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192 results for “environmental modelling”
Data from: Spatial scaling of environmental variables improves species-habitat models of fishes in a small, sand-bed lowland river
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Data from: Modeling effects of environmental change on wolf population dynamics, trait evolution, and life history
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Spatiotemporal monitoring of the rare Northern dragonhead, Dracocephalum ruyschiana (Lamiaceae): SNP genotyping and environmental niche modelling herbarium specimens
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Data from: Environmental filtering improves ecological niche models across multiple scales
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Data for: Habitat functionality: integrating environmental and geographic space in niche modelling for conservation planning
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Data from: Plantago spp. as models for studying the ecology and evolution of species interactions across environmental gradients
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Data from: Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta-analysis
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Data from: Modeling spatial expansion of invasive alien species: relative contributions of environmental and anthropogenic factors to the spreading of the harlequin ladybird in France
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Climate change and alpine-adapted insects: modelling environmental envelopes of a grasshopper radiation
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Data from: Importance of antecedent environmental conditions in modeling species distributions
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Data from: A dynamic state model of migratory behavior and physiology to assess the consequences of environmental variation and anthropogenic disturbance on marine vertebrates
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Data from: Modeling the influence of genetic and environmental variation on the expression of plant life cycles across landscapes
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Data: Using environmental DNA and occupancy modeling to estimate rangewide metapopulation dynamics
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Environmental exposure model for copper oxide nanoparticles impact in estuarine ecosystem services
<p>Modelling the effect of copper oxide nanoparticles (< 50 nm) on denitrification rate and abundance and transcription of genes of the denitrification pathway on a temperate estuary.</p>
Data from: A dynamic model of facilitation on environmental stress gradients
Theories based on competition for resources in animals and other non-sessile organisms rarely consider the role of facilitative interactions. Yet these interactions are important for community assembly, especially under stressful environments (e.g. the stress-gradient hypothesis, SGH). To make an explicit link between species interaction theory and SGH patterns, I used a classic resource competition model promoting coexistence between a beneficiary and its facilitator sharing a common resource along a stress gradient. I compared model outcomes for two fundamentally different mechanisms of facilitation (alleviation of resource vs. non-resource stress), and also tested the effect of a reciprocal cost of facilitation from the beneficiary. I then tested model's biological relevance using experimental data from two tuber moth species (Lepidoptera, Gelechiidae) for which facilitation in resource access was previously established. Simulation outcomes revealed that both the mode of facilitation and the incorporation of facilitation costs affected the shape of the facilitation-stress relationship. These predictions are in line with current SGH observations and experiments on both plants and animals and reconcile the frequently reported variability of this relationship in nature. Moreover, a sensitivity analysis of model's parameters confirmed the robustness of the modelling framework to uncover the mechanisms responsible for observed species interaction–stress patterns. Finally, when parameterized with tuber moth demographic data, model's results corresponded to observed interaction outcomes along resource stress gradients. Overall, having a common model for plants and animals may simplify assumptions in SGH studies, allow contrasting the shapes of different consumer-resource relationships and specifying the conditions that favour one type of interaction outcome over another.
Data from: Correlative changes in life history variables in response to environmental change in a model organism
Global change alters the environment, including increases in the frequency of (un)favorable events and shifts in environmental noise color. However, how these changes impact the dynamics of populations, and whether these can be predicted accurately has been largely unexamined. Here we combine recently developed population modeling approaches and theory in stochastic demography to explore how life history, morphology, and average fitness respond to changes in the frequency of favorable environmental conditions and in the color of environmental noise in a model organism (an acarid mite). We predict that different life-history variables respond correlatively to changes in the environment, and we identify different life-history variables, including lifetime reproductive success, as indicators of average fitness and life-history speed across stochastic environments. Depending on the shape of adult survival rate, generation time can be used as an indicator of the response of populations to stochastic change, as in the deterministic case. This work is a useful step toward understanding population dynamics in stochastic environments, including how stochastic change may shape the evolution of life histories.
Data from: Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values
Ellenberg indicator values (EIVs) are a widely used metric in plant ecology comprising a semi-quantitative description of species' ecological requirements. Typically, point estimates of mean EIV scores are compared to infer differences in the environmental conditions structuring plant communities – particularly in resurvey studies with no historical environmental data available. However, the use of point estimates as a basis for inference does not take into account variance among species EIVs within sampled plots, and gives equal weighting to means calculated from sites with differing numbers of species. We present a set of multilevel models – fitted with and without group-level predictors – to improve precision and accuracy of site mean EIV scores, and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in re-visitation studies. We compare multilevel model performance to GLMM's fitted to point estimates of site mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample sites. Hierarchical modelling led to more accurate and precise estimates of site-level differences in mean EIV scores between time-periods, particularly for datasets with incomplete records of species occurrence. They also revealed directional environmental change within ecological habitat types, which estimates from GLMM's were inadequate to detect. Multilevel models also highlighted a prominent role of hydrological differences as a driver of community change in our case study, which traditional use of EIVs failed to reveal. We have demonstrated that multilevel modelling of EIVs allows for a nuanced estimation of environmental change underlying ecological communities from plant assemblage data, leading to a better understanding of temporal dynamics of ecosystems. Further, the ability of these methods to perform well with missing data should increase the total set of historical data which can be used to this end.
