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89 results for “ecological time”
Data from: Invasion timing affects multiple scales, metrics and facets of biodiversity outcomes in ecological restoration experiments (Missouri, 2009-2016)
Vegetation responses to experimental ecological restoration treatments at Tyson Research Centre of Washington University in Missouri, USA. These data include species-level cover responses to various factorial restoration treatments. Treatments were applied starting in 2009 and were measured in 2016. Treatment responses reflect these long term responses, but the dataset is comprised to one time point.
LakeBeD-US: Ecology Edition - a benchmark dataset of lake water quality time series and vertical profiles
LakeBeD-US: Ecology Edition is a harmonized lake water quality dataset containing time series and vertical profiles of 21 lakes in the United States monitored by long-term monitoring institutions. These institutions include the North Temperate Lakes Long-Term Ecological Research program (NTL-LTER), Niwot Ridge Long-Term Ecological Research program (NWT-LTER), National Ecological Observatory Network (NEON), and the Carey Lab at Virginia Tech as part of the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) site in collaboration with the Western Virginia Water Authority. The data include depth-discrete observations of 17 water quality variables including temperature, dissolved oxygen, chemical properties, Secchi depth, and more. Observations are divided into data collected by automated sensors at a relatively high temporal frequency and manually sampled data at a relatively low temporal frequency. All data were collected in situ. The data are available as Apache Parquet files, and the included R scripts give guidance on how to utilize and query the dataset in R. LakeBeD-US: Ecology Edition is an ecological science-oriented companion to LakeBeD-US: Computer Science Edition. The Computer Science Edition is available on the Hugging Face Hub.
Long time-series ecological niche modelling using archaeological settlement data.
<p><strong>CR_settlement_niche_[N]_[Yr]_[BC/AD].tif</strong></p> <p>Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement.<br> <strong>N</strong> - chronological ordering<br> <strong>Yr, BC/AD</strong> - calendar years BC or AD</p> <p> </p> <p><strong>CR_settlement_niche_combined.tif</strong></p> <p>All models combined by averaging.</p> <p> </p> <p><strong>CR_settlement_archeo.zip</strong></p> <p>Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields:</p> <p><strong>Site_Type:</strong> Cemetery or Settlement</p> <p><strong>Archeo_Dat:</strong> Archaeological dating (culture or period)</p> <p><strong>Source:</strong> Source dataset (AMCR or LONGWOOD)</p> <p>AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/.</p> <p>LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016). Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554.</p> <p><strong>Yrs_BP_Avg:</strong> Average dating in calendar years BP (based on the archaeological dating)</p> <p><strong>Yrs_BP_Unc:</strong> Temporal uncertainty of the dating (half of the culture or period's duration)</p> <p><strong>Loc_Accur:</strong> Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)</p>
Data from: Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time
<p>We used hierarchical random-effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [<em>Mareca americana</em>], blue-winged teal [<em>Spatula discors</em>], gadwall [<em>Mareca strepera</em>], green-winged teal [<em>Anas crecca</em>], mallard [<em>Anas platyrhynchos</em>] and northern pintail [<em>Anas acuta</em>]) across six distinct ecostrata in the Prairie Pothole Region of North America (Alberta parkland, Alberta prairie, Saskatchewan parkland, Saskatchewan prairie, Manitoba parkland, US prairie). We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross-seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. This submission comprises model code and data of banded birds by species, breeding population survey by species, proportion of ecostratum in conservation tillage (a proxy for agriculutral intensification), proportion of ecostratum in grassland, mean winter precipitation for Pacific Coast and Gulf Coast, total hectares of rice planted in the US, as well as hectares of flooded rice in the Pacific Coast and Gulf Coast. </p>
Artemisia pollen dataset for exploring the potential ecological indicators in deep time
<p>To cover the maximum range of <em>Artemisia</em> pollen morphological variation, we provide a pollen dataset of 36 species from 9 clades and 3 outgroups of <em>Artemisia </em>constrained by the phylogenetic framework, containing 4018 original pollen photographs under LM and SEM, 9360 pollen morphological trait measurements, together with their 30858 source plant occurrences, and corresponding environmental factors. Here, we attempt to decipher the underlying causes of the long-standing disagreement in the palynological community on the correlation between <em>Artemisia</em> pollen and aridity by using this pollen dataset to recognize the different ecological implications of <em>Artemisia</em> pollen types.</p> <p>The <em>Artemisia</em> pollen dataset as designed is open and expandable for new pollen data from<em> Artemisia</em> worldwide in order to better serve the global environment assessment and refined reconstruction of vegetation in the geological past.</p>
FIGURE 18 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 18. Ancestral state reconstruction of prey capture strategies and prey type preference in stem and crown Mysticeti. Topology follows Gatesy et al. (2013) and Fordyce and Marx (2018). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information (https://doi.org/10.6086/d14671).
