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4,059 results for “Mammals”

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edi48/100

CSM08 Small mammal host-parasite sampling data for 16 linear trapping transects located in 8 LTER burn treatment watersheds at Konza Prairie

Data set contains summaries (summer) of the number of individuals of each species of small mammal captured (relative abundance) on each transect. Each record contains date, treatment, transect, trap station, species, specimen number, recapture status, specimen disposition, external body measurements (where applicable), reproductive information, and miscellaneous associated comments. These sampling records are based on nightly captures during one 4-night trapping period in summer (June through August) for each of 16 permanent transects established on eight fire treatments (two transects per treatment). These treatments include two seasonal burn watersheds (SpB, SuB), two reversal burn watersheds (R1A, R20A), one annual burn watershed (1D), two 4-year burn watersheds (4B, 4F, and one 20-year burn watershed (20B). None of these treatments implement bison grazing.

openCC0May 2023View details →

edi48/100

Heavy metals in mammal tissue over the last 100 years and their proximity to populated places in Minnesota

This dataset makes use of the University of Minnesota's Bell Museum of Natural History collection examining specimens of four mammal species (a mouse, shrew, bat and squirrel) to ask how tissue metal content has changed over a 94-year time period (1911-2005), and implications for measures of individual performance (body size and cranial capacity). The metal content of organisms is often elevated closer to cities, so these specimens were examined for spatial variation in metal exposure based on their proximity to human populations and the size of those populated areas at the time of collection. Analysis of mammal tissues focused on six heavy metals associated with human activity (Pb, Cd, Zn, Cu, Cr, Ni, Mn), to address whether these anthropogenic metal pollutants vary in concert with human activity.

openCC (other)May 2024View details →

edi48/100

Marine Mammal Survey, Sightings and Sampling Event Log at Palmer Station, Antarctica, 2020-2024

Seasonal sea ice-influenced marine ecosystems at both poles are characterized by high productivity concentrated in space and time by local, regional, and remote physical forcing. These polar ecosystems are among the most rapidly changing on Earth. The PALmer (PAL) LTER seeks to build on three decades of long-term research along the western side of the Antarctic Peninsula (WAP) to gain new mechanistic and predictive understanding of ecosystem changes in response to disturbances spanning long-term, subdecadal, and higher-frequency “pulses” driven by a range of processes, including long-term climate warming, natural climate variability, and storms. These disturbances alter food-web composition and ecological interactions across time and space scales that are not well understood. We seek to determine the differential effects of disturbance and resilience on krill predators with different life histories, foraging behaviors, and demographic patterns. Specifically, changes in foraging behavior can affect adult fitness, body condition, and reproductive rates, as well as offspring survival. Preliminary analyses suggest mean chick fledgling mass decreases later in the austral summer as storm disturbances increase. If storms are not a factor influencing chick mass, parental effects or ecosystem phenology may play a larger role. For whales, changes in foraging effort and increases in body condition should correlate with increased pregnancy rates. We will test for linkages between whale foraging efficiency related to storms with female pregnancy rates the following year. Our prediction is that in seasons with more storms and poorer foraging conditions, fewer whales will become pregnant. However, as whales are long-lived, we predict this will not have a major effect on the long-term positive population trend. We will contribute fundamental understanding of how population dynamics and physiological processes are responding within a polar marine ecosystem undergoing profound change

openCC (other)Jul 2024View details →

edi48/100

Small Mammal Mark-Recapture Population Dynamics at Core Research Sites at the Sevilleta National Wildlife Refuge, New Mexico (1989-present)

This file contains mark/recapture trapping data collected from 1989-present on permanently established web trapping arrays at sites on the Sevilleta National Wildlife Refuge in central New Mexico.. The trapping sites are representative of Chihuahuan Desert Grassland, Chihuahuan Desert Shrubland, Pinyon-Juniper Woodland, Juniper Savanna, Plains-Mesa Sand Scrub and Blue Grama Grassland. Not all sites have been trapped for the entire period: goatdraw (1992-2008), blue grama (2002-2004) rsgrass (1989-1998), rslarrea (1989-2009), two2 (1989-1998), savanna (1999-2002). Only 2 sites have been continuously been sampled since 1989 (5pgrass and 5plarrea). At each site 3 trapping webs are sampled for 3 consecutive nights in spring and fall. Each trapping web consists of 145 rebar stakes numbered from 1-145. There are 148 traps deployed on each web: 12 along each of 12 spokes radiating out from a central point (stake #145) plus 4 traps placed at the center of each web. The wide format facilitates community composition and species diversity analyses. Wide format has been reshaped so that the count data for each species are presented in a unique column. Data are summarized for each trapping web X trapping bout to present the mean number of animals per trap per night of the trapping bout. Wide format fills zeros for species that were not captured on a web during a given trapping bout. Long format facilitates filtering the dataset to a particular small mammal species of interest, but this format requires the addition of zeros to be functional for accurate data analysis requiring counts of animals.

openCC (other)Mar 2024View details →

edi48/100

Small Mammal Exclosure Study (SMES) Surface Soil Disturbance in the Chihuahuan Desert Grassland and Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (1995-2005)

The purpose of this study is to determine whether or not the activities of small mammals regulate plant community structure, plant species diversity, and spatial vegetation patterns in Chihuahuan Desert shrublands and grasslands. What role if any do indigenous small mammal consumers have in maintaining desertified landscapes in the Chihuahuan Desert? Additionally, how do the effects of small mammals interact with changing climate to affect vegetation patterns over time? This is data for animal created soil surface disturbance measured from each of the SMES study plots. Soil surface disturbance was measured from each of the 36 one-meter2 quadrats twice each year when vegetation was measured.

openCC0Jan 2021View details →

zenodo44/100

What do studies in wild mammals tell us about human emerging viral diseases in Mexico? database

The database used in the article "What do studies in wild mammals tell us about human emerging viral diseases in Mexico?". It contains all available records of viral zoonotic and potential zoonotic species in Mexican wild mammals. The first file is a .csv file and the second one is .xls

opencc-by-4.0Jun 2019View details →

dryad44/100

Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation

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.

opencc-zeroDec 2019View details →

zenodo44/100

Mammals under pressure: presence data for assessing extinction of endemic, threatened, and mammals subject to use, in Colombia

This is the first dataset that provides a complete compilation of mammal records based on camera traps, human observations, and specimens deposited in biological collections in Colombia. We compiled a dataset with unpublished information, including 97,943 records corresponding to 136 species, of which 38 are endemic, 92 are identified as species subject to use by humans in the literature, and 33 are categorized either as Data Deficient or threatened according to international or unofficial national assessments. The information comes from 31 out of 32 departments of Colombia and constitutes relevant input for future distribution and conservation assessments. Most records (n=96,417, 98.44%) come from non-invasive sampling methods such as camera traps. However, we highlight the contribution of museum specimens (n= 1,332), especially for small and medium-sized species, many of them with restricted distributions in the country. This dataset constitutes a joint collaborative and interinstitutional effort that serves as the basis for cooperative work to comprehensively assess the current conservation status of all mammal species in Colombia.

opencc-by-sa-4.0May 2024View details →

zenodo44/100

Data inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al.

