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      Consequence of habitat specificity: a rising risk of habitat loss for endemic and sub-endemic woody species under climate change in the Hyrcanian ecoregion

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          Abstract

          Endemic species are more impacted by climate change than other taxa. However, assessing the vulnerability of endemics to these changes in some regions, such as the Hyrcanian forest, is limited, despite its importance for biodiversity and ecosystem function. To address the question of expected habitat shifts under climate change across the Hyrcanian ecoregion, we built an ensemble of species distribution models (SDM) under two emission scenarios (RCP 4.5 and RCP 8.5) for 15 endemic woody taxa. To identify the potential priority conservation areas, we also applied a spatial prioritization approach. Overall, our results suggest that the impacts of climate change are more severe on the eastern parts of the region (Golestan) and the Talysh Mountains (north-western Hyrcanian ecoregion) with over 85% and 34% loss of suitable habitats over the next 80 years. The central part of the Alborz Mountains (Mazandaran) and some areas in the Talysh Mountains could be potential climatic refugia under the future conditions for endemic taxa. The most prominent changes are expected for Ruscus hyrcanus, Gleditsia capsica, Acer velutinum, Frangula grandifolia, and Buxus hyrcana. The worrying predicted loss of suitable habitats for most studied taxa would dramatically affect the stability and resilience of forests, threatening thus biodiversity of the Hyrcanian ecoregion. We present the first estimation of the potential risks involved and provide useful support for regional climate-adaptation strategy, indicating potential conservation priority areas for maintaining and preserving its resources. Notably, only 13.4% of areas designated for conservation and management under climate change will be located within the current Hyrcanian protected areas, yet the majority of these areas are classified as low priority.

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          Climatologies at high resolution for the earth’s land surface areas

          High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.
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            A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests

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              SoilGrids250m: Global gridded soil information based on machine learning

              This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

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                Contributors
                (View ORCID Profile)
                Journal
                Regional Environmental Change
                Reg Environ Change
                Springer Science and Business Media LLC
                1436-3798
                1436-378X
                June 2024
                April 20 2024
                June 2024
                : 24
                : 2
                Article
                10.1007/s10113-024-02222-7
                ff68ec01-45dd-4068-9e74-ce095a30838b
                © 2024

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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