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Drought is considered to be the most complex but least understood of all natural hazards, affecting more people. Its reappearance in drought-prone areas every few years is almost certain. Also, they lack sudden and easily identified onsets and terminations. Under the background of global climate change, the impact from drought exhibits the characteristics of complexity and multi-process. It has a significant impact on the water resources, agriculture, society, and economy hence needs attention. Vegetation Condition Index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the Temperature Condition Index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and a comprehensive assessment of vegetation and temperature stress is achieved from MODIS satellite data in Google Earth Engine (GEE) platform for pre and post monsoon season from 2008 to 2022 (15- year period). Vegetation Condition Index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the Temperature Condition Index (TCI) is used for studying the temperature change. The research also incorporates precipitation data from WorldClim to investigate its influence on the Vegetation Health Index (VHI). Mann Kendall trend analysis is employed to examine spatio-temporal variations in drought severity, for both pre-monsoon and post-monsoon seasons. The results emphasize the sensitivity of VHI to shifts in rainfall patterns, providing valuable insights for drought monitoring and management. In essence, this study enhances understanding of drought dynamics and emphasizes the significance of Remote Sensing data and climate information for effective drought assessment and mitigation strategies. [ABSTRACT FROM AUTHOR]

Climate change is reshaping forest ecosystems, presenting urgent and complex challenges that demand attention. In this context, research that quantifies interactions between climate and forests is substantial. However, modelling at a spatial resolution relevant for ecological processes presents a significant challenge, especially given the diverse geographical contexts in which it is applied. In our study, we aimed to assess the effects of applying CHELSA v.2.1 and WorldClim v2.1 data on bioclimatic analysis within the Río Puelo catchment area in northern Patagonia. To achieve this, we inter-compared and evaluated present and future bioclimates, drawing on data from both climate datasets. Our findings underscore substantial consistency between both datasets for temperature variables, confirming the reliability of both for temperature analysis. However, a strong contrast emerges in precipitation predictions, with significant discrepancies highlighted by minimal overlap in bioclimatic classes, particularly in steep and elevated terrains. Thus, while CHELSA and WorldClim provide valuable temperature data for northern Patagonia, their use for precipitation analysis requires careful consideration of their limitations and potential inaccuracies. Nevertheless, our bioclimatic analyses of both datasets under different scenarios reveal a uniform decline in mountain climates currently occupied by N. pumilio, with projections suggesting a sharp decrease in their coverage under future climate scenarios. [ABSTRACT FROM AUTHOR]

Drought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins. [ABSTRACT FROM AUTHOR]

The spatial divergence of climatic changes necessitates the creation of predictive models of the state of vegetation cover. Our proposed solution for the spatial modeling of the biological productivity of natural ecosystems creates the basis for a further quantitative assessment of the potential absorption of CO2, which is currently considered one of the most urgent environmental issues. In the proposed work, using the methods of geoanalysis of growing conditions, an original technology for modeling the potential spread of biocenoses and their productivity has been developed, reflecting the internal attractor of the development of biocenoses under the influence of local hydrological and climatic conditions of their growth. The methodology was implemented for the southeast of Western Siberia within the framework of publicly available GIS Saga based on a digital terrain model and data from the WorldClim 2.0 climate reanalysis. To forecast data for the period of the third decade of the 21st century, V.V. Paromov's regional climate forecast was used on the basis of an adaptive model—the exponential smoothing method. The verification of the simulation results was carried out on the basis of the Productivity of Ecosystems of Northern Eurasia database. As a result, spatially distributed data were obtained in the form of rasters with high spatial resolution for the average long-term potential bioproductivity according to reanalysis data for 1970–2000 and predicted data for 2021–2030. Both positive and negative trends of potential bioproductivity for various natural zones of the southeast of Western Siberia due to the spatial divergence of changes in heat and energy resources and precipitation over the territory have been revealed. In general, warming in sufficiently drained areas contributes to an increase in the biological productivity of agro and biocenoses and, in hydromorphic areas, to a decrease. [ABSTRACT FROM AUTHOR]

Mongolian herder households maintain the health and condition of their livestock by adapting to the characteristics of the local vegetation distribution. Thus, predicting future vegetation changes is important for stable livestock grazing and sustainable rangeland use. We predicted the distributional extent of rangeland vegetation, specifically desert steppe, steppe and meadow steppe communities, for the period 2081–2100, based on vegetation data obtained from a previous study. Rangeland vegetation data collected in Mongolia (43–50° N, 87–119° E) between 2012 and 2016 (278 plots) were classified into community types. Species distribution modeling was conducted using a maximum entropy (MaxEnt) model. Distribution data for desert steppe, steppe and meadow steppe communities were used as objective variables, and bioclimatic data obtained from WorldClim were used as explanatory variables. CMIP6-downscaled future climate projections provided by WorldClim were used for future prediction. The area under the curve values for the desert steppe, steppe and meadow steppe models were 0.850, 0.847 and 0.873, respectively. Suitable habitat was projected to shrink under all scenarios and for all communities with climate change. The extent of reduction in potential suitable areas was greatest for meadow steppe communities. Our results indicate that meadow steppe communities will transition to steppe communities with future climate change. [ABSTRACT FROM AUTHOR]

Edaphic specialization is considered to promote ecological differentiation among closely related species of Damburneya (Lauraceae) occurring in sympatry. However, little is known about the effects of soil and other key environmental factors like climate on the ecological niche and distributionof these tree species. Here, we assessed the role of climate and soil on niche divergence and potential distribution of four Damburneya species whose distributions span Central America and Mexico. We performed ecological niche modeling with MaxEnt using three sets of environmental data: climatic-only, edaphic-only, and a combination of both, to characterize species niches and suitable distribution areas. Niche overlap was quantified, and niche similarity was tested to assess niche differentiation among species. Climate and soil determined species’ potential distribution. While climatic niches were mostly similar, edaphic niches tended to differ. Warm and moist tropical forests with no water deficit and low seasonality in precipitation are the most suitable environments for the four species. This study supports previous reports of wide ecological plasticity of Damburneya salicifolia based on its distribution and leaf trait variation, as it occurred in drier environments with wider temperature and soil pH ranges than the other species. The observed patterns of niche similarity did not reflect the phylogenetic relationships between species, suggesting that the modeled niches do not necessarily reflect past evolutionary processes but rather the current environmental variation across the geographical ranges of the species. The results suggest that the studied species are similarly constrained by climate and toleratewide edaphic variation, supporting a potential role for soils on ecological divergence within the genus. On the other hand, performance and predictions varied between models built with different datasets. This research supports the utility of including climate and soil data in ecological niche models to comprehensively analyze the niche and distribution of plant species.