This data packet provides inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al., currently in the submission process. This repository will eventually be updated to link to the published manuscript.   =============================================================================== =============================================================================== Overview =============================================================================== =============================================================================== Data and model products associated with the manuscript "Mammal niches are not  conserved over continental scales" by Goldstein et al.  Files are organized into two subdirectories. The first, "model_inputs/",  contains 8 data files intended to be used as part of the reproducible code  repository at  https://github.com/dochvam/Mammal_SVCs_ISDM_reproducible.  The second subdirectory, "model_outputs/", contains modeled products giving estimated spatially varying niche relationships and predictions of relative abundance. Below, we describe the contents of each file type. See the main manuscript  for full methodology, data sources, and discussions of spatial scales. NOTE: Version 1 of this dataset contained some errors that have been corrected in Version 2. Version 2 was used as the input dataset for the analyses in the associated manuscript. Version 1 should not be used. =============================================================================== =============================================================================== Subdirectory 1: "model_inputs/" =============================================================================== =============================================================================== Two versions of each of four files are provided, corresponding to analyses  that do or do not consider ancient genetic lineages as potential sources of  spatial nonstationarity in mammal niches. Each file type is formatted the same, and the versions are differentiated by either the suffix "nolineage" or  "lineage" in the filename.  =============================================================================== File 1: gridcell_covars_lineage.csv and gridcell_covars_nolineage.csv =============================================================================== These files are .csvs giving spatial covariate data for each scale 2 cell in North America, summarized to 5000 m. All percentage values are given in  10ths of a percent (scale of 0-1000). The following columns are provided: - grid_cell: Scale 2 cell ID - Arable: pct arable land (Jung et al. 2020) - EVI_mean: mean enhanced vegetation index (Didan 2021) - EVI_Q95: 95th quantile of EVI (Didan 2021) - Forest: Pct forest cover (Jung et al. 2020) - Grassland: pct grassland (Jung et al. 2020) - Pastureland: pct pastureland (Jung et al. 2020) - Pop_den: Human population density, from Gridded Population of the World (CIESIN 2018) - Precipitation: avg annual precip. (Vega et al. 2017) - Shrubland: pct shrubland (Jung et al. 2020) - Temp_max: Average maximum daily temperature (Vega et al. 2017) - Terrain_roughness (Amatulli et al. 2018) - Wetlands: pct wetlands (Jung et al. 2020) - is_land: Whether or not the cell is on land vs. ocean, used for filtering - Agriculture: Pct. agricultural land (Jung et al. 2020) - Pop_den_sqrt: Square root of human population density (CIESIN 2018) - EVI_variability: Distance btw the 95% inner quantiles of EVI (Didan 2021)   =============================================================================== File 2: inat_cts_lineage.csv and inat_cts_nolineage.csv =============================================================================== These files give summaries of iNaturalist sampling effort and detections for target species. The following columns are provided: - grid_cell: Scale 3 cell ID - n: Total iNaturalist effort in the cell (number of obs. of all mammals) - The remaining columns are named for species. Each column gives the count     of observations of the species in the cell. =============================================================================== File 3: ct_datlist_lineage.RDS and ct_datlist_nolineage.RDS =============================================================================== The ct_datlist files contain R objects that are lists of lists. These objects ultimately contain all of the camera detection histories and camera-level covariate data used in modeling. We use the nice data type "unmarkedFrameOccu" from the unmarked R package to organize these detection data. Each outer list is of length equal to the number of species. The ith element of each list contains the following named slots: - species: a string giving the name of the ith species - umf: an unmarkedFrameOccu object. This object has three important slots:     - y: a (# deployments) x (max # replicates) matrix giving 1s, 0s, or NAs indicating          whether the target species was observed in that 10-day window;     - siteCovs: a (# deployments) x 2 data frame with the following columns:         - site_ID: A unique ID of the exact location, shared by deployments with the same            coordinates         - subproject_ID: A unique ID indicating which camera array is associated                 with this deployment     - obsCovs: a (# deployments * max # replicates) x 6 data frame with the following columns:             - year: the year of survey, relative to 2020 (zero-year is 2020)             - yday_scaled: the (scaled) Julian date of the beginning of the window             - yday_scaled_sq: yday_scaled^2, for use in estimating a quadratic effect             - log_roaddist_scaled: Scaled distance to nearest road (Meijer et al. 2018)             - Canopy_height_scaled: Scaled canopy height (Potapov et al. 2021)             - obs_len_scaled: Scaled duration of window, to account for some windows                                being cut off at < 10 days - coords: a data frame. Originally, this file gave the exact position for each camera,       but these exact locations have been scrubbed for privacy. See the original sources           cited in the manuscript for full details. This data frame contains the following column:     - scale2_grid_ID: the ID of the Scale-2 5000 m grid cell containing the camera =============================================================================== File 4: grid_translator_wspecs_nolineage.csv and grid_translator_wspecs_lineage.csv =============================================================================== These files are used for bookkeeping to track the relationships between the  three spatial scales in this study. Each row corresponds to a single "scale 2" cell, giving the ID of the corresponding S3 and S4 grid and also an ID for each species indicating whether and where it is in the species' range.  Note that the scale names in the code don't match the manuscript. In the code, "scale 1" is the level of an individual camera, "scale 2" is the 5 km intensity grid, "scale 3" is the 50 km iNaturalist grid, and "scale 4" is the 100 km SVC grid. The following columns are provided: - scale2_grid_ID: unique ID for each cell in the 5 km intensity grid - scale3_grid_ID: unique ID for each cell in the 50 km iNaturalist aggregation - scale4_grid_ID: unique ID for each cell in the 100 km SVC grid - GRID_ID_[species]: for each species, a column is provided on the S4 scale                      counting each cell in the species' modeled range. NAs                  indicate that the S2 cell defined in the row is not              included in the species' modeled range. =============================================================================== =============================================================================== Subdirectory 2: "model_outputs/" =============================================================================== =============================================================================== =============================================================================== File 1: svc_estimates.csv =============================================================================== This file gives an estimate of the effect of each covariate on each species' intensity, and the uncertainty in that estimate, for each species/covariate pair. Results correspond to lineage models for species with phylogeographies and non-lineage species otherwise. Each row represents the effect of one  covariate on one species' relative intensity process within one 100 km cell g.  Note that many estimates of beta_g are uncertain even for strong spatial  effects---the model is often confident that a spatial process is supported  while estimates of the realized process are uncertain. The following columns are provided: - x: the x-coordinate of the 100 km cell - y: the y-coordinate of the 100 km cell - species - parname: the name of the covariate - mean: the mean of the posterior samples of beta_g - 2.5%: the 2.5th quantile of the posterior samples of beta_g - 50%: the 50th quantile of the posterior samples of beta_g - 97.5%: the 97.5th quantile of the posterior samples of beta_g The following spatial projection is used to define X/Y coordinates: "+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83" =============================================================================== File 2: predicted_intensity.tif =============================================================================== This file contains a raster "brick" giving the predicted intensity surface  and uncertainty in this surface for each species. All predictions are generated using models that do *not* account for lineage information---this means that  predictions for species with lineages are not from the models reported in the main manuscript. The reason for this is that we found that lineages were  overall unsupported, so better predictions can be arrived at by excluding this source of uncertainty in the underlying intensity process. The raster brick has 66 layers. Each layer provides either the mean predicted log intensity in each grid cell across the species range or else provides the standard error of that predicted log intensity. Layer names indicate output type and species associated with each layer. =============================================================================== =============================================================================== References =============================================================================== =============================================================================== Camera data are obtained from the following sources, which can be consulted to obtain the original raw camera data - Cove, Michael V., et al. "SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States." (2021): e03353. - Kays, Roland, et al. "SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID‐19 pandemic." (2022): e3775. - Shamon, H., et al. “SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.” Ecology, 105.6 (2024): e4318. - Rooney, B., et al. “SNAPSHOT USA 2019–2023: The first five years of data from a coordinated camera trap survey of the United States.” In Press (2024). - Kays, Roland, et al. "Does hunting or hiking affect wildlife communities in protected areas?." Journal of Applied Ecology 54.1 (2017): 242-252. - Roberts, R. California Department of Fish and Wildlife, Bobcat Program Initiative. wildlifeinsights.org (2023). - Lasky, Monica, et al. "CAROLINA CRITTERS: a collection of camera trap data from wildlife surveys across North Carolina." Ecology 102.7 (2021): e03372. - Forrester, T. (2000). Urban to Wild Project. http://n2t.net/ark:/63614/w12004302. Accessed via wildlifeinsights.org on 2024-08-29. - McMurry, S. et al. In review (2024). - Forrester, T. (2011) Okaloosa S.C.I.E.N.C.E. Project. http://n2t.net/ark:/63614/w12004287.  - Myers, J. (2014) Tyson Research Center ForestGEO Project. http://n2t.net/ark:/63614/w12004295. - McMurry, S., and Kays, R.(2023). Calloway Forest Preserve. http://n2t.net/ark:/63614/w12006449. Accessed via Wildlife Insights on 2024-08-29. - McMurry, S., Parsons, A., Lasky, M., Luongo, K.,  Clark, J., McShea, W., Scher, L., Kays, R., Spurlin, J., Martin, G., Frech, G., Barajas-Salazar, K.,  Snider, M. (2022). Last updated October 2023. Calloway Forest Preserve. http://n2t.net/ark:/63614/w12004251. Accessed via wildlifeinsights.org on 2024-08-29. - Kays, R.. (2008). Last updated March 2024. Albany Area Camera Trapping Project. http://n2t.net/ark:/63614/w12003860. Accessed via wildlifeinsights.org on 2024-08-29. - Kays, R., Snider, M., McMurry, S., Alyetama, M. (2024). Last updated April 2024. Pilot Mountain Density 2024. http://n2t.net/ark:/63614/w12007160. Accessed via wildlifeinsights.org on 2024-08-29. - Malleshappa, V., Smithsonian, E., Kays, R., Schuttler, S. (2015). Last updated December 2022. Museums Connect Mexico. http://n2t.net/ark:/63614/w12004298. Accessed via wildlifeinsights.org on 2024-08-29. Covariate data are obtained from the following sources: - Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017). - Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020). - Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018). - Center For International Earth Science Information Network-CIESIN-Columbia University. Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. (2018) doi:10.7927/H45Q4T5F. - Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD13A2.061 (2021). - Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018). - Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021). - Jensen, A. J. et al. Geographic barriers but not life history traits shape the phylogeography of North American mammals. Glob. Ecol. Biogeogr. e13875 (2024). iNaturalist data are obtained from inaturalist.org via the data exporter (see manuscript for details).

