Future Climate Change Scenarios Research Articles

Influence of incubation temperature, maternal effects, and paternity on quality of olive ridley hatchlings (Lepidochelys olivacea) from a mass-nesting beach in the Mexican Pacific.

Future climate change scenarios project that the increase in surface temperatures will affect ocean temperatures, inducing shifts in marine biodiversity. Sea turtles are species that are particularly vulnerable to the effects of climate change because temperature is a factor that influences embryonic development. We collected clutches of olive ridley turtles from a mass-nesting beach in the Mexican Pacific, which were incubated in ex situ conditions. When the hatchlings emerged, we measured the body condition index-which evaluates the weight-length relationship-and swim thrust, both were considered traits associated with fitness, termed "fitness proxies," and evaluated the effects of incubation temperature, maternal effects, and paternity on these fitness proxies. The body condition index was correlated positively and significantly with the arribada month and temperature during the last third of the incubation period but showed an inverse relationship with the maternal effect. While swim thrust was positively correlated with the maternal effect and the arribada month, there was an inverse relationship with incubation temperature during the first third of the period. Paternity, whether single or multiple, did not have a significant effect on either fitness proxies; however, it may have effects on the average fitness of a population of hatchlings. These results underscore the need to expand research on the sublethal effects of high incubation temperatures on the adaptation and survival of sea turtles, particularly in scenarios of rapid climate change.

Effects of grazing and climate change on aboveground standing biomass and sheep live weight changes in the desert steppe in Inner Mongolia, China

CONTEXTThe Stipa breviflora desert steppe ecosystem is fragile and sensitive to climate change and grazing disturbance. Previous studies have reported the effects of climate change and grazing on aboveground standing biomass of the plant community and sheep live weight, however, the interaction between climate change and grazing remains unclear. Process models have become ideal tools for the evaluation of the effects of grazing management practices under climate change. OBJECTIVEWe used the Soil-Plant-Atmosphere Continuum System (SPACSYS) model to investigate aboveground standing biomass and the live weight of sheep in the desert steppe of Inner Mongolia under future climate change scenarios and different grazing management. The results will be used to inform adaptive management strategies. METHODSThe grazing experiment consisted of four treatments: no grazing (0 sheep ha−1 half year−1), light stocking rate (0.91 sheep ha−1 half year−1), moderate stocking rate (1.82 sheep ha−1 half year−1), and high stocking rate (2.71 sheep ha−1 half year−1). We used observed data on soil temperature, soil volumetric water content, changes to sheep live weight, and aboveground standing biomass of plant community to provide parameterization and validation for the SPACSYS model. We then predicted aboveground standing biomass of the plant community and sheep live weight changes for different grazing management under three representative concentration pathways (RCP) scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) from 2021 to 2100. RESULTS AND CONCLUSIONSModerate and high stocking rates decreased aboveground standing biomass and sheep live weight changes more than the light stocking rate. A light stocking rate can maintain higher aboveground standing biomass and sheep live weight as well as meet production requirements. Therefore, a light stocking rate is a potentially effective management approach to improve food production security and combat global climate change in the desert steppe. SIGNIFICANCEThe model can inform management strategies for grazing in the desert steppe under climate change, supporting efforts to maintain the stability of the steppe ecosystem and increase economic benefits, while also providing a theoretical basis for adaptive management in the desert steppe.

Determination of crop water requirements and potential evapotranspiration for sustainable coffee farming in response to future climate change scenarios