FIGURE 3 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 3. Anatomical features associated with suction feeding in walrus (Odobenus rosmarus skull, from Jefferson et al., 2015) and North Sea beaked whale (Mesoplodon bidens skull, authors' work).
FIGURE 11 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 11. Sirenia stem and familial level diversity through time. "Protosirenidae / Prorastomidae" includes all taxa that are not included in the two extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
FIGURE 7 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 7. Pinnipedimorpha stem taxa and familial level diversity through time. "Stem-Pinnipedimorpha" includes all stem taxa that are not included in Desmatophocidae and the three extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
FIGURE 5 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 5. Anatomical features associated with grazing in African manatee (Trichechus senegalensis skull, from Werth, 2000), Desmostylia (Paleoparadoxia skull, public domain), and aquatic sloth (Thalassocnus sp. skull, modified from de Muizon et al., 2004).
FIGURE 8 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 8. Cetacean stem and Neoceti taxa diversity through time. "Archaeoceti" includes all stem taxa that are not included in the two extant groups. Dashed vertical lines: black, epoch boundaries; gray, age boundaries.
FIGURE 16 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 16. Ancestral state reconstruction of prey capture strategies and tooth pattern and cusp shape in Odobenidae. Topology follows Berta et al. (2018). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information uploaded to Dryad repository (https:// doi.org/10.6086/d14671).
FIGURE 17 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 17. Ancestral state reconstruction of prey type preference in stem and crown Pinnipedimorpha. Topology follows Rybczynski et al. (2009), Dewaele et al. (2017), and Berta et al. (2018). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch can be found in the Supplementary Information (https:// doi.org/10.6086/d14671).
FIGURE 19 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 19. Ancestral state reconstruction of prey capture strategies and tooth pattern and cusp shape in stem Odontoceti. Topology follows Gatesy et al. (2013) and Boessenecker et al. (2017). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information (https://doi.org/10.6086/d14671).
FIGURE 20 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 20. Ancestral state reconstruction of prey capture strategies and tooth pattern and cusp shape in crown Odontoceti. Topology follows McGowen et al. (2009) and Gatesy et al. (2013). Details on the tree can be found in Appendix 1. All data matrices and complete trees with branch lengths can be found in the Supplementary Information (https://doi.org/10.6086/d14671).
FIGURE 1 in Feeding in marine mammals: An integration of evolution and ecology through time
FIGURE 1. Feeding strategies of extant marine mammal predators (from Kienle et al., 2017) with the addition of grazing (sirenians, authors' work).
Fig. 6. Divergence times estimated from a in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism
Fig. 6. Divergence times estimated from a concatenated data set of per, COX1 and ITS2 sequences for the Lucilia bufornivora species group. Substitution model and relaxed clock models were unlinked for each gene. The tree was calibrated by setting the root to the node age corresponding to the split between Luciilinae and Calliphorinae subfamilies (~19 mya) as estimated by Wallman et al. (2005). Blue bars represent 95% highest posterior density (HPD) of each node age. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
FIGURE 3 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
FIGURE 3 Variation in HeV AUC from flying foxes. (A) The forest plot displays annual estimates and 95% confidence intervals ordered by latitude and year; points are scaled by the inverse sampling variance. The horizontal axis uses a modulus transformation to accommodate wide upper bounds of some confidence intervals. (B) Fitted values and 95% confidence intervals for the top GAM, with raw data (scaled by inverse sampling variance) and modelled means coloured by roost type. Transparency denotes AUC derived from truncated time series (≤20 weeks)
FIGURE 2 Fitted HeV urine pool prevalence and 95 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
FIGURE 2 Fitted HeV urine pool prevalence and 95% confidence intervals from the most parsimonious GAMM with week, seasonal interactions with roost type and previous food shortages, and an adjustment for relative abundance of Pteropus alecto. Weekly data are overlaid, coloured by roost type, and sized by corresponding P. alecto relative abundance. Thin lines show the fitted curves from the random factor smooth including each roost per year
FIGURE 1 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
FIGURE 1 Spatiotemporal variation in HeV shedding for the nine Australian flying fox roosts sampled from 2012 through 2014. Curve height indicates the weekly proportion of HeV-positive urine pools, with roosts shown in order of latitude and coloured by roost type. Ticks show sampling time points. Dark grey shading indicates regional acute food shortage events, and dashed lines with light grey shading indicate the Austral winter (i.e. June through August)
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.