opencc-by-4.0Nov 2024View details →

zenodo44/100

EOL Mammals Patch (MAM)

Mammal taxa to complement ITIS mammal data & support mammal trait data sets. <h3>References</h3> Abello, M.A., Reyes, M.D.L., Candela, A.M., Pujos, F., Voglino, D., Quispe, B.M., 2015. Description of a new species of Sparassocynus (Marsupialia: Didelphoidea: Sparassocynidae) from the late Miocene of Jujuy (Argentina) and taxonomic review of Sparassocynus heterotopicus from the Pliocene of Bolivia. Zootaxa 3937, 147. <a href="https://doi.org/10.11646/zootaxa.3937.1.7">https://doi.org/10.11646/zootaxa.3937.1.7</a> Agnarsson, I., May-Collado, L.J., 2008. The phylogeny of Cetartiodactyla: The importance of dense taxon sampling, missing data, and the remarkable promise of cytochrome b to provide reliable species-level phylogenies. Molecular Phylogenetics and Evolution 48, 964–985. <a href="https://doi.org/10.1016/j.ympev.2008.05.046">https://doi.org/10.1016/j.ympev.2008.05.046</a> Almendra, A.L., Rogers, D.S., González-Cózatl, F.X., 2014. Molecular phylogenetics of the Handleyomys chapmani complex in Mesoamerica. J Mammal 95, 26–40. <a href="https://doi.org/10.1644/13-MAMM-A-044.1">https://doi.org/10.1644/13-MAMM-A-044.1</a> Álvarez, A., Arévalo, R.L.M., Verzi, D.H., 2017. Diversification patterns and size evolution in caviomorph rodents. Biol J Linn Soc 121, 907–922. <a href="https://doi.org/10.1093/biolinnean/blx026">https://doi.org/10.1093/biolinnean/blx026</a> ASM Mammal Diversity Database. American Society of Mammalogists. <a href="https://mammaldiversity.org/">https://mammaldiversity.org/ </a> Averianov, A.O., Lopatin, A.V., 2014. High-level systematics of placental mammals: Current status of the problem. Biol Bull Russ Acad Sci 41, 801–816. <a href="https://doi.org/10.1134/S1062359014090039">https://doi.org/10.1134/S1062359014090039</a> Bai, W., Dong, W., Zhang, L., Wei, Q., Liu, W., Chen, Z., 2019. New material of the Early Pleistocene spiral horned antelope spirocerus (Artiodactyla, Mammalia) from North China and discussion on its evolution. Quaternary International 522, 94–102. <a href="https://doi.org/10.1016/j.quaint.2019.06.029">https://doi.org/10.1016/j.quaint.2019.06.029</a> Balakirev, A.E., Phuong, B.X., Phuong, P.M., Rozhnov, V.V., 2011. On taxonomic status of Pseudoberylmys muongbangensis new species and genus described from Son La Province, Vietnam; Does it new or pseudo-new species? Proceedings of the 4th National Scientific Conference on Ecology and Biological Resources, 11-18. Bates, H., Travouillon, K.J., Cooke, B., Beck, R.M.D., Hand, S.J., Archer, M., 2014. Three new Miocene species of musky rat-kangaroos (Hypsiprymnodontidae, Macropodoidea): description, phylogenetics and paleoecology. Journal of Vertebrate Paleontology 34, 383–396. <a href="https://doi.org/10.1080/02724634.2013.812098">https://doi.org/10.1080/02724634.2013.812098</a> Begun, D.R., 2010. Miocene Hominids and the Origins of the African Apes and Humans. Annual Review of Anthropology 39, 67–84. <a href="https://doi.org/10.1146/annurev.anthro.012809.105047">https://doi.org/10.1146/annurev.anthro.012809.105047</a> Begun, D.R., 2015. Fossil Record of Miocene Hominoids, in: Henke, W., Tattersall, I. (Eds.), Handbook of Paleoanthropology. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 1261–1332. <a href="https://doi.org/10.1007/978-3-642-39979-4_32">https://doi.org/10.1007/978-3-642-39979-4_32</a> Benton, M., 2014. Vertebrate Palaeontology. John Wiley & Sons. Bhandari, A., Kay, R.F., Williams, B.A., Tiwari, B.N., Bajpai, S., Hieronymus, T., 2018. 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Morgan Ernest, 2003: Life history characteristics of placental non-volant mammals

