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6 results for “WorldClim”
Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim
<p>Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (<a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a>). <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/clim1km">Downscaled to 1 km resolution using gdalwarp</a> (cubic splines) and combined with WorldClim (<a href="https://worldclim.org/data/worldclim21.html">https://worldclim.org/data/worldclim21.html</a>) and CHELSA Climate (<a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a>) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the <a href="https://gpm.nasa.gov/data/imerg">monthly IMERGE dataset</a>, however this requires transformation and is available only for limited span of years.</p> <p>Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Antarctica is not included. Standard deviation (sd) indicates a difference between the 3 data sources. To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a>.<strong> </strong>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>precipitation = variable: precipitation,</li> <li>wc.v2.1.chelsa.v2.1.sm2rain.oct = determination method: long-term average values for October,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1980..2020 = time reference: from 1980 to 2020,</li> <li>v0.3 = version number: 0.3,</li> </ul>
WorldClim, elevation and distribution data for all palms from: The ecology of palm genomes: Repeat-associated genome size expansion is constrained by aridity
<p>Genome size varies 2,400-fold across plants, influencing their evolution through changes in cell size and cell division rates which impact plants' environmental stress tolerance. Repetitive element expansion explains much genome size diversity, and the processes structuring repeat 'communities' are analogous to those structuring ecological communities. However, which environmental stressors influence repeat community dynamics has not yet been examined from an ecological perspective.</p> <p>We measured genome size and leveraged climatic data for 91% of genera within the ecologically diverse palm family (Arecaceae). We then generated genomic repeat profiles for 141 palm species, and analysed repeats using phylogenetically-informed linear models to explore relationships between repeat dynamics and environmental factors.</p> <p>We show that palm genome size and repeat 'community' composition are best explained by aridity. Specifically, <em>Ty3-gypsy</em> and <em>TIR </em>elements were more abundant in palm species from wetter environments, which generally had larger genomes, suggesting amplification. In contrast, <em>Ty1-copia</em> and <em>LINE </em>elements were more abundant in drier environments.</p> <p>Our results suggest that water stress inhibits repeat expansion through selection on upper genome size limits. However, elements which may associate with stress-response genes (e.g., <em>Ty1-copia</em>) have amplified in arid-adapted palm species. Overall, we provide novel evidence of climate influencing the assembly of repeat 'communities'. </p>
WorldClim, elevation and distribution data for all palms from: The ecology of palm genomes: Repeat-associated genome size expansion is constrained by aridity
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Worldclim 2.1 versus Worldclim 1.4: climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe
<p>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.</p>
SRTM y WorldClim para Curso de mapeo digital de suelos
<p>Datos requeridos para Curso Introductorio al Mapeo digital de suelos</p> <p>https://github.com/angelini75/Curso-introductorio-a-MDS</p>
Worldclim 2.1 versus Worldclim 1.4: climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe
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