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Dataset results
74 results for “Environment Prediction”
Data from: Environment predicts repeated body size shifts in a recent radiation of Australian mammals
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Data from: Partner’s age, not social environment, predicts extrapair paternity in wild great tits (Parus major)
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Data from: Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment
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Genomic prediction applied to multiple traits and environments in second season maize hybrids
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Predictive ability of hop (Humulus lupulus L.) grown in single hills on plot environments
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Life history and environment predict variation in testosterone across vertebrates
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Data from: Predicting genotypes environmental range from genome-environment associations
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Data from: Predicting ecological and phenotypic differentiation in the wild: a case of piscivorous fish in a fishless environment
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Data from: Nest size is predicted by female identity and the local environment in the blue tit, but is not related to genetic or foster mother's nest size
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History and environment shape spatial genetic variation and predict climate maladaptation in a narrowly distributed serotinous pine, Pinus muricata
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Developmental Cost Theory predicts thermal environment and vulnerability to global warming
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Performance Curves for better Performance Predictions of Parallel Applications in Multicore Environments
<p><br> Model-based performance prediction for parallel applications on architectural models suffers from significant inaccuracies. <br> A major reason is that current model-based performance prediction approaches consider CPU speed as a single metric for multicore performance. </p> <p>Thus, in this paper, we investigate performance-influencing factors for multicore environments, execute extensive experiments to determine their impact on the performance, and extract performance curves for characteristic behaviours.</p> <p>As a result, we present a set of performance curves to software engineers which enables them to increase the performance prediction power in an easy to use manner. <br> Further, we evaluate the approach using 13 SPEC Benchmarks and could show that our approach reduces the prediction error by up to 60% and increases the accuracy by up to 98% for certain scenarios. </p>
Data from: Predicting biotic interactions and their variability in a changing environment
Global environmental change is altering the patterns of biodiversity worldwide. Observation and theory suggest that species' distributions and abundances depend on a suite of processes, notably abiotic filtering and biotic interactions, both of which are constrained by species' phylogenetic history. Models predicting species distribution have historically mostly considered abiotic filtering and are only starting to integrate biotic interaction. However, using information on present interactions to forecast the future of biodiversity supposes that biotic interactions will not change when species are confronted with new environments. Using bacterial microcosms, we illustrate how biotic interactions can vary along an environmental gradient and how this variability can depend on the phylogenetic distance between interacting species.
Data from: Amphibian species' traits, evolutionary history, and environment predict Batrachochytrium dendrobatidis infection patterns, but not extinction risk
The fungal pathogen Batrachochytrium dendrobatidis (Bd) has emerged as a major agent of amphibian extinction, requiring conservation intervention for many susceptible species. Identifying susceptible species is challenging, but many aspects of species' biology are predicted to influence the evolution of host resistance, tolerance, or avoidance strategies towards disease. In turn, we may expect species exhibiting these distinct strategies to differ in their ability to survive epizootic disease outbreaks. Here we test for phylogenetic and trait-based patterns of Bd infection risk and infection intensity among 302 amphibian species by compiling a global dataset of Bd infection surveys across 95 sites. We then use best-fit models that associate traits, taxonomy, and environment with Bd infection risk and intensity to predict host disease mitigation strategies (tolerance, resistance, avoidance) for 122 Neotropical amphibian species that experienced epizootic Bd outbreaks, and noted species' persistence or extinction from these events. Aspects of amphibian species' life history, habitat use, and climatic niche were consistently linked to variation in Bd infection patterns across sites around the world. However, predicted Bd infection risk and intensity based on site environment and species traits did not reveal a consistent pattern between the predicted host disease mitigation strategy and extinction outcome. This suggests that either tolerant or resistant species may have no advantage in ameliorating disease during epizootic events, or that other factors drive the persistence of amphibian populations during chytridiomycosis outbreaks. These results suggest that using a trait-based approach may allow us to identify species with resistance or tolerance to endemic Bd infections, but that this approach may be insufficient to ultimately identify species at risk of extinction from epizootics.