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Mammal occurrence records (2024) in the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India

This dataset contains Mammal occurrence records (November 2023 - October 2024) in the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India. It includes a few occurrence records from other parts of southern India. Occurrence records were gathered in the field by researchers of the Nature Conservation Foundation, India, using a mobile data collection application (EpiCollect5). Suggested citation is: Nature Conservation Foundation (2024). Mammal occurrence records (2024) in the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India. Nature Conservation Foundation, India. Dataset, Zenodo. DOI: 10.5281/zenodo.13910696   CONTACT #1 1. Name: T. R. Shankar Raman  2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: trsr@ncf-india.org  5. ORCID: https://orcid.org/0000-0002-1347-3953 CONTACT #2 1. Name: Divya Mudappa  2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: divya@ncf-india.org  5. ORCID: https://orcid.org/0000-0001-9708-4826 Keywords: tropical rainforest, plantations, Anamalai Hills, Western Ghats, animal distribution, mammals  Geographic Coverage: 1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India 2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E) Temporal Coverage: 1. Begins: 2023-11-01 (Year, Month, Day) 2. Ends: 2024-10-01 (Year, Month, Day) Besides the 00_readMe.txt file containing this information, the dataset includes 23 images (photographs) and two comma-delimited text (csv) files as explained below: 1) 01_anamalai-mammals-2024.csv -- This file has the main mammal occurrence data with relevant and renamed columns derived from the original downloaded csv file from the EpiCollect5 application website. 2) 02_nameMatch.csv -- This file matches the vernacular name as originally recorded with the correct common name and scientific name +23 image files (with ".jpg" file extension) FILES INCLUDED IN DATASET 01_anamalai-mammals-2024.csv This file has the main mammal occurrence data with relevant and renamed columns derived from the original downloaded csv file from the EpiCollect5 application website. ec5_uuid: Unique ID for each observation created_at: Automatic time stamp of date and time when record was created on the mobile app uploaded_at: Automatic time stamp of date and time when record was uploaded using the mobile app recordedBy: Name of observer title: Title assigned to each record (composite of date, species, and type of observation) lat_gps: Latitude in decimal degrees N long_gps: Longitude in decimal degrees E accuracy_gps: Horizontal accuracy of GPS location in metres UTM_Northing_gps: Latitude in UTM UTM_Easting_gps: Longitude in UTM UTM_Zone_gps: UTM Zone eventDate: Date in ISO format (yyyy-mm-dd) verbatimEventDate: Date in format originally recorded (dd/mm/yyyy) eventTime: Time of observation vernacularName: Species common name as initially recorded individualCount: Number of individuals observed occurrenceRemarks: type of observation habitat: Habitat type photo: Filename of photo if available (NA otherwise) eventRemarks: Notes or remarks about the observation 02_nameMatch.csv This file matches the name as originally recorded with the correct common name and scientific name. vernacularName: Common or English name as initially recorded  scientificName: Scientific name of the species +23 image files (.jpg extension)