Based on the information obtained on organic carbon content in soils and remote sensing data, a mapping model reflecting the spatial variation of organic carbon content in the upper horizons (0–20 cm) of soils in Central Caucasus was created using digital soil modelling and mapping technology. For modelling we applied a multifunctional approach involving a combination of actual data on the organic carbon content (training set) with data derived from external sources of information (remote sensing data) that was processed using a stepwise discriminant analysis. The necessity of creating a model of organic carbon distribution in soils separately for artificial (agrocenoses) and natural biogeocenoses was established using statistical methods of analysis. As a result of combining two hypothetical models, a verified model reflecting the real picture of changes in the organic carbon content in soils of Central Caucasus was obtained. The reliability of the model was 68%. It contains actual data on organic carbon content in natural and agrogenic soils of Central Caucasus. This model is a necessary tool for making decisions to maintain or increase current soil carbon stocks under conditions of climate change and increasing anthropogenic impact. [ABSTRACT FROM AUTHOR]

Understanding the relationship between climate and vegetation requires climate data to be linked with ecological data, including habitat types and vegetation mapping. Our new R package climenv allows researchers to efficiently acquire, extract and visualise data sets that are commonly used by researchers to quantify the climatic envelope of vegetation. climenv offers integrated downloading and processing capabilities for three globally recognised data sets: WorldClim 2, CHELSA and the National Aeronautics and Space Administration's (NASA) Shuttle Radar Topography Mission (SRTM) elevation data. The package allows users to easily download and extract these data sets for single and multi‐geospatial polygon and point data sets, facilitating studies that explore the relationship between vegetation and climate. Furthermore, climenv allows users to plot traditional Holdridge life zone classification, Walter–Lieth climate diagrams and new customised plots, which combine aspects of both these systems with other biologically relevant climate variables. By enhancing the usability and flexibility of these data sets, climenv helps to explore the intricacies of the relationships between climate and vegetation. Our package is accessible from CRAN (https://CRAN.R‐project.org/package=climenv) or GitHub (https://github.com/jamestsakalos/climenv). [ABSTRACT FROM AUTHOR]

Bioclimatic variables (BCVs) are the most widely used predictors within the field of species distribution modeling, but recent studies imply that BCVs alone are not sufficient to describe these limits. Unfortunately, the most popular database, WorldClim, offers only a limited selection of bioclimatological predictors; thus, other climatological datasets should be considered, and, for data consistency, the BCVs should also be derived from the respective datasets. Here, we investigate how well the BCVs are represented by different datasets for the extended Mediterranean area within the period 1970–2020, how different calculation schemes affect the representation of BCVs, and how deviations among the datasets differ regionally. We consider different calculation schemes for quarters/months, the annual mean temperature (BCV‐1), and the maximum temperature of the warmest month (BCV‐5). Additionally, we analyzed the effect of different temporal resolutions for BCV‐1 and BCV‐5. Differences resulting from different calculation schemes are presented for ERA5‐Land. Selected BCVs are analyzed to show differences between WorldClim, ERA5‐Land, E‐OBS, and CRU. Our results show that (a) differences between the two calculation schemes for BCV‐1 diminish as the temporal resolution decreases, while the differences for BCV‐5 increase; (b) with respect to the definition of the respective month/quarter, intra‐annual shifts induced by the calculation schemes can have substantially different effects on the BCVs; (c) all datasets represent the different BCVs similarly, but with partly large differences in some subregions; and (d) the largest differences occur when specific month/quarters are defined by precipitation. In summary, (a) since the definition of BCVs matches different calculation schemes, transparent communication of the BCVs calculation schemes is required; (b) the calculation, integration, or elimination of BCVs has to be examined carefully for each dataset, region, period, or species; and (c) the evaluated datasets provide, except in some areas, a consistent representation of BCVs within the extended Mediterranean region. [ABSTRACT FROM AUTHOR]

This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The analysis involved two main segments: (1) evaluation of base environmental covariates, including surface reflectance, phenology, and derived covariates, compared to the addition of bioclimatic covariates; and (2) assessment of three individual machine learning methods, including random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM), as well as their ensemble for soil TC prediction. Among the evaluated machine learning methods, the ensemble approach resulted in the highest prediction accuracy overall, outperforming the individual models. The ensemble method with bioclimatic covariates achieved an R2 of 0.580 and an RMSE of 10.392, demonstrating its effectiveness in capturing complex relationships among environmental covariates. The results of this study suggest that the ensemble model consistently outperforms individual machine learning methods (RF, XGB, and SVM), and adding bioclimatic covariates improves the predictive performance of all methods. The study highlights the importance of integrating bioclimatic covariates when modeling environmental covariates and demonstrates the benefits of ensemble machine learning for the geospatial prediction of soil TC. [ABSTRACT FROM AUTHOR]

The raccoon is listed among the invasive alien species of EU concern requiring management actions. Projections of its global distribution have been mainly based on climatic variables so far. In this study, we aim to address the impact of land cover (LC) on the raccoon distribution in North America and Europe. First, we identified the LC types in which the observation sites are predominantly located to derive preferred LC types. Second, we used an ecological niche modelling (ENM) approach to evaluate the predictive power of climatic and LC information on the current distribution patterns of raccoons in both ranges. Raccoons seem to be more often associated to forested areas and mixed landscapes, including cropland and urban areas, but underrepresented in vegetation-poor areas, with patterns largely coinciding in both ranges. In order to compare the predictive power of climate variables and land cover variables, we conducted principal component analyses of all variables in the respective variable sets (climate variables and land cover variables) and used all PC variables that together explain 90% of the total variance in the respective set as predictors. Land cover only models resulted in patchy patterns in the projected habitat suitabilities and showed a higher performance compared to the climate only models in both ranges. In Europe, the land cover habitat suitability seems to exceed the current observed occurrences, which could indicate a further spread potential of the raccoon in Europe. We conclude that information on land cover types are important drivers, which explain well the spatial patterns of the raccoon. Consideration of land cover could benefit efforts to control invasive carnivores and contribute to better management of biodiversity, but also human and animal health. [ABSTRACT FROM AUTHOR]

Abstract: Many Orchidaceae, especially those occupying periodically dry, epiphytic microhabitats in the humid tropics, are believed to engage in the water-conserving crassulacean acid metabolism (CAM) photosynthetic pathway. However, the photosynthetic pathway has been studied in only c. 5% of all orchid species. Here we extend the survey to 1079 orchid species, mainly from Colombia, by assessing the presence of CAM based on the carbon isotopic signature (δ 13C values) of herbarium specimens. Ninety-six species, representing 8.9% of those analysed, had δ 13C values less negative than −20‰, indicating CAM. Epiphytism was the predominant life form (75.2% of species sampled), and 9.4% of these epiphytes showed a CAM-type isotopic signature. Isotope values suggested CAM in 19 terrestrial orchid species, 14 species from high elevation (2000–3400 m) and species from six genera that were previously unknown to engage in CAM (Jacquiniella, Meiracyllium, Pabstiella, Psychopsis, Pterostemma and Solenidium). We conclude that CAM is the major pathway of carbon acquisition in a small but broadly distributed fraction of tropical orchids and is more prevalent at lower elevations.

