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252 results for “Variability Modelling”
Figure 9 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175
Figure 9 Distribution of suitable niches of P. discordans (A), P. omphalodes (B) and P. pinnatifida (C) in Eurasia.
Model and observational dataset used in Tsiringakis, A. , Holtslag, A.A.M., Grimmond, S. and Steeneveld, G.J. Surface and atmospheric driven variability of the single-layer urban canopy model under clear sky conditions over London, Journal of Geophysical Research: Atmospheres
<p>This dataset contains:</p> <p>- Model output from the single-column version of the Weather Research and Forecasting model v3.8.1.</p> <p>- Observations from the KSSW tower (King's College London, United Kingdom) used for model evaluation.</p> <p>- The Single-column Urban Boundary Layer Inter-comparison Model Experiment (SUBLIME) case study description and external forcing file for the single-column version of Weather Research and Forecasting model.</p> <p>This dataset is used in :</p> <p>~Tsiringakis, A., Holtslag, A.A.M., Grimmond, S. and Steeneveld, G.J. Surface and atmospheric driven variability of the single-layer urban canopy model under clear sky conditions over London. Journal of Geophysical Research: Atmospheres</p>
Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling
Remote sensing techniques offer an opportunity to improve biodiversity modeling and prediction worldwide. Yet, to date, the weather-station based WorldClim dataset has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those datasets suffer from uneven geographic coverage, with many areas of Earth poorly represented. Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR-E, a pure remote sensing data source. We use these data to generate novel temperature-based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado, and Patagonia. We compare the bioclimatic datasets derived from MERRA and AMSR-E information with in situ station data, and contrast species distribution models based on these two products to models built with WorldClim. Surface temperature estimates provided by MERRA and AMSR-E showed warm temperature biases relative to the in situ data fields, but the reliability of these datasets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia, and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR-E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental datasets with higher spatial and temporal fidelity may improve upon these results.
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: Allometric convergence in savanna trees and implications for plant scaling models in variable ecosystems
Theoretical models of allometric scaling provide frameworks for understanding and predicting how and why the morphology and function of organisms vary with scale. It remains unclear, however, if the predictions of 'universal' scaling models for vascular plants hold across diverse species in variable environments. Phenomena such as competition and disturbance may drive allometric scaling relationships away from theoretical predictions based on an optimized tree. Here, we use a hierarchical Bayesian approach to calculate tree-specific, species-specific, and 'global' (i.e. interspecific) scaling exponents for several allometric relationships using tree- and branch-level data harvested from three savanna sites across a rainfall gradient in Mali, West Africa. We use these exponents to provide a rigorous test of three plant scaling models (Metabolic Scaling Theory (MST), Geometric Similarity, and Stress Similarity) in savanna systems. For the allometric relationships we evaluated (diameter vs. length, aboveground mass, stem mass, and leaf mass) the empirically calculated exponents broadly overlapped among species from diverse environments, except for the scaling exponents for length, which increased with tree cover and density. When we compare empirical scaling exponents to the theoretical predictions from the three models we find MST predictions are most consistent with our observed allometries. In those situations where observations are inconsistent with MST we find that departure from theory corresponds with expected tradeoffs related to disturbance and competitive interactions. We hypothesize savanna trees have greater length-scaling exponents than predicted by MST due to an evolutionary tradeoff between fire escape and optimization of mechanical stability and internal resource transport. Future research on the drivers of systematic allometric variation could reconcile the differences between observed scaling relationships in variable ecosystems and those predicted by ideal models such as MST.
Data from: A prediction model of compressor with variable geometry diffuser based on elliptic equation and Partial Least Squares
In order to fulfill more and more extensive intake air flow range of diesel engine, variable geometry compressor (VGC) is introduced into turbocharged diesel engine. However, due to the variable diffuser vanes angle (DVA), the prediction for the performance of VGC becomes more difficult than normal compressor. In the present study, a prediction model comprised of elliptical equation and PLS (Partial Least Squares) model was proposed to predict the performance of VGC. The speed lines of pressure ratio map and efficiency map with elliptical equation were fitted, and the coefficients of elliptical equation was introduced into PLS model to build the polynomial relationship between the coefficients and relative speed, DVA. And further, the maximal order of polynomical was detailed investigated to reduce the number of sub-coefficients and acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority in compressor performance prediction, the prediction results of this model were compared with those of look-up table and BPNN. The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than look-up table and BPNN under the same condition in the VGA performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.
Data from: Modelling the co-evolution of indirect genetic effects and inherited variability
When individuals interact, their phenotypes may be affected by genes in their social partners, a phenomenon known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, competition not only affects trait levels of individuals, but also inflates variation of trait values among individuals. Variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, models of IGE and inherited variability do not allow for such relationship. Models of trait levels show IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to inherited variability. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of genetic and environmental effects of the focal individual and of the difference in trait values between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait which is measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. Our simulations show that the model results in increased variability of body weight with increase of competition. When competition decreases, variability becomes significantly smaller. Our findings suggest we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.