Climate change (CC) is causing a significant threat to agriculture, a sector complicatedly tied to natural resources. Changes in precipitation patterns, atmospheric water content, and rising temperatures intensely affect global agriculture, especially in tropical regions. In this intense CC scenario, potential evapotranspiration (PET) and crop water requirement (CWR) are critical components of agricultural water management. This study evaluates the future impact of CC on precipitation, CWR, and PET in different provinces of Thailand's northern and northeastern regions. Three bias correction methods (Delta (DT), Empirical Quantile Mapping (EQM), and Quantile Mapping (QM)) were employed for precipitation downscaling from the CanESM5 CMIP6-GCM across selected 13 coffee farms with different coffee species. Arabica and Robusta coffee were carefully selected for this analysis. The DT method demonstrated superiority, exhibiting lower RMSE and higher correlation coefficients than EQM and QM. Farm-specific assessments illuminated water demand's complex dynamics during critical growth stages, showcasing variable CWR and PET. During the blooming stage in N-F1, CWR ranged from 16.7 to 33.7 mm/stage, highlighting the variability in water needs. Projected CC impacts on Arabica and Robusta coffee farms in Chiang Rai and Sisaket presented challenges, emphasizing farm-specific strategies to address potential water deficits or surpluses during critical growth phases. Projected 2023, 2028, and 2033 precipitation demonstrated incongruities with CWR and PET. The findings emphasize the crucial role of farm-specific adaptive strategies in mitigating the impacts of changing precipitation patterns on coffee cultivation.

Assessing the growing threat of heat stress in the North Africa and Arabian Peninsula region connected to climate change

Climate change exacerbates extreme heat events in the North Africa and Arabian Peninsula (NAAP) region, posing a significant threat to human health and well-being. This study proposes a novel approach for reliable mapping of population exposure in NAAP to different UTCI levels under future climate change scenarios by improved prediction of UTCI using machine learning (ML) methods. Daily mean UTCI was calculated using hourly ERA-HEAT data from 1979 to 2022. Sobol's global sensitivity analysis was used to identify the most influential meteorological variables affecting UTCI. Three ML models were trained to predict monthly UTCI based on these variables. The best-performing model was used to generate historical and future projections of UTCI for four CMIP6 climate models under Paris Agreement goal scenarios. Finally, the projected change in population exposure to mean UTCI was assessed by comparing historical and future projections. The results showed a significant influence of air temperature, wind speed, surface pressure, solar radiation, and humidity on UTCI in NAAP. Estimation of UTCI using these variables showed a higher performance of random forest (RF) (Kling-Gupta Efficiency = 0.84) compared to other models. The projection of UTCI from CMIP6 climatic model simulations using the RF model revealed a decrease in UTCI for the 1.5 °C temperature rise scenario by 0.25–0.75 °C, while an increase by 0.25–0.75 °C for the 2.0 °C temperature rise scenario in most of NAAP. The study revealed that even a modest increase of 0.5 °C in global warming could lead to a 20-day increase in the number of days with moderate thermal stress in Egypt and Sudan and a significant increase in the number of days with strong thermal stress in the Arabian Peninsula. The population exposure to heat stress would increase significantly even under the most ambitious Paris Agreement goals. These findings highlight the urgent need for adaptation and mitigation strategies to protect public health and well-being in the NAAP region from escalating risks.

Interactions of moisture and light drive lichen growth and the response to climate change scenarios - experimental evidence for Lobaria pulmonaria.

There is growing interest in the functional ecology of poikilohydric nonvascular photoautotrophs (NVPs), including 'cryptogamic' bryophytes and lichens. These organisms are structurally important in many ecosystems, contributing substantially to ecosystem function and services, while also being sensitive to climate change. Previous research has quantified the climate change response of poikilohydric NVPs using predictive bioclimatic models with standard climate variables including precipitation totals and temperature averages. This study aimed for an improved functional understanding of their climate change response based on their growth rate sensitivity to moisture and light. We conducted a 24-month experiment to monitor lichen hydration and growth. We accounted for two well-known features in the ecology of poikilohydric NVPs, and exemplified here for a structurally dominant lichen epiphyte, Lobaria pulmonaria: (i) sensitivity to multiple sources of atmospheric moisture including rain, condensed dew-formation and water vapour, and (ii) growth determined by the amount of time hydrated in the light, driving photosynthesis, referred to as the Iwet hypothesis. First, we found that even within an oceanic high-rainfall environment, lichen hydration was better explained by vapour pressure deficit (VPD) than precipitation totals. Second, growth at a monthly resolution was positively related to the amount of time spent hydrated in the light, and negatively related to the amount of time spent hydrated in the dark. Using multimodel averaging to project growth models for an ensemble of future climate change scenarios, we demonstrated reduced net growth for L. pulmonaria by the late 21st Century, explained by extended climate dryness and lichen desiccation for periods when there is otherwise sufficient light to drive photosynthesis. The results further emphasise a key issue of photoperiodism when constructing functionally relevant models to understand the risk of climate change, especially for poikilohydric NVPs.

