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1,042 results for “model species”
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance
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Spatial confounding in Bayesian species distribution modeling
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Hierarchical heuristic species delimitation under the multispecies coalescent model with migration
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Revisiting the multispecies coalescent model fit with an example from a complete molecular phylogeny of the Liolaemus wiegmannii species group (Squamata: Liolaemidae)
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Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling
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Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions
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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: Species distribution models of the Spotted Wing Drosophila (Drosophila suzukii, Diptera: Drosophilidae) in its native and invasive range reveal an ecological niche shift
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Empirical data for: Extending phylogenetic regression models for comparing within-species patterns across the Tree of Life
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Environmental niche models improve species identification in DNA barcoding
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Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
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Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
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Code and data for Bayesian joint species distribution model selection for community-level prediction
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Data from: Parameters used in the endotherm biophysical model for each species
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Data for: An integrated population model and population viability assessment for the southern population of a data-poor species
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Vertebrate-habitat relationships: Logistic regression models predict probability of occurrence of bird and small mammal species in western Oregon
Logistic regression models predicting probability of occurrence of bird and of small-mammal species were produced using animal-habitat data sets from throughout western Oregon (Garman and Cole 1999 - Vertebrate Habitat Relationships Data Bank (VHRDB), Report to Coastal Landscape Analysis and Modeling Study). Regression coefficients, variables, and metrics related to model predictions are provided here under Entity 1, and in VHRDB as VERTLOGR.
Data from: Modeling the mito-nuclear compatibility and its role in species identification
<p>Mitochondrial genetic material (mtDNA) is widely used for phylogenetic reconstruction and as a barcode for species identification. The utility of mtDNA in these contexts derives from its particular molecular properties, including its high evolutionary rate, uniparental inheritance, and small size. But mtDNA may also play a fundamental role in speciation -- as suggested by recent observations of coevolution with the nuclear DNA, along with the fact that respiration depends on coordination of genes from both sources. Here we study how mito-nuclear interactions affect the accuracy of species identification by mtDNA, as well as the speciation process itself. We simulate the evolution of a population of individuals who carry a recombining nuclear genome and a mitochondrial genome inherited maternally. We compare a null model fitness landscape that lacks any mito-nuclear interaction against a scenario in which interactions influence fitness. Fitness is assigned to individuals according to their mito-nuclear compatibility, which drives the coevolution of the nuclear and mitochondrial genomes. Depending on the model parameters, the population breaks into distinct species and the model output then allows us to analyze the accuracy of mtDNA barcode for species identification. Remarkably, we find that species identification by mtDNA is equally accurate in the presence or absence of mito-nuclear coupling and that the success of the DNA barcode derives mainly from population geographical isolation during speciation. Nevertheless, selection imposed by mito-nuclear compatibility influences the diversification process and leaves signatures in the genetic content and spatial distribution of the populations, in three ways. First, speciation is delayed and the resulting phylogenetic trees are more balanced. Second, clades in the resulting phylogenetic tree correlate more strongly with the spatial distribution of species and clusters of more similar mtDNA's. Third, there is a substantial increase in the intraspecies mtDNA similarity, decreasing the number of alleles substitutions per locus and promoting the conservation of genetic information. We compare the evolutionary patterns observed in our model to empirical data from copepods (<em>T. californicus</em>). We find good qualitative agreement in the geographic patterns and the topology of the phylogenetic tree, provided the model includes selection based on mito-nuclear interactions. These results highlight the role of mito-nuclear compatibility in the speciation process and its reconstruction from genetic data.</p>
Disentangling drivers of spatial autocorrelation in species distribution models
