Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
355
datasets available to search
ShareScore release 0.9.0
Dataset results
355 results for “mammal species”
Mammal Species of the World
<p>Mammal species of the world checklist from GBIF. This is snapshot of the version online 2015-06-25.</p>
Figure 2 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 2. Binary maps of potential distribution of (A) chacoan peccary, (B) cougar, (C) brown brocket deer, (D) collared peccary and (E) anteater. The gray pixels indicate the places of presence of the species.
Figure 1 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 1. Study area. Geometric figures of different colors indicating the sites of presence of the selected mammalian species used for the distribution models.
Figure 3 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 3. Response graphs of habitat suitability (ordinate axis) according to the explanatory variables that intervened in the adjustment of the model for cougar (A, B, C). Precipitation is expressed in mm and altitude in meters. Source of bioclimatic variables (bio), site https://www.worldclim.org/data/bioclim.html.
FIGURE 3 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 3. Google Earth© (2020) image of Campbell Island within the Montebello Island group.
Fig. 34 in The Mammals Of Paracou, French Guiana: A Neotropical Lowland Rainforest Fauna Part 2. Nonvolant Species
Fig. 34. Limits of four craniodental dimensions as measured for caviomorph rodents.
Data from: Island biodiversity in peril: anticipating a loss of mammals' functional diversity with future species extinctions
<p>Islands are biodiversity hotspots that host unique assemblages. However, a substantial proportion of island species are threatened and their long-term survival is uncertain. Identifying and preserving vulnerable species has become a priority, but it is also essential to combine this information with other facets of biodiversity like functional diversity, to understand how future extinctions might affect ecosystem stability and functioning. Focusing on mammals, we (i) assessed how much functional space would be lost if threatened species go extinct, (ii) determined the minimum number of extinctions which would cause a significant functional loss, (iii) identified the characteristics (e.g., biotic, climatic, geographic, or orographic) of the islands most vulnerable to future changes in the functional space, and (iv) quantified how much of that potential functional loss would be offset by introduced species. Using trait information for 1,474 mammal species occurring in 318 islands worldwide, we built trait probability density functions to quantify changes in functional richness and functional redundancy in each island if the mammals categorized by IUCN as threatened disappeared. We found that the extinction of threatened mammals would reduce the functional space in 63% of the assessed islands, although these extinctions in general would cause a reduction of less than 15% of their overall functional space. Also, on most islands, the extinction of just a few species would be sufficient to cause a significant loss of functional diversity. The potential functional loss would be higher on small, isolated and/or species rich islands and, in general, the functional space lost would not be offset by introduced species. Our results show that the preservation of native species and their ecological roles remains crucial for maintaining the current functioning of island ecosystems. Therefore, conservation measures considering functional diversity are imperative to safeguard the unique functional roles of threatened mammal species on islands.</p> <p><strong>Datasets and R scripts provided: </strong></p> <p><strong>1. Mammals_occurrence_islands.xlxs</strong> - Matrix of presence/absence of mammals species (columns) per island (rows) and bibliographic sources from which the information has been extracted. Islands are grouped according to the zoogeographical regions proposed by Holt el al. (2013).</p> <p><strong>2. Traits_matrix.csv</strong> – Matrix of species functional traits included in this study and the bibliographic sources of this information. Note that trait values are scaled and centered and that some of them have been imputed (see Methods section of the original manuscript).</p> <p><strong>3.</strong> <strong>Functional_space_analysis.R</strong> – script to:</p> <p>· Build the functional space of islands</p> <p>· Calculate observed functional richness and functional redundancy</p> <p>· Calculate functional richness and functional redundancy after simulating the extinction of threatened species</p> <p>· Calculate the functional space offset by introduced species </p> <p><strong>4. mixed_models_islands.R</strong> - R script to perform the linear mixed models to explore whether islands with higher values of predicted functional diversity loss due to threatened species extinction share some characteristics.</p> <p><strong>5. data_models_islands.csv </strong>- Dataset used to perform the models. For each island the following information is provided:</p> <ul> <ul> <li>Island ID (ID)</li> <li>Number of species (SppRich)</li> <li>Number of threatened species (Thre_sp)</li> <li>Functional richness (Island_FRic)</li> <li>Functional redundancy (Island_Red)</li> <li>Functional richness standard effect size (SES_FRic)</li> <li>Functional redundancy standard effect size (SES_FRed)</li> <li>Island group (Archipielago)</li> <li>Island past connectivity (Type)</li> <li>Percentage of protected area coverage (protected_percentageI_VI)</li> <li>Distance to the nearest continent (dContinent_km)</li> <li>Mean annual temperature (Anntemp_promedio)</li> <li>Mean annual precipitation (Annprec_promedio)</li> <li>Island area (Area_km2)</li> <li>Maximum elevation (Elev_max)</li> <li>Species richness (SR)</li> <li>Mean human footprint (Human_foot)</li> <li>Distance to the nearest larger landmass (distance_biggerLandmass)</li> </ul> </ul> <p>Island