Plant pathogen infection risk and climate change in the Nordic and Baltic countries

Climate change and global warming are already affecting food production, and the impact is predicted to intensify in the future. Previous studies have been based on global data and have provided general information about climate change effects on food production. Regional high-resolution data are, however, needed to evaluate the effect of future scenarios of climate change to support strategic and tactical planning to safeguard food production. Here, we provide results on the future potential distribution range of fungal plant pathogens in the Nordic and Baltic countries. This is done using regional climate model data at 12.5 km horizontal resolution. The temperature dependent infection risk and species richness are calculated using data for 80 plant pathogens. Within the region the studied pathogens will in most cases thrive more and be more abundant in a warmer climate; leading to a longer infection risk season and the introduction of new pathogens. This applies to all emissions scenarios, even though the effects are stronger with high emissions. Our results indicate that plant diseases will increase, and this will negatively affect crop production and food security.

Uncovering Current and Future Variations of Irrigation Water Use Across China Using Machine Learning

AbstractAccurately characterizing changes in irrigation water use (IWU) is crucial for formulating optimal water resource allocation policies, particularly in the context of climate change. However, existing IWU estimation methods suffer from uncertainties due to limited data availability and model constraints, restricting their applicability on a national scale and under future climate change scenarios. We present a robust framework leveraging machine learning and multiple data sets to estimate IWU across China. Forced with an ensemble of climate and socio‐economic projections, we appraise future trends and additional costs of IWU. Our model shows high accuracy in reproducing IWU, with coefficient of determination (R2) ranging from 0.86 to 0.91 and root mean square error from 0.261 to 0.361 km3/yr when compared to reported values in Chinese prefectures. Independent validation at 11 cropland sites further confirms the model's predictive power (R2 = 0.67). Under different emissions scenarios, China's IWU is projected to increase by 8.5%–17.1% (6.8%–34.8%) by 2050s (2100s) compared to the historical period (1981–2010), with higher emissions leading to more significant increases. This rise in IWU by 2050s (2100s) comes with an estimated additional cost of US $1.65–3.91 ($2.28–6.5) billion/year, highlighting the urgency for sustainable water management. Our study provides an effective approach for estimating current and future IWU using machine learning techniques, transferable to other countries facing increasing irrigation demands.

The past, present, and future of predator-prey interactions in a warming world: Using species distribution modeling to forecast ectotherm-endotherm niche overlap.

Climate change has the potential to disrupt species interactions across global ecosystems. Ectotherm-endotherm interactions may be especially prone to this risk due to the possible mismatch between the species in physiological response and performance. However, few studies have examined how changing temperatures might differentially impact species' niches or available suitable habitat when they have very different modes of thermoregulation. An ideal system for studying this interaction is the predator-prey system. In this study, we used ecological niche modeling to characterize the niche overlap and examine biogeography in past and future climate conditions of prairie rattlesnakes (Crotalus viridis) and Ord's kangaroo rats (Dipodomys ordii), an endotherm-ectotherm pair typifying a predator-prey species interaction. Our models show a high niche overlap between these two species (D = 0.863 and I = 0.979) and further affirm similar paleoecological distributions during the last glacial maximum (LGM) and mid-Holocene (MH). Under future climate change scenarios, we found that prairie rattlesnakes may experience a reduction in overall suitable habitat (RCP 2.6 = -1.82%, 4.5 = -4.62%, 8.5 = -7.34%), whereas Ord's kangaroo rats may experience an increase (RCP 2.6 = 9.8%, 4.5 = 11.71%, 8.5 = 8.37%). We found a shared trend of stable suitable habitat at northern latitudes but reduced suitability in southern portions of the range, and we propose future monitoring and conservation be focused on those areas. Overall, we demonstrate a biogeographic example of how interacting ectotherm-endotherm species may have mismatched responses under climate change scenarios and the models presented here can serve as a starting point for further investigation into the biogeography of these systems.