Introduction Global climate change has led to change in the ecological amplitude of plant growth, expand plant adaptation to hot climates, and decrease plant adaptation to cold climates. Climate change resulting from human activities occurs at such a speed that many species will not be able to adapt to it. These changes have led to a change in the range of plants growth. Such high-speed changes have caused subsequent changes in the structure and entire ecosystems of the earth, therefore predicting the effect of climate change on the distribution of plant species has become a major field of research for its conservation measures and programs. Changes in the range of distribution of plants are mostly predicted by species distribution models. In this sense, every environmental factor affecting the distribution of plant species has a minimum, maximum and optimal value, which, in combination with other factors, separates the territory of the species and forms an ecological niche. These models are used to investigate species distribution and are based on ecological niche theory. This research was conducted with the aim of determining the potential habitats of Pistacia vera L. species and the factors affecting it in the present and future in Razavi Khorasan province. Materials and Methods For this purpose, 28 bioclimatic variables including topographic (4 cases), climatic (19 cases), soil (4 cases), and geological (1 case) factors as prediction variables have been analyzed for the correlation coefficient. The variables with high correlation (more than 80 %) have been removed. Environmental variables in ASCII format along with presence points were added for modeling in R software of the desired species. According to the size of the study area, sampling of data points was done based on the field visit during the period 2021-2022 from the introduced areas. through using the Global Positioning System (GPS) of 129 points from 8 regions (as points of presence) were recorded. Then, in order to prevent spatial autocorrelation and reduce the sampling error, the useful areas were converted into 1000×1000 meters grids in ArcGIS 10.5 software, and one presence point was obtained from each cell. In the modeling process, 70 % of the presence points (Pistacia vera L.) were used to generate models and 30 % of the presence points were used to evaluate the performance of the models. Also, to increase the modeling accuracy, the number of repetitions was considered 10. Then all data and points through R software and using Biomed 2 package models including GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt Phillips models, in determining the relationship between vegetation and environmental factors in rangelands of Khorasan Razavi province at current and future distribution of this species in 2080-2100 were predicted under climate scenarios ssp1-2.6 and ssp5-8.5 model. The accuracy of the models was evaluated using the values of KAPPA, TSS and ROC indices, which are prominent and widely used indices for determining and identifying the areas of equal potential. Results and Discussion The variables of climatic factors were removed from the modeling due to the high correlation of 80 %, and the analysis was done using four topographic factors, eight climatic factors, four soil factors and one geological factor. The results of this research showed that according to the accuracy evaluation index, the best modeling for the present time is done by the random forest (RF) model with the ROC, KAPPA, and TSS equal to 100. In the future, the 2.6 and 8.5 scenarios of the random forest model for the ROC, KAPPA, and TSS indicators, with the accuracy of 0.999, 0..982, and 0.989 respectively, have the highest level of accuracy; Also, in the random forest model, the factors that had the greatest impact included: Bio12 (annual precipitation) and Bio15 (seasonal precipitation changes) and land unit at the present time, in the future time under the scenario 2.6 Bio12 (annual precipitation) and Bio15 (seasonal precipitation changes) and DEM and in the scenario 8.5 Bio15 (seasonal precipitation changes) and Bio12 (annual precipitation) and aspect. The results of the relative importance show the great influence of climatic factors on the distribution of this species. It is most present in the habitat with an annual rainfall of 200-285 mm, and more than this amount of rainfall was associated with a decrease in suitability for the establishment of the species. Besides, the height of 800-1300 meters above sea level and rainfall changes up to 7.8 mm in seasonal rainfall also had a positive effect on the suitability of the habitat for the presence of wild pistachio. Also, the most desirable habitat is in low to relatively high hills with a rounded and sometimes flat top consisting of limestone, metamorphic, conglomerate, and shale sandstones and a slope of 40 to 50 % and with shallow to relatively deep gravelly soils. The highest distribution of Pistacia vera L. species is in the northeastern region to the east of Khorasan province. In general, by examining the outputs of the random forest model and comparing the areas prone to the growth of Pistacia vera L. species in the present and future climate scenarios, it can be stated that the trend of stable habitat in the province can be expected. Conclusion The results of this research can be used to identify areas prone to growth, improvement, development, protection, economic exploitation, and expansion of the habitat of Pistacia vera L. species. From the ecological point of view, the wild pistachio species is considered as one of the most important factors preventing and destroying land in the high mountains of arid and semi-arid regions in many geographical and ecological regions. On the other hand, the economic importance and the income-generating aspect of wild pistachios are also important for local operators. In general, it can be stated that vector machine models provide very good performance for identifying such prone areas. In this research, an attempt was made to evaluate different species distribution vector machine models, and then the most suitable model, which was random forest, was selected.

Simple Summary: The species distribution model has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of bioclimate variables may affect the performance of the species distribution model. Here, we tested a new bioclimate variable dataset (i.e., CMCC BioClimInd) and used it in the species distribution model. We evaluated the predictive performance and explanatory power of WorldClim and CMCC-BioClimInd using AUC and omission rate, and also used the ODMAP protocol to record CMCC-BioClimInd to ensure reproducibility. The results indicate that CMCC BioClimInd can effectively simulate the distribution of invasive plant species. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, we inferred that the modified simplified continentality index and modified Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. Our research provides a new perspective for risk assessment and management of global invasive plant species. Species distribution modeling (SDM) has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of these variables may affect the performance of SDM. This investigation elucidates a new bioclimate variable dataset (i.e., CMCC-BioClimInd) for its use in SDM. The predictive performance of SDM that includes WorldClim and CMCC-BioClimInd was evaluated by AUC and omission rate and the explanatory power of both datasets was assessed by the jackknife method. Furthermore, the ODMAP protocol was used to record CMCC-BioClimInd to ensure reproducibility. The results indicated that CMCC-BioClimInd effectively simulates invasive plant species' distribution. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, it was inferred that the modified and simplified continentality and Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical, and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. This method has great potential to improve the efficiency of species distribution modeling, thereby providing a new perspective for risk assessment and management of global invasive plant species. [ABSTRACT FROM AUTHOR]

Various edaphoclimatic factors influence mango productivity (dendrometry, flowering and fruiting). The objective of the study was to analyze the edaphoclimatic factors that influence the productivity of 3 mango varieties (Keitt, Ataúlfo and Manila) in the Papaloapan Basin in Veracruz, Mexico. Dendrometric measurements were carried out on trees of 3 mango varieties in 15 orchards (n = 5 orchards per variety) and the values of 33 edaphoclimatic variables were acquired using geographic information systems; a comparison analysis was carried out with the dendrometric and edaphoclimatic variables. The Manila variety stood out in dendrometric terms as a response to ideal edaphoclimatic conditions for its physiological development. However, the 3 varieties are developed under unique edaphoclimatic values to which they are already adapted, with Keitt and Manila having the highest productivity. [ABSTRACT FROM AUTHOR]

Forest fires are becoming a serious concern in Central European countries such as Austria (AT) and the Czech Republic (CZ). Mapping fire ignition probabilities across countries can be a useful tool for fire risk mitigation. This study was conducted to: (i) evaluate the contribution of the variables obtained from open-source datasets (i.e., MODIS, OpenStreetMap, and WorldClim) for modeling fire ignition probability at the country level; and (ii) investigate how well the Random Forest (RF) method performs from one country to another. The importance of the predictors was evaluated using the Gini impurity method, and RF was evaluated using the ROC-AUC and confusion matrix. The most important variables were the topographic wetness index in the AT model and slope in the CZ model. The AUC values in the validation sets were 0.848 (AT model) and 0.717 (CZ model). When the respective models were applied to the entire dataset, they achieved 82.5% (AT model) and 66.4% (CZ model) accuracy. Cross-comparison revealed that the CZ model may be successfully applied to the AT dataset (AUC = 0.808, Acc = 82.5%), while the AT model showed poor explanatory power when applied to the CZ dataset (AUC = 0.582, Acc = 13.6%). Our study provides insights into the effect of the accuracy and completeness of open-source data on the reliability of national-level forest fire probability assessment. [ABSTRACT FROM AUTHOR]