Data from: Control of entropy in neural models of environmental state
Humans and animals construct internal models of their environment in order to select appropriate courses of action. The representation of uncertainty about the current state of the environment is a key feature of these models that controls the rate of learning as well as directly affecting choice behaviour. To maintain flexibility, given that uncertainty naturally decreases over time, most theoretical inference models include a dedicated mechanism to drive up model uncertainty. Here we probe the long-standing hypothesis that noradrenaline is involved in determining the entropy, and thus flexibility, of neural models. Pupil diameter, which indexes neuromodulatory state including noradrenaline release, predicted increases (but not decreases) in entropy in a neural state model encoded in human medial orbitofrontal cortex, as measured using multivariate functional MRI. Activity in anterior cingulate cortex predicted pupil diameter. These results provide evidence for top-down, neuromodulatory control of entropy in neural state models.
Causality analysis, path analyses and models between Chlorophyll a and environmental factors
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FESOM-REcoM model data: 21st-century environmental change decreases habitat overlap of Antarctic toothfish (Dissostichus mawsoni) and its prey
<p>This data set belongs to "<strong>21st-century environmental change decreases habitat overlap of Antarctic toothfish (Dissostichus mawsoni) and its prey</strong>" by Cara Nissen, Jilda Alicia Caccavo and Anne L. Moree (accepted for publication in "Global Change Biology")</p> <p>Contact: cara.nissen@colorado.edu</p> <p>The data provided here are post-processed from the raw FESOM1.4-REcoM2 model output which can be obtained <br>at the World Data Center for Climate (WDCC): <a href="https://www.wdc-climate.de/ui/project?acronym=HighRes_highLat_SO">https://www.wdc-climate.de/ui/project?acronym=HighRes_highLat_SO</a></p> <p>simA (historical simulation): <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a><br>simA-ssp126: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC</a><br>simA-ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC</a><br>simA-ssp370: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC</a><br>simA-ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a><br>simB (control simulation): <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC</a></p> <p>The raw model output was first post-processed with <strong>MASTER_toothfish_postprocessing_AGI_save_netcdf_files_monthly_with_drift_correction.ipynb</strong> to get fields of in-situ temperature (t_insitu) and partial pressure of oxygen (pO2) on the regular grid used in this study (see <strong>Mesh_ancillary_information_v20220919.nc</strong>).</p> <p>Subsequently, these data are post-processed with <strong>reduce_depth_levels_drift_corr_files.sh</strong>. </p> <p>2-dimensional distribution data of Antarctic Toothfish prey used in this study can be accessed via<br><a href="https://doi.org/10.5281/zenodo.10598488">https://doi.org/10.5281/zenodo.10598488</a></p> <p><strong>The following files are provided here: </strong></p> <p><strong>Monthly climatological pO2 and t_insitu 1995-2014</strong> (used to compute preferred temperature, pO2 threshold and critical AGI of each species):<br>- pO2_fesom_simA_monthly_clim_1995_2014_v2.nc<br>- t_insitu_fesom_simA_monthly_clim_1995_2014_v2.nc</p> <p><strong>Annual pO2 and t_insitu for the historical period 1995-2014</strong>:<br>- pO2_fesom_historical_1995_2014_annual_mean_AGImesh.tar.gz<br>- t_insitu_historical_1995_2014_annual_mean_AGImesh.tar.gz</p> <p><strong>Drift-corrected annual pO2 and t_insitu 2091-2100 for four emission scenarios</strong>: <br>- pO2_fesom_ssp126_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- pO2_fesom_ssp245_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- pO2_fesom_ssp370_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- pO2_fesom_ssp585_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp126_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp245_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp370_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp585_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz</p> <p><strong>Sensitivity to chosen future time period / Drift-corrected annual pO2 and t_insitu 2081-2100 and 2098-2100 for the highest-emission scenario SSP5-8.5</strong>: <br>- pO2_fesom_ssp585_2081_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- pO2_fesom_ssp585_2098_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp585_2081_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp585_2098_2100_drift_corrected_annual_mean_AGImesh.tar.gz</p> <p><strong>Attributing change to warming and deoxygenation / Drift-corrected annual pO2 2091-2100 at clim. t_insitu for the highest-emission scenario SSP5-8.5</strong>:<br>- pO2_fesom_ssp585_2091_2100_drift_corrected_at_clim_t_insitu_annual_mean_AGImesh.tar.gz</p> <p><strong>Information on model drift</strong> (used to correct the above files): <br>- oxygen_fesom_simB_1995_2014_2091_2100_monthly.tar.gz<br>- pO2_fesom_simB_1995_2014_2091_2100_monthly.tar.gz<br>- salinity_fesom_simB_1995_2014_2091_2100_monthly.tar.gz<br>- t_insitu_fesom_simB_1995_2014_2091_2100_monthly.tar.gz</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.