area, distance to the nearest continent and distance to the nearest larger landmass were calculated with ArcMap (ESRI, 2019) and the ‘terra 1.7-71’ R package (Hijmans, 2023), using the shapefile of the world’s islands available in Martin et al. (2022). Distance to the nearest mainland and to the nearest larger landmass were calculated as the shortest distance between coastlines (Weigelt & Kreft, 2013). Maximum elevation of each island was extracted from the Global Bathymetry and Elevation Database (Becker et al., 2009). We also used this database to access the bathymetry around the continents and islands and determine whether an island was connected to the mainland during the Last Glacial Maximum (about 20,000 years ago), assuming a sea level of 122 m below the present level (glacial maximum mainland connection; Weigelt, Jetz and Kreft, 2013). Averaged values of annual temperature and annual precipitation for each island were calculated using the climatic variables available in the CHELSA 2.1 database (Karger et al., 2018) at a resolution of 30 arc seconds. To calculate the percentage of protected area on each island, we gathered the protected surface’s shapefile from The World Database on Protected Areas (UNEP-WCMC & IUCN, 2022). Finally, we used the mean human footprint index from Human Footprint maps (see Venter et al., 2018).</p> <p><strong>References</strong></p> <p>Becker, J. J., Sandwell, D. T., Smith, W. H. F., Braud, J., Binder, B., Depner, J., Fabre, D., Factor, J., Ingalls, S., Kim, S.-H., Ladner, R., Marks, K., Nelson, S., Pharaoh, A., Trimmer, R., Von Rosenberg, J., Wallace, G., & Weatherall, P. (2009). Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. <em>Marine Geodesy</em>, 32(4), 355–371. <a href="https://doi.org/10.1080/01490410903297766">https://doi.org/10.1080/01490410903297766</a></p> <p>ESRI (2019). <em>ArcGis for Desktop</em>. Retrieved from https://desktop.arcgis.com/en/</p> <p>Hijmans, R. (2023). terra: Spatial Data Analysis_. R package version 1.7-3, <https://CRAN.R-project.org/package=terra>.</p> <p>Holt, B. G., Lessard, J.-P., Borregaard, M. K., Fritz, S. A., Araújo, M. B., Dimitrov, D., Fabre, P.-H., Graham, C. H., Graves, G. R., Jønsson, K. A., Nogués-Bravo, D., Wang, Z., Whittaker, R. J., Fjeldså, J., & Rahbek, C. (2013). An update of Wallace’s zoogeographic regions of the world. <em>Science</em>, 339(6115), 74–78. https://doi.org/10.1126/science.1228282</p> <p>Karger D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2018). Data from: Climatologies at high resolution for the earth's land surface areas [Dataset]. <em>Dryad</em>. https://doi.org/10.5061/dryad.kd1d4</p> <p>Martin, M., Sayre, R., VanGraafeiland, K., McDermott Long, O., Weatherdon, L., Will, D., Spatz, D. R., & Holmes, N. D. (2020). Global Islands (M. I. Goldstein & D. A. B. T.-E. of the W. B. DellaSala (eds.); pp. 47–50). Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.12475-3</p> <p>UNEP-WCMC & IUCN (2022). <em>Protected Planet: The World Database on Protected Areas (WDPA).</em> Cambridge, UK: UNEP-WCMC and IUCN. Retrieved from <a href="http://www.protectedplanet.net">www.protectedplanet.net</a>. [Accessed 12/2022]</p> <p>Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J., Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete, B. M., Levy, M. A., & Watson, J. E. (2018). <em>Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release</em>. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). <a href="https://doi.org/10.7927/H46T0JQ4">https://doi.org/10.7927/H46T0JQ4</a></p> <p>Weigelt, P., & Kreft, H. (2013). Quantifying island isolation – insights from global patterns of insular plant species richness. <em>Ecography,</em> 36(4), 417-429. https://doi.org/10.1111/j.1600-0587.2012.07669.</p> <p>Weigelt, P., Jetz, W., & Kreft, H. (2013). Bioclimatic and physical characterization of the world’s islands. <em>Proceedings of the National Academy of Sciences</em>, 110(38), 15307–15312. https://doi.org/10.1073/pnas.1306309110</p> <p> </p>
Number of seeds recorded from the feces of different species of mammals during the study
Open the record for dataset details and reuse information.
Gridded Species Distribution: Global Mammal Richness Grids, 2015 Release
The 2015 Release of the Global Mammal Richness Grids data set of the Gridded Species Distribution collection are aggregations of the presence grids data for the entire class, individual families, and International Union for the Conservation of Nature (IUCN) Red List status categories. The data are available in 30 arc-second (~1 km) resolutions. The grid cell values represent the number of species in a particular class, family or IUCN threatened category. The input vector layers are based on the IUCN Red List and the grids are compiled by the Columbia University Center for International Earth Science Information Network (CIESIN). The data from IUCN were downloaded in April 2013.
FIGURE 3 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 3. Phytogeographical subregions of Australia (Ebach et al. 2015), based on the analysis by González-Orozco et al. (2014b).
FIGURE 6 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 6. The Bassian subregion as proposed by Main et al. (1958). Note that the Bassian includes the South-West Australia subregion.
FIGURE 2 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 2. Map of Australia with the location of the 3 Clusters and their corresponding Subclusters (a-c) in relation to the regions.
Fig. 4 in Newly described and already endangered: a new mammal species endemic to Corsica
Fig. 4. Lateral and occlusal views of the skull and mandible of the holotype female Myotis nustrale sp. nov. (MNHN-ZM-2023-12). The scale is 5 mm.
FIGURE 5 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 5. Faunal subregions of Australia by Spencer (1896).
FIGURE 7 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 7. The interim zoogeographic dominions of Australia.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.