The observed climate change determines the silvicultural and productive perspectives of the different species. The use of stand growth simulators, which are important tools for predicting future tree growth, requires verified and consistent data, such as length of forest growing season (FGS). The aim of this study is to determine the current and future FGS on the territory of Poland, which has a highly variable transition climate between maritime and continental types. The analysis is based on the WorldClim grid dataset corrected with the constructed model based on the FGS derived from 245 meteorological stations covering the whole territory of the country. In addition, predictions of changes in FGS depending on different climate scenarios were considered. The results show that the inclusion of geographical location components, i.e., longitude, latitude and especially altitude, is important for the correction of FGS calculated on the basis of raster datasets such as WorldClim. The prediction of climatic changes shows a significant increase in FGS duration in Poland, ranging from 18 to 52 days, mainly affecting the mountainous regions with the shortest actual FGS. [ABSTRACT FROM AUTHOR]

This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The analysis involved two main segments: (1) evaluation of base environmental covariates, including surface reflectance, phenology, and derived covariates, compared to the addition of bioclimatic covariates; and (2) assessment of three individual machine learning methods, including random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM), as well as their ensemble for soil TC prediction. Among the evaluated machine learning methods, the ensemble approach resulted in the highest prediction accuracy overall, outperforming the individual models. The ensemble method with bioclimatic covariates achieved an R2 of 0.580 and an RMSE of 10.392, demonstrating its effectiveness in capturing complex relationships among environmental covariates. The results of this study suggest that the ensemble model consistently outperforms individual machine learning methods (RF, XGB, and SVM), and adding bioclimatic covariates improves the predictive performance of all methods. The study highlights the importance of integrating bioclimatic covariates when modeling environmental covariates and demonstrates the benefits of ensemble machine learning for the geospatial prediction of soil TC.

The effect of future climate change is poorly studied in the tropics, especially in mountainous areas, yet species living in these environments are predicted to be strongly affected. Newly available high‐resolution environmental data and statistical methods enable the development of forecasting models, but the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. Predictive studies aimed at providing conservation guidelines often account for a range of future climate predictions (climate scenarios and global circulation models). However, very few studies consider potential differences related to the source of climate data and/or do not account for spatial information (overlap) in uncertainty assessments. We modelled the environmental suitability for Phelsuma borbonica, an endangered reptile native to Reunion Island. Using two metrics of species range change (difference in overall suitability and spatial overlap), we quantified the uncertainty related to the modelling technique (n = 10), sample bias correction, climate change scenario, global circulation models (GCM) and data source (CHELSA vs. Worldclim). Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap, the uncertainty related to data source became more important than that of GCMs. The uncertainty driven by sample bias correction and variable selection was much higher when assessed based on the spatial overlap. The modelling technique was a strong driver of uncertainty in both cases. We provide a consensus ensemble prediction map of the environmental suitability of P. borbonica to identify the areas predicted to be the most suitable in the future with the highest certainty. Predictive studies aimed at identifying priority areas for conservation in the face of climate change need to account for a wide panel of modelling techniques, GCMs and data sources. We recommend the use of multiple approaches, including spatial overlap when assessing uncertainty in species distribution models. [ABSTRACT FROM AUTHOR]

Precipitation and temperature data of Tigray region of Ethiopia for baseline (1950-2000), future climate projection for mid-century (2050) and end-century (2070) based on medium emission scenario (RCP 4.5) and high emission scenario (RCP 8.5) were obtained from climate model ensembles, Coupled Model Intercomparison Project 5 (CMIP5). Arc GIS 10.1 and Diva GIS 7.5.0 was used for mapping climatic variables and area coverage. Area coverage were computed based on number of cell (1km2). The result indicted that precipitation of the study might be increased 308-1054 mm to 301-1236 mm under both scenario compared with baseline. Temperature of the study area might be increased from 8.1-29 to 11.3-32.4ºC. The area coverage is decreasing at 8.1-16.2 and 16.2-24.3ºC temperature classes at both time slices and RCPs. In the 24.3-32.4ºC the area might be increased at both time slices and RCPs compared with the baseline. In addition, the area coverage for 301-612mm precipitation class might be increased but decreased at 612-924mm at both RCPs and time slices. The area coverage for 924-1236mm class increased at both time slices and RCPs compared with the baseline. Overall, amount of precipitation and temperature might increase at both time slices and RCPs. [ABSTRACT FROM AUTHOR]

Drought episodes across the Himalayas are inevitable due to rapidly increasing atmospheric temperatures and uncertainties in rainfall patterns. Tarai of Nepal is a tropical region located in the foothills of the Central Himalaya as a country's food granary with a contribution of over 50% to the entire country's agricultural production. However, there is a lack of detailed studies exploring the spatiotemporal occurrence of drought in these regions under the changing climate. In this study, we used the ensemble of nine climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two shared socio-economic pathways (SSPs), namely SSP245 (an intermediate development pathway) and SSP585 (a high development pathway), to assess anticipated drought during the mid-century. We used bias-corrected gridded data from the Worldclim to project drought events by the end of the mid-century based on the historical period (1989–2018). We computed historical and projected Thornthwaite moisture index (TMI) to evaluate soil moisture conditions on a seasonal scale for the Tarai region's Eastern, Central, and Western parts. The model ensemble projected a significant increase in precipitation and temperature for the entire Tarai by the end of mid-century. However, the winter and spring seasons are projected to suffer precipitation deficiency and a temperature rise. Our results indicated that the Eastern Tarai would likely experience a decrease in winter precipitation. We emphasize that the presented spatiotemporal pattern of the MI will be instrumental in addressing the irrigation facility's needs, choice, and rotation of crops under the changing climate scenarios and in improving our mitigation measures and adaptation plans for sustainability of the agriculture in drought-prone areas. [ABSTRACT FROM AUTHOR]

Climate changes indulge the spread of pests outside their active range by increasing, decreasing, or shifting appropriate climatic conditions and niche of a particular species. Modeling the future potential distribution of pests using MaxEnt under different climate change scenarios is an effective method for prevention and management protocol. This study was conducted to predict the potential distribution of Schistocerca gregaria in future for better management plans based on two socio-economic pathways (SSPs) for 2050 and 2070. To evaluate the predictive performance of the model, the area under the receiver operating characteristic curve (AUC) was used. We used 226 occurrence records and 9 bioclimatic variables to simulate the current and future distributions of S. gregaria. The precision of the MaxEnt model was highly significant, with mean AUC value ranging from 0.929 to 0.940 for all of the evaluated models. The jackknife test showed that Bio 11 and Bio18 contributed 51.4% and 17.3% to the MaxEnt model. A total of 2,557,856 km2 (1.69%) area were recognized as high potential habitats of S. gregaria. However, in 2050 and 2070 high-potential areas would decrease for both of the SSP scenarios.