Data from: Nonlinear averaging of thermal experience predicts population growth rates in a thermally variable environment
As thermal regimes change worldwide, projections of future population and species persistence often require estimates of how population growth rates depend on temperature. These projections rarely account for how temporal variation in temperature can systematically modify growth rates relative to projections based on constant temperatures. Here,we tested the hypothesis that time-averaged population growth rates in fluctuating thermal environments differ from growth rates in constant conditions as a consequence of Jensen's inequality, and that the thermal performance curves (TPCs) describing population growth in fluctuating environments can be predicted quantitatively based on TPCs generated in constant lab conditions. With experimental populations of the green alga Tetraselmis tetrahele, we show that nonlinear averaging techniques accurately predicted increased as well as decreased population growth rates influctuating thermal regimes relative to constant thermal regimes. We extrapolate from these results to project critical temperatures for population growth and persistence of 89 phytoplankton species in naturally variable thermal environments. These results advance our ability to predict population dynamics in the context of global change.
Data from: How feathered are birds? environment predicts both the mass and density of body feathers
1.Studies modelling heat transfer of bird plumage design suggest that insulative properties can be attributed to the density and structure of the downy layer, while waterproofing is the result of the outer layer, comprised of contour feathers. In this study, we test how habitat and thermal condition affect feather mass and density of body feathers (contour, semiplume and downy feathers) measured on the ventral and dorsal sides of the body, using a phylogenetic comparative analysis of 152 bird species. 2.Our results demonstrate that feather mass and the density of downy feathers are higher in species that inhabit colder environments, while total feather density is higher of species breeding under intermediate temperatures compared to the ones breeding under more extreme conditions. The density of contour feathers, depending on the body region, is either quadratically related or negatively correlated to minimum winter temperature. 3.The density of contour and downy feathers, measured on both sides of the body, is higher in aquatic than in terrestrial birds. However, among the former, diving behaviour does not select for further increases in body feather mass or density. 4.The results of this study provides key insights into how the plumage of birds is adapted to different environments and lifestyles and provides a basis for understanding the diverse range and the evolution of variation in these characteristics.
Data of the article "Predicting the 2023/24 El Niño from a multi-scale and global perspective" that published in Communications Earth & Environment
<p>MATLAB data of the article "Predicting the 2023/24 El Niño from a multi-scale and global perspective" that published in Communications Earth & Environment.</p> <p>The article is available on the Communications Earth & Environment at <a title="Predicting the 2023/24 El Niño from a multi-scale and global perspective" href="https://www.nature.com/articles/s43247-024-01867-w">https://www.nature.com/articles/s43247-024-01867-w</a>.</p> <p>These data include ocean temperature, current, atmosphere wind, pressure, precipitation, heat flux, and more, that from reanalysis data and coupled model outputs. Additionaly, there are four figures of the article.</p> <p>The related MATLAB codes can be accessed from <a href="https://github.com/HRKsince1993/COMMSENV-24-1347.git">https://github.com/HRKsince1993/COMMSENV-24-1347.git</a></p> <p>If these data are helpful to you, please cite our article or acknowledge us in your publication, e. g.</p> <p>Hu, R. <em>et al.</em> Predicting the 2023/24 El Niño from a multi-scale and global perspective. <em>Commun. Earth Environ.</em> <strong>5</strong>, 675 (2024). </p> <p>If you have any questions, please contact Tao Lian (<a href="mailto:liantao@sio.org.cn">liantao@sio.org.cn</a>) and/or Dake Chen (<a href="mailto:dchen@sio.org.cn">dchen@sio.org.cn</a>) and/or me.</p> <p> </p> <p>Best wishes</p> <p>Ruikun Hu</p> <p>Ph.D of physical oceanography</p> <p>State Key Laboratory of Satellite Ocean Environment Dynamics,</p> <p>Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China</p> <p><a href="mailto:ruikunhu@sio.org.cn">ruikunhu@sio.org.cn</a></p> <p>October 18, 2024</p> <p>Updated on November 8, 2024</p>
Prediction of olivine in distinct forming-environments using machine learning and implications for magmatic sulfide prospectivity
<p>Olivine compositions from global volcanic and plutonic samples.</p>
Data from: An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments
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Data from: DOG1 expression is predicted by the seed-maturation environment and contributes to geographic variation in germination in Arabidopsis thaliana.
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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.