WorldClim Dataset Research Articles

Anthropogenic Pressures Lead to Different Patterns of Niche Contraction and Protected Area Cover in Three Species Procapra Gazelles on Qinghai‐Tibet Plateau and Mongolia

ABSTRACTAimAnthropogenic threats often lead to range contraction towards the margins of a species historic niche, resulting in increased extinction risk. Here, we investigate niche characteristics of current and historic populations to evaluate changes in ‘Area of Niche’ (AON) following range loss from different levels of anthropogenic threats three congeneric Asian gazelle species are facing: Przewalski's (Procapra przewalskii), Tibetan (P. picticaudata) and Mongolian gazelles (P. gutturosa).LocationTibet, Qinghai and Mongolia.MethodsWe collated range maps for historic and contemporary distributions of Przewalski's, Tibetan and Mongolian gazelles and created 3‐dimensional hypervolume and convex hull niche models using environmental variables from the Worldclim dataset (v2) together with topographic information from SRTM elevation data from historic and contemporary Area of Habitat maps and evaluated changes over time. We calculated Area of Niche (AON) maps by projecting a scaled Mahalanobis distance from the historic niche centroid of each grid cell onto each species' historic range. Finally, we evaluated how the protected area network overlaps with historic niche characteristics.ResultsThe endangered Przewalski's gazelle has lost almost all its range and niche, with remaining populations at niche peripheries. In contrast, the near‐threatened Tibetan and least‐concern Mongolian gazelles have lost less range and niche and contracted towards their historic niche centre. Protected areas for each species were biased towards the ecological margins of their historic ranges, which can result in sub‐optimal conservation strategies.Main ConclusionsThis study uses niche modelling to evaluate changes in Area of Niche (AON) occupied by a species that has undergone range contraction. We highlight that species most affected by anthropogenic threats are most vulnerable to niche shift and contraction. These species are also vulnerable to a mismatch between the protected area network and species historic niche space. We advocate that conservation strategies should include niche dynamics as an indicator of the species risk.

Expanded Bioclimatic Variables Extracted from Monthly Climate Predictions under the SSP Climate Scenarios over South Korea

Numerous studies, including the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report, have documented species habitat shifts caused by climate change. These shifts lead to transformations in ecosystem structure, components, and functions. Exploring the connections between species and climate change is essential for developing adaptation strategies. Many studies use species distribution models (SDMs), which are based on the correlation between species habitats and climatic surroundings, to predict ecological shifts under climate change. The primary climate variables for these models are the only 19 variables whose concepts are based on monthly average temperature and precipitation from the BIOCLIM package developed in 1984. These 19 bioclimatic variables usually are obtained from WorldClim data set and other datasets. However, they have limitations in reflecting local climate characteristics and their association with ecology. Firstly, future projection data from global dataset including WorldClim dataset is derived directly from global climate models rather than regional climate models. Secondly, the 19 variables based on monthly temperature and precipitation do not adequately express hydrological characteristics of terrestrial ecosystem which are crucial for species habitats. Lastly, although there are various biogeographical indices excepts the 19 bioclimatic variables, there have been just a few cases that they were applied to SDMs for Korea. To overcome these limitations, this study expands the various bioclimatic variables, using regionally specialized climate data from Korea Meteorology Administration (KMA). The newly extended indices, which can reflect water availability, are expected to improve the prediction of SDMs, enabling more precise assessment of ecological risks due to climate change and effective adaptation strategies to mitigate the impacts of climate change on ecosystems.

Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China.

The alpine wetlands in western Sichuan are distributed along the eastern section of the Qinghai-Tibet Plateau (QTP), where the ecological environment is fragile and highly sensitive to global climate change. These wetlands are already experiencing severe ecological and environmental issues, such as drought, retrogressive succession, and desertification. However, due to the limitations of computational models, previous studies have been unable to adequately understand the spatiotemporal change trends of these alpine wetlands. We employed a large sample and composite supervised classification algorithms to classify alpine wetlands and generate wetland maps, based on the Google Earth Engine cloud computing platform. The thematic maps were then grid-sampled for predictive modeling of future wetland changes. Four species distribution models (SDMs), BIOCLIM, DOMAIN, MAXENT, and GARP were innovatively introduced. Using the WorldClim dataset as environmental variables, we predicted the future distribution of wetlands in western Sichuan under multiple climate scenarios. The Kappa coefficients for Landsat 8 and Sentinel 2 were 0.89 and 0.91, respectively. Among the four SDMs, MAXENT achieved a higher accuracy (α = 91.6%) for the actual wetland compared to the thematic overlay analysis. The area under the curve (AUC) of the MAXENT model simulations for wetland spatial distribution were all greater than 0.80. This suggests that incorporating the SDM model into land change simulations has high generalizability and significant advantages on a large scale. Furthermore, simulation results reveal that between 2021 and 2100 years, with increasing emission concentrations, highly suitable areas for wetland development exhibit significant spatial differentiation. In particular, wetland areas in high-altitude regions are expected to increase, while low-altitude regions will markedly shrink. The changes in the future spatial distribution of wetlands show a high level of consistency with historical climate changes, with warming being the main driving force behind the spatiotemporal changes in alpine wetlands in western Sichuan, especially evident in the central high-altitude and northern low-altitude areas.

