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70 results for “Climatic suitability”
Baseline and Future (2050s and 2090s) Climate Suitability Scores for 116 Useful Tree Species and 220 locations from Côte d'Ivoire, Ghana and Guinea
<p>Climate suitability scores were calculated for 116 Useful Tree Species identified by filtering Top830+ native tree species from Côte d'Ivoire, Ghana and Guinea via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database 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. For some variables, the planting site occurs outside the 25% - 75% species's range.</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables. For some variables, the planting site occurs outside the 5% - 95% species's range.</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> <p>Locations corresponded to cities and weather stations from the three target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> and <a href="https://doi.org/10.5281/zenodo.12679832">ClimateForecasts</a> databases, respectively. Both these databases provide 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 (mean annual temperature), BIO12 (total annual precipitation), climaticMoistureIndex, monthCountByTemp10 (number of months with average temperature above 10 degrees), growingDegDays5, BIO05 (maximum temperature of the warmest month), BIO06 (minimum temperature of teh coldest month), BIO16 (precipitation of the wettest quarter), BIO17 (precipitation of the driest quarter) and MCWD (Maximum Climatological Water Deficit). 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 single bioclimatic variables available for 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 18 species not documented by the TreeGOER.</p> <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> </ul> <p> </p> <p><strong>Funding</strong></p> <p>The data sets and maps available in this archive were created within the context of an agreement between The International Centre for Research in Agroforestry (ICRAF) and WORLD UNIVERSITY SERVICE OF CANADA (WUSC) for a <em><a href="https://ceci.org/en/projects/nature-based-climate-adaptation-guinean-forest-west-africa-sbn-guinean-forests">Nature-based climate adaptation project in the Guinean forests of West Africa (NbS Guinean Forests)</a></em> funded by <a href="https://www.international.gc.ca/global-affairs-affaires-mondiales/home-accueil.aspx?lang=eng">Global Affairs Canada</a>.</p>
Data for: Predicting habitat suitability for Townsend's big-eared bats across California in relation to climate change
<p>Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend's big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions.</p> <p>Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species' distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts.</p> <p>Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species' responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions.</p>
PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS.
<p>This is the data for "PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS."</p>
Climate change shrinks environmental suitability for a viviparous Neotropical skink
<p>Anthropogenic global warming and deforestation are significant drivers of the global biodiversity crisis. Ectothermic and viviparous animals are especially vulnerable since high environmental temperatures can impair embryonic development, but we lack knowledge about these effects upon Neotropical organisms. Here, we estimate how much of the current area with suitable habitats overlaps with protected areas and model the combined effects of climate change and deforestation on the geographic distribution of the viviparous Neotropical lizard <em>Notomabuya frenata</em> (Scincidae). This species ranges in Brazil, Argentina, Paraguay, and Bolivia. We use environmental and physiological variables (locomotor performance and hours of activity) to predict suitable present and future areas, considering different scenarios of greenhouse gas emissions and deforestation. The most critical predictors of habitat suitability were isothermality (i.e., the ratio between mean diurnal temperature range and annual temperature range), precipitation during winter, and hours of activity under lower thermal extremes. Still, our models predict a contraction of suitable habitats in all future scenarios and the displacement of these areas towards eastern South America. In addition, protected areas are not enough to ensure suitable habitats for this species. Our findings highlight the vulnerability of tropical and viviparous ectotherms and suggest that even widely distributed species, such as <em>N. frenata</em>, may have their conservation compromised shortly due to the low representativeness of their suitable habitats in protected areas combined with the synergistic effects of climate change and deforestation. We stress the need for decision‐makers to consider the impact of range shifts in creating protected areas and managing endangered species.</p>
SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections
<p>For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.</p> <p> </p> <p>The SECURES-Met dataset provides variables visible in the table.</p> <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> <p>°C</p> <p>°C</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> <p>GLO (mean global radiation)</p> <p>BNI (direct normal irradiation)</p> </td> <td> <p>Wm-2</p> <p>Wm-2</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th><strong>Potential Wind Power </strong></th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th><strong>Hydro Power Potential</strong></th> <td> <p>HYD-RES (reservoir)</p> <p>HYD-ROR (run-of-river)</p> </td> <td> <p>MW</p> <p>1</p> </td> <td> <p>summed power production</p> <p>summed power production normalized with average daily production</p> </td> <td>daily</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the <strong>spatial aggregation level</strong></p> <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As <strong>population weighted</strong> temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.</p> <p>The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p>
Comparing climatic suitability and niche distances to explain populations responses to extreme climatic events
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Data for: Predicting habitat suitability for Townsend’s big-eared bats across California in relation to climate change
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Climate change shrinks environmental suitability for a viviparous Neotropical skink