Modelling tools for including climate change in pest risk assessments

AbstractThis paper provides a comprehensive overview of the modelling tools available for integrating climate change impacts into pest risk assessments (PRA), elucidating the existing methodologies and models employed to understand the potential distributions of pests based on historical data and under future climate change scenarios. We highlight the strengths and weaknesses of these models and provide commentary on their ability to identify emerging threats due to climate change accurately and adequately, considering pest establishment likelihood, host crop exposure and the distribution of impacts. The simplest methods are based on climate‐matching models, degree‐day development models and Köppen–Geiger climate classification. Correlative species distribution models derive species–environment relationships and have been applied to PRA with mixed success. When fitted models are applied to different continents they are usually challenged to extrapolate climate suitability patterns outside the climate space used to train them. Global climate change is creating novel climates, exacerbating this ‘transferability’ problem. Some tools have been developed to reveal when these models are extrapolating. Process‐oriented models, which focus on mechanisms and processes rather than distribution patterns, are inherently more reliable for extrapolation to novel climates such as new continents and future climate scenarios. These models, however, require more skill and generally more knowledge of the species to craft robust models.

Vulnerability assessment of hydrological ecosystem services under future climate and land use change dynamics

Hydrological Ecosystem Services (HESs), as an important part of ecosystem services, are those services that depend on hydrological processes. HESs vulnerability assessment at the watershed scale is used to explore highly vulnerable areas for management and protection measures. This study aimed at developing a method for HESs vulnerability assessment and achieve a quantitative integrated index under future climate and land use change scenarios. We focused on three regulating HESs, including water flow regulation, erosion regulation, and local climate regulation (LC). An integrated model of Markov chain and automatic cells was used to predict land use changes. Future climate data were estimated using the Climate Change Toolkit (CCT). The Soil and Water Assessment Tool (SWAT) model was developed for HESs vulnerability assessment in the Taleghan Watershed in Iran. The model was calibrated by river discharge using the Sequential Uncertainty Fitting (SUFI-2) and the Parallel Processing option. The results show that sub-watersheds with higher precipitation (PCP) and lower evapotranspiration (ET) are more efficient in water flow and local climate regulation. However, sub-watersheds with lower PCP and higher ET are more vulnerable to erosion. Changes in water flow regulation services and local climate regulation were more consistent with climate changes, and conversely, changes in erosion and sediment regulation services had a very high level of compliance with land use changes. The method applied in this study can easily be adapted to other regions to identify areas with more vulnerability and less resilience in improving the water and soil structure and function in the watershed.

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

BackgroundIn Zimbabwe, anthrax is endemic with outbreaks being reported almost annually in livestock, wildlife, and humans over the past 40 years. Accurate modelling of its spatial distribution is key in formulating effective control strategies. In this study, an Ensemble Species Distribution Model was used to model the current and future distribution of anthrax occurrence in Zimbabwe.MethodsBioclimatic variables derived from the Beijing Climate Centre Climate System Model were used to model the disease. Collinearity testing was conducted on the 19 bioclimatic variables and elevation to remove redundancy. Variables that had no collinearity were used for anthrax habitat suitability modelling. Two future climate change scenarios for different Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 were used. Model evaluation was done using true skill, Kappa statistics and receiver operating characteristics.ResultsThe results showed that under current bioclimatic conditions, eastern and western districts of Zimbabwe were modelled as highly suitable, central districts moderately suitable and southern parts marginally suitable for anthrax occurrence. Future predictions demonstrated that the suitable (8%) and highly suitable (7%) areas for anthrax occurrence would increase under RCP4.5 scenario. In contrast, a respective decrease (11%) and marginal increase (0.6%) of suitable and highly suitable areas for anthrax occurrence were predicted under the RCP8.5 scenario. The percentage contribution of the predictors varied for the different scenarios; Bio6 and Bio18 for the current scenario, Bio2, Bio4 and Bio9 for the RCP4.5 and Bio3 and Bio15 for the RCP8.5 scenarios.ConclusionsThe study revealed that areas currently suitable for anthrax should be targeted for surveillance and prevention. The predicted future anthrax distribution can be used to guide and prioritise surveillance and control activities and optimise allocation of limited resources. In the marginally to moderately suitable areas, effective disease surveillance systems and awareness need to be put in place for early detection of outbreaks. Targeted vaccinations and other control measures including collaborative ‘One Health’ strategies need to be implemented in the predicted highly suitable areas. In the southern part where a high decrease in suitability was predicted, continued monitoring would be necessary to detect incursions early.