Assessing drought in Turkish basins through satellite observations

AbstractDrought 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.

Historic Changes and Future Projections in Köppen–Geiger Climate Classifications in Major Wine Regions Worldwide

A valuable tool for comprehending and characterizing climate patterns on a global scale is the Köppen–Geiger climate classification system. When it comes to wine production, the climate of a region plays an essential role in determining whether specific grape varieties can be cultivated, largely determining the style of wine that can be made, and influencing the consistency of overall wine quality. In this study, the application of the Köppen–Geiger classification system to the latest Coupled Model Intercomparison Project (CMIP6) experiments has been explored. To establish a baseline for the historical period (1970–2000), the WorldClim dataset was used alongside a selection of an ensemble of 14 Global Climate Models. The evaluation of climate variability across winemaking regions is conducted by considering future climate projections from 2041 to 2060, which are based on different anthropogenic radiative forcing scenarios (Shared Socioeconomic Pathways, SSP2–4.5, and SSP5–8.5). The results are the most comprehensive documentation of both the historical climate classifications for most wine regions worldwide and the potential changes in these classifications in the future. General changes in climate types are projected to occur largely in a significant shift from a warm summer climate to a hot summer climate in temperate and dry zones worldwide (climate types C and B, respectively). This shift poses challenges for grape cultivation and wine production. The grape development process can be significantly affected by high temperatures, which could result in early ripening and changes in the grape berry’s aromatic compounds. As regions transition and experience different climates, wine producers are required to adapt their vineyard management strategies by implementing suitable measures that can effectively counter the detrimental impacts of abiotic stresses on grape quality and vineyard health. These adaptation measures may include changes in canopy and soil management, using different variety-clone-rootstock combinations, adopting irrigation methods, or shifting into other microclimatic zones, among other effective techniques. To ensure long-term sustainability, wine producers must consider the climatic change projections that are specific to their region, allowing them to make more informed decisions about vineyard management practices, reducing risks, and ultimately making the wine industry more resilient and adaptive to the ongoing effects of climate change.

Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin

Surface gauge measurements have been commonly employed to analyze the precipitation's high spatial and temporal variability. However, incomplete area coverage and deficiencies over most tropical and complex topography mean significant limitations of this measurement type. Satellite-derived datasets, combined with the integration of in-situ observations with satellite data, are an alternative to address these limitations by offering a more spatially homogeneous and temporally comprehensive coverage for scarce data areas of the globe. Nevertheless, applying a bias correction technique on the precipitation datasets is still necessary before they are used for research due to their considerable bias. Here, we analyze the performance of CHIRPS, WorldClim, and TerraClimate datasets compared to data from 30 rain gauge stations over the South-West of Colombia, specifically in the Upper Cauca River Basin-UCRB between 1981 and 2018. Additionally, we applied the Quantile Mapping correction to all gridded precipitation products, and subsequently, the corrected rainfall is compared to the observed data on the monthly, seasonal, and annual scale. Our results show that the CHIRPS dataset better captures the seasonal and monthly variability. CHIRPS presents the best performance during less rainy seasons and at low elevation zones (900–2000 m above sea level-m.a.s.l.), followed by TerraClimate. Utilizing the bias correction methodology, we generated a new, corrected, and more reliable monthly precipitation time series for each location from all gridded precipitation products. Additionally, we found that the correction of the CHIRPS dataset presented the best performance across all spatiotemporal scales in the UCRB. Therefore, this study provides an accurate precipitation database for a complex topographic tropical region with limited data availability.