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Data from: Population analysis reveals genetic structure of an invasive agricultural thrips pest related to invasion of greenhouses and suitable climatic space
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Data associated with: Climate change will likely threaten areas of suitable habitats for the most relevant medicinal plants native to the Caatinga dry forest
<p>Medicinal plants play an important role in providing ecosystem services, such as local cultural and economic value, and human well-being, especially in poor regions. The use of plants to improve living conditions and increase the chances of survival comes from the beginning of human life. Climate change has the potential to contract areas of suitable habitat for medicinal plant species across different regions. As a consequence of climate change, the possibility of treating diseases can be compromised, and even interrupted. <em>We collected data from the medicinal applications and the parts that are used of 10 species of medicinal plants native to the Caatinga dry forest [i.e., </em><em>Myracrodruon urundeuva</em><em> Allemão (Anacardiaceae), </em><em>Cereus jamacaru </em><em>DC (Cactaceae), </em><em>Neocalyptrocalyx longifolium</em><em> (Mart) Cornejo & Iltis (Caparaceae),</em><em> Maytenus rigida </em><em>Mart (Celastraceae), </em><em>Operculina hamiltonii</em><em> (G Don) DF Austin Staples, </em><em>Operculina macrocarpa</em><em> (L) Urb (Convolvulaceae), </em><em>Amburana cearensis </em><em>(Allemao) AC Sm, </em><em>Anadenantehra colubrina</em><em> (Vell) Brenan, </em><em>Bauhinia cheilantha</em><em> (Bong) Steud and </em><em>Erythrina velutina</em><em> Willd (Legimonosae). </em>In addition, we also collected precise georeferenced data (native occurrence) of these medicinal plant species, that was accessed in 1) The Global Biodiversity Information Facility platform (GBIF) is an international data network funded by governments around the world, providing open access to data on all life on Earth (https:// www.gbif.org, accessed May 2022); 2) REFLORA - Herbário Virtual, virtual herbarium network that contains information on Brazilian plants that are deposited in 63 herbaria in Brazil and 10 international herbaria (http://reflora.jbrj.gov.br/reflora/herbarioVirtual, accessed May 2022); 3) Botanical Information and Ecology Network Platform (BIEN), a global information network that helps to document patterns of plant diversity, trait records and distribution, which includes georeferenced plant observation data from herbarium records, plots, survey inventories (https://bien .nceas.ucsb.edu/bien/biendata, accessed May 2022) and 4) 95 botanical monographs and floras. We excluded all repeated and mismatch occurrence data for each species. We collected all the available points for the studied species.</p>
Data for: Forecasting shifts in habitat suitability of three marine predators suggests a rapid decline in inter-specific overlap under future climate change
<p><strong><span>Aim:</span></strong><span> To estimate spatiotemporal changes in habitat suitability and inter-specific overlap among three marine predators: Baltic grey seals (<em>Halichoerus grypus grypus</em>), harbour seals (<em>Phoca vitulina</em>), and harbour porpoises (<em>Phocoena phocoena</em>) under contemporary and future conditions.</span></p> <p><strong><span>Location: </span></strong><span>The southwestern region of the Baltic Sea, including the Danish Straits and the Kattegat, one of the fastest-warming semi-enclosed seas in the world.</span></p> <p><strong><span>Methods: </span></strong><span>Location data (>200 tagged individuals) were analysed within the </span><span>maximum entropy (MaxEnt) </span><span>algorithm to estimate changes in total area size and overlap of species-specific habitat suitability between 1997-2020 and 2091-2100. A total of eleven candidate predictor variables were considered </span><span>representing anthropogenic activity, environmental, and climate sensitive oceanographic conditions in the area. Sea surface temperature and salinity</span><span> data were taken from </span><span>representative concentration pathways [RCPs] scenarios 6.0 and 8.5</span><span> to forecast potential </span><span>climate change effects</span><span>.</span></p> <p><strong><span>Results:</span></strong><span> Model output suggests that habitat suitability of Baltic grey seals will decline drastically over space and time, largely driven by changes in sea surface salinity and a loss of currently available haulout sites following sea level rise in the future. A similar though weaker response was observed for harbour seals, while suitability of habitat for harbour porpoises was predicted to remain fairly stable over space and time. Inter-specific overlap in highly suitable habitat was predicted to increase slightly under RCP scenario 6.0 when compared to contemporary conditions but to largely disappear under RCP scenario 8.5.</span></p> <p><strong><span>Main conclusions:</span></strong><strong> </strong><span>Marine predators in the southwestern Baltic Sea and adjacent waters may respond differently to future climatic conditions, leading to divergent shifts in habitat suitability that are likely to decrease inter-specific overlap.<strong> </strong>We, therefore, conclude that climate change can lead to a marked redistribution of area use by marine predators in the region, which may influence local food-web dynamics and ecosystem functioning.</span></p>
Climate change effects on deep-water corals – habitat suitability model input data
<p>Deep-water corals are protected in the seas around New Zealand by legislation that prohibits intentional damage and removal, and by marine protected areas where bottom trawling is prohibited. However, these measures do not protect them from the impacts of a changing climate and ocean acidification. To enable adequate future protection from these threats we require knowledge of the present distribution of corals and the environmental conditions that determine their preferred habitat, as well as the likely future changes in these conditions, so that we can identify areas for potential refugia.</p> <p>In this study, we built habitat suitability models for 12 taxa of deep-water corals using a comprehensive set of sample data and predicted present and future seafloor environmental conditions from an earth system model specifically tailored for the South Pacific. These models predicted that for most taxa there will be substantial shifts in the location of the most suitable habitat and decreases in the area of such habitat by the end of the 21st century, driven primarily by decreases in seafloor oxygen concentrations, shoaling of aragonite and calcite saturation horizons, and increases in nitrogen concentrations. The current network of protected areas in the region appear to provide little protection for most coral taxa, as there is little overlap with areas of highest habitat suitability, either in the present or the future. We recommend an urgent re-examination of the spatial distribution of protected areas for deep-water corals in the region, utilising spatial planning software that can balance protection requirements against value from fishing and mineral resources, take into account the current status of the coral habitats after decades of bottom trawling, and consider connectivity pathways for colonisation of corals into potential refugia.</p>
