests to future disturbance regimes, by reducing their susceptibility to these threats and, therefore, increasing their adaptation capacity. However, few comprehensive studies have integrated the factors involved in adaptive forest management by comparing the biomass of national parks of the same region, or examined how global change might affect them in the near future. In this study, we integrate data of forest inventories, wildfires, high-resolution climate and pests information in climate model projections to assess climate-sensitive risks associated with key abiotic disturbances (such as fires, droughts and pests) in coniferous Mediterranean mountain forests, considering various forest management approaches and climate scenarios in three Spanish national parks. We employed the conceptual framework introduced by Lecina-Diaz et al. (2021a) to evaluate risks to forests and considered Exposed values (E), Hazard Magnitude (HM), Susceptibility (S) and Lack of Adaptive Capacity (LAC). We applied statistical weights using expert weights and the analytic hierarchy process (AHP) as the basis for determining the global ranking score of each indicator of the risk components. We extracted information about the climatic scenarios from the Coupled Model Intercomparison Project Phase 6. Three forest management scenarios were generated, named “low intervention», «traditional silviculture”, and "reduction of climate vulnerability", and mapped the risks of carbon sequestration for the different scenarios. The importance of each component was estimated using the AHP. HM was most significantly affected by the Aridity Index and the Fire Risk Index, with weights of 27.7 % and 24.2 %, respectively; S was affected by fuel load (29.0 %) and the Suppression Difficulty index (21.0 %), and LAC was influenced by forest management (32.0 %) and resprouting capacity (22.0 %). The three forest management scenarios exhibited different risk values that decreased with increasing intensity of the intervention in the three protected areas. Likewise, when two climate scenarios were considered, risk increased in the protected areas (Teide National Park, 10.69 %; Sierra Nevada National Park, 10.9 %; Sierra de las Nieves National Park, 6.17 %). Patterns of spatial arrangement of risk, which included HM, S and LAC, were consistent with the risk level, with the highest risk values being concentrated in the buffer areas (natural parks), with areas with a greater presence of coniferous plantations. The risk maps allowed us to identify areas that are critical under extreme conditions and where the greatest disturbance events may occur. Our study underscores that risks of disturbances in mountain ecosystems dominated by conifers within protected areas can be mitigated by forest management. The application of forest management adaptation strategies in national parks and buffer areas has proven effective in reducing risks of disturbances and preserving certain ecosystem services, such as carbon sequestration.

Modelling extreme precipitation projections under the effects of climate change: case study of the Caspian Sea

ABSTRACT Climate change is expected to alter climate extremes. This paper evaluates future changes in extreme precipitation events over the Caspian Sea, where the changes are noticeable due to its geographical location and remoteness from the ocean. Using bias-corrected data from the Coupled Model Intercomparison Project Phase 6 and extreme value analysis, this study shows increased precipitation from 18.48 mm/month in 1980–2014 to 19.50–19.82 mm/month in 2015–2100, depending on the climate scenario. Additionally, extreme events in the future are projected to increase over the sea due to climatic changes, depicting the emergence of droughts/floods that could occur more frequently in the region.

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.