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73 results for “Evolutionary Constraint”
Data from: Do evolutionary constraints on thermal performance manifest at different organizational scales?
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Data from: Genetic constraints predict evolutionary divergence in Dalechampia blossoms
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Evolutionary constraints and adaptation shape the size and colour of rain forest fruits and flowers at continental scale
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Data from: Evolutionary constraint on low elevation range expansion: Defense‐abiotic stress‐tolerance trade‐off in crosses of the ecological model Boechera stricta
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Data from: Using artificial intelligence classification of videos to examine the environmental, evolutionary and physiological constraints on provisioning behavior
<p>The use of artificial intelligence (AI) technologies can revolutionize how we approach data collection and analysis in behavioral ecology. One such example is in provisioning behavior. Parents of altricial species are selected to provide parental care (such as food provisioning) for their offspring, but there is substantial variation in the level of this care. Provisioning rate may be determined environmentally, by the physiological ability of parents and needs of nestlings, or by evolutionary incentives. We quantified provisioning rate in 20 purple martin (<i>Progne subis</i>) nests in the context of an experimental reduction of nest ectoparasites. 10 nests had a parasite reduction treatment, and 10 nests were controls. By using AI to automate the analysis of nest camera videos we were able to obtain nearly-continuous provisioning rate information at a high temporal resolution for the first half of the nestling period. We used random forest modeling to assess the factors determining provisioning rate and found evidence for environmental, evolutionary and physiological constraints and incentives on provisioning. Birds appeared to be environmentally limited in their provisioning in cool, wet conditions, especially later in the breeding season; but adjusted their provisioning according to the changing physiological needs of nestlings. We found evidence for a compensatory response to increased parasite load, in which parents increased provisioning to more heavily parasitized nests.</p>
Data from: What affects the predictability of evolutionary constraints using a G-matrix? The relative effects of modular pleiotropy and mutational correlation
Phenotypic traits do not always respond to selection independently from each other and often show correlated responses to selection. The structure of a genotype-phenotype map (GP map) determines trait covariation, which involves variation in the degree and strength of the pleiotropic effects of the underlying genes. It is still unclear, and debated, how much of that structure can be deduced from variational properties of quantitative traits that are inferred from their genetic (co)variance matrix (G-matrix). Here we aim to clarify how the extent of pleiotropy and the correlation among the pleiotropic effects of mutations differentially affect the structure of a G-matrix and our ability to detect genetic constraints from its eigen decomposition. We show that the eigenvectors of a G-matrix can be predictive of evolutionary constraints when they map to underlying pleiotropic modules with correlated mutational effects. Without mutational correlation, evolutionary constraints caused by the fitness costs associated with increased pleiotropy are harder to infer from evolutionary metrics based on a G-matrix's geometric properties because uncorrelated pleiotropic effects do not affect traits' genetic correlations. Correlational selection induces much weaker modular partitioning of traits' genetic correlations in absence then in presence of underlying modular pleiotropy.
Data from: Evolutionary constraints in high-dimensional trait sets
Genetic variation for individual traits is typically abundant, but for some multivariate combinations it is very low, suggesting that evolutionary limits might be generated by the geometric distribution of genetic variance. To test this prediction, we artificially selected along all eight genetic eigenvectors of a set of eight quantitative traits in Drosophila serrata. After six generations of 50% truncation selection, at least one replicate population of all treatments responded to selection, allowing us to reject a null genetic subspace as a cause of evolutionary constraint in this system. However, while all three replicate populations of the first five selection treatments displayed a significant response, the remaining three, characterized by low genetic variance in their selection indexes in the base population, displayed inconsistent responses to selection. The observation that only four of the nine replicate populations evolved in response to the direct selection applied to them in these low genetic variance treatments, led us to conclude that a nearly null subspace did limit evolution. Dimensions associated with low genetic variance are often found in multivariate analyses of standing genetic variance in morphological traits, suggesting that the nearly null genetic subspace may be a common mechanism of evolutionary constraint in nature.
FIGURE 5 in Constraints on the timescale of animal evolutionary history
FIGURE 5. Calibration diagram for actinopterygians (basal teleosts and Clupeocephala).
FIGURE 6 in Constraints on the timescale of animal evolutionary history
FIGURE 6. Calibration diagram for actinopterygians (Acanthomorpha).
FIGURE 3 in Constraints on the timescale of animal evolutionary history
FIGURE 3. Calibration diagram for gnathostomes.
FIGURE 8 in Constraints on the timescale of animal evolutionary history
FIGURE 8. Calibration diagram for amniotes.
FIGURE 1 in Constraints on the timescale of animal evolutionary history
FIGURE 1. Calibration diagram for metazoans.
FIGURE 4 in Constraints on the timescale of animal evolutionary history
FIGURE 4. Calibration diagram for actinopterygians (Chondrostei, Holostei).
FIGURE 7 in Constraints on the timescale of animal evolutionary history
FIGURE 7. Calibration diagram for tetrapods.
Data from: High evolutionary constraints limited adaptive responses to past climate changes in toad skulls
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Data from: What affects the predictability of evolutionary constraints using a G-matrix? The relative effects of modular pleiotropy and mutational correlation
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Data from: Genetic variation, simplicity and evolutionary constraints for function-valued traits
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Data from: Testing the evolutionary constraints of metamorphosis: the ontogeny of head shape in Triturus newts
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Data from: Using artificial intelligence classification of videos to examine the environmental, evolutionary and physiological constraints on provisioning behavior
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Data from: Evolutionary constraints in high-dimensional trait sets
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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)
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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.