Precipitation and temperature data of Tigray region of Ethiopia for baseline (1950-2000), future climate projection for mid-century (2050) and end-century (2070) based on medium emission scenario (RCP 4.5) and high emission scenario (RCP 8.5) were obtained from climate model ensembles, Coupled Model Intercomparison Project 5 (CMIP5). Arc GIS 10.1 and Diva GIS 7.5.0 was used for mapping climatic variables and area coverage. Area coverage were computed based on number of cell (1km2). The result indicted that precipitation of the study might be increased 308-1054 mm to 301-1236 mm under both scenario compared with baseline. Temperature of the study area might be increased from 8.1-29 to 11.3-32.4ºC. The area coverage is decreasing at 8.1-16.2 and 16.2-24.3ºC temperature classes at both time slices and RCPs. In the 24.3-32.4ºC the area might be increased at both time slices and RCPs compared with the baseline. In addition, the area coverage for 301-612mm precipitation class might be increased but decreased at 612-924mm at both RCPs and time slices. The area coverage for 924-1236mm class increased at both time slices and RCPs compared with the baseline. Overall, amount of precipitation and temperature might increase at both time slices and RCPs.

Aim: Species' climatic niches may be poorly predicted by regional climate estimates used in species distribution models (SDMs) due to microclimatic buffering of local conditions. Here, we compare SDMs generated using a locally validated below‐canopy microclimate model to those based on interpolated weather station data at two spatial scales to determine the effects of scale and topography on potential future below‐canopy warming and species distributions. Location: Great Smoky Mountains National Park (2,090 km2; NC, TN, USA). Time period: 1970–2006, late‐century warming. Major taxa studied: Vascular plant species of Southern Appalachian forests. Methods: We compared the fit and spatio‐temporal predictions of SDMs generated using occurrence records of 154 plant species and three climate models: macroclimate (1 km, WorldClim), downscaled climate (based on a 30‐m digital elevation model), and fine‐scale microclimate (30 m) from a below‐canopy sensor network. Results: We found that, although SDM fit was similar across models, microclimate‐derived SDMs predicted substantially greater species persistence with 4°C of regional warming, with a difference of 50% of the species pool in some areas. Microclimate models predicted that warming trends will be buffered in high‐elevation and near‐stream habitats. Microclimate SDMs predicted higher stability of mid‐elevation species, particularly in thermally buffered areas near streams, and critically, less change in species composition at high elevation. In contrast, predictions of macroclimate and downscaled climate models were similar despite improved resolution. Main conclusions: Our results demonstrate that careful selection of climate drivers, including local near‐ground validation rather than downscaling solely with elevation, is critical for projecting distributions. They also suggest that some species at risk from climate change might persist, even with 4°C of macroclimate warming, in cryptic refugia buffered by microclimate, pointing to the roles of forest cover and topography in explaining slower‐than‐expected changes in understorey communities. However, certain species, such as those currently occurring on low‐elevation ridges that are sensitive to atmospheric changes, may be at more risk than macroclimate or downscaled climate SDMs suggest. [ABSTRACT FROM AUTHOR]

In Ukraine during 2008–2010, the first SDM matched the red squirrel (S. vulgaris) based on GLM-by-2050, and which covered Eastern Europe, was developed and used by the Land and Resource Management Center (ULRMC, Kyiv). Our new study reveals further development of the analysis by using relevant IPCC climate change scenarios. We took into account materials on S. vulgaris (and S. anomalus) distribution, as well as selected associated species, and the WorldClim with respective maps and current bioclimatic variables, and its projections for four relevant scenarios which combined SSPs & RCPs by 2100. The simulations of scenario SSP1 & RCP2.6 associated with an average temperature increase of 1.5 °C show that climate change could cause the loss of 12 % of suitable habitats of the species in Eastern Europe and 49 % in Ukraine. The simulations for SSP2 & RCP4.5 (with average temperature increase of 1.8 °C) demonstrates, respectively, a potential loss of 14 % and 57 % of suitable habitats. Simulations of SSP3 & RCP7.0 and SSP5 & RCP8.5 scenario (with average temperature increase of >> 2 °C) shows a loss of 30 % and 41 % of suitable habitats within Eastern Europe, and more than 90 % in Ukraine. Since each percent of such changes provokes enormous losses in ecosystems and biodiversity, we emphasize the current need for countries to aim and achieve the most ambitious climate change commitments to stabilize the increase of temperature, i.e. within 1.5 °C. Our comparison platform included also SDMs of some trees (oak, beech, spruce, pine, linden, and birch — Quercus robur, Fagus sylvatica, Picea abies, Pinus silvestris, Tilia cordata, Betula spp.), as well as SDM for the marten (Martes martes), for all of which we had already developed GLM-by-2050. Consequently, the new projections demonstrated that all habitats of the red squirrel and associated species are expected to shift mostly ‘to the north’ by 2100, and their localities in the Caucasus Mountain areas might be fragmented. Most likely, in nature, this complicated displacement revealed by the mentioned modelling will happen not in the form of direct migration of individuals ‘to the north’ directly, but through active synanthropization of the red squirrel. How durable and satisfactory this mechanism is for natural selection remains a mystery. The territories from which S. v. ukrainicus (Mygulin, 1928) historically originated and was described have changed significantly: the respective landscape ecosystem losses have reached up to 50 % and more. By 2100, significant habitat changes are likely to be also demonstrated by beech and birch. This research can be used by educators in teaching the history of science, applied ecology, nature conservation, and geoinformatics in biology. This research is dedicated to the Squirrel Year 2020.

The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs‐based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between‐version differences). To this aim, we simulated occurrence probability and presence‐absence across Europe of four virtual species (VS) with differing climate‐occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate‐occurrence relationships for the climate‐tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between‐version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between‐version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between‐version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re‐evaluating previous biodiversity‐related works relying on geographical predictions from Worldclim‐based HSMs. [ABSTRACT FROM AUTHOR]

The climate change in the Selenga River basin observed over the period from 1961 to 2018 is considered. The WorldClim dataset and the GHCN-Daily global weather station database are used to map mean and extreme climatic characteristics. These maps are included in the geoinformation system for hydroecological safety in the Selenga River basin under development. An interpolation technique that takes into account the dependence of temperature and precipitation extremes on their average long-term values is used for mapping extreme indices. It was found that in contrast to most of Russia, average annual temperature in the Selenga River basin increases mainly due to the warm season. At the same time, the amount of precipitation decreases, which leads to the increasing risk of droughts. A substantial increase in the number of days with maximum temperature C, the number of consecutive dry days, as well as the frequency of droughts, is revealed. It is most pronounced in the south of the basin. [ABSTRACT FROM AUTHOR]

The article presents the technique of landscape ecological modeling of water balance for the Southeast of Western Siberia. This technique enables to assess the impact of different types of land use on the structure of the water balance. For all elements of the landscape that make up the catchment area, i.e. surface complexes, characterized by their physical and geographical conditions, the main of them are the following: climate, topography, soil and vegetation. The combination of these conditions determines the features of structure of the water balance and hydrological regime of the territory. Knowledge of the structure of the water balance gives us an idea of the possibilities of using water resources of different geographical zones. In modelling we used the genetic method of flow formation, or the method of hydrological-climatic calculations (HCC) developed by V. S. Mezentsev implemented on the basis of landscape-hydrological approach in public geographic information system (GIS). The initial data for modeling are the following: digital terrain model, satellite sensing data and climatic characteristics. The system of equations of the HCC method describes the processes of formation of local climatic elementary runoff, changes in soil moisture and evaporation from the surface of catchments with the accuracy, which is sufficient for many practical purposes. In this method, the physical and geographical factors of flow formation are taken into account by the parameter n (reflects the influence of landscape conditions) and r (characterizes the ability of the soil to supply moisture to the evaporating surface and spend it on evaporation).