A machine learning approach to mapping suitable areas for forest vegetation in the eThekwini municipality

Driven by climate change, global forests are undergoing significant transformations in growth, ecology, and distribution, necessitating informed restoration and conservation strategies, particularly in the eThekwini Municipality where anthropogenic activities exacerbate these trends. Modelling current forest suitability (2023) utilized bioclimatic variables from the WorldClim dataset, alongside elevation and slope from the Shuttle Radar Topography Mission (SRTM) dataset, with remote sensing data acquired from Landsat 9 and Sentinel 2A. Future forest suitability (2021–2040) was projected also using bioclimatic variables from two Global Climate Models (GCMs) under four WorldClim Shared Socioeconomic Pathway (SSP)-based Representative Concentration Pathway (RCP) scenarios. Employing Random Forests (RF), Light Gradient Boosting (LightGBM), and Artificial Neural Networks (ANN), data processing was carried out using Google Earth Engine (GEE), QGIS and Python, with model accuracy primarily assessed using the Receiver Operating Characteristic (ROC) curves and the Area Under the ROC Curve (AUC). LightGBM demonstrated superior performance, achieving AUCs of 96.88% and 93.75% for current and future suitability mapping, respectively, with annual precipitation and vegetation changes identified as crucial variables. Currently, 30% of the municipality's land is deemed suitable, primarily concentrated in the central region. Future projections highlight the mountainous north-western region as most suitable, notably under the SSP370 scenario with a projected suitable area of 63%. Strategic recommendations include prioritizing reforestation efforts, engaging private landowners, exploring urban reforestation opportunities, and implementing continuous monitoring for adaptive management, thereby enhancing carbon sequestration, biodiversity conservation, and ecosystem resilience. This study provides valuable insights for informed decision-making in forest restoration and conservation, despite inherent uncertainties.

An Endemic Vascular Plant Species for Türkiye, Ferulago Humilis Boiss., and Its Potential Distribution Areas

Turkey is expected to be affected considerably in future adverse climatic conditions. Plant species are one of the most vulnerable to these climatic changes. In this study, we aimed to investigate current and future potential distribution areas of Ferulago humilis Boiss., which is an endemic vascular plant species for Turkey, using CMIP5 projected to year 2070. For this purpose, we obtained occurence data (presence-only) from Global Biodiversity Information Facility (GBIF). Regarding bioclimatic data we used WorldClim dataset with 10 km2 resolution. Using both plant occurrence and bioclimatic data, we performed species distribution modelling analysis. We used two methods namely Boosted Regression Trees (BRT) and Random Forest (RF). Additionally, we used bootstrapping method as partitioning resampling for all analysis. Our analysis has showed that potential distribution areas of the species has slightly changed for the future projection. The species movement is towards slightly upwards as higher latitudes. We believe that our study shows the importance and relevance of the endemic species in the scope of species distribution models for plant conservation topics.

Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

AbstractDelineation of carbon management zones (CMZs) by capturing geospatial distribution of soil organic carbon (SOC) stock down the profile is an effective strategy for precision agriculture and climate change mitigation. Satellite (Landsat OLI 8), terrain (SRTM 30 m DEM) and bioclimatic (WorldClim dataset) factors were used as covariables in this digital soil mapping approach. Depth harmonization using the quadratic spline method (equal‐area) was carried out prior to quantile regression forest (QRF) algorithm‐based modelling to estimate SOC stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). Soil depth and SOC stock for the whole soil profile were used for the delineation of CMZs using fuzzy k‐means clustering. The predicted SOC stock, varied from 14.68 to 42.35 Mg ha−1 in the top layer (0–5 cm depth), while 17.91 to 36.88, 14.15 to 34.70, 12.55 to 35.59, 10.30 to 28.52 and 7.26 to 20.16 Mg ha−1 in the depths of 5–15, 15–30, 30–60, 60–100 and 100–200 cm, respectively. The QRF algorithm performed well in predicting SOC stock with high R2, which ranged from .67 to .83 for all the soil depths. To delineate three CMZs, modified partitioning entropy and the fuzzy performance index were used. In CMZ2, there was a significant increase in SOC stock, followed by CMZ1 and CMZ3. This zone (CMZ2) was located in the central region of the study area and was mostly covered by dense forest and perennial plantations (rubber). The CMZs provided the necessary foundation for the development of site‐specific carbon management techniques that can enhance ecosystem service and meet climate change mitigation goals.

