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238 results for “temporal distribution”
Linking temporal changes in species composition and biomass in a globally distributed grassland experiment: The Nutrient Network
Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition, and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonization, and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained, or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e., local extinctions) were balanced by gains (i.e. colonization). Under fertilization, the decline in species richness resulted from increased species loss and from decreases in species gained. Biomass increase under fertilization resulted mostly from species that persist, and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition, and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained, or persisting.
Supplemental data for "Inequitable spatial and temporal patterns in the distribution of multiple environmental risks and benefits in Metro Vancouver"
<p><strong>DemoEnPoC2016.csv/DemoEnPoC2006.csv:</strong></p> <p>This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ). </p> <p><strong>DGRwithLable:</strong></p> <p>This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer. </p> <p><strong>Aggregation and SS Determination:</strong></p> <p>This script contains code for:</p> <ul> <li>Aggregating postal code level data to the Dissemination Area (DA) level.</li> <li>Eliminating rural DAs.</li> <li>Converting environmental data into ordinal categories using quartile and even break methods.</li> <li>Identifying sweet and sour spots for each DA based on these methods.</li> </ul> <p><strong>SSEJ Analysis:</strong></p> <p>This script includes code for:</p> <ul> <li>Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).</li> <li>Performing linear regression analyses between environmental categories and demographic variables.</li> </ul> <p><strong>SS Heatmap:</strong></p> <p>This script comprises code for:</p> <ul> <li>Summarizing the results of the linear regression analyses.</li> <li>Assessing changes in inequities among demographic groups between 2006 and 2016.</li> <li>Visualizing regression coefficients through heatmaps.</li> </ul> <p> </p>
Dataset of E. huxleyi blooms: spatio-temporal distribution and their impact on high-latitudinal marine environments (1998-2016)
<p>Dataset of coccolithophore blooms in polar seas of the Northern Hemisphere, viz. the North, Labrador (with adjacent North Atlantic open waters), Norwegian, Barents, Greenland and Bering seas are presented for the period 1998-2016. Seas are divided into 4 regions, for each of them continuous data series (as 8-days composites) are published, including information about bloom spatial masks, coccolith concentration, particulate inorganic carbon content and CO<sub>2</sub> partial pressure in water increment driven by coccolithophores.</p> <p>Datasets are published as NetCDF files with full metadata/descriptions and with GDAL support.</p> <p>Additional information (regions configuration, data access instructions) is provided alongside the data.</p> <p>Naming convention is: <strong>niersc_cocco_<version of dataset>_<region>_<start date>_<end date>.nc</strong></p>
Data from: The spatial distribution and temporal trends of livestock damages caused by wolves in Europe
<p>The preprint of the corresponding manuscript can be found here: doi: https://doi.org/10.1101/2022.07.12.499715</p> <p>Wolf populations are recovering and expanding across Europe, causing conflicts with livestock owners. We here compiled incident-based livestock damage data caused by wolves across 21 European countries for the years 2018, 2019 and 2020.</p> <p>The file "<strong>wolf_damages_2018_2019_2020_complete_data_to_publish.csv</strong>" contains the following information per incident: country, target species, cause, number of animals killed/injured/missing, assessment level probability, reported date, number of days until inspection, location, incidentID, uniqueID, NUS1_ID, NUTS2_ID, NUTS3_ID, damage prevention measure, number of wolves attacking, latitude, longitude, comments, metadata constraints.</p> <p>The file "<strong>nuts3_regions_and_LC_where_wolves_are_present.csv</strong>" contains information of the percentage of area occupied by wolves per NUTS3 region for selected land cover variables.</p> <p>The file "<strong>prevention_measures.csv</strong>" contains information about the financial support of livestock damage prevention measure per country or NUTS region</p> <p>The file "<strong>wolf_presence_now_vs_50_years_ago_nuts3.csv</strong>" contains information on NUTS3 regions that had a documented wolf presence 50 years ago.</p> <p>The "<strong>scripts_to_publish.zip</strong>" folder contains the scripts that we used to conduct the analyses.</p>
Spatial and temporal distribution and abundance of moths in the Andrews Experimental Forest, 1994 to 2008
The distribution and abundance of macromoth species is strongly influenced by geographical (region-neighboring plots) scale, elevation, aspect, plant community, management regime, and time of year. Noctural macromoths have been observed at a total of 263 sample sites throughout the Andrews Forest watershed since 1994. Only a limited subset of these sites is sampled each year. From 2004 to 2008, 20 sites were sampled consistently using a hierarchical sampling design stratified by elevation and vegetation type. Moths are sampled using blacklight traps deployed for one night every two weeks at each site from April through October. A total of 503 species have been observed, and approximately 300 species may be observed in any given year. The watershed can be divided into 13 distinct zones. The northwest ridge above the Andrews headquarters has the highest number of species (n = 321) and the lowest number of species occurred at upper Lookout Creek (n = 239). Each of 13 zones is missing ca. 200 of the 500 resident species, suggesting that heterogeneity in the landscape is important. A breakdown of the species into functional groups based on larval feeding habits: conifers, hardwood, herb, mix, unknown shows that 43% of Andrews species rely on a hardwoods and 63% rely on hardwoods and herbaceous angiosperms. Conifer-feeders only represent 8% of moth species. However, moths associated with conifer hosts are the most abundant; for instance, in the zone representing the midlevel of Carpenter Mountain 67% of moth individuals are conifer feeders, but only 14% of the species feed on conifers. In contrast, within the zone represented by the Headquarters site, only 32% of the individual moths feed on conifers whereas 56% feed on hardwoods. Moth biogeographic zones correspond to elevation zones and to potential vegetation.