A novel flood risk management approach based on future climate and land use change scenarios

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools – particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Decadal soil total carbon loss in northern hinterland of Tibetan Plateau

As the largest and highest plateau in the world, ecosystems on the Tibetan Plateau (TP) imply fundamental ecological significance to the globe. Among the variety, alpine grassland ecosystem on the TP forms a critical part of the global ecosystem and its soil carbon accounts over nine tenths of ecosystem carbon. Revealing soil carbon dynamics and the underlying driving forces is vital for clarifying ecosystem carbon sequestration capacity on the TP. By selecting northern TP, the core region of the TP, this study investigates spatiotemporal dynamics of soil total carbon and the driving forces based on two phases of soil sampling data from the 2010s and the 2020s. The research findings show that soil total carbon density (STCD) in total-surface (0–30 cm) in the 2010s (8.85 ± 3.08 kg C m−2) significantly decreased to the 2020s (7.15 ± 2.90 kg C m−2), with a decreasing rate (ΔSTCD) of −0.17 ± 0.39 kg C m−2 yr−1. Moreover, in both periods, STCD exhibited a gradual increase with soil depth deepening, while ΔSTCD loss was more apparent in top-surface and mid-surface than in sub-surface. Spatially, ΔSTCD loss in alpine desert grassland was −0.41 ± 0.48 kg C m−2 yr−1, which is significantly higher than that in alpine grassland (−0.11 ± 0.31 kg C m−2 yr−1) or alpine meadow (−0.04 ± 0.28 kg C m−2 yr−1). The STCD in 2010s explained >30 % of variances in ΔSTCD among the set of covariates. Moreover, rising temperature aggravates ΔSTCD loss in alpine desert grassland, while enhanced precipitation alleviates ΔSTCD loss in alpine meadow. This study sheds light on the influences of climate and background carbon on soil total carbon loss, which can be benchmark for predicting carbon dynamics under future climate change scenarios.

Proximity to freshwater and seagrass availability mediate the impacts of climate change on the distribution of the West Indian manatee

How climate change alters persistence and distribution of endangered species is an urgent question in current ecological research. However, many species distribution models do not consider consumers in the context of their resources. The distribution and survival of the West Indian manatee (Trichechus manatus), listed as a Vulnerable species on the IUCN Red List, critically depend on seagrass resources and freshwater sources for drinking. We parameterized Maxent models with Bio-ORACLE environmental layers, freshwater proximity data, and modelled seagrass distance layers, to determine manatee and seagrass distributions under future climate change scenarios. We used two plausible IPCC Representative Concentration Pathways (RCP45 and RCP26, respectively) for the year 2050. The model fits had high accuracies and predicted a marked decline in seagrass coverage (RCP26: -1.9%, RCP45: -6%), coinciding with declines in manatee ranges (RCP26: -9%, RCP45: -11.8%). We also found that over 94% of the projected manatee distribution for all scenarios fell within the seagrass distribution. The analysis showed a decline in seagrass coverage to significantly impact manatee distributions, since the distance to seagrass ecological layer contributed significantly to manatee distributions, along with distance to freshwater sources. Our findings suggest that manatees will lose substantial range due to future climate change, but the extent and direction of this change will be mediated by the degree of warming and its impact on the resources manatees depend on.