opencc-by-4.0Dec 2023View details →

zenodo44/100

Data from: Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India

This dataset contains data from the following publication: Sridhar, H., Raman, T. R. S. & Mudappa, D. 2008. <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. Current Science 94: 748-757. URL: <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">https://www.currentscience.ac.in/Volumes/94/06/0748.pdf</a> URL2: <a href="https://www.jstor.org/stable/24100628">https://www.jstor.org/stable/24100628</a> Corrigendum: Sridhar, H., Raman, T. R. S. & Mudappa, D. 2009. <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">Corrigendum: mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. Current Science 97: 612-613. URL: <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">https://www.currentscience.ac.in/Volumes/97/05/0612.pdf</a> Description of dataset: The data contains detections of mammals and hornbills (and few incidental records of other species) made along line transect surveys and opportunistic surveys in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. Further details of the Study Area and methods are available in Sridhar et al. (2008), but methods are briefly described below. Five rainforest patches were chosen within IGWLS and four privately-owned rainforest fragments in the Valparai plateau. Fifteen line transects, ranging in length from 1 to 3 km were laid across the nine sites, with the three largest sites having 2–4 transects each. The total distance covered by all transects was 32.02 km. Each transect was walked five times between September 2005 and April 2006 following standard distance sampling protocol. Two observers walked each transect at 0.75–1 km/h. For each detection, we recorded species, group size and perpendicular distance (measured using a rangefinder) from the transect. For animals which occurred in groups, perpendicular distances were measured to group centres. Apart from detections on transects, attempts were made to obtain group sizes of mammal species whenever incidentally detected. All transects were walked between 0630 and 1000 h. Indirect evidence (scat, tracks) on transects and incidental sightings (direct and indirect) of mammals were also recorded. AUTHOR #1 1. Name: Hari Sridhar 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Current Work Address: Konrad Lorenz Institute for Evolution and Cognition Research, A-3400 Klosterneuburg, Austria 4. Email address: harisridhar1982@gmail.com 5. ORCID: https://orcid.org/0000-0003-3286-0120 AUTHOR #2 1. Name: T. R. Shankar Raman 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: trsr@ncf-india.org 5. ORCID: https://orcid.org/0000-0002-1347-3953 AUTHOR #3 1. Name: Divya Mudappa 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: divya@ncf-india.org 5. ORCID: https://orcid.org/0000-0001-9708-4826 Keywords: tropical rainforest, tea plantation, coffee plantation, line transect, population density, distance sampling, Anamalai Tiger Reserve, Valparai Plateau, Anamalai Hills, Western Ghats, mammals, hornbills Geographic Coverage: 1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India 2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E) Temporal Coverage: 1. Begins: 2005-09-01 (Year, Month, Day) 2. Ends: 2006-10-31 (Year, Month, Day)   Dataset files: Besides the 00_README.txt file, the dataset includes 4 comma-delimited text (csv) files with the data in columns as explained below: 01_Transect_locations.csv — contains transect location details and descriptions 02_Transects_and_opportunistic_surveys.csv — contains main dataset of observations on line transect and opportunistic surveys 03_Opportunistic_observations_locations.csv — contains location details of opportunistic surveys 04_Lion-tailed_macaque_counts.csv — contains counts of lion-tailed macaque (Macaca silenus) troops 05_allmammals_raw.xls — raw data NOT for use, for reference only in original Microsoft Excel format   Data variables and descriptions: 01_Transect_locations.csv TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations TransectLength_m: Length of line transect in metres decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum StartLat: transect starting point latitude in decimal degrees (N), WGS84 datum StartLon: transect starting point longitude in decimal degrees (E), WGS84 datum EndLat: transect ending point latitude in decimal degrees (N), WGS84 datum EndLon: transect ending longitude in decimal degrees (E), WGS84 datum MidLat: approximate mid-way location latitude in decimal degrees (N), WGS84 datum MidLon: approximate mid-way longitude in decimal degrees (E), WGS84 datum ExtraLatLon: additional pairs of latitude and longitude points along transect in decimal degrees (E, N), WGS84 datum RouteDescription: description of transect route 02_Transects_and_opportunistic_surveys.csv eventID: unique ID of sampling event corresponding to a single on-foot survey of a line transect, with elements separated by colons and last two elements referring to TransectCode and replicate survey number occurrenceID: unique ID assigned to each occurrence (detection) along line transect Sno: serial number TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations locality: name of transect Transectno: unique number assigned to each transect survey or resurvey Replicate: number indicating repeat survey of same transect SiteCategory: Category indicating whether transcet was in Protected Area or Rainforest Fragment Date: date of transect survey or opportunistic observation Weather: Weather at time of survey Habitat: Habitat where observation was made Time: time in 24 h HH:MM format verbatimIdentification: Identification as originally entered scientificName: Scientific name of species or taxon observed vernacularName: Common English name of species or taxon observed Perpdist: Perpendicular distance in metres Freshness: rating of freshness of faeces found (d=day, wk=week, mt=month) individualCount: number of individuals counted, taken as minimum 1 if not noted in field rawNumber: number as originally entered DetectionType: type of observation classified as Call, Faeces, Indirect, Sighting, Track verbatimDetection: raw entry corresponding to previous column Height: height of observed animal above ground in metres occurrenceRemarks: remarks on occurrence 03_Opportunistic_observations_locations.csv locality: name of place or transect where opportunistic observation was made decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum coordinateUncertaintyInMeters: approximate/estimated uncertainty in location coordinates (in metres) 04_Lion-tailed_macaque_counts.csv Sno: Serial number of entry Place_or_Transect: Transect (TransectCode) or place where lion-tailed macaques were counted Date: Date of observation Time: Time of observation in 24h HH:MM format Weather: Weather Groupid: ID of Lion-tailed macaque troop, if known Total: Total number of individuals recorded AM: number of adult males AF: number of  adult females A: number of adults (unsexed) SA: number of sub-adults (unsexed) SAM: number of sub-adult males SAF: number of sub-adult females JUV: number of juveniles INF: number of infants CARINF: number of infants carried by mother UNID: number of unclassified Remarks: other notes   05_allmammals_raw.xls Raw data file in Microsoft Excel format -- for reference only (not advised for use) ADDITIONAL NOTES General notes taken about survey: Pannimade transect 3/11/05 - Most giant squirrel detections were made after squirrel alarm called on seeing a soaring raptor. 36TH hpb transect - very poor visibility on one side as it is very steep Giant squirrels present within LTM  troops might go undetected. Need to look carefully and check every movement KO transect 20/01/06 - 1 GS which wasn’t detected when walking transect detected when measuring at less than 20 metres SHK transect 23/01/06 - Abandoned 100 metres from end because of elephants BAN - Ignore detections after 2.05 KM for first two replicates KSPV 26/01/2006 - 1 GS  not detected on transect detected while returning at < 40 m Anaigundi - Transect in december strayed slightly from correct path Var 30/01/2006 transect  Do not include for indirect signs encounter rate since replicates were done on consecutive days Is there a difference in visibility between wet and dry months; atleast in the more deciduous forests like varagaliar that is the case should I consider only january and afterwards for MGH  numbers since vocal activity is much higher then? TF transect 12/02/06 4 GS heard calling from coffee estate adjoining TF; could fewer detections on last transect be because they are moving into coffee, maybe because some tree is fruiting Do NL individuals move solitarily; what average group size to use visibility in BAN and VAR is much better than other sanctuary sites such as IYAK, AN, MA rained on 1st & 2nd of March after a long dry spell KSPV  31/03/06 - Could have missed some calls because of cicada noise KSWT  01/04/06 - Could have missed some calls because of cicada noise KSPV  02/04/06 - Could have missed some calls because of cicada noise KSPV 02/04/06  Do not include for indirect signs encounter rate since replicates were done on consecutive days Great hornbills seem to be more vocal during april. To do with end of nesting?? malabar grey hornbills more vocal from february onwards

