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252 results for “Variability Modelling”
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
Data accompanying "Diurnal variability of the upper ocean simulated by a climate model"
<p>Data used for creating figures in the draft article "Diurnal variability of the upper ocean simulated by a climate model". This includes:</p> <ul> <li>Multi-year, monthly mean diurnal cycle metrics at all model grid points.</li> <li>Monthly mean diurnal cycle data for individual years at selected locations.</li> </ul> <p>Code used to create these data files, and to create the plots, is in a Github repository (https://github.com/JackReevesEyre/cfs-analysis-gaea/). The repository is also archived on Zenodo (https://doi.org/10.5281/zenodo.7846095).</p>
Pulse Profile Modeling of Thermonuclear Burst Oscillations I: The Effect of Neglecting Variability
<p>Pulse Profile Modeling of Thermonuclear Burst Oscillations I: The Effect of Neglecting Variability</p>
Dataset for "Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers" by Verjans et al.
<p>Code and data products associated with "Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers" by Verjans V., Robel A., Thompson A. F., and Seroussi H.</p> <p>Please see readme file for all the information.</p> <p>Contact: vverjans3@gatech.edu</p> <p>Author: Vincent Verjans</p>
Individual variability and versatility in an eco-evolutionary model of avian migration
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Quantitative variables related to color, territory, behavior, and morphology for male lesser prairie-chickens used in discrete choice models in mate choice study
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Niche modelling and correlation analyses of climate variables
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Model data to investigate wood frog abundance in 17-year post harvest variable retention mixed wood forests
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A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean
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Natural disease history of a canine model of oligogenic RPGRIP1-cone-rod dystrophy establishes variable effects of previously and newly mapped modifier loci
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Derived variables and coordinates to assess the ecological relevance of multiscale bathymetry for coral species distribution modelling across the Great Barrier Reef
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Shelled Pteropod individual-based model output for the publication: The impact of aragonite saturation variability on shelled pteropods: An attribution study in the California current system
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Soil chemical variables improve models of understory plant species distributions
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Data and model output for figures in "Variable particle size distributions reduce the sensitivity of global export flux to climate change"
<p><strong>Associated publication</strong></p> <p>This dataset was used to generate analyses and figures in the following publication:</p> <p>Leung, S., Weber, T., Cram, J. A., & Deutsch, C. Variable particle size distributions reduce the sensitivity of global export flux to climate change. <em>Submitted to Biogeosciences.</em></p> <p><strong>Associated code</strong></p> <p>After downloading this dataset, run the associated MATLAB code at the following link to generate the figures and analyses in the above publication:</p> <p>https://doi.org/10.5281/zenodo.4117382</p>
The way bioclimatic variables are calculated has impact on potential distribution models
<p>1. Bioclimatic variables (BCVs) are routinely used in potential distribution models, typically without considering their calculation options in detail. We aimed at studying the impact of a decision, yet unexamined, on the calculation of BCVs, namely whether the identity of specific months/quarters in the calculation of BCVs should be updated for the future periods (temporal context). Effects on the performance of potential distribution models and on their projections were investigated. Additionally, we also aimed at comparing the impact of month/quarter shifts to that of climate model selection and covariate selection.</p> <p>2. Potential natural vegetation models encompassing eight habitat types and the whole territory of Hungary were created using boosted regression trees. We tested multiple initial covariate sets to compare the impact of the temporal context to that of covariate selection. The resulting models were applied to the reference and one future time period (with data from two regional climate models). The effect of the BCV calculation approach was tested by linear mixed-effects models and model goodness-of-fit measures in a comprehensive framework of 192 predictions. Area Under the ROC Curve (AUC) and True Positive Rate (TPR) curves were used to evaluate the models.</p> <p>3. Our results show that (1) temporal context of BCVs in interaction with covariate selection had a strong effect on model structure as well as on projections; (2) no evidence supporting the superiority of the widely applied calculation approach of BCVs was found. However, we found notable differences under the two approaches and examples of projection artefacts when applying the widespread way of calculation.</p> <p>4. We conclude that (1) more attention and more transparent communication is needed when BCVs are used as covariates in distribution models; (2) not only ecophysiology but also the way covariates are calculated should be considered when preselecting covariates for potential distribution models.</p>
Temporal variability is key to modelling the climatic niche
<p><strong>Aim</strong><i>:</i> Niche-based species distribution models (SDMs) have become a ubiquitous tool in ecology and biogeography. These models relate species occurrences with the environmental conditions found at these sites. Climatic variables are the most commonly used environmental data, and are usually included in SDMs as averages of a reference period (30-50 years). In this study we analyze the impact of including inter-annual climatic variability on the estimation of species niches and predicted distributions when assessing plant demographic response to extreme climatic episodes.</p> <p><strong>Location</strong><i>:</i> Mediterranean basin, SE Iberian Peninsula.</p> <p><strong>Methods</strong><i>:</i> We first characterized species niches with inter-annual and average climate in the same environmental space. We then compare the respective capacities of climatic suitability obtained from averaged climate-based and from inter-annual variability-based niches to explain population demographic responses to extreme drought. Furthermore, we assessed the relative increase in niche size when including climatic variability for a set of Mediterranean species exhibiting a wide range of distribution areas.</p> <p><strong>Results</strong><i>:</i> We found that climatic suitability obtained from inter-annual variability-based niches showed higher explanatory capacity than average climate-based suitability, especially for populations living in climatically marginal conditions, although both niches quantifications significantly explained species demographic responses. In addition, species with restricted distribution ranges increased relatively more their niche space when considering climatic variability, probably because in widely distributed species spatial variability compensates for temporal variability.</p> <p><strong>Main Conclusions</strong><i>:</i> The common use of climatic averages when characterizing species niches could lead to underestimations of species distribution and misunderstanding of demographic behavior, with implications for conservation plans derived from SDMs, e.g. overestimations of species extinction risk under climate change, or underestimations of alien species invasion' risk. We highlight that including climatic variability in niche modelling can be particularly important when dealing with species with restricted distribution and populations at the margin of their species niche.</p>
Data from: Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change.
Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine (Pinus ponderosa), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.
Data from: Spatial scaling of environmental variables improves species-habitat models of fishes in a small, sand-bed lowland river
Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species' presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale.
Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe.
Data for GRL Article "Resolved Convection Improves the Representation of Equatorial Waves and Tropical Rainfall Variability in a Global Nonhydrostatic Model"
This repository contains mandatory material to reproduce the results of the GRL article "Resolved Convection Improves the Representation of Equatorial Waves and Tropical Rainfall Variability in a Global Nonhydrostatic Model" [Paper #2021GL093265RR].
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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.