Abstract The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs‐based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between‐version differences). To this aim, we simulated occurrence probability and presence‐absence across Europe of four virtual species (VS) with differing climate‐occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate‐occurrence relationships for the climate‐tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between‐version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between‐version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between‐version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re‐evaluating previous biodiversity‐related works relying on geographical predictions from Worldclim‐based HSMs.

The dramatic rise of human population growth and urban development, especially in developing countries, has led to a decline in the quality, fragmentation and isolation of habitats which support rare or endangered fauna species. New areas designed to protect such species and form corridors and networks to support populations are needed. Two rare species of Chinkara and Blanford's fox habitats in Isfahan Province are used to evaluate the optimal method to determine suitable habitat. Eight statistical methods, including four widely-used methods (SVM, Random Forest, GBM and MARS) and five new methods (XGBtree, XGBLinear, Treebag and SVMRadial) are evaluated in this study; to do so two Modis products and 19 worldclim parameters are applied. In addition, the ensemble of these methods was applied using gap analysis and areas with potential for protection were prioritized. Finally, minimum cost analysis was utilized to identify possible corridors between new areas and current protected areas. Results demonstrate that the eastern parts of Isfahan province have the highest potential for these two species, and because of the diverse habitats demands for these two species, the regions are able to support a large number of fauna and flora species. [ABSTRACT FROM AUTHOR]

The observed climate change determines the silvicultural and productive perspectives of the different species. The use of stand growth simulators, which are important tools for predicting future tree growth, requires verified and consistent data, such as length of forest growing season (FGS). The aim of this study is to determine the current and future FGS on the territory of Poland, which has a highly variable transition climate between maritime and continental types. The analysis is based on the WorldClim grid dataset corrected with the constructed model based on the FGS derived from 245 meteorological stations covering the whole territory of the country. In addition, predictions of changes in FGS depending on different climate scenarios were considered. The results show that the inclusion of geographical location components, i.e., longitude, latitude and especially altitude, is important for the correction of FGS calculated on the basis of raster datasets such as WorldClim. The prediction of climatic changes shows a significant increase in FGS duration in Poland, ranging from 18 to 52 days, mainly affecting the mountainous regions with the shortest actual FGS.

More than half of the European population of the Barbary Partridge is in Sardinia; nonetheless, the researches concerning this species are very scarce, and its conservation status is not defined because of a deficiency of data. This research aimed to analyse the habitat selection and the factors affecting the abundance and the density of the Barbary Partridge in Sardinia. We used the data collected over 8 years (between 2004 and 2013) by spring call counts in 67 study sites spread on the whole island. We used GLMM to define the relationships between the environment (topography, land use, climate) both the occurrence and the abundance of the species. Moreover, we estimated population densities by distance sampling. The Barbary Partridge occurred in areas at low altitude with garrigue and pastures, avoiding woodlands and sparsely vegetated areas. We found a strong relationship between the occurrence probability and the climate, in particular, a positive relation with temperature and a negative effect of precipitation, especially in April–May, during brood rearing. Furthermore, dry crops positively affected the abundance of the species. We estimated a density of 14.1 partridges per km2, similar to other known estimates. Our findings are important both because they increase the knowledge concerning this species, which is considered data deficient in Italy, and because they are useful to plan management actions aimed to maintain viable populations if necessary. [ABSTRACT FROM AUTHOR]

Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.

Differences between climatic databases have been reported to alter the spatial predictions of species distribution models (SDM). In the present study, the global WorldClim v.2 database (WC) and the regional Iberian Climate Atlas (ICA) were compared in the geographical context of the Iberian Peninsula. Six climatic variables were considered: BIO1, BIO5 and BIO6 (temperature-related variables) and BIO12, BIO13 and BIO14 (precipitation-related variables). We performed regression analyses between values for each pair of homologous variables and generated quantile–quantile plots to compare the distribution of ranges within 10 × 10 grid cells. Pearson correlations were used to determine whether absolute differences between homologue variables were related to elevation. We modelled the occurrence of 48 woody plant species using either WC or ICA variables, and tested for differences in the estimated suitability values, discrimination power and importance of variables. Precipitation values varied considerably between databases, with WC variables reaching lower maximum and less variable values than ICA. Regarding temperature values, BIO1 had the highest correlation value between both datasets, whereas we observed substantial differences in the case of BIO5, which showed consistently lower values in WC than in ICA. Higher discrepancies between datasets, especially for temperature variables, were found in high elevation areas. As regards distribution models, the climate data source affected estimated suitability values, discrimination capacity and estimated variable importance. In addition, the rarer the species, the higher the uncertainty associated with the climate source. Climate data source is another uncertainty factor to add to all those that have already been highlighted in SDM. [ABSTRACT FROM AUTHOR]

A pesar de la numerosa información científica sobre cambio climático mundial, no existen estudios que demuestren los efectos sobre la biodiversidad a menor escala. Por ello, utilizando 19 variables bioclimáticas, cinco de radiación solar, altitud, software especializado (MaxEnt) y coordenadas geográficas de presencia de cinco especies de flora categorizada verificadas en campo se modelaron sus nichos ecológicos actuales y proyectados a los cuatro escenarios futuros de emisiones (2050 y 2070). Se demostró que el de Buddleja coriacea disminuirá en más del 80 % por las variaciones futuras de precipitación y temperatura consecuencia del cambio climático, mientras que Carica candicans, Haplorhus peruviana, Kageneckia lanceolata y Weberbauerella brongniartioides se mantendrán e incrementarán, deduciéndose que sus categorías de amenaza derivan principalmente por actividad antropogénica. Estos modelos mejoran significativamente la comprensión del funcionamiento ecosistémico, otorgando información útil para diseñar políticas y acciones de conservación que orienten la gestión territorial hacia la estrategia de adaptación basada en ecosistemas.