opencc-by-4.0Apr 2023View details →

zenodo44/100

Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals

Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals Sophie Teulleta,#, Marie-Ka Tilaka, Amandine Magdeleinea, Roxane Schaubb,c, Nora M. Weyerd, Wendy Panainod,e, Andrea Fullerd, William. J. Loughryf, Nico L. Avenantg, Benoit de Thoisyh,i, Guillaume Borrelj and Frédéric Delsuca,# aInstitut des Sciences de l’Evolution de Montpellier (ISEM), Univ Montpellier, CNRS, IRD, Montpellier, France bCIC AG/Inserm 1424, Centre Hospitalier de Cayenne Andrée Rosemon, Cayenne, French Guiana cTropical Biome and immunopathology, Université de Guyane, Labex CEBA, DFR Santé, Cayenne, French Guiana dBrain Function Research Group, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa eCentre for African Ecology, School of Animals, Plant, and Environmental Sciences, University of the Witwatersrand, Johannesburg, South Africa fDepartment of Biology, Valdosta State University, Valdosta, GA, USA gNational Museum and Centre for Environmental Management, University of the Free State, Bloemfontein, South Africa hInstitut Pasteur de la Guyane, Cayenne, French Guiana, France iKwata NGO, Cayenne, French Guiana, France jInstitut Pasteur, Université Paris Cité, UMR CNRS 6047, Evolutionary Biology of the Microbial Cell, Paris, France #Corresponding authors: sophie.teullet@umontpellier.fr; frederic.delsuc@umontpellier.fr   Abstract In mammals, myrmecophagy (ant and termite consumption) represents a striking example of dietary convergence. This trait evolved independently at least five times in placentals with myrmecophagous species comprising aardvarks, anteaters, some armadillos, pangolins, and aardwolves. The gut microbiome plays an important role in dietary adaptation, and previous analyses of 16S rRNA metabarcoding data have revealed convergence in the composition of the gut microbiota among some myrmecophagous species. However, the functions performed by these gut bacterial symbionts and their potential role in the digestion of prey chitinous exoskeletons remain open questions. Using long- and short-read sequencing of fecal samples, we generated 29 gut metagenomes from nine myrmecophagous and closely related insectivorous species sampled in French Guiana, South Africa, and the USA. From these, we reconstructed 314 high-quality bacterial genome bins of which 132 carried chitinase genes, highlighting their potential role in insect prey digestion. These chitinolytic bacteria belonged mainly to the family Lachnospiraceae, and some were likely convergently recruited in the different myrmecophagous species as they were detected in several host orders (i.e., Enterococcus faecalis, Blautia sp), suggesting that they could be directly involved in the adaptation to myrmecophagy. Others were found to be more host-specific, possibly reflecting phylogenetic constraints and environmental influences. Overall, our results highlight the potential role of the gut microbiome in chitin digestion in myrmecophagous mammals and provide the basis for future comparative studies performed at the mammalian scale to further unravel the mechanisms underlying the convergent adaptation to myrmecophagy.   Main figures and corresponding datasets Figure_1_dataset.zip contains: <ul> <li>FIGURE 1. Phylogenetic position of the 314 high-quality selected bins reconstructed from 29 gut metagenomes of the nine focal myrmecophagous species within a reference prokaryotic phylogeny. A: Phylogeny of the 314 selected bins (red branches) with 2496 prokaryote reference genomes. Circles respectively indicate (from inner to outer circles): the bacterial phyla and kingdom to which these genome bins were assigned based on the Genome Taxonomy Database release 7 (Parks et al, 2021). Clades, where a subtree was defined, are highlighted in blue for the Firmicutes (Fig. 1B), green for the Bacteroidetes, and pink for the Proteobacteria (Figs. S2 A and B, respectively). B: Subtree within Fimircutes showing myrmecophagous-specific clades (blue highlights; dark blue corresponds to the three clades mentioned in the results, light blue to the other clades). The outer circle indicates the bacterial family to which these genome bins were assigned based on the Genome Taxonomy Database. Bins’ names of the myrmecophagous-specific clades are indicated at leaves of the phylogenetic tree together with the genus to which they were assigned to.</li> <li>phylophlan_LR_SR_ToL_FINAL_concatenated.aln: Alignment of the concatenated markers assembled by PhyloPhlAn v3.0.58.</li> <li>phylophlan_LR_SR_ToL_FINAL.tre: Phylogenetic tree reconstructed by PhyloPhlAn v3.0.58 for the 314 high quality selected genome bins and the 2496 prokaryote reference genomes.</li> </ul> Figure_2_dataset.zip contains: <ul> <li>FIGURE 2. Phylogeny of the 394 GH18 sequences identified in 132 high-quality selected bins reconstructed from 29 gut metagenomes of the nine focal myrmecophagous species and relatives. Red branches indicate the 237 sequences having an active chitinolytic site (DXXDXDXE). Circles respectively indicate (from inner to outer circles): the bacterial family and phyla of the bin the sequence was retrieved from. Colored sequence names indicate the host species. Colored circles at certain nodes indicate enzymes to which sequences are similar when blasting them against the NCBI non-redundant protein database. Sequence names are indicated at leaves of the tree and begin with the genus to which the bin they were identified in was assigned to. </li> <li>GH18_sequences_from_selected_bins_alignment.fasta: Alignment of the 394 GH18 sequences identified in 132 high quality selected bins computed with MAFFT v7.450.</li> <li>GH18__sequences_from_selected_bins_tree.newick: Phylogenetic tree of the 394 GH18 sequences inferred with RAxML v8.2.11 within Geneious Prime 2022.0.2.</li> </ul> Figure_3_dataset.zip contains: <ul> <li>FIGURE 3. Detection of the 314 high-quality bacterial genomes (lines) in the 29 gut metagenomes (columns) of the nine focal species. Each square indicates the detection of a genome bin in a sample as estimated by anvi’o v7 (Eren et al, 2021). Names of bins are indicated on the left with red indicating chitinolytic bins (Table S2). The names begin with the genus to which the bin was assigned to. Asterisks (*) indicate bins detected in at least one soil sample (detection > 0.25) (Fig. S4, Table S2, and detection table available via Zenodo). Phylogenetic relationships of host species distinguished by different color strips are represented at the bottom of the graph. Columns on the right indicate (from left to right): the number of GH18 sequences identified in each bin (from 0 to 17), the bin’s taxonomic phylum, class, order, and family. The phylogeny of the 314 selected bins inferred with PhyloPhlAn v3.0.58 (Asnicar et al, 2020) is also represented on the right of the graph (see Fig. S1). Silhouettes were downloaded from phylopic.org.</li> <li>detection_bins_across_gut_metagenomes.txt: Detection table as tab-delimited file containing the detection values inferred by anvi'o v7 for the 314 high quality selected bins across the 29 gut metagenomes from the nine focal myrmecophagous species. </li> </ul> Figure_4_dataset.zip contains: <ul> <li>FIGURE 4. Distribution of chitinolytic selected bins (red links) among the nine focal myrmecophagous species and relatives. Phylogenies of the 314 high-quality selected bins (Fig. S1) and of the nine host species (downloaded from timetree.org) are represented respectively on the left and the right of the graph. Links illustrate, for each bin, in which host species the bin was detected (detection threshold > 0.25). Red links indicate bins in which at least one GH18 sequence with an active chitinolytic site (DXXDXDXE) was found (chitinolytic bins). The size of the circles at the tips of the host phylogeny is proportional to the number of samples (n = 1 for D. kap; n = 2 for D. nov, C. uni and M. tri; n = 3 for T. tet and O. afe; n = 4 for D. sp. nov FG; n = 6 for P. cri and S. tem). Bins’ names are indicated at the tip of the bins’ phylogeny and main bacterial phyla are indicated by colored vertical bars. This graph was done with the cophylo R package within the phytools suite (Revell, 2012). Silhouettes were downloaded from phylopic.org.</li> <li>presence_absence_MAGs_in_metagenomes.txt: Presence/absence matrix of the 314 selected genome bins across the 29 gut metagenomes.</li> <li>host_species_phylo_reduced_fig4.newick: Host phylogenetic timetree.</li> </ul> Table_1_sample_infos.xls: Detailed sample information for the 33 fecal samples collected. N.B.: Diet was determined based on field observations (i.e., dissections) and the literature.   Supplementary results Supplementary_results_Teullet_etal_2023.zip includes a comparison of genome statistics of the selected bins reconstructed from the long-read vs the short-read datasets, a phylogeny of the set of selected bins before dereplication (n = 407) and a comparison of the distribution of shared and specific genome bins carrying GH18 among host orders.   Supplementary material Supplementary_material_Teullet_etal_2023.zip contains <ul> <li>Supplementary figures (S1-S4) and tables (S1-S4).</li> <li>phylophlan_314_bins_phylogeny_FINAL_concatenated.aln and phylophlan_314_bins_phylogeny_FINAL.tre: Alignment of the concatenated markers and the final tree (respectively) reconstructed by PhyloPhlAn v3.0.58 for the 314 high-quality selected and dereplicated genome bins.</li> <li>phylophlan_407_selected_bins_nodRep_concatenated.aln and phylophlan_407_selected_bins_phylogeny_FINAL.tre: Alignment of the concatenated markers and the final tree (respectively) reconstructed by PhyloPhlAn v3.0.58 for the 407 high-quality selected genome bins before dereplication.</li> <li>abundance_bins_across_gut_metagenomes.txt: A tab-delimited file corresponding to the absolute abundance values inferred by anvi'o v7 for the 314 high-quality selected bins across the 29 gut metagenomes from the nine focal myrmecophagous species. </li> <li>detection_bins_across_soil_samples.txt: A tab-delimited file corresponding to the detection values inferred by anvi'o v7 for the 140 high-quality selected bins reconstructed from the aardvark, ground pangolin and southern aardwolf gut metagenomes across the eight soil samples collected on sample sites in South Africa.</li> </ul>   Assemblies Long-read_metagenomic_assemblies_polished.zip contains the 31 long-read metagenomes assembled with metaFlye strain v2.9 and polished with short reads using Pilon v1.4, which were used for binning. Long-read_metagenomic_assemblies_not_polished.zip contains the 33 long-read metagenomes assembled with metaFlye strain v2.9 before polishing. Short-read_metagenomic_assemblies.zip contains the 31 short-read metagenomes assembled with metaSPAdes and MEGAHIT. N.B: <ol> <li>Two samples (DASY M1746 and DASY VLD168) were not sequenced using Illumina short reads. Only long reads were generated and assembled for these two samples and are made available here. As these assemblies could not be polished, these samples were not included in downstream analyses.</li> <li>Two samples (CAB M3141 and MYR M5293) were highly contaminated by host reads and not used in downstream analyses. As they were still assembled with the other samples, the corresponding metagenomes are made available here.</li> </ol>   Binning: genome bins and dereplication results High-quality_selected_bins_dereplicated.zip contains the 314 high quality selected bins (>90% completion, <5% redundancy) reconstructed from long- and short-read metagenomes with metaBAT2 and dereplicated with dRep at 98% ANI. metaBAT2_short-read_assemblies_bins.zip contains all bins reconstructed from the short-read assemblies with metaBAT2 (i.e., output of metaBAT2). metaBAT2_long-read_assemblies_bins.zip contains all bins reconstructed from the long-read polished assemblies with metaBAT2 (i.e., output of metaBAT2). Output_dRep_98ANI_407_bins_long-short-reads.zip contains the output of the dereplication analysis done on the set of 407 high-quality selected genome bins reconstructed from long- (n = 201) and short-read (n = 206; labeled "spad") metagenomes. It was performed with dRep using default parameters. After this step, the final dataset included 314 high-quality non-redundant genome bins. This folder includes: <ul> <li>LR_SR_407_bins_dRep_98ANI_Primary_clustering_dendrogram.pdf: The primary clustering of selected genome bins using the Mash algorithm with an ANI threshold of 90%.</li> <li>LR_SR_407_bins_dRep_98ANI_Secondary_clustering_dendrograms.pdf: The secondary clustering of selected genome bins using the fastANI algorithm with an ANI threshold of 98%.</li> <li>LR_SR_407_bins_dRep_98ANI_Cluster_scoring.pdf: The clustering score attributed to each genome bin during dereplication. Asteriks (*) indicate genomes chosen to be the representative genomes of their cluster.</li> </ul> <ul> </ul>