Identifying the geographic distribution pattern of venomous snakes and regions of high snakebite risk in Iran

We describe species richness patterns of venomous snakes in Iran in order to produce snakebite risk prediction maps and identify gaps in regional health care centers capable of managing snakebites. We digitized distribution maps from the literature, Global Biodiversity Information Facility (GBIF), and the results of our own field studies of 24 terrestrial venomous snake species (including 4 endemic to Iran). Species richness patterns were associated with eight environmental factors. The variables have been extracted from the WorldClim dataset (bio12 = annual precipitation, bio15 = precipitation seasonality, bio17 = precipitation of the driest quarter, bio2 = mean diurnal range, bio3 = isothermality (bio2/bio7), bio4 = temperature seasonality, bio9 = mean temperature of the driest quarter and slope). Based on spatial analyses, species richness in Iran is highly affected by three environmental variables (bio12, 15, and 17) associated with precipitation. The relationship patterns among these predictors and species richness were strong and linear. The hotspot regions for venomous snakes species are concentrated on the western to southwestern and north to northeastern regions of Iran, which is partially consistent with the known Irano-Anatolian biodiversity hotspot. Because of the high number of endemic species and climatic conditions on the Iranian Plateau, the venoms of snakes distributed in those areas may contain novel properties and components.

Comparing the Performance of CMCC-BioClimInd and WorldClim Datasets in Predicting Global Invasive Plant Distributions.

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.

Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity

Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC’s accurate and high-resolution spatial information is needed for the sustainability of agricultural management on farms in the Nagaland state (northeast India) which are fragmented and intertwined with the forest ecosystem. The current study applied the digital soil mapping (DSM) methodology, based on the CEC values determined in soil samples obtained from 305 points in the region, which is mountainous and difficult to access. Firstly, digital auxiliary data were obtained from three open-access sources, including indices generated from the time series Landsat 8 OLI satellite, topographic variables derived from a digital elevation model (DEM), and the WorldClim dataset. Furthermore, the CEC values and the auxiliary were used data to model Lasso regression (LR), stochastic gradient boosting (GBM), support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN) machine learning (ML) algorithms were systematically compared in the R-Core Environment Program. Model performance were evaluated with the square root mean error (RMSE), determination coefficient (R2), and mean absolute error (MAE) of 10-fold cross-validation (CV). The lowest RMSE was obtained by the RF algorithm with 4.12 cmolc kg−1, while the others were in the following order: SVR (4.27 cmolc kg−1) <KNN (4.45 cmolc kg−1) <LR (4.67 cmolc kg−1) <GBM (5.07 cmolc kg−1). In particular, WorldClim-based climate covariates such as annual mean temperature (BIO-1), annual precipitation (BIO-12), elevation, and solar radiation were the most important variables in all algorithms. High uncertainty (SD) values have been found in areas with low soil sampling density and this finding is to be considered in future soil surveys.

PROJECTION OF CLIMATE CHANGE IMPACTS ON NET PRIMARY PRODUCTIVITY OF THE LEGAL AMAZON – BRAZIL

The Amazon Rainforest is one of the main carbon sinks (CO2) on the planet. However, recently, due to anthropic activities and climate change, it has lost its stability in CO2 absorption. Therefore, understanding the dynamics of future climate change scenarios becomes essential. We assess the influence of future climate change scenarios on NPP (biomass) levels in the Amazon Forest using ML models. The tested models were Bayesian, Linear Model, and Random Forest. The current scenario was evaluated using 19 bioclimatic covariates (worldclim dataset). While the future scenarios were based on RCPs 2.6 and 8.5 (based on the models of the MIROC5 and HadGEM2-ES models). Random Forest had the best performance statistic (R² = 0.71 in training and 0.68 in holdout-test). The climate change scenarios will imply an increase in the average NPP for the Amazon forest, especially with greater intensification in RCP 2.6 (2 and 7.69 % for the HadGEM2-ES and MIROC5 models, respectively). Forests (Evergreen Broadleaf Forests areas) will have greater carbon fixation capacity. In general, the Amazon forest will have increased carbon fixation capacity by the end of the century.