Spatial and temporal distribution and abundance of butterflies in the Andrews Experimental Forest, 1994-1996
This database contains information on species abundance according to date and location within the H.J. Andrews Experimental Forest Lookout Creek watershed. The database provides the information needed to assess patterns in the abundance of butterflies across time and space. The distribution and abundance of butterfly species on the Andrews Forest is strongly influenced by geographical scale, elevation, aspect, plant community, management regime, and time of year. Patterns of distribution and abundance are based on an historical total of 80 species, of which 73 are resident species and about 55 of which may be observed in any given year. Butterflies were surveyed at two- week intervals from late April through early October over a three-year period (1994-6). Approximately one-third of the watershed was covered during each visit, thus each area was sampled at about 6 week intervals within each sample season.
IVMOOC 2017 - GloBI Data for Interactive Tableau Map of Spatial and Temporal Distribution of Interactions
<p>Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to <em>identify ‘gaps’ in knowledge of species interactions</em>. We examine the richness of GloBI’s datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education.</p> <p>Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI <em>Kingdom</em> Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper.</p> <p>The <strong>interactive Tableau map</strong> can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm</p> <p>The<strong> companion paper</strong> can be found here: doi.org/10.5281/zenodo.814979</p> <p><strong>Complementary high resolution visualizations </strong>can be found here: doi.org/10.5281/zenodo.814922</p> <p><strong>Project-specific data </strong>can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)</p>
Livestock activity shifts large herbivore temporal distributions to their crepuscular edges
<p>Wildlife species are transitioning to greater crepuscular and nocturnal activity in response to high human densities. This plasticity in temporal niches may partially mitigate the impacts of human activity but may also result in underestimating human effects on species foraging, predator-prey relationships, and community level interactions. We deployed remote cameras to characterize shifts in herbivore diel activity in protected habitat vs pastoralist landscapes. We then compared species traits including body mass, dietary preferences, and behavioral characteristics as potential predictors of species sensitivity to livestock. Our data capture a significant temporal shift away from core cattle activity for nearly every herbivore species in our study, leading to more crepuscular activity patterns. As livestock were primarily diurnal and predators primarily nocturnal in pastoralist habitat, species that decreased their overlap with livestock were more likely to increase their overlap with potential predators. Other than species' typical daytime activity levels, we found no evidence that any particular trait significantly predicted temporal shifts in response to livestock. Instead, species generally trended toward greater activity levels at dawn, suggesting that cattle have a homogenizing effect on community-wide activity patterns. Our findings highlight how cohabitation with livestock can profoundly alter the temporal niches of wild herbivores. Shifts in diel activity patterns may reduce herbivore foraging time or efficiency and potentially have cascading shifts on predator-prey dynamics. Given that species traits could not predict responses to livestock, our analysis suggests that conservation strategies should consider each species separately when designing interventions for wildlife management.</p>
Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts
<p><strong>Aim</strong>: Climate change and habitat loss or degradation are some of the greatest threats that species face today, often resulting in range shifts. Species traits have been discussed as important predictors of range shifts, with the identification of general trends being of great interest for conservation efforts. However, studies reviewing relationships between traits and range shifts have questioned the existence of such generalized trends, due to mixed results and weak correlations, as well as analytical shortcomings. The aim of this study was to test this relationship empirically, using analytical approaches that account for common sources of bias when assessing range trends.<br><strong>Location</strong>: Tanzania, East Africa.<br><strong>Time period</strong>: 1980-1999 and 2000-2020.<br><strong>Major taxa studied</strong>: 57 savannah specialist birds found in Tanzania, belonging to 26 families and 11 orders.<br><strong>Methods</strong>: We applied recently developed integrated spatio-temporal species distribution models in R-INLA, combining citizen science and bird atlas data to estimate ranges of species, quantify range shifts, and test the predictive power of traditional trait groups, as well as exposure-related and sensitivity traits. We based our study on 40 years of bird observations in East African savannahs, a biome that has experienced increasing climatic and non-climatic pressures over recent decades. We correlated patterns of change with species traits.<br><strong>Results</strong>: We find indications of relationships identified by previous research, but low average explanatory power of traits from an ecological perspective, confirming the lack of meaningful general associations. However, our analysis finds compelling species-specific results.<br><strong>Main conclusions</strong>: We highlight the importance of individual assessments, while demonstrating the usefulness of our analytical approach for analyses of range shifts.</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
FIGURE 21 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 21. Latest Cretaceous Transylvanian kogaionid occurrences, highlighting the spatial distribution of the different chronofaunal tiers represented. Numbers within kogaionid silhouettes refer to their respective tiers (see fig. 3). A. Hațeg Basin; B. southwestern Transylvanian Basin; C. Rusca Montană Basin.