opencc-by-4.0Jun 2023View details →

zenodo44/100

Diversity and evolution of cerebellar folding in mammals

Coronal cerebellar mid-sections for 56 mammalian species, at the same scale.   This figure is from our open access paper: Heuer, K., Traut, N., de Sousa, A. A., Valk, S., & Toro, R. (2022). Diversity and evolution of cerebellar folding in mammals. bioRxiv. <a href="https://doi.org/10.1101/2022.12.30.522292">https://doi.org/10.1101/2022.12.30.522292</a>   Abstract The process of brain folding is thought to play an important role in the development and organisation of the cerebrum and the cerebellum. The study of cerebellar folding is challenging due to the small size and abundance of its folia. In consequence, little is known about its anatomical diversity and evolution. We constituted an open collection of histological data from 56 mammalian species and manually segmented the cerebrum and the cerebellum. We developed methods to measure the geometry of cerebellar folia and to estimate the thickness of the molecular layer. We used phylogenetic comparative methods to study the diversity and evolution of cerebellar folding and its relationship with the anatomy of the cerebrum. Our results show that the evolution of cerebellar and cerebral anatomy follows a stabilising selection process. We observed 2 groups of phenotypes changing concertedly through evolution: a group of “diverse” phenotypes – varying over several orders of magnitude together with body size, and a group of “stable” phenotypes varying over less than 1 order of magnitude across species. Our analyses confirmed the strong correlation between cerebral and cerebellar volumes across species, and showed in addition that large cerebella are disproportionately more folded than smaller ones. Compared with the extreme variations in cerebellar surface area, folial anatomy and molecular layer thickness varied only slightly, showing a much smaller increase in the larger cerebella. We discuss how these findings could provide new insights into the diversity and evolution of cerebellar folding, the mechanisms of cerebellar and cerebral folding, and their potential influence on the organisation of the brain across species.