Dataset to reproduce the figures in "Parameterizing the Impact of Unresolved Temperature Variability on the Large-Scale Density Field: Part 2. Modeling." in Journal of Advances in Modeling Earth Systems (JAMES)
Ocean circulation models have systematic errors in large-scale horizontal density gradients due to estimating the grid-cell-mean density by applying the nonlinear seawater equation of state to the grid-cell-mean water properties. In frontal regions where unresolved subgrid-scale (SGS) fluctuations are significant, dynamically relevant errors in the representation of current systems can result. A previous study developed a novel and computationally efficient parameterization of the unresolved SGS temperature variance andresulting density correction. This parameterization was empirically validated but not tested in an ocean model. In this study, we implement deterministic and stochastic variants of this parameterization in the pressure-gradient force term of a coupled ocean-sea ice configuration of CESM-MOM6 and perform a suite of hindcast sensitivity experiments to investigate the ocean response. The parameterization leads to coherent changes in the large-scale ocean circulation and hydrography, particularly in the Nordic Seas and Labrador Sea, which are attributable in large part to changes in the seasonally varying upper-ocean exchange through Denmark Strait. In addition, the separated Gulf Stream strengthens and shifts equatorward, reducing a common bias in coarse-resolution ocean models. The ocean response to the deterministic and stochastic variants of the parameterization is qualitatively, albeit not quantitatively, similar, yet qualitative differences are found in various regions.
Data for "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density"
<p>This dataset contains all the postprocessed data required to reproduce the figures in the publication Simpson et al (2022) "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density", in the Journal of Advances in Modelling the Earth System.</p>
Pulse Profile Modelling of Thermonuclear Burst Oscillations II: Handling variability
<p>Pulse Profile Modelling of Thermonuclear Burst Oscillations II: Handling variability</p>
Model data for "Factors Modulating Variability of Eddy Kinetic Energy in the Southern Ocean from Idealized Simulations" "
<p>This dataset contains the all the idealized simulations with different topographic features.</p>
Performance results from species distribution models considering historical occurrences and variables of varying persistency
<p>Occurrence data used to build species distribution models often include historical records from locations in which the species no longer exists. When these records are paired with contemporary environmental values that no longer represent the conditions the species experienced, the model creates false associations that hurt predictive performance. The extent of mismatching increases with the number of historical occurrences and with inclusion of environmental variables that are prone to change over time. Indeed, the mismatch between occurrence data and contemporaneous environmental variables is a common dilemma when modeling rare or cryptic species, especially those of conservation concern that were once more abundant. Herein, we assess (1) the impact of historical occurrences on model performance across three sets of environmental variables of increasing persistency, and (2) the performance of models built using selected-historical occurrences from locations that showed evidence of limited environmental change over time. Concepts are tested on federally listed flatwoods salamanders, reflecting real-world conservation management efforts. We predicted that, compared to other occurrence sets, (1) historical occurrences would perform best with environmental variables that were more persistent, (2) recent occurrences would perform best when the environmental variables were more impersistent, and that (3) our selected-historical occurrences would perform best with a combination of persistent and impersistent variables. Our results showed the expected inversion of model performance of recent and historical occurrences across environmental variables of increasing persistency when evaluated by correct predictions. However, the inversion was not seen in AUC performance, in which historical occurrences outperformed recent occurrence models across all variable sets. Selected-historical occurrences did not notably improve performance over all-historical occurrences in any metric or variable set. To maximize utility and performance, modelers could acknowledge potential tradeoffs from inclusion of historical occurrences and consider number and age of recent and historical occurrences available, the persistency of environmental variables considered, and how their conservation goals are reflected in model design and evaluation, particularly with respect to sensitivity vs. specificity. Our study lends support for inclusion of historical occurrences, with the potential exception of mostly impersistent variables when sensitivity is the highest priority.</p>
Ranking Variable Importance for US Commercial Buildings via Sensitivity Analysis of Building Energy Models
<p>This zip file contails all code and simulation results which was used for the analysis. </p>
Data from: miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs
<p>These datasets can be used to reproduce all analyses from the publication "miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs" in conjunction with codes found at https://github.com/mccall-group/miRglmm_paper. </p> <p>"Monocyte_data_subset.rda", "monocyte_exact_subset_filtered2.rda" and "sims_N100_m2_s1_rtruncnorm.rda" can be used to reproduce the simulation analysis. </p> <p>"panel_B_SE.rda" and "ERCC_filtered.rda" can be used to reproduce the ERCC synthetic data analysis with known ground truth.</p> <p>"study89_data_subset.rda" and "study89_data_subset_filtered2.rda" can be used to reproduce the immune cell-type analysis. </p> <p>"bladder_testes_data_subset.rda" and "bladder_testes_data_subset_filtered2.rda" can be used to reproduce the bladder vs testes tissue analysis.</p>