Files associated with: Migration-based simulations for Canadian trees show limited tracking of suitable climate under climate change
<p><strong>Aim</strong></p> <p>Species distribution models typically project climatically suitable habitat for trees in eastern North America to shift hundreds of kilometers this century. We simulated potential migration considering species' life history and traits for 10 tree species and their ability to track climatically suitable habitat.</p> <p><strong>Location</strong></p> <p>Eastern Canada, covering ~3.7 million km<sup>2</sup></p> <p><strong>Methods</strong></p> <p>We simulated migration-constrained range shifts through 2100 using a hybrid approach combining projections of climatically suitable habitat based on two Representative Concentration Pathways (RCP4.5, RCP8.5) for three time periods and two species distribution modelling approaches with process-based models parameterized using data related to <span>dispersal ability and generation time</span>. We developed a unique 'migration kernel' that uses seed dispersal traits and observed migration velocities to obtain kernel shape and dispersal probabilities. We then calculated lags between the migration-constrained range limits obtained through simulations and limits of climatically suitable habitat.</p> <p><strong>Results</strong></p> <p>All species demonstrated northward range shifts at the leading edge of their simulated distribution through 2100, but the magnitude and rate of that shift varied by species and time period. Climatically suitable habitat limits were found to be north of simulated distribution limits across both RCPs, with lags increasing through time. On average, the simulated distribution that remained within climatically suitable habitat showed higher decreases under RCP8.5 than RCP4.5, with large areas of the rear edge of the simulated distribution becoming partially or completely climatically unsuitable for many species.</p> <p><strong>Main conclusions</strong></p> <p><span>Climatically suitable habitat limits projected for 2100 far exceeded migration-constrained range limits for all 10 species, particularly for temperate species. This study underlines the limited extent to which species will track climate change via natural migration. Integrating observed migration velocities, seed dispersal and generation time with SDM outputs allows for more realistic evaluations of tree migration ability under climate change and may help orient forest conservation and restoration efforts.</span></p>
Data from: Future climatically suitable areas for bats in South Asia
<p>Climate change majorly impacts biodiversity in diverse regions across the world, including South Asia, a megadiverse area with heterogeneous climatic and vegetation regions. However, climate impacts on bats in this region are not well‐studied, and it is unclear whether climate effects will follow patterns predicted in other regions. We address this by assessing projected near‐future changes in climatically suitable areas for 110 bat species from South Asia. We used ensemble ecological niche modelling with four algorithms (random forests, artificial neural networks, multivariate adaptive regression splines and maximum entropy) to define climatically suitable areas under current conditions (1970–2000). We then extrapolated near future (2041–2060) suitable areas under four projected scenarios (combining two global climate models and two shared socioeconomic pathways, SSP2: middle‐of‐the‐road and SSP5: fossil‐fuelled development). Projected future changes in suitable areas varied across species, with most species predicted to retain most of the current area or lose small amounts. When shifts occurred due to projected climate change, new areas were generally northward of current suitable areas. Suitability hotspots, defined as regions suitable for >30% of species, were generally predicted to become smaller and more fragmented. Overall, climate change in the near future may not lead to dramatic shifts in the distribution of bat species in South Asia, but local hotspots of biodiversity may be lost. Our results offer insight into climate change effects in less studied areas and can inform conservation planning, motivating reappraisals of conservation priorities and strategies for bats in South Asia.</p>
Figure 1 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 1. Modeled range and presence records for Arborophila crudigularis.
Prediction of potential suitable areas for Phoebe zhennan in future different climate scenarios
<p>This dataset includes sample collection data of existing <em>Phoebe zhennan</em> in China, as well as historical climate data and future climate data (with a resolution of 2.5 minutes and using the BCC-CSM2-MR GCM model) collected by Worldclim, along with geographical elevation data. These data are used to predict the potential distribution range of <em>Phoebe zhennan</em> in the future.</p>
Genetic data and climate niche suitability models highlight the vulnerability of a functionally important plant species from south-eastern Australia
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Climate change effects on deep-water corals – habitat suitability model input data
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Data for: Forecasting shifts in habitat suitability of three marine predators suggests a rapid decline in inter-specific overlap under future climate change
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Files associated with: Migration-based simulations for Canadian trees show limited tracking of suitable climate under climate change
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.