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238 results for “temporal distribution”
Fig. 1 in Environmental influences on the spatial and temporal distribution of the puffer fish Sphoeroides greeleyi and Sphoeroides testudineus in a Brazilian subtropical estuary
Fig. 1. Map of the estuarine complex of Paranaguá, northern shore of the State of Paraná, Brazil, locating the sampling sites.
Fig. 2 in Environmental influences on the spatial and temporal distribution of the puffer fish Sphoeroides greeleyi and Sphoeroides testudineus in a Brazilian subtropical estuary
Fig. 2. Graphic representation of the Principal Component Analysis (PCA) conducted for the sampling sites and for the seasons of the year on the north-south axis of the estuarine complex of Paranaguá, Paraná State.
FIGURE 4 Temporal estimates derived from a in Split distribution, biogeography and morphological and genetic diversity of the Iberobathynellini Tribe in the family Parabathynellidae (Crustacea, Malacostraca, Bathynellacea)
FIGURE 4 Temporal estimates derived from a relaxed molecular clock analysis. Divergence times are depicted in the coalescence-based MCC tree. Units of X-axis address Mya and blue bars at nodes indicate the 95% highest posterior density intervals (HPD).
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
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Livestock activity shifts large herbivore temporal distributions to their crepuscular edges
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Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts
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Data from: Temporal distribution of endophytic and exophytic insect guilds responds to host plant phenology in the Brazilian Savannah
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Data from: Overlap of spatial and temporal spawning distributions of spring and summer Chinook Salmon results in hybridization in the upper Columbia River
<p>The upper Columbia River in Washington State (main-stem and tributary habitat between McNary and Chief Joseph dams) is inhabited by two major lineages of Chinook Salmon (<i>Oncorhynchus tshawytscha</i>); endangered spring Chinook Salmon and summer Chinook Salmon which are not ESA listed. The lineages are highly genetically divergent from one another and historically spatial and temporal isolating mechanisms maintained these genetic differences. Both lineages occur in the Entiat River, a system where anthropogenic activity has changed habitat, flows, species composition, and the distribution of the two lineages over the past century. We examined the spatial and temporal overlap in spawning distributions between Entiat River spring and summer Chinook Salmon and we used genetic markers to assess the level of introgression between lineages. Redd surveys were conducted from 2003 to 2017 to describe spatial and temporal spawning patterns of both lineages. We genotyped sub-yearling juvenile Chinook Salmon captured in the Entiat River from 2009–2014 at 90 SNP loci to determine lineage and hybridization status. There was temporal overlap in spawning between lineages in several years and considerable spatial overlap in redd locations annually. Genetic analysis revealed hybridization between lineages does occur, albeit at relatively low rates (2.6% of sub-yearling juveniles genotyped). We detected hybrids each year samples were collected and they were distributed throughout the Entiat River basin. Hybridization between lineages of Chinook Salmon could result in introgression and a loss of genetic diversity between the lineages, and/or, a loss of production by ESA-listed spring Chinook Salmon. The presence of hybrids warrants concern for ESA-listed spring Chinook Salmon in both the Entiat River system and throughout the upper Columbia River basin.</p>
A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean
<p>The importance of scale when investigating ecological patterns and processes is recognised across many species. In marine ecosystems, the processes that drive species distribution have a hierarchical structure over multiple nested spatial and temporal scales. Hence, multi-scale approaches should be considered when developing accurate distribution models to identify key habitats, particularly for populations of conservation concern. Here, we propose a modelling procedure to identify the best spatial and temporal scale for each modelled and remotely sensed oceanographic variable to model harbour porpoise (<em>Phocoena phocoena</em>) distribution. Harbour porpoise sightings were recorded during dedicated line-transect aerial surveys conducted in the summer of 2016, 2021 and 2022 in the Northeast Atlantic. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5, 20 and 40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll-a, sea surface height, mixed layer depth and salinity. A total of 30,514 km was covered on-effort with 216 harbour porpoise sightings recorded. Overall, the best spatial scale corresponded to the coarsest resolution considered in this study (40 km), while porpoise presence showed stronger association with oceanographic variables summarised at a longer temporal scale (monthly and averaged over survey period). Habitat models including covariates at coarse spatial and temporal scales may better reflect the processes driving availability and abundance of prey resources at the large scales covered during the surveys. These findings support the hypothesis that a multi-scale approach should be applied when investigating species distribution. Identifying suitable spatial and temporal scale would improve the functional interpretation of the underlying relationships, particularly when studying how a small marine predator interacts with its environment and responds to climate and ecosystem changes. </p>
Fig. 5 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 5: Spatial and temporal distribution of Gobius niger in the Marchica Lagoon.