Smart farming: modeling distribution of Xanthomonas campestris pv. oryzae as a leaf blight-causing bacteria in rice plants

Regarding plant protection in agriculture, it has been known that the cause of leaf blight is the bacteria named Xanthomonas campestris pv. oryzae. This paper aims to conduct a species distribution model of leaf blight-causing bacteria in rice plants and elaborates on its habitat suitability throughout Indonesia within the climate change context. The occurrences data was extracted from the Global Biodiversity Information Facility (GBIF), whereas the climate data was obtained from the Worldclim data set. The Species Distribution Model used the Maximum entropy method available on the Ecocommons website. The result shows that the bacteria occurrences positively correlate with several climatic variables and spread throughout the archipelago presented into five classes. Main islands such as Java, Bali, and Sumatra share areas with the highest suitability values. While Kalimantan and Sulawesi only share small areas with high suitability area. Papua has a less suitable location for the bacteria to spread. Rice cultivation is inseparable from the threat of pests and diseases. It can cause losses in the form of decreased production to crop failure. Therefore, The Species Distribution Model needed to identify areas where the vector is likely to occur. This way, mitigation or even prevention efforts could be made effectively.

The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study

The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984-2010 and the WorldClim dataset. A temperature-dependent R0 model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from -5·6% (95% CI -4·5 to -6·6) to -9·2% (95% CI -7·6 to -10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. Scientific and Technological Innovation 2030: Major Project of New Generation Artificial Intelligence, National Natural Science Foundation of China, Beijing Natural Science Foundation, National Key Research and Development Program of China, Young Elite Scientist Sponsorship Program by CAST, Research on Key Technologies of Plague Prevention and Control in Inner Mongolia Autonomous Region, and Beijing Advanced Innovation Program for Land Surface Science.

The Impacts of Current Climate Variability on Coffee Production in the Northern and Southern Highlands of Tanzania

Coffee is the most traded commodity in the world. In Tanzania, Coffee is the second largest traditional commodity. However, several climate change studies have predicted that coffee production will be reduced as a result of climate change. Therefore, the study aimed to assess the impact of current climate change on Tanzania’s Arabica coffee production and determine the most significant climatic variables, which influence coffee production in the respective regions. Global interpolated climatic database (Worldclim dataset) and official historical coffee production data from Tanzania Coffee Board for a period of 40 years (1970-2018) were used. Climatic parameters and coffee production were compared through descriptive statistics, correlation analysis, and multiple regressions. The Mann-Kendall method was used to detect significant trends in climatic data. The minimum temperature has been increasing at a higher rate than the maximum temperature in the Northern and Southern Highlands zones. A 1 °C increase in minimum temperature (Tmin) during short rains and annual mean temperature (Tmean) resulted in a significant coffee production decrease (-6,041 and -4,450 tons) in Kilimanjaro and Arusha regions respectively. In the Southern Highlands zone coffee production positively correlated with temperature. A significant reduction in coffee production due to a decline in long rains was also observed in the Kilimanjaro region. The warming and drought trends are likely to continue with significant implications on coffee production and this, calls for the development of suitable adaptation strategies to sustain production. Such strategies may include, re-adapting the coffee agronomic practices to climate change, improving water and nutrient use efficiency in coffee trees, and developing genetically improved coffee cultivars that will tolerate the impact of climate change.

КАРТОГРАФИРОВАНИЕ СОВРЕМЕННЫХ ИЗМЕНЕНИЙ КЛИМАТА В БАССЕЙНЕ РЕКИ СЕЛЕНГА

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 ≥25 °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.

Will climate change be harmful for small tropical islands? The case of Fernando de Noronha Archipelago, Brazil

The Fernando de Noronha archipelago is a key site for biodiversity conservation, besides being one of the most searched destinies for ecotourism in Brazil. We present the first study focusing on the evaluation of this important archipelago’s exposure to climate change. Our metric was based on the differences between current and future predictions of climatic and bioclimatic variables obtained from the WorldClim dataset. For the predictions, we considered two models of radiative forcing, the first optimistic and the second a business-as-usual scenario of greenhouse gases emissions. We showed an increase in average temperature and decrease in annual precipitation for the archipelago, although for the driest period of the year the precipitation is likely to increase. We also recovered a decrease in differences between interannual temperature variation and diurnal temperature variation, indicating a shift in the seasonality patterns. These changes can be potentially harmful to local biodiversity and consequently to local economy, since it is based on touristic activities that involves natural environments. The maintenance of local vegetation cover is likely to be a good strategy to avoid the local increase of environmental temperature, however, a global commitment to decrease the greenhouse gases emissions is paramount to avoid a potential collapse of small islands around the world, including the Fernando de Noronha archipelago.