Predicting potential occurrence of Adelges tsugae (Homoptera: Adelgidae) on a global scale under climate change scenarios using maximum entropy model

Invasive alien species pose grave threats to natural ecosystems and human society. The hemlock woolly adelgid (HWA; Adelges tsugae Annand) is an invasive alien species in several countries, causing severe damage to forest mainly composed of hemlock and spruce. This study employed a maximum entropy model to predict the global potential occurrence areas of HWA based on worldwide HWA occurrence data and four bioclimatic variables: mean diurnal range (BIO2), mean temperature of the wettest quarter (BIO8), mean temperature of the coldest quarter (BIO11), and annual precipitation (BIO12). Future climate change scenarios were used to assess the effects of climate change on HWA distribution. The developed model showed good predictive performance for HWA occurrence (AUC:0.992, F1 score: 0.876), with BIO11 being the most influential factor for HWA occurrence, followed by BIO12. The model identified high-risk areas in North America and eastern Asia including Korea, the Himalayan regions, central and western Europe, Oceania including southeastern Australia and New Zealand, and western part of South America. Additionally, most distribution areas of hemlock trees were in areas where HWA could potentially occur. According to climate change, the model predicted a northward shift in the average latitude of the predicted potential distribution areas showing that the range of HWA may potentially expand even into subarctic regions, where the occurrence probability is currently lower than that of temperate regions. The findings of this study provide valuable insights into the potential occurrence and invasion risk of HWA in current and future climates and emphasize the importance of proactive measures to monitor and manage its spread.

Climatic variables are more effective on the spatial distribution of oak forests than land use change across their historical range.

The current research is conducted to model the effect of climate change and land use change (LUC) on the geographical distribution of Quercus brantii Lindl. (QB) forests across their historical range. Forecasting was done based on six general circulation models under RCP 2.6 and RCP 8.5 future climate change scenarios for the future years 2050 and 2070. In order to model the species distribution, different modeling methods were used. The results indicated that, in general, climatic variables had a higher influence on the distribution of QB than land use-related attributes. The mean diurnal range (bio2), the precipitation seasonality (bio15), and the mean temperature of the driest quarter (bio9) were the main predictors in the distribution of QB forests, while land use variables were less important in oak species distribution. The GBM, MaxEnt, and RF had higher accuracy and performance in modeling species distribution. The outputs also showed that in the current climate circumstances, 97,608.81 km2 of the studied area has high desirability for the presence of QB, and by 2070, under the pessimistic scenario, 96.29% of these habitats will be lost under the concomitant effect of LUC and climate change. By using the results of this research, it is possible to predict and identify the effective factors in changing the habitat of this oak species with more certainty. Based on the insights obtained from the results of such studies, the protection and restoration planning of the habitat of this key species, which supports diverse species, will be provided more efficiently.

Developing a Social Vulnerability Index (SVI) for Risk Mapping

community, in Jilotepec, State of Mexico, with risk mapping purposes. Design/Methodology/Approach: A Social Vulnerability Index (SVI) was developed based on sociodemographic indicators, housing characteristics, and the conditions of the production systems. The information sources for the SVI indicators were obtained from official Basic Geostatistical Area (AGEB) censuses and from an online survey carried out in the community under study. The SVI was cross-referenced with data from four previously published natural hazard studies for risk mapping purposes. Results: The methodological proposal uses equal weight index statistical techniques to develop the SVI. Using online surveys is a clearly viable option for research studies that require to obtain more detailed data on housing characteristics, as well as the population’s perception of certain changes in precipitation and temperature patterns that are happening in their community and the measures, they take to face these natural phenomena. Study Limitations/Implications: Online surveys are relatively recent, which implies the need to design and implement validation and sampling mechanisms for the results. Findings/Conclusions: Risk mapping enabled the territorial visualization and identification of the communal vulnerability conditions, which facilitates a conceptual approach to the social reality of the population and will allow the formulation of potential future scenarios of climate change and the implementation of public policies.

Climate change impact on small mammals from two Neotropical hotspots

Abstract Current climate change represents the unprecedented change in weather patterns across global and regional scales over a short period due to human activity. It is expected to drive changes in species distributions across the globe. Our goal here was to analyse (i) how climate change can impact the future distribution of species in two Neotropical hotspots, and (ii) how future distribution can impact the threatened status of species. We used ecological niche modelling to estimate suitable areas in the present and four future climate change scenarios (SSPs) for 40 species of nonvolant small mammals endemic to the Atlantic Forest and Cerrado biomes of South America. We also inferred species’ future threat levels according to the IUCN’s A3 criteria using future relative area estimates. We found that species will gradually lose more area in all future scenarios, from the most optimistic Shared Socio-economic Pathway (SSP1-2.6) to the most pessimistic (SSP5-8.5) scenario. Species endemic to the Cerrado will see a greater area reduction than species endemic to the Atlantic Forest. Consequently, the Cerrado will have more threatened species than the Atlantic Forest. Species that occupy both biomes will lose proportionally less area than those endemic to the Cerrado but more area than those endemic to the Atlantic Forest. In conclusion, small mammal species will, in general, be negatively impacted by climate change. However, the degree of impact depends on the trajectory of greenhouse gas concentrations and where the species live.

