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70 results for “Climatic suitability”
Data from: Quaternary climatic fluctuations and resulting climatically suitable areas for Eurasian owlets
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Terrain- and climate-based habitat suitability indices for the reef-building coral Lophelia pertusa on the Blake Plateau (southeastern United States)
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Data from: Combining US and Canadian forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change
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Data from: Community science validates climate suitability projections from ecological niche modeling
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Baseline and Future (2050s and 2090s) Climate Suitability Scores for 137 Useful Tree Species and 273 locations from the United Republic of Tanzania
<p>Climate suitability scores were calculated for 137 Useful Tree Species identified by filtering native tree species from the United Republic of Tanzania via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database, matching species with those described in the <a href="https://apps.worldagroforestry.org/usefultrees/">RELMA-ICRAF Useful Tree and Shrub Species for Tanzania manual</a> and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database.</p> <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variable</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> <p> </p> <p>Locations corresponded to cities within the target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> database. This database provides bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s.</p> <p>Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis:</p> <ul> <li>One set of bioclimatic variables included BIO01, BIO12, climaticMoistureIndex, monthCountByTemp10, growingDegDays5, BIO05, BIO06, BIO16, BIO17 and MCWD. These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (= Mean Annual Temperature), which is the same bioclimatic variables available from the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> <p>Calculations were made with similar scripting pipelines in the <em>R</em> statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.</p> <p>The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 6 species not documented by the TreeGOER.</p> <p> </p> <p>The Excel database allows filtering useful tree species by some of the attributes available in the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database.</p> <p>Species can be filtered for ten categories of documented human uses (see <a href="https://kew.iro.bl.uk/concern/datasets/7243d727-e28d-419d-a8f7-9ebef5b9e03e">Diazgranados et al. 2020</a> for details):</p> <ul> <li>AF: Animal Food.</li> <li>EU: Environmental Uses.</li> <li>FU: Fuel.</li> <li>GS: Gene Sources.</li> <li>HF: Human Food.</li> <li>IF: Invertebrate Food.</li> <li>MA: Materials.</li> <li>ME: Medicines.</li> <li>PO: Poisons.</li> <li>SU: Social Uses</li> </ul> <p>Species can also be filtered for the Climatic Moisture Index (CMI). See this Zenodo archive (<a href="https://zenodo.org/records/8252756">https://zenodo.org/records/8252756</a>) to see the distribution of CMI zones across the United Republic of Tanzania. Codings refer to the species reaching the upper part of the range in the zone (code: 2), the zone being included in teh middle part of the ranage (code: 9) or the species reaching the lower part of the range in this zone (code:3).</p> <ul> <li>CMI.A (CMI ≥ 0.5 ; P >= 2 * PET; ‘extremely humid’ lands)</li> <li>CMI.B (0 ≤ CMI < 0.5 ; PET <= P < 2 * PET ; ‘very humid’ lands)</li> <li>CMI.C (−0.35 ≤ CMI < 0 ; 0.65 <= P/PET < 1 ; ‘humid’ lands)</li> <li>CMI.D ( −0.5 ≤ CMI < −0.35 ; 0.50 <= P/PET < 0.65 ; dry sub-humid drylands)</li> <li>CMI.E (−0.8 ≤ CMI < −0.5 ; 0.20 <= P/PET < 0.50 ; semi-arid drylands)</li> <li>CMI.F (−0.95 ≤ CMI < −0.8 ; 0.05 <= P/PET < 0.20 ; arid drylands)</li> <li>CMI.G (CMI < −0.95 ; P/PET < 0.05 ; hyper-arid drylands)</li> </ul> <p> </p> <p><strong>References</strong></p> <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. <em>et al.</em> (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> <li>Diazgranados, M., Allkin, B., Black, N., Cámara-Leret, R., Canteiro, C., Carretero, J., Eastwood, R., Hargreaves, S., Hudson, A., Milliken, W. and Nesbitt, M., 2020. World checklist of useful plant species. Royal Botanic Gardens, Kew. <a href="https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34">https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34</a></li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>The data sets and maps available in this archive were created through funding by the <strong>U. S. Agency for International Development (USAID)</strong> to CIFOR-ICRAF, here specifically in the context of the <em>On-farm Land Restoration for Livelihoods and Environmental Benefits</em> project.</p> <p> </p>
Data from: Climatic suitability, isolation by distance and river resistance explain genetic variation in a Brazilian whiptail lizard
Spatial patterns of genetic variation can help understand how environmental factors either permit or restrict gene flow and create opportunities for regional adaptations. Organisms from harsh environments such as the Brazilian semiarid Caatinga biome may reveal how severe climate conditions may affect patterns of genetic variation. Herein we combine information from mitochondrial DNA with physical and environmental features to study the association between different aspects of the Caatinga landscape and spatial genetic variation in the whiptail lizard Ameivula ocellifera. We investigated which of the climatic, environmental, geographical and/or historical components best predict: (1) the spatial distribution of genetic diversity, and (2) the genetic differentiation among populations. We found that genetic variation in A. ocellifera has been influenced mainly by temperature variability, which modulates connectivity among populations. Past climate conditions were important for shaping current genetic diversity, suggesting a time lag in genetic responses. Population structure in A. ocellifera was best explained by both isolation by distance and isolation by resistance (main rivers). Our findings indicate that both physical and climatic features are important for explaining the observed patterns of genetic variation across the xeric Caatinga biome.
The dataset of predicting the potential habitat suitability of Saussurea species in China under future climate change using the optimized Maximum Entropy (MaxEnt) model
<p><strong>Description:</strong></p> <p>This dataset accompanies the study on the Saussurea species, renowned for its biodiversity and medicinal significance in high-elevation regions, which faces endangerment due to climate change and human activities. Despite its importance, conservation research on Saussurea has been limited. To address this gap, the study employed the optimized MaxEnt model to simulate Saussurea's habitat suitability and analyze key environmental factors influencing its distribution.</p> <p>The dataset includes:</p> <ol> <li><strong>Model and Parameter Optimization Code</strong>: The code used for optimizing the MaxEnt model parameters, ensuring reproducibility of the habitat suitability models.</li> <li><strong>Saussurea Distribution Points</strong>: Georeferenced points indicating the observed locations of Saussurea species.</li> <li><strong>Current Environmental Variables</strong>: Data on key environmental factors influencing Saussurea distribution, such as Elevation, Isothermality (Bio3), and Temperature Annual Range (Bio7).</li> <li><strong>Future Environmental Variables: </strong>Data on key environmental factors influencing Saussurea distribution under SSP126, SSP245, SSP370 and SSP585 in 2020-2100s.</li> </ol>
Data from: Climatic suitability, isolation by distance and river resistance explain genetic variation in a Brazilian whiptail lizard
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Dataset of "Impact of climate change on the distribution and habitat suitability of the world's main commercial squids"
<p>Data of the manuscript "Impact of climate change on the distribution and habitat suitability of the world’s main commercial squids"</p>
Data for: Extreme shifts in habitat suitability under contemporary climate change for a high-Arctic herbivore
<p>Data and code associated with MaxEnt analyses to quantify shifts in habitat suitability of muskoxen in the Northeast Greenland National Park. Details on how to use the files are provided in the README.docx file</p>
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Allen Brain Atlas
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