Spatio-temporal distribution of LSD outbreaks in the Balkan region since deecember 2016 until November 2017 and cattle density (animals/square km)
<p>The European Food Safety Authority (EFSA), under request of the European Commission, performed an epidemiological analysis of the lumpy skin disease (LSD) epidemics based on the data collected from the affected and at-risk Member States and non-EU countries in south-eastern Europe. Spatial and temporal (monthly) distribution of LSD outbreaks reported in the Balkan region between December 2016 and November 2017 and cattle density (animals/square km) shown as green shade (for Bosnia and Herzegovina data at regional level are not available). LSD outbreaks were reported in this time frame only in Albania, the former Yugoslav Republic of Macedonia and Greece. Red and grey dots indicate new and past outbreaks, respectively.</p> <p>*This designation is without prejudice to positions on status and is in line with UNSCR 1244 and the ICJ Opinion on the Kosovo Declaration of Independence</p>
Figure 5 in Occurrence and temporal variation in the size-frequency distribution of 2 bloom-forming jellyfishes, Catostylus perezi (L. Agassiz, 1862) and Rhizostoma pulmo (Cuvier, 1800), in the Indus Delta along the coast of Sindh, Pakistan
Figure 5. Temporal variation of jellyfish medusae in zooplankton samples.
Figure 3 in Occurrence and temporal variation in the size-frequency distribution of 2 bloom-forming jellyfishes, Catostylus perezi (L. Agassiz, 1862) and Rhizostoma pulmo (Cuvier, 1800), in the Indus Delta along the coast of Sindh, Pakistan
Figure 3. Temporal variation in size distribution in 3 populations of C. perezi.
Figure 1. Map showing 3 in Occurrence and temporal variation in the size-frequency distribution of 2 bloom-forming jellyfishes, Catostylus perezi (L. Agassiz, 1862) and Rhizostoma pulmo (Cuvier, 1800), in the Indus Delta along the coast of Sindh, Pakistan
Figure 1. Map showing 3 sampling sites: Bhanbore, Mirpur Sakro, and Keti Bunder.
Figure 2 in Spatial and temporal distribution of aquatic insects in the Dicle (Tigris) River Basin, Turkey, with new records
Figure 2. Psychomyia sp. larva: a- head, b- trochantin, c- anal claws.
Figure 1 in Spatial and temporal distribution of aquatic insects in the Dicle (Tigris) River Basin, Turkey, with new records
Figure 1. The locations of the selected sampling sites in the Tigris River Basin.
Fig. 1 in South American Sea Lions Otaria flavescens, a good indicator of relative spatial and temporal changes in the distribution and abundance of marine resources?
Fig. 1. Study area showing the location of the rookeries analysed at RÍo Negro Province, Argentina.
Fig. 1 in Patterns of spatio-temporal distribution as criteria for the separation of planktic foraminiferal species across the Danian-Selandian transition in Spain
Fig. 1. Geographical location of the Caravaca and Zumaia sections (Spain).
Dataset from Makino H. and Suhaimi A. Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.
<p>Dataset from the paper:</p> <p>Makino H. and Suhaimi A. Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.</p> <p>Each variable is described in Description.pdf.</p> <p>Analysis code is available at <a href="https://github.com/HiroshiMakinoLaboratory/RPEAccumulation" target="_blank" rel="noopener">https://github.com/HiroshiMakinoLaboratory/RewardPredictionErrorAccumulation</a>.</p>
Data from: Odonate species occupancy frequency distribution and abundance – occupancy relationship patterns in temporal and permanent water bodies in a subtropical area
<p>This paper investigates species richness and species occupancy frequency distributions (SOFD) as well as patterns of abundance-occupancy relationship (SAOR) in Odonata (dragonflies and damselflies) in a subtropical area. A total of 82 species and 1983 individuals were noted from 73 permanent and temporal water bodies (lakes and ponds) in the Pampa biome in southern Brazil. Odonate species occupancy ranged from 1 to 54. There were few widely distributed generalist species and several specialist species with a restricted distribution. About 70% of the species occurred in less than 10% of the water bodies, yielding a surprisingly high number of rare species, often making up the majority of the communities. No difference in species richness was found between temporal and permanent water bodies. Both temporal and permanent water bodies had odonate assemblages that fitted best with the unimodal satellite SOFD pattern. It seems that unimodal satellite SOFD pattern frequently occurred in the aquatic habitats. The SAOR pattern was positive and did not differ between permanent and temporal water bodies. Our results are consistent with a niche-based model rather than a metapopulation dynamics model.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.