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9 results for “fitness surface”
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Concrete habitat: Impervious surface in nest vicinity is associated with avian fitness decline in two urban adapters
<p>The conversion of natural habitats to impervious surfaces in cities affects biotic and abiotic attributes of urban ecosystems. Detailed information on the gradual influence of impervious surfaces on reproductive success, however, is lacking. Using five years of nestbox-breeding great tit and blue tit breeding data collected across various habitat types within and outside a Central-Eastern European capital city, we quantified the impact of impervious surfaces on avian reproductive success. Impervious surfaces strongly and negatively covaried with the number of fledged young in both species: a 50% increase in impervious surface resulted in 2.93 (95% CI: -4.27; -1.58) fewer blue tit offspring fledging the nest, and 3.51 (95% CI: -4.79; -2.23) fewer great tit offspring fledging the nest, thus halving the reproductive output of two widespread urban species. These results provide benchmark values of avian productivity for ecologists and urban policy makers, and for the management of urban areas.</p>
Concrete habitat: Impervious surface and avian fitness decline in two urban adapters
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Data from: Fitness surfaces and local thermal adaptation in Drosophila along a latitudinal gradient
<p><span>Local adaptation is commonly cited to explain species distribution, but how fitness varies along continuous geographical gradients is not well understood. Here we combine thermal biology and life-history theory to demonstrate that <em>Drosophila </em>populations along a 2,500 km latitudinal cline are adapted to local conditions. We measured how heat tolerance and viability rate across 8 populations vary with temperature in the laboratory, and then simulated their expected cumulative Darwinian fitness employing high-resolution temperature data from their 8 collection sites. Simulations indicate a trade-off between annual survival and cumulative viability, as both mortality and the recruitment of new flies are predicted to increase in warmer regions. Importantly, populations are locally adapted and exhibit the optimal combination of both traits to maximize fitness where they live. In conclusion, our method is able to reconstruct fitness surfaces employing empirical life-history estimates and reconstructs peaks representing locally adapted populations, allowing to study geographic adaptation <em>in silico</em>.</span></p>
Data from: Fitness surfaces and local thermal adaptation in Drosophila along a latitudinal gradient
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Marginal Fit of Porcelain Veneers After Finishing Prepared Surfaces With Ultrasonic Tips
ClinicalTrials.gov study NCT02683499. IPD Sharing: NO. Countries: 1. Publications: 9.
Data from: Exploring variation in fitness surfaces over time or space
As the number of studies estimating selection on multiple traits has increased in recent years, fitness surfaces have become a fundamental tool for understanding multivariate selection and evolution. However, rigorous statistical comparisons of multivariate selection surfaces over time or space have been limited to parametric analyses of selection coefficients estimated using a quadratic regression model. Although parametric comparisons are useful when selection is approximately linear or quadratic in nature, they are limited when confronting the complex nature of rugged fitness surfaces. Here, I present a novel solution to comparing non-parametric fitness surfaces over time or space. Using a Tucker3 tensor decomposition, which is essentially a higher-order principal components analysis, I show how major features of fitness surfaces can be compared statistically. Combined with a bootstrap algorithm, I develop three statistical tests that identify 1) Differences in the shape of non-parametric fitness surfaces, 2) Differences in the contribution of each surface to variation in fitness across time or space, and 3) Specific areas of the surfaces (trait combinations) that vary significantly over time or space. I illustrate the tensor decomposition and statistical analyses using idealized fitness surfaces.
Accuracy of Fitting Surface of Orbital Prosthesis Constructed by Photogrammetry Scan
ClinicalTrials.gov study NCT07078162. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Exploring variation in fitness surfaces over time or space
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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.