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124 results for “terrestrial mammals”
Figure 1. Phylogenetic relationships between the 76 in Locomotion in terrestrial mammals: the influence of body mass, limb length and bone proportions on speed
Figure 1. Phylogenetic relationships between the 76 species of mammals used in the study. Numbers adjacent to the nodes refer to split ages in units of millions of years. Total height of tree is 85 million years. Literature sources used in constructing the tree are Kielan-Jaworowska et al. (1979), Bennett (1980), Janis (1982), Savage & Russel (1983), Lanave et al. (1985), Shoshani (1986), Janis & Scott (1987), Wayne & O'Brien (1987), Gentry & Hooker (1988), Flynn et al. (1988), Novacek et al. (1988), Padmadisastra (1988), Prothero et al. (1988), Tassy & Shoshani (1988), Georgiadis et al. (1990), Marshall (1990), Miyamoto et al. (1990), Nowak (1991), Geffen et al. (1992), Novacek (1992a,b), Garland & Janis (1993), Wyss & Flynn (1993), Flynn (1996), Hunt (1996), Foote et al. (1999) and Penny et al. (1999).
Figure 3 in Locomotion in terrestrial mammals: the influence of body mass, limb length and bone proportions on speed
Figure 3. Regression plots of independent contrasts for fore limb parameters. Log running speed is in km h-1. a, Fore limb length in mm/3÷body mass in kg; b, radius/humerus ratio; c, metacarpus/humerus ratio; d, olecranon process length in mm/ 3÷body mass in kg. Regression lines fitted to the contrasts by means of least squares (model I) analysis.
Figure 4 in Locomotion in terrestrial mammals: the influence of body mass, limb length and bone proportions on speed
Figure 4. Regression plots of independent contrasts for hind limb parameters. Log running speed is in km h-1. a, Hind limb length in mm/3÷body mass in kg; b, metatarsus/femur ratio; c, cnemial crest height in mm/3÷body mass in kg; d, calcaneal tuber length in mm/3÷body mass in kg. Regression lines fitted to the contrasts by means of least squares (model I) analysis.
Figure 2 in Locomotion in terrestrial mammals: the influence of body mass, limb length and bone proportions on speed
Figure 2. Plots of standardized contrasts to their standard deviations. a, log forelimb length in mm; b, radius/humerus ratio; c, metacarpus/humerus ratio; d, tibia/femur ratio; e, cnemial crest height in mm/3÷body mass in kilograms; f, calcaneal tuber/metatarsus ratio.
Behavioral responses of terrestrial mammals to COVID-19 lockdowns
<p>COVID-19 lockdowns in early 2020 reduced human mobility, <span>providing an opportunity to disentangle its effects on animals from those of landscape modifications. Using GPS data, we compared movements and road avoidance of 2300 terrestrial mammals (43 species) during the lockdowns to the same period in 2019. Individual responses were variable, with no change in average movements or road avoidance behavior, likely due to variable lockdown conditions. However, under strict lockdowns, 10-day 95th percentile displacements increased by 73%, suggesting increased landscape permeability. Animals' 1-hour 95th percentile displacements declined by 12%, and animals were 36% closer to roads in areas of high human footprint, indicating reduced avoidance during lockdowns. Overall, lockdowns rapidly altered some spatial behaviors, highlighting variable but substantial impacts of human mobility on wildlife worldwide.</span></p>
Eurasian beaver – A semi-aquatic ecosystem engineer rearranges the assemblage of terrestrial mammals in winter - dataset
<p>Dataset to paper of Fedyń, I., Przepióra, F., Sobociński, W., Wyka, J., & Ciach, M. (2022). Eurasian beaver–A semi-aquatic ecosystem engineer rearranges the assemblage of terrestrial mammals in winter. <em>Science of The Total Environment</em>, <em>831</em>, 154919 (<a href="https://doi.org/10.1016/j.scitotenv.2022.154919">https://doi.org/10.1016/j.scitotenv.2022.154919</a>).</p>
Behavioral responses of terrestrial mammals to COVID-19 lockdowns
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Investigating the biotic and abiotic drivers of body size disparity in communities of non-volant terrestrial mammals
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Date From: The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: A global analysis
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No evidence for the consistent effect of supplementary feeding on home range size in terrestrial mammals
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Terrestrial mammals from America with dengue incidence
<p>This dataset contains dengue virus incidence in mammals from published papers.</p> <p>It has 17 fields:</p> <ol> <li>Reference</li> <li>Sampling year</li> <li>Country</li> <li>Diagnostic test</li> <li>Diagnostic test type</li> <li>Subclade</li> <li>Virus</li> <li>Order</li> <li>Family</li> <li>Mammal species</li> <li>Individual tested</li> <li>Incidence</li> <li>Prevalence</li> <li>Susceptibility</li> <li>Location</li> <li>latitude</li> <li>longitude</li> </ol>
Species richness, composition and microhabitat characteristics of non-volant terrestrial mammals in disturbed habitats