Dünyamız doğal dengenin insan tarafından bozulduğu ve bozulmalardan kaynaklı olumsuz etkilerin giderek arttığı bir dönemin içindedir. İklim değişikliğinin olumsuz etkilerinin sınırları yoktur ve bu olumsuz etkiler yeterince önemsenmediği sürece her alana hızla yayılmaktadır. Tüm canlılar için önemli olan bitkiler ise iklim değişikliği sebebiyle sıcaklık ve yağış gibi parametrelerin coğrafi değişkenlik göstermesi sonucunda, gelecekte, ölümler, göçler veya adaptasyon zorunlulukları ile karşı karşıya kalacaktır. Bu sebepler iklim değişikliğinin olası etkileri altında bitkilerin gelecekteki coğrafi dağılımlarının tahmin edilmesini gerekli kılmaktadır. Çalışmada, model olarak seçilen mısır, aspir, kanola, pamuk, buğday ve dallı darı bitkilerinin gelecekteki iklimsel değişikliklere uygunluğu incelenmiş ve elde edilen gelecek tahminleri ile günümüz için elde edilen iklimsel uygunluklar karşılaştırılmıştır. Uygulanan adımlar; (1) İklimsel parametrelerin günümüze ait ve 2070 yılı projeksiyonuna ait verilerinin Worldclim veritabanından elde edilmesi, (2) Model bitkilerin büyümeleri için gerekli sıcaklık ve yağış aralıklarının EcoCrop veritabanı ile tespit edilmesi, (3) Coğrafi Bilgi Sistemleri (CBS) ve Uzaktan Algılama yazılımı olan TerrSet ortamında, Climate Change Adaptation Modeler (CCAM)’in alt modeli olan Crop Climatic Suitability Modeling (CCSM) uygulanarak (4) iklimsel uygunluk haritalarının her bitki için günümüz ve gelecek projeksiyonu şeklinde üretilmesi şeklindedir. Köppen iklim sınıflandırma sistemine göre %41.1 oranında Akdeniz ikliminin hâkim olduğu ülkemizde bitkilerin iklimsel uygunluk değerlendirilmesi yapılmıştır. Çalışmada, mısır, dallı darı ve pamuk bitkilerinin yetiştirilmesine uygun olan alanların nispeten artsa bile günümüze göre önemli ölçüde değişmeyeceği; aspirin uygunluğunun alansal olarak önemli ölçüde genişleyeceği ve kanolaya yönelik uygun alanların yer değiştireceği tespit edilmiştir. Burada önemli ölçüde kaybeden hem alansal olarak değişikliğe ve daralmaya uğrayacak olan ülkemizin kayda değer tarım ürün bitkisi buğdaydır. Çalışma, gelecek projeksiyonlarının yapılması, acil durum önlemlerinin veya tarımsal yönetim planlamalarının yapılması açısından önem taşımaktadır.

Dünyamız doğal dengenin insan tarafından bozulduğu ve bozulmalardan kaynaklı olumsuz etkilerin giderek arttığı bir dönemin içindedir. İklim değişikliğinin olumsuz etkilerinin sınırları yoktur ve bu olumsuz etkiler yeterince önemsenmediği sürece her alana hızla yayılmaktadır. Tüm canlılar için önemli olan bitkiler ise iklim değişikliği sebebiyle sıcaklık ve yağış gibi parametrelerin coğrafi değişkenlik göstermesi sonucunda, gelecekte, ölümler, göçler veya adaptasyon zorunlulukları ile karşı karşıya kalacaktır. Bu sebepler iklim değişikliğinin olası etkileri altında bitkilerin gelecekteki coğrafi dağılımlarının tahmin edilmesini gerekli kılmaktadır. Çalışmada, model olarak seçilen mısır, aspir, kanola, pamuk, buğday ve dallı darı bitkilerinin gelecekteki iklimsel değişikliklere uygunluğu incelenmiş ve elde edilen gelecek tahminleri ile günümüz için elde edilen iklimsel uygunluklar karşılaştırılmıştır. Uygulanan adımlar; (1) İklimsel parametrelerin günümüze ait ve 2070 yılı projeksiyonuna ait verilerinin Worldclim veritabanından elde edilmesi, (2) Model bitkilerin büyümeleri için gerekli sıcaklık ve yağış aralıklarının EcoCrop veritabanı ile tespit edilmesi, (3) Coğrafi Bilgi Sistemleri (CBS) ve Uzaktan Algılama yazılımı olan TerrSet ortamında, Climate Change Adaptation Modeler (CCAM)’in alt modeli olan Crop Climatic Suitability Modeling (CCSM) uygulanarak (4) iklimsel uygunluk haritalarının her bitki için günümüz ve gelecek projeksiyonu şeklinde üretilmesi şeklindedir. Köppen iklim sınıflandırma sistemine göre %41.1 oranında Akdeniz ikliminin hâkim olduğu ülkemizde bitkilerin iklimsel uygunluk değerlendirilmesi yapılmıştır. Çalışmada, mısır, dallı darı ve pamuk bitkilerinin yetiştirilmesine uygun olan alanların nispeten artsa bile günümüze göre önemli ölçüde değişmeyeceği; aspirin uygunluğunun alansal olarak önemli ölçüde genişleyeceği ve kanolaya yönelik uygun alanların yer değiştireceği tespit edilmiştir. Burada önemli ölçüde kaybeden hem alansal olarak değişikliğe ve daralmaya uğrayacak olan ülkemizin kayda değer tarım ürün bitkisi buğdaydır. Çalışma, gelecek projeksiyonlarının yapılması, acil durum önlemlerinin veya tarımsal yönetim planlamalarının yapılması açısından önem taşımaktadır.

El clima del mundo está cambiando de forma signicativa en los últimos años y con ello la distribución de las especies. El objetivo del estudio fue generar modelos de distribución potencial de las especies de pinos piñoneros Pinus cembroides, P. culminicola y P. nelsonii del noroeste de México. Para establecer el modelo más preciso, se usaron dos escenarios: a) con 19 variables bioclimáticas Worldclim, y b) con las mismas variables más las variables topográcas de altitud, pendiente y exposición. Se modeló con registros REMIB CONABIO, un total de 208, 89 y 67 registros para cada especie con ajuste logístico y 500 iteraciones. Se realizó la prueba de Jacknife para establecer el porcentaje de participación de las variables en el modelo. Los resultados muestran que ambos escenarios predicen de forma adecuada la distribución potencial de las especies estudiadas. De acuerdo al valor AUC, los ajustes del modelo son mejores para las especies de distribución restringida con 0.999 para P. nelsonii y P. culminicola y de 0.998 para P. cembroides, integrando el modelo la altitud, la temperatura mínima y la pendiente, con contribución promedio de 18.6, 16.6 y 32.8% cada variable. La distribución potencial de P. cembroides puede ampliarse en el noreste de México, debido a que el modelo integró de forma signicativa variables de temperatura máxima y discriminó aquellas derivadas de la precipitación.