<p>Species distribution models (SDMs) are frequently used to understand the influence of site properties on species occurrence. For robust model inference, SDMs need to account for the spatial autocorrelation of virtually all species occurrence data. Current methods do not routinely distinguish between extrinsic and intrinsic drivers of spatial autocorrelation, although these may have different implications for conservation. Here, we present and test a method that disentangles extrinsic and intrinsic drivers of spatial autocorrelation using repeated observations of a species. We focus on unknown habitat characteristics and conspecific interactions as extrinsic and intrinsic drivers, respectively. We model the former with spatially correlated random effects and the latter with an autocovariate, such that the spatially correlated random effects are constant across the repeated observations whereas the autocovariate may change. We tested the performance of our model on virtual species data and applied it to observations of the corncrake Crex crex in the Netherlands. Applying our model to virtual species data revealed that it was well able to distinguish between the two different drivers of spatial autocorrelation, outperforming models with no or a single component for spatial autocorrelation. This finding was independent of the direction of the conspecific interactions (i.e., conspecific attraction versus competitive exclusion). The simulations confirmed that the ability of our model to disentangle both drivers of autocorrelation depends on repeated observations. In the case study, we discovered that the corncrake has a stronger response to habitat characteristics compared to a model that did not include spatially correlated random effects, whereas conspecific interactions appeared to be less important. This implies that future conservation efforts should primarily focus on maximizing habitat availability. Our study shows how to systematically disentangle extrinsic and intrinsic drivers of spatial autocorrelation. The method we propose can help to correctly identify the main drivers of species distributions.</p>
Processed data of grasshoppers, butterflies and moths for analyses of species trend models for the regional WWF Living Planet Index for Belgium
<p>This archive contains pre-processed datasets used for the analysis of species occupancy models, the results of which were used in the calculation of multi-species indices as part of the regional WWF Living Planet Index for Belgium.</p> <p>The datasets are csv files (comma separated and . as decimal mark). </p> <p>For each species group (moths, butterflies and grasshoppers), the following files are available:</p> <ul> <li>a species list (files with 'species' in the name)</li> <li>an observations list (files with 'observations' in the name - for butterfly or moth species with two distinct flight periods, also a file with the data for the second generation is available)</li> </ul> <p>For one species, <em>Fabriciana adippe</em>, separate files are available with corrected data.</p> <p>The species list files contain the following variables:</p> <ul> <li>species_id (unique species id)</li> <li>scientific_name (accepted scientific name according to the GBIF taxonomic backbone)</li> <li>species_name_NL (Dutch species name)</li> <li>species_name_FR (French species name)</li> <li>season_start (n-th day of the year that marks the beginning of the first -and possibly only- generation)</li> <li>season_end (n-th day of the year that marks the end of the first -and possibly only- generation)</li> </ul> <p>The observations files contain the following variables:</p> <ul> <li>species_id (a unique identifier)</li> <li>year (year of observation)</li> <li>month (month of observation)</li> <li>day (day of observation)</li> <li>julian_day (n-th day of the year)</li> <li>site_id (unique identifier for the 1 km x 1km EEA 1 km x 1 km reference grid square <a href="https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2">https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2</a>)</li> <li>source (name of data provider)</li> <li>count (max number of sightings for the species for that day and site</li> </ul>
Eco‐evolutionary dynamics driven by fishing: from single species models to dynamic evolution within complex food webs
<p>Evidence of contemporary evolution across ecological time scales stimulated research on the eco-evolutionary dynamics of natural populations. Aquatic systems provide a good setting to study eco-evolutionary dynamics owing to a wealth of long-term monitoring data and the detected trends in fish life-history traits across intensively harvested marine and freshwater systems. In the present study, we focus on modelling approaches to simulate eco-evolutionary dynamics of fishes and their ecosystems. Firstly, we review the development of modelling from single-species to multispecies approaches. Secondly, we advance the current state-of-the-art methodology by implementing evolution of life-history traits of a top predator into the context of complex food web dynamics as described by the allometric trophic network (ATN) framework. The functioning of our newly developed eco-evolutionary ATNE framework is illustrated using a well-studied lake food web. Our simulations show how both natural selection arising from feeding interactions and size-selective fishing cause evolutionary changes in the top predator and how those feed back to its prey species and further cascade down to lower trophic levels. Finally, we discuss future directions, particularly the need to integrate genomic discoveries into eco-evolutionary projections.</p>
ScienceDex guides
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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.