Projections of soil loss by water erosion in Europe by 2050

Changes in future soil erosion rates are driven by climatic conditions, land use patterns, socio-economic development, farmers’ choices, and importantly modified by agro-environmental policies. This study simulates the impact of expected climatic and land use change projections on future rates of soil erosion by water (sheet and rill processes) in 2050 within the agricultural areas of the European Union and the UK, compared to a current representative baseline (2016). We used the Revised Universal Soil Loss Equation (RUSLE) adjusted at continental scale with projections of future rainfall erosivity and land use change. Future rainfall erosivity is predicted using an average composite of 19 Global Climate Models (GCMs) from the Coupled Model Inter-comparison Projects (CMIP5) WorldClim dataset across three Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5). Concerning future land use change and crop dynamics, we used the projections provided by the Common Agricultural Policy Regional Impact Analysis (CAPRI) model.Depending on the RCP scenario, we estimate a +13 %-22.5 % increase in the mean soil erosion rate in the EU and UK, rising from an estimated 3.07 t ha−1 yr−1 (2016) to between 3.46 t ha−1 yr−1 (RCP2.6 scenario) and 3.76 t ha−1 yr−1 (RCP8.5 scenario). Here, we disentangle the impact of land use change and climate change in relation to future soil losses. Projected land use change in the EU and UK indicates an overall increase of pasture coverage in place of croplands. This land use change is estimated to reduce soil erosion rates (-3%). In contrast, the increases in future rainfall erosivity (+15.7 %–25.5 %) will force important increases of soil erosion requiring further targeted intervention measures.Given that agro-environmental policies will be the most effective mechanisms to offset this future increase in soil erosion rates, this study proposes soil conservation instruments foreseen in the EU Common Agricultural Policy (CAP) to run policy scenarios. A targeted application of cover crops in soil erosion hotspots combined with limited soil disturbance measures can partially or completely mitigate the effect of climate change on soil losses. Effective mitigation of future soil losses requires policy measures for soil conservation on at least 50 % of agricultural land with erosion rates above 5 t ha−1 yr−1.

Effects of Climate Change on Distribution Areas of Former Endemic Plant Species Campanula lyrata Lam.

Species distribution models (SDMs) are useful tools for future potential distribution patterns of species in the face of climate change. Turkey is expected to be affected considerably from climatic change i.e., up to 6°C increase in temperature and 50% decrease in precipitation by 2070. Therefore, there is an urgent need for conservation and management practices for future patterns of species. It is aimed current and future (using CMIP5 projected to 2070) potential distribution areas of Campanula lyrata Lam., which is formerly an endemic species. To do this, presence-only data was used, which is obtained from the Global Biodiversity Information Facility (GBIF). Bioclimatic data from was downloaded from WorldClim dataset with 10 km2 resolution. Species distribution modelling was performed using R program. Two regression techniques and two machine learning techniques namely Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), Support Vector Machine (SVM) and Random Forest (RF), were used, respectively. The bootstrapping method as partitioning resampling was also used for all analysis. Considerably high model performances as well as AUC values for all possible models were found. Significant range shifts between current and future climatic conditions were found. The most relevant relative importance variables were precipitation seasonality and precipitation of the wettest month. This study reveals the importance of the future distributional areas of species.Species distribution models (SDMs) are useful tools for future potential distribution patterns of species in the face of climate change. Turkey is expected to be affected considerably from climatic change i.e., up to 6°C increase in temperature and 50% decrease in precipitation by 2070. Therefore, there is an urgent need for conservation and management practices for future patterns of species. It is aimed current and future (using CMIP5 projected to 2070) potential distribution areas of Campanula lyrata Lam., which is formerly an endemic species. To do this, presence-only data was used, which is obtained from the Global Biodiversity Information Facility (GBIF). Bioclimatic data from was downloaded from WorldClim dataset with 10 km2 resolution. Species distribution modelling was performed using R program. Two regression techniques and two machine learning techniques namely Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), Support Vector Machine (SVM) and Random Forest (RF), were used, respectively. The bootstrapping method as partitioning resampling was also used for all analysis. Considerably high model performances as well as AUC values for all possible models were found. Significant range shifts between current and future climatic conditions were found. The most relevant relative importance variables were precipitation seasonality and precipitation of the wettest month. This study reveals the importance of the future distributional areas of species.