FIGURE 23 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 23. Chronostratigraphic distribution of the sedimentary facies represented, combined with the nature of the kogaionid remains recovered, at the different uppermost Cretaceous Transylvanian kogaionid sites. Dark gray, marine deposits; light gray, continental deposits.
FIGURE 22 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 22. Chronostratigraphic distribution of the estimated body sizes of known latest Cretaceous Transylvanian kogaionid occurrences. Dark gray, marine deposits; light gray, continental deposits.
FIGURE 20 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 20. Chronostratigraphic distribution of the different kogaionid taxa identified in the uppermost Cretaceous beds of Transylvania. Dark gray, marine deposits; light gray, continental deposits. For color-coding of the taxa, see legend in figure 19.
FIGURE 12 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 12. In situ remains of the referred specimen of Barbatodon transylvanicus, LPB (FGGUB) M.1635, Pui Beds, site PB3, documenting their associated nature. A. Remains of M.1635 as first spotted in the field, July 2002, showing the dentaries and one of the femora exposed on the bed surface by river erosion. B. Closeup from the initial preparation stage of the plaster jacket containing the partial skeleton M.1635, with the fully exposed dentaries and right femur. C. Presence of further postcranial remains revealed within the plaster jacket; boxed area in left center (B) highlights the position of the first discovered remains. Abbreviations: de, dentary; fe, femur; fi, fibula; l, left; r, right.
FIGURE 14 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 14. Isolated kogaionid teeth from the Pui Beds, site PB4. A–C. Isolated left I2, LPB (FGGUB) M.1706, in A. labial, B. distal, and C. mesial views. D–F. Isolated left I3, LPB (FGGUB) M.1670, in D. mesial, E. labial, and F. distal views. Arrows in B, C, and F point to the demarcation line between thicker and thinner enamel cover. G. Isolated left M1, LPB (FGGUB) M.1671, in occlusal view. H. Holotype of Litovoi tholocephalos, LPB (FGGUB) M.1700, left M1 in occlusal view, for comparison.
FIGURE 16 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 16. Kogaionid-bearing fossiliferous localities from the Densuș-Ciula Formation, Hațeg Basin. A. The Tuștea-Oltoane nesting locality, corresponding to site DC1; large-scale excavation on the newly created platform, August 1998. B. Close-up of the fossiliferous bed at Vălioara-Fântânele, hosting site DC2; C. Close-up of the fossiliferous bed at Vălioara-Fântânele 2, location of site DC3. D. Ravines north of Livezi with outcrops of uppermost Cretaceous continental beds; in the middle foreground, below the grass cover, is site DC4; E. Limited exposures of the uppermost Cretaceous continental beds north of General Berthelot, locality GB1, with the lower red silty mudstones hosting site DC5.
FIGURE 19 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 19. Latest Cretaceous Transylvanian kogaionid occurrences, highlighting the spatial distribution of the different taxa; estimated body sizes taken from figure 18. A. Hațeg Basin; B. southwestern Transylvanian Basin; C. Rusca Montană Basin. Question-mark refers to taxonomical uncertainty concerning the small kogaionid taxon from site RB1, in the Hațeg Basin.
FIGURE 9 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 9. Kogaionid remains from the Sînpetru Formation. A. Close-up of the left upper molars (M1 and M2) of Kogaionon ungureanui (ISER SPT/001), site SP1. B, C. Isolated right M2, LPB (FGGUB) M.1631, of an indeterminate kogaionid, site SP3, occlusal view as B. reflected light stereomicroscopic image; C. SEM image.
FIGURE 18 in Spatial And Temporal Distribution Of The Island-Dwelling Kogaionidae (Mammalia, Multituberculata) In The Uppermost Cretaceous Of Transylvania (Western Romania)
FIGURE 18. Latest Cretaceous Transylvanian kogaionid occurrences, highlighting the spatial distribution of their estimated body size (see text and figure 2 for identity of the fossiliferous sites marked in red in figures 18–23; see text and tables 4–7 for details of body size estimates). A. Hațeg Basin; B. southwestern Transylvanian Basin; C. Rusca Montană Basin.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.