opencc-by-4.0Jun 2023View details →

zenodo44/100

Data and code: Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology

Zip folder conaining the data and code that support the findings of Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology. The Data_code.zip folder contains four subfolders with the following files: <ul> <li>Data_raw <ul> <li>AgriDiv_data.csv</li> <li>AgriDiv_metadata.docx</li> <li>Species_data.csv</li> <li>Species_metadata.docx</li> <li>Landscape_data.csv</li> <li>Landscape_metadata.docx</li> </ul> </li> <li>Data_derived <ul> <li>RIA_RSR_effect_sizes.csv</li> <li>MSA_effect_sizes.csv</li> </ul> </li> <li>Data_output <ul> <li>Response_estimation.csv</li> </ul> </li> <li>R_scripts <ul> <li>01_Effect_size_calculation.R</li> <li>02_Null_model_analysis.R</li> <li>03_Model_selection.R</li> <li>04_Model_analysis.R</li> <li>05_Response_estimation.R</li> <li>06_Figures.R</li> <li>README.md</li> </ul> </li> </ul>

opencc-by-4.0Jul 2023View details →

zenodo44/100

Data from: Sex differences in the relationship between maternal and foetal glucocorticoids in a free-ranging large mammal

Description This registration contains the data and scripts used for the analysis in the manuscript "Sex differences in the relationship between maternal and neonate cortisol in a free-ranging large mammal" (https://doi.org/10.1101/2023.05.04.538920). All versions and additional material are posted on the OSF project page: https://osf.io/4ymc8/  Important to note: this is an updated registration from a previous version (www.osf.io/wynke). OSF currently does not allow to change the files that were uploaded previously, which is why we switched to Zenodo. Future updates to the dataset and scripts will take place here.  Changes to previous version Following our review process at PCI Ecology, we made some changes (mostly additions) to the previous version. This includes the addition of more raw data.  Changes to the previous version include:  -    We added the raw faecal data (“Raw_faecal_data.csv) along with a script (“Faecal exploration .R”), which loads the raw faecal datafile and does some minor investigations on it. These include checking whether the sampling time or day matter, and calculating a repeatability estimate. -    In “Faecal exploration.R”, we highlight the outlier that we removed.  -    We have added the t-test reported in the manuscript to the script “Analysis.R”. -    In “Analysis.R”, we now also run the analysis both with as well as without the outlier. We also have adapted our code to plot the outlier in the figure along with the other values.  -    We have updated the README file to include a description of both the data files and the scripts.

opencc-by-4.0Sep 2023View details →

zenodo44/100

Data supporting Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland

Data supporting the paper: <blockquote> Land-Miller, H., A. Roos, M. Simon, R. Dietz, C. Sonne, S. Pedro, A. Rosing-Asvid, F. Rigét, and M. McKinney. 2023. Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland. Marine Ecology Progress Series. </blockquote> This data is in five files: 1. greenland_marmam_metadata.csv contains metadata for all samples used in this project, including sample identifiers: <ul> <li>Sample: unique sample ID per individual animal</li> <li>Species</li> </ul> and details of collection, including Year, Location (general area), Lat, Long, and Date. It also includes other data on the animal (Sex, Age, Length), when available, as well as the co-author who provided the sample to the project (Sample sender) and the tissues available/analyzed for each individual (Tissues received). 2. all_sample_locations.csv includes latitude/longitude of each sample for mapping. Latitude and longitude are consistent with the full metadata file when coordinates were available, and estimated based on general sampling area (Location or Area) when not. The variable estimate denotes samples for which coordinates were estimated. 3. fatty_acids_greenland_marmams.csv contains fatty acid data for all samples. Variables 8:00 to 24:1n9 represent the proportion of each individual fatty acid, out of total fatty acids in that sample. Data are represented as whole number percents (i.e., 10 = 10% and all fatty acids sum to 100 for each sample).  4. CNS_greenland_McGill.csv contains bulk stable isotope data for all samples analyzed at McGill. In addition to Sample and Species, this includes: <ul> <li>treatment: whether a sample was lipid-extracted (LE) or non-lipid-extracted (nLE) prior to analysis</li> <li>d15N: stable isotope ratio δ15N</li> <li>d13C: stable isotope ratio δ13C</li> <li>d34S: stable isotope ratio δ34S</li> <li>perc.C: mass percent of carbon in the sample</li> <li>perc.N: mass percent of nitrogen in the sample</li> <li>perc.S: mass percent of sulfur in the sample</li> <li>C.N.ratio: mass ratio of carbon to nitrogen in the sample </li> </ul> 5. CN_greenland_nLE_copenhagen.csv contains stable isotope data for non-lipid-extracted samples analyzed at the University of Copenhagen for δ13C and δ15N. Variables treatment, d13C, and d15N are consistent with CNS_greenland_McGill.csv.

opencc-by-4.0Oct 2023View details →

dryad44/100

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.

publicDec 2019View details →

edi44/100

Frugivoria: an open trait database of birds and mammals exhibiting frugivory in moist montane Neotropical forest

Biodiversity in many areas is rapidly shifting and declining as a consequence of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor, and conserve biodiversity hotspots. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the functional trait information required to evaluate functional diversity is often lacking and is difficult to harmonize across disparate data sources. Biodiversity hotspots are particularly lacking in this information. To address this knowledge gap, we compiled Frugivoria, a trait database containing dietary, life-history, and morphological traits as well as IUCN conservation status, for mammals and birds exhibiting frugivory, which are important for seed dispersal, an essential ecosystem service. Accompanying Frugivoria is an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyze traits in space. This version of Frugivoria encompasses species in moist montane forests of Central and South America. Compared with existing trait databases, Frugivoria adds 25 species (reclassifying 199 to align with the most recent taxonomic changes), adds new traits such as observed and inferred range size, habitat specialization, and body size, and also fills gaps in trait categories from other databases such as diet category, home range size, generation time, and longevity. Overall, Frugivoria adds 2,045 new trait values for mammals and 4,022 for birds, and includes a total of 17,454 trait entries with minimums and maximums reported for certain traits. Frugivoria and its workflow enables researchers to quantify relationships between traits and the environment, as well as spatial trends in functional diversity, contributing to basic knowledge and applied conservation of frugivores

openCC (other)Nov 2021View details →

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Allen Brain Atlas

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Last verified 2026-04-30Open record

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behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

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dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record