A MaxEnt Model of Citrus Black Fly Aleurocanthus woglumi Ashby (Hemiptera: Aleyrodidae) under Different Climate Change Scenarios.

The citrus blackfly (CBF), Aleurocanthus woglumi Ashby, is an exotic pest native to Southeast Asia that has spread rapidly to the world's main centers of citrus production, having been recently introduced to Brazil. In this study, a maximum entropy niche model (MaxEnt) was used to predict the potential worldwide distribution of CBF under current and future climate change scenarios for 2030 and 2050. These future scenarios came from the Coupled Model Intercomparison Project Phase 6 (CMIP6), SSP1-2.6, and SSP5-8.5. The MaxEnt model predicted the potential distribution of CBF with area under receiver operator curve (AUC) values of 0.953 and 0.930 in the initial and final models, respectively. The average temperature of the coldest quarter months, precipitation of the rainiest month, isothermality, and precipitation of the driest month were the strongest predictors of CBF distribution, with contributions of 36.7%, 14.7%, 13.2%, and 10.2%, respectively. The model based on the current time conditions predicted that suitable areas for the potential occurrence of CBF, including countries such as Brazil, China, the European Union, the USA, Egypt, Turkey, and Morocco, are located in tropical and subtropical regions. Models from SSP1-2.6 (2030 and 2050) and SSP5-8.5 (2030) predicted that suitable habitats for CBF are increasing dramatically worldwide under future climate change scenarios, particularly in areas located in the southern US, southern Europe, North Africa, South China, and part of Australia. On the other hand, the SSP5-8.5 model of 2050 indicated a great retraction of the areas suitable for CBF located in the tropical region, with an emphasis on countries such as Brazil, Colombia, Venezuela, and India. In general, the CMIP6 models predicted greater risks of invasion and dissemination of CBF until 2030 and 2050 in the southern regions of the USA, European Union, and China, which are some of the world's largest orange producers. Knowledge of the current situation and future propagation paths of the pest serve as tools to improve the strategic government policies employed in CBF's regulation, commercialization, inspection, combat, and phytosanitary management.

Goliath is not the enemy: Fiddler crab species of similar size are stronger competitors than their larger adversaries in the climate change context

Many intertidal species are expanding their distribution limits towards the poles as a consequence of increased temperature due to climate change. At the same time, some intertidal habitats have been reduced due to rising sea levels. Fiddler crabs inhabit mangroves, and different species occupy well-established zones. Due to the restricted mangrove areas and consequent latitudinal expansion of the species, the distribution and density of fiddler crabs may be subject to change. The overlap forces several species to cohabit in a smaller territorial space. Fiddler crab species can vary in size, which could decisively affect territorial competition. Using Leptuca urguayensis as a reference, we tested how competing species of different sizes (larger size: L. thayeri and Minucapanema; similar size: L. cumulanta and L. leptodactyla) might interact in future climate change scenarios. First, we showed that within the territory of L. uruguayensis, the species L. cumulanta and L. thayeri occurred mainly in the lower mesolittoral zone, while L. leptodactyla and M.panema predominated in the upper mesolittoral zone. In a laboratory experiment, we evaluated whether the density and size of the heterospecific competitors could alter the agonistic behavior of Leptuca uruguayensis. Our results indicated that increasing the density can increase the agonistic behavior of L. uruguayensis with a greater intensity against species of a similar size. In a second field experiment, we demonstrated that the presence of a burrow enhances the intensity of agonistic interactions between L. uruguayensis and species of similar size. Therefore, in a scenario of territorial expansion of fiddler crabs due to climate change, it is expected that similarly sized species represent a greater competitive threat to populations of L. uruguayensis.