Modelling palaeoglaciers in mountainous terrain is challenging due to the need for detailed ice flow computations in relatively narrow and steep valleys, high‐resolution climate estimations, knowledge of pre‐ice topography, and proxy‐based palaeoclimate forcing. The Parallel Ice Sheet Model (PISM), a numerical model that approximates glacier sliding and deformation to simulate large ice sheets such as Greenland and Antarctica, was recently adapted to alpine environments. In an attempt to reconstruct the climate conditions during the Last Glacial Maximum (LGM) on Mount Dedegöl in SW Turkey, we used PISM and explored palaeoglacier dynamics at high spatial resolution (100 m) in a relatively small domain (225 km2). Palaeoice‐flow fields were modelled as a function of present temperature and precipitation. Nine different palaeoclimate simulations were run to reach the steady‐state glacier extents and the modelled glacial areas were compared with the field‐based and chronologically well‐established ice extents. Although our results provide a non‐unique solution, best‐fit scenarios indicate that the LGM climate on Mount Dedegöl was between 9.2 and 10.6 °C colder than today, while precipitation levels were the same as today. More humid (20% wetter) or arid (20% drier) conditions than today bring the palaeotemperature estimates to 7.7–8.8 or 11.5–13.2 °C lower than present, respectively. [ABSTRACT FROM AUTHOR]

Brown, Hill & Haywood (2020) offered a critical evaluation of the theory and methods of the Oscillayers approach and attempted to test its utility by reproducing global circulation model (GCM)‐based palaeoclimatic reconstructions of two variables (Bio1 and Bio12) for four different time points (130 ka, 787 ka, 3.2 Ma and 3.3 Ma). They concluded that Oscillayers show poor agreement with independent GCMs and thus do not provide a robust approximation of palaeoclimate throughout the Plio‐Pleistocene. Here, I demonstrate that the authors underestimated the ability of Oscillayers to reproduce independent GCMs by not taking into account inter‐framework differences between the models used to generate the Oscillayers and PaleoClim datasets. However, upon correcting this systematic error, differences in Bio1 and Bio12 between Oscillayers and PaleoClim GCMs are less than ± 1°C or ± 50 mm, on average, in 35.9% (range: 11.8–59.0%) or 46.7% (20.8–66.0%) of values, respectively. Thus, the agreement between Oscillayers and PaleoClim is, on average, c. 1.5–2 times higher than estimated by Brown et al. (2020) and mostly well above the inter‐model agreement between two commonly used GCMs (CCSM and MIROC) for the Last Glacial Maximum. Consequently, I conclude that the Oscillayers approach does provide reasonably robust approximations of palaeoclimate throughout the Plio‐Pleistocene. Some clarifications are given. [ABSTRACT FROM AUTHOR]

The Iberian Peninsula (Portugal and Spain) is a great production area of olives. The fruit production can be severely affected by the olive fruit fly, Bactrocera oleae (Rossi, 1790) (Diptera). Detailed geographical distribution maps of key pests, such as B. oleae, are essential for their integrated management. Although different sources reporting the occurrence of B. oleae are available for sub‐regions of Portugal and Spain, the data available are dispersed and centralisation of this information considering the Iberian Peninsula as a faunistic geographical unit is currently lacking. In this work, we built two distribution maps of B. oleae throughout the Iberian Peninsula, one based on occurrence sites and another based on its bioclimatic habitat suitability. After modelling the bioclimatic suitability of B. oleae using a maximum entropy model, three potential distribution areas beyond the previously known occurrence range of the olive fruit fly were identified corresponding to the autonomous community of Galicia (Spain), the Spanish and Portuguese sides of the International Douro Natural Park, and the autonomous community of Castilla y León (Spain). Interestingly, each region houses nowadays autochthonous olive cultivars. The drivers that most contributed to the model were the precipitation of the coldest quarter and the precipitation of driest month which agrees with the B. oleae bioecology. Although our approach is not fully‐comprehensive in terms of occurrence sites, we show how a maxent modelling approach can be useful to identify potential risk areas of B. oleae occurrence throughout a target geographical extent such as the Iberian Peninsula. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator‐Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%–84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision‐makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease. [ABSTRACT FROM AUTHOR]

Despite the numerous scientific information on global climate change, there are no studies that show effects on biodiversity on a smaller scale. Therefore, using 19 bioclimatic variables, five solar radiation, altitude, specialized software (MaxEnt) and geographical coordinates of the presence of five species of categorized flora verified in the field, their current ecological niches were modeled and projected to the four future emission scenarios (2050 and 2070) showing that Buddleja coriacea will decrease by more than 80% due to future variations in precipitation and temperature due to climate change, while Carica candicans, Haplorhus peruviana, Kageneckia lanceolata and Weberbauerella brongniartioides will remain and increase, deducing that Its threat categories derive mainly from anthropogenic activity. These models significantly improve the understanding of ecosystem functioning, providing useful information to design conservation policies and actions that guide territorial management towards the Ecosystem-based Adaptation strategy. [ABSTRACT FROM AUTHOR]

Background: Climate change is believed to be continuously affecting ticks by influencing their habitat suitability. However, we attempted to model the climate change-induced impacts on future genus Rhipicephalus distribution considering the major environmental factors that would influence the tick. Therefore, 50 tick occuance points were taken to model the potential distribution using maximum entropy (MaxEnt) software and 19 climatic variables, taking into account the ability for future climatic change under representative concentration pathways (RCPs) 4.5 and 8.5, were used. Results: MaxEnt model performance was tested and found with the AUC value of 0.99 which indicates excellent goodness-of-fit and predictive accuracy. Current models predict increased temperatures, both in the mid and end terms together with possible changes of other climatic factors like precipitation which may lead to higher tick-borne disease risks associated with expansion of the range of the targeted tick distribution. Distribution maps were constructed for the current, 2050, and 2070 for the two greenhouse gas scenarios and the most dramatic scenario; RCP 8.5 produced the highest increase probable distribution range. Conclusions: The future potential distribution of the genus Rhipicephalus show potential expansion to the new areas due to the future climatic suitability increase. These results indicate that the genus population of the targeted tick could emerge in areas in which they are currently lacking; increased incidence of tick-borne diseases poses further risk which can affect cattle production and productivity, thereby affecting the livelihood of smallholding farmers. Therefore, it is recommended to implement climate change adaptation practices to minimize the impacts. [ABSTRACT FROM AUTHOR]

In studies of flow regionalization, the uncertainties associated with the precariousness or non-existence of flow and precipitation data are problems faced by researchers and managers of water resources, especially in emerging countries such as Brazil. Given this, the usage of precipitation databases obtained by satellites has become more prevalent, allowing accurate precipitation estimates in regions where punctual data obtained from rain gauges are precarious. Therefore, the objective of this study was to compare different precipitation databases, used as predictive variables, in flow regionalization studies. The study area considered was the hydrographic basin of the Paranaíba River. Flow data from streamflow gauges present in the study area and precipitation data obtained from rain gauges were used, which were interpolated by the simple kriging method, the TRMM satellite and WorldClim. The study area was divided into four homogeneous regions, and only for region 4 the generated regionalization equations have some use restriction, since some adjustments proved to be unsatisfactory. The best fits of the regionalization equations were obtained using the precipitation data from the rain gauges interpolated by simple kriging, however, the use of precipitation data from TRMM and WorldClim as predictive variables of the flow provided similar results. Therefore, it can be considered that the alternative databases (TRMM and WorldClim) used in the study are presented as an option to replace the data observed in the rain gauges, resulting in a gain of time by the researchers and management bodies in the studies of flow regionalization. • The potential regression model was the one that presented the best results. • WorldClim and TRMM had good performance in the analysis. • Interpolated data had the best results for streamflow regionalization. • WorldClim and TRMM are a promising alternative for streamflow regionalization. [ABSTRACT FROM AUTHOR]