<b>Description: </b><p>A study on the small mammals communities was carried out in disturbed habitats aroundsabah, namely university malaysia sabah (ums), klias peat swamp forest reserve(klias), kawang forest reserve (kawang), kalabakan forest reserve (safe) and maliaubasin conservation area (maliau). the objectives were (1) to determine the speciesrichness and composition of non-volant small mammal communities in disturbedhabitats; (2) to characterize the microhabitat-use patterns of the non-volant smallmammal communities in disturbed habitats; and (3) to determine the microhabitatpreferences of the non-volant small mammal communities in disturbed habitats. the aimof this study was to investigate how the habitat disturbance affects the species richness,community compositions and microhabitat-use pattern of the small mammals. this studywas conducted from october 2014 to march 2015 with a total sampling effort of 540trap-nights. overall, 71 individuals representing 14 species were successfully caughtduring this study. the species richness peaked at safe, and then declined at the rest ofthe study sites. habitat variables analysis showed that all study sites were divided intothree distinctive groups in terms of habitat types. canonical discriminant functionanalysis were used to analyze the microhabitat preferences and use-pattern of smallmammals and results showed the preferences of small mammals towards shrub cover(rattus rattus and callosciurus notatus), litter cover (callosciurus prevostii and echinorexgymnurus) and herbs limber (tupaia gracilis). The locations of the traps have not been given longitute and latitute as there were set on animal trails approximately 20metres from one another. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/150"><b>Species richness, composition and microhabitat characteristics of non-volant terrestrial mammals in disturbed habitats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Universiti Malaysia Sabah (Grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiverstiy Council (Research licence NA)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3265704">here</a></p><p><b>Files: </b>This consists of 1 file: Veg_Volent_mammals.xlsx</p><p><b>Veg_Volent_mammals.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Vegetation cover</b> (described in worksheet Vegetation_cover)</p><p>Description: The estimated canopy cover and percentage of ground cover of traps across disturbed habitats</p><p>Number of fields: 19</p><p>Number of data rows: 180</p><p>Fields: </p><ul><li><b>Date</b>: Date the vegetation cover was collected (Field type: Date)</li><li><b>Location</b>: Where data was collected (Field type: Location)</li><li><b>Habitat_type</b>: Habitat type (Field type: Categorical)</li><li><b>Trap_station</b>: Transect and trap number (Field type: ID)</li><li><b>Canopy_cover</b>: Percentage of canopy cover (Field type: Numeric)</li><li><b>Canopy_height</b>: Canopy height (Field type: Numeric)</li><li><b>Herbs_Climber</b>: Any climbers seen on trees for example vines and lianas (Field type: Numeric)</li><li><b>Tree_GBH_>10CM</b>: Tree girth at breast height of 10 cm (Field type: Numeric)</li><li><b>Tree_GBH_>30CM</b>: Tree girth at breast height of 30 cm (Field type: Numeric)</li><li><b>Tree_GBH_>60CM</b>: Tree girth at breast height of 60 cm (Field type: Numeric)</li><li><b>Tree_GBH_>90CM</b>: Tree girth at breast height of 90 cm (Field type: Numeric)</li><li><b>Fallen_trees_bran</b>: Percentage cover (Field type: Numeric)</li><li><b>Bareground</b>: Percentage cover (Field type: Numeric)</li><li><b>Shrub</b>: Percentage cover (Field type: Numeric)</li><li><b>grass</b>: Percentage cover (Field type: Numeric)</li><li><b>Rock</b>: Percentage cover (Field type: Numeric)</li><li><b>Litter</b>: Percentage cover (Field type: Numeric)</li><li><b>Water</b>: Percentage cover (Field type: Numeric)</li><li><b>Twig</b>: Percentage cover (Field type: Numeric)</li></ul></li><li><p><b>Non volent mammal abundance</b> (described in worksheet Non_volent_mammals)</p><p>Description: The abundance of non-volant terrestrails mammals caught across disturbed habitats. Growth and sex measurements takens</p><p>Number of fields: 17</p><p>Number of data rows: 490</p><p>Fields: </p><ul><li><b>Date</b>: Date the vegetation cover was collected (Field type: Date)</li><li><b>Location</b>: Where data was collected (Field type: Location)</li><li><b>Transect</b>: Transect number (Field type: ID)</li><li><b>Habitat</b>: Habitat type (Field type: Categorical)</li><li><b>Trap_station</b>: Transect and trap number (Field type: ID)</li><li><b>Species</b>: Species of non volent mammals caught in trap (Field type: Taxa)</li><li><b>Weight</b>: Weight of caught non volent mammal (Field type: Numeric)</li><li><b>Ear</b>: Ear measurment of caught non volent mammal (Field type: Numeric)</li><li><b>Hind_leg</b>: Hind leg measurement of caught non-volent mammal (Field type: Numeric)</li><li><b>Head_body</b>: Head to body measurement of caught non volent mammal (Field type: Numeric)</li><li><b>Tail</b>: Tail length of caught non volent mammal (Field type: Numeric)</li><li><b>Sex</b>: Sex of caught non volent mammal (Field type: Categorical)</li><li><b>Sexual_activity</b>: Sexual maturity of caught non volent mammal (Field type: Categorical)</li><li><b>Age</b>: Age class of caught non volent mammal (Field type: Categorical)</li><li><b>Trap_condition</b>: Trap condition (Field type: Categorical)</li><li><b>Trap_open_closed</b>: Trap open or closed (Field type: Categorical)</li><li><b>Bait</b>: Bait taken or intact (Field type: Categorical)</li></ul></li></ol><p><b>Date range: </b>2014-10-22 to 2015-03-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Erinaceomorpha<br> -  -  -  - Erinaceidae<br> -  -  -  -  - <i>Echinosorex</i><br> -  -  -  -  -  - <i>Echinosorex gymnura</i><br> -  -  - Rodentia<br> -  -  -  - Muridae<br> -  -  -  -  - <i>Lenothrix</i><br> -  -  -  -  -  - <i>Lenothrix canus</i><br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys rajah</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Niviventer</i><br> -  -  -  -  -  - <i>Niviventer cremoriventer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus rattus</i><br> -  -  -  - Sciuridae<br> -  -  -  -  - <i>Callosciurus</i><br> -  -  -  -  -  - <i>Callosciurus adamsi</i><br> -  -  -  -  -  - <i>Callosciurus notatus</i><br> -  -  -  -  -  - <i>Callosciurus prevostii</i><br> -  -  -  -  - <i>Sundasciurus</i><br> -  -  -  -  -  - <i>Sundasciurus lowii</i><br> -  -  - Scandentia<br> -  -  -  - Tupaiidae<br> -  -  -  -  - <i>Tupaia</i><br> -  -  -  -  -  - <i>Tupaia dorsalis</i><br> -  -  -  -  -  - <i>Tupaia glis</i><br> -  -  -  -  -  - <i>Tupaia gracilis</i><br></div><p></p>
Data used, summary and codes: A validation standard for Area of Habitat maps for terrestrial birds and mammals
<p>Birds_AOH_Metadata: Logistic modelling and point validation summary along with pixel summary of AOH maps for Birds .</p> <p>Mammals_AOH_Metadata: Logistic modelling and point validation summary along with pixel summary of AOH maps for Mammals.</p> <p>Column_Names_Index_AOH_Metedata: Description of column names of Birds_AOH_Metadata and Mammals_AOH_Metadata</p> <p>Point_localities_ebird: Latitude and longitude of points per species for birds downloaded from eBird used to validate the AOH maps. </p> <p>Point_localities_gbif: Latitude and longitude of points per species for mammals downloaded from GBIF used to validate the AOH maps. </p> <p>Logistic_Regression_Codes_AOH_Validation: R script for logistic modelling.</p> <p>Point_Validation_Codes_AOH_Validation: R script for point validation of AOH maps.</p> <p>Sample_AOH_Maps: Few sample AOH maps which were validated.</p> <p> </p>
Figure 3 in Deforestation affects biogeographical regionalization: a case study contrasting potential and extant distributions of Mexican terrestrial mammals
Figure 3. Mexican regionalization based on consensus cladogram of t1.
Emergence and radiation of distemper viruses in terrestrial and marine mammals - Input files, bash and R codes for analysing PDV and CDV sequence data
<p><span>Canine distemper virus (CDV) and phocine distemper virus (PDV) are major pathogens to terrestrial and marine mammals. Yet little is known about the timing and geographical origin of distemper viruses and to what extent it was influenced by environmental change and human activities. To address this, we i) performed the first comprehensive time-calibrated phylogenetic analysis of the two distemper viruses; ii) mapped distemper antibody and virus detection data from marine mammals collected between 1972-2018; iii) and compiled historical reports on distemper dating back to the 18<sup>th</sup> century. We find that CDV and PDV diverged in the early 17<sup>th</sup> century. Modern CDV strains last shared a common ancestor in the 19<sup>th</sup> century with a marked radiation during the 1930s-50s. Modern PDV strains are of more recent origin, diverging in the 1970s-80s. Based on the compiled information on distemper distribution, the diverse host range of CDV and basal phylogenetic placement of terrestrial morbilliviruses, we hypothesize a terrestrial CDV-like ancestor giving rise to PDV in the North Atlantic. Moreover, given the estimated timing of distemper origin and radiation, we hypothesize a prominent role of environmental change such as the Little Ice Age, and human activities like globalisation and war in distemper virus evolution. </span></p>
Drivers of change in the realised climatic niche of terrestrial mammals
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Data from:Identifying cryptic mammals with non-invasive methods: An effective molecular species identification tool to survey southern African terrestrial carnivores
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Emergence and radiation of distemper viruses in terrestrial and marine mammals - Input files, bash and R codes for analysing PDV and CDV sequence data
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Data from: Distribution and protection of ecological specialists in Chinese terrestrial mammals
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The effects of body mass on immune cell concentrations of terrestrial mammals
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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)
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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.