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355 results for “mammal species”
Small mammal species composition data for Niwot Ridge, 1981 - 1990.
Small mammals were live trapped during the summer of each year at various locations on Niwot Ridge. Species trapped included pikas (Ochotona princeps), marmots (Marmota flaviventris), deer mice (Peromyscus maniculatus), voles (Microtus longicaudus, Microtus montanus, and Phenacomys intermedius), and pocket gophers (Thomomys talpoides). Pocket gophers were only trapped through the summer of 1983. Mice and voles were trapped using Sherman traps, while Tomahawk traps were used to trap both the pikas and marmots. Sherman gopher traps were used to trap gophers. The mice and vole traps were placed 10 meters apart in parallel rows in grids containing from 25 to 100 traps; weather permitting, these traps were placed for 3 consecutive nights every 1 or 2 weeks from June to August. The pikas, marmots, and gophers were trapped during late summer of each year; consult Halfpenny et al. (1984; 1987) for a general description of these trap locations, as well as descriptions of baiting and handling techniques and surrounding vegetation communities. Pika and marmot traps were selectively placed in areas where evidence of these animals existed. In the case of the pocket gophers, traps were placed below ground in active mounds. For pikas, marmots and pocket gophers, traps were set during the day and were checked every 2 hours. After determining and recording species, sex, age, reproductive status, weight, health, and trap location, trapped animals were permanently tagged and released. An additional quality-controlled, derived data-table (pikas_no_recap.cr.data.csv) was added to the original dataset in 2022. This data table is derived from the original capture data, but includes only year-unique captures, where each year-unique capture is the "best" one for comparison with other year-unique captures. For comparability among years, the best capture for comparability among years is usually the first capture of the year, representing the animal's baseline weight for the year (weight appea
Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation
<p>Big, time-scaled phylogenies are fundamental to connecting evolutionary processes to modern biodiversity patterns. Yet inferring reliable phylogenetic trees for thousands of species involves numerous trade-offs that have limited their utility to comparative biologists. To establish a robust evolutionary timescale for all ~6000 living species of mammals, we developed credible sets of trees that capture root-to-tip uncertainty in topology and divergence times. Our 'backbone-and-patch' approach to tree-building applies a newly assembled 31-gene supermatrix to two levels of Bayesian inference: (i) backbone relationships and ages among major lineages, using fossil node- or tip-dating; and (ii) species-level 'patch' phylogenies with non-overlapping in-groups that each correspond to one representative lineage in the backbone. Species unsampled for DNA are either excluded ('DNA-only' trees) or imputed within taxonomic constraints using branch lengths drawn from local birth-death models ('completed' trees). Joining time-scaled patches to backbones results in species-level trees of extant Mammalia with all branches estimated under the same modeling framework, thereby facilitating rate comparisons among lineages as disparate as marsupials and placentals. We compare our phylogenetic trees to previous estimates of mammal-wide phylogeny and divergence times, finding that (i) node ages are broadly concordant among studies, and (ii) recent (tip-level) rates of speciation are estimated more accurately in our study than in previous 'supertree' approaches where unresolved nodes led to branch length artifacts. Credible sets of mammalian phylogenetic history are now available for download at <a href="http://vertlife.org/phylosubsets">http://vertlife.org/phylosubsets</a>, enabling investigations of long-standing questions in comparative biology.</p>
Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation
Open the record for dataset details and reuse information.
Mammal species recorded within the Hubbard Brook Experimental Forest and vicinity (1963-2020; updated January 2021).
This dataset contains confirmed observations of mammal species at the Hubbard Brook Experimental Forest and adjacent Mirror Lake. The original list was published in Holmes, R. T. and G. E. Likens. 1999. Organisms of the Hubbard Brook Valley, New Hampshire. USDA Forest Service, Northeastern Research Station, General Tech. Report NE-257. 32 pp. The list is updated here (January 2021) to include additional species observed since the original publication, with annotated comments by R.T. Holmes, H. ter Hofstede (bats) and L. Christenson (from motion-detecting cameras, 2014-2019).
Figures 9–10 in Studies on palearctic Onthophagus associated with burrows of small mammals. IV. A new Iranian species belonging to the furciceps group (Coleoptera, Scarabaeidae, Onthophagini)
Figures 9–10. Onthophagus (Paleonthophagus) psychopompus sp. n. Male, paratype (Iran, Sirdan, Qazvin prov.) and female, paratype (Iran, Saqqez, Kordestan prov.). 9 Dorsum of male 10 Dorsum of female. Photos by A. Ballerio, scanned by G. Fiumi.
Figures 1–7 in Studies on palearctic Onthophagus associated with burrows of small mammals. IV. A new Iranian species belonging to the furciceps group (Coleoptera, Scarabaeidae, Onthophagini)
Figures 1–7. Onthophagus (Paleonthophagus) psychopompus sp. n. Male, holotype, and female, allotype (Iran, Hashtgerd, Tehran prov.). 1 Male: head and pronotum, dorsal view 2 Male: head, frontal view 3 Female: head and pronotum, dorsal view 4 Female: head, frontal view 5 Parameres, lateral view 6 Parameres, dorsal view 7 Lamella copulatrix, ventral side. Drawings by I. Gudenzi.