Table ¹: Model performance in relation to predictor variables combinations and MaxEnt parameters for the two best models identified in our analysis. in Understanding habitat suitability and road mortality for the conservation of the striped hyaena (Hyaena hyaena) in Batna (East Algeria)
<p><b>Table ¹:</b> Model performance in relation to predictor variables combinations and MaxEnt parameters for the two best models identified in our analysis.</p><table><tbody><tr><th><b>Prediction set</b></th><th><b>FC</b></th><th><b>RM</b></th><th><b>P</b> <b>ROC</b></th><th><b>OR</b></th><th><b>AICc</b></th><th><b>ΔAICc</b></th><th><b>WAICc</b></th><th><b>Number of parameters**</b></th></tr></tbody><tbody><tr><th>313</th><td>lqp</td><td>0.7</td><td>0</td><td>0</td><td>752.875</td><td>1.226</td><td>0.0337</td><td>10</td></tr><tr><th>314*</th><td>lqp</td><td>1</td><td>0</td><td>0</td><td>751.649</td><td>0.000</td><td>0.057</td><td>7</td></tr></tbody></table><p>The selected model set (*) met the statistical significance and omission rate criteria during evaluation with train and test data.**Number of environmental variables. FC, feature class; RM, regularization multiplier; OR = omission rate; ΔAICc, Akaike information criterion corrected for small simple sizes; WAICc, weighted AICc.</p>
Table ²: Percentage of contribution of the selected variables for habitat suitability modeling for the striped hyaena in Batna, Algeria. in Understanding habitat suitability and road mortality for the conservation of the striped hyaena (Hyaena hyaena) in Batna (East Algeria)
<p><b>Table ²:</b> Percentage of contribution of the selected variables for habitat suitability modeling for the striped hyaena in Batna, Algeria.</p><table><tbody><tr><th>Environmental variables</th><th>Contribution to prediction capacity (%)</th></tr></tbody><tbody><tr><th>Shrubland</th><td>37.5</td></tr><tr><th>Slope</th><td>30.2</td></tr><tr><th>Built-up areas</th><td>12</td></tr><tr><th>Distance to roads</th><td>7.9</td></tr><tr><th>DEM</th><td>5.2</td></tr><tr><th>BIO3 (isothermality)</th><td>4.9</td></tr><tr><th>Distance to waterbodies</th><td>2.3</td></tr></tbody></table>
Processed Data, Code, & Supplementary Material for de la Torre Cerro et al. (2024) Modelling asynchrony in phenology using a dynamic representation of meteorological variables
Open the record for dataset details and reuse information.
Skabbholmen data from: Concurrent ordination: Simultaneous unconstrained and constrained latent variable modeling
<ol> <li>In community ecology, unconstrained ordination can be used to indirectly explore drivers of community composition, while constrained ordination can be used to directly relate predictors to an ecological community. However, existing constrained ordination methods do not explicitly account for community composition that cannot be explained by the predictors, so that they have the potential to misrepresent community composition if not all predictors are available in the data.</li> <li>We propose and develop a set of new methods for ordination and Joint Species Distribution Modelling (JSDM) as part of the Generalized Linear Latent Variable Model (GLLVM) framework, that incorporate predictors directly into an ordination. This includes a new ordination method that we refer to as concurrent ordination, as it simultaneously constructs unconstrained and constrained latent variables. Both unmeasured residual covariation and predictors are incorporated into the ordination by simultaneously imposing reduced rank structures on the residual covariance matrix and on fixed-effects.</li> <li>We evaluate the method with a simulation study, and show that the proposed developments outperform Canonical Correspondence Analysis (CCA) for Poisson and Bernoulli responses, and perform similar to Redundancy Analysis (RDA) for normally distributed responses, the two most popular methods for constrained ordination in community ecology. Two examples with real data further demonstrate the benefits of concurrent ordination, and the need to account for residual covariation in the analysis of multivariate data.</li> <li>This article contextualizes the role of constrained ordination in the GLLVM and JSDM frameworks, while developing a new ordination method that incorporates the best of unconstrained and constrained ordination, and which overcomes some of the deficiencies of existing classical ordination methods.</li> </ol>
Interspecific trait variability and local soil conditions modulate grassland model community responses to climate
<p><span><span><span><span><span><span><span><span><span><span><span><b> </b>High elevation grasslands provide critical services in agriculture and ecosystem stabilization. However, these ecosystems face elevated risks of disturbance due to predicted soil and climate changes. We experimentally exposed model grassland communities, comprised of three species grown on either local or reference soil, to varied climatic environments along an elevational gradient in the European Alps, measuring the effects on species and community traits. Although species-specific biomass varied across soil and climate, species' proportional contributions to community-level biomass production remained consistent. Where species experienced low survivorship, species-specific biomass production was maintained through increased production of surviving individuals. Species responded directionally to climatic variation, segregating differentially by plant traits (including height, reproduction, biomass, survival, leaf dry weight, and leaf area) across all sites. Local soil variation drove stochastic trait responses across all species. This soil variability obscured climate-driven responses: we recorded no directional trait responses driven by climate. Our species-based approach contributes to our understanding of grassland community stabilization and suggests that these communities show some stability under climatic variation. </span></span></span></span></span></span></span></span></span></span></span></p>
One Minute Heart Rate Variability Quantification in Airway Obstruction Model
ClinicalTrials.gov study NCT03733704. IPD Sharing: NO. Countries: 0. Publications: 1.
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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.