Fig. 2. Species accumulation curves for 45 in Mammal inventories in Seasonal Neotropical Forests: traditional approaches still compensate drawbacks of modern technologies
Fig. 2. Species accumulation curves for 45 sampling days for each method used to sample mammals in Serra do Japi Biological Reserve, JundiaÍ, State of SÃo Paulo, Brazil in July and August 2009 and January and February 2010.
Figure 3 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 3. Published values of percent divergence between cetacean subspecies (black bars), species (white bars), and taxa of uncertain taxonomic status (gray bars). Values are based on mtDNA control region sequence data. Not all values represent net sequence divergence. See Table 1 for list of papers corresponding to each value. Since completing this work, Sousa species have been supported ((Mendez et al. 2013) and Inia subspecies changed.
Figure 1 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 1. Sample sizes used in publications of molecular genetic studies of marine mammals at different taxonomic levels. Graphs present the proportion of studies at each taxonomic level that fall into each sample size category. (A) minimum total sample size per focal taxon; (B) maximum sample size per single sampling locality. Papers were categorized as examining taxonomic questions at: species = subspecies/species boundary; subspecies = population/subspecies boundary; uncertain = taxonomic boundary uncertain (see text).
Figure 2 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species
Figure 2. Types of molecular genetic data used in published studies examining questions at the species-level, subspecies-level, or undefined taxonomic level for marine mammals. Note that studies may have used more than one data type. Mitochondrial DNA sequence data (MtDNASeq), nuclear DNA sequence data (NuSeq), microsatellites (Msats), morphological data (Morph).
Fig. 3 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places
Fig. 3. Consensus results of species delimitation analyses. Phylogenetic distribution of hidden diversity estimated from strict consensus of delimitation results (SI Appendix, Table S1). Each silhouette represents a mammalian order with its shadow reflecting the ratio of predicted species to recognized species. Striped silhouettes represent orders with conflicting delimitation results that were not included in the predictive analysis. Phylogeny was adapted from ref. 31.
Fig. 4 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places
Fig. 4. Important predictors of hidden species in mammals. (A) From Top to Bottom, the 50 most important predictive variables (judged by MDA), for the consensus random forest classification model. In both plots, variables are color coded by life history, geographic, climatic, taxonomic, and environmental. (B) Boxplots representing values of the top predictive variables for species included in the consensus model. Values from species identified as hidden are shown at the Bottom of each plot (labeled "H"), and values from species not identified as hidden are shown Above (labeled "NH"). Outliers are excluded from boxplots.
Fig. 1 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places
Fig. 1. Predictive modeling workflow. The framework proposed for identifying named mammal species that are likely to contain hidden diversity utilizes barcoding gene sequences and machine learning models built from environmental, geographic, climatic, taxonomic, and life history variables.
Fig. 2 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places
Fig. 2. Scope of the dataset. Genetic sequences for ∼70% of currently recognized mammalian species were obtained. All mammalian orders are represented, with 23 orders containing sequences from both COI and cytb and 4 having only sequences from cytb. (A) Circle plots reflect species representation for the COI and cytb genes in each order. Dark bars represent the species present in the dataset and light bars represent species for which no genetic data are available. (B) Blue bars represent the proportion of the sequence database represented by each order, and gray bars represent the proportion of recognized species in each order. (C) A total of 3,205,630 geographic occurrence records were obtained for species present in the genetic database.
Fig. 9 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species
Fig. 9. Morphometric parameters of free-living females of S. papillosus: а — body length; b — body width; c — length of esophagus; d — length of intestine; e — length of tail; f — distance to vulva; g — number of eggs; on the ordinate axis — length in µm, on the abscissa axis — species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).
Fig. 8 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species
Fig. 8. Morphometric parameters of S. papillosus males: a — body length; b – body width; c — length of esophagus; d — length of intestine; e — length of the tail; f — length of spicules; on the ordinate axis — length in µm, on the abscissa axis — species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).
Fig. 7 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species
Fig. 7. Free-living female of S. papillosus: a — general view; b — anterior part (the arrowheads indicate the bulbs); c — uterus with eggs (the pointer indicates the genital opening); scale bars 100 µm.
Fig. 5 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species
Fig. 5. Morphometric parameters of L3: а — body length; b — body width; c — length of esophagus; d — length of intestine; e — length of the tail end; on the ordinate axis — length in µm, on the abscissa axis — species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).
Fig. 6 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species
Fig. 6. Mature male of S. papillosus: a — general view; b — anterior part (arrowheads indicate bulbs); c — caudal part with spicules in lateral view; d — caudal part with spicules in dorsal view; scale bars 100 µm.
Fig. 3 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species
Fig. 3. Morphometric parameters of L2 of S. papillosus: а — body length; b — body width; c — length of esophagus; d — length of intestine; e — length of the tail; on the ordinate axis — length in µm, on the abscissa axis – species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).
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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.