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28 results for “hierarchical Bayesian”
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Data for: Reintroduced Oriental stork bayesian hierarchical model data
<p>Long-lived territorial bird populations often consist of a few territorial breeding adults and many non-breeding individuals. Some populations are threatened by anthropogenic activities, because of human conflicts for high-quality breeding habitat. Therefore, habitat restoration projects have been widely implemented to improve avian population status. In conjunction with habitat restoration, conservation translocations have been increasingly implemented. Adequate non-breeder survival can be a key factor in the success of these attempts because non-breeding birds may represent reservoirs for the replacement of breeders. The maintenance of breeding pair numbers is also influenced by the transition rate of non-breeders to breeders. The reintroduction of Oriental stork (<em>Ciconia boyciana</em>), a long-lived, territorial, endangered species, was initiated in Japan in 2005 using captive birds in hopes of increasing the population's use of restored habitat. Our objective of this study was to elucidate the factors determining reintroduced stork survival and recruitment to the breeding populations. We estimated the survival rate and breeding participation rate by sex, age, generation, wild-born or not, haplotypes, and breeding status in storks reintroduced during 2005–2022 using Bayesian hierarchical models. There was no significant difference in survival rate between non-breeders and breeders. However, the survival rate was lower in wild-born birds than released birds, which may be related to the longer-distance natal dispersal of new generations. Accelerated habitat restoration around breeding areas and preventive measures for collision with human-built structures should be implemented for the sustained growth of reintroduced populations. A low survival rate was also detected for a specific mtDNA haplotype that accounts for the majority of the reintroduced population. This phenomenon might be explained by mtDNA-encoded mutations. Moreover, captive breeding and release history might contribute to an increase in the proportion of this haplotype in the wild.</p>
Datasets for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants"
<p>All data used for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants", Criswell, A.W., et al. (2022). The code used to create the paper results from this data can be found at <a href="https://github.com/criswellalexander/hbpm_paper">https://github.com/criswellalexander/hbpm_paper</a> and the underlying software package can be found at <a href="https://github.com/criswellalexander/bayestack">https://github.com/criswellalexander/bayestack</a>.</p>
Data for: Reintroduced Oriental stork bayesian hierarchical model data
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Data from: Disentangling elevational richness: a multi-scale hierarchical Bayesian occupancy model of Colorado ant communities
Understanding the forces that shape the distribution of biodiversity across spatial scales is central in ecology and critical to effective conservation. To assess effects of possible richness drivers, we sampled ant communities on four elevational transects across two mountain ranges in Colorado, USA, with seven or eight sites on each transect and twenty repeatedly sampled pitfall trap pairs at each site each for a total of 90 days. With a multi-scale hierarchical Bayesian community occupancy model, we simultaneously evaluated the effects of temperature, productivity, area, habitat diversity, vegetation structure, and temperature variability on ant richness at two spatial scales, quantifying detection error and genus-level phylogenetic effects. We fit the model with data from one mountain range and tested predictive ability with data from the other mountain range. In total, we detected 105 ant species, and richness peaked at intermediate elevations on each transect. Species-specific thermal preferences drove richness at each elevation with marginal effects of site-scale productivity. Trap-scale richness was primarily influenced by elevation-scale variables along with a negative impact of canopy cover. Soil diversity had a marginal negative effect while daily temperature variation had a marginal positive effect. We detected no impact of area, land cover diversity, trap-scale productivity, or tree density. While phylogenetic relationships among genera had little influence, congeners tended to respond similarly. The hierarchical model, trained on data from the first mountain range, predicted the trends on the second mountain range better than multiple regression, reducing root mean squared error up to 65%. Compared to a more standard approach, this modeling framework better predicts patterns on a novel mountain range and provides a nuanced, detailed evaluation of ant communities at two spatial scales.
River dams and the stability of bird communities: A hierarchical Bayesian analysis in a tropical hydroelectric power plant
<ol> <li>The effects of anthropogenic disturbance upon the stability of wildlife communities depend on the heterogeneity and connectivity of habitat remnants on multiple scales. The number of hydroelectric dams in biodiversity hotspots (Africa, South America and Asia) is growing rapidly. To establish their environmental impact, it is essential to understand the dynamics of wildlife communities before and following the establishment of dams.</li> <li>We evaluated the impacts of the filling of the Serra do Facão hydroelectric reservoir in the São Marcos river, central Brazil, upon the bird community. Using data from 1,145 surveys across 20 sampling sites over eight years, two years before and six years after the filling of the reservoir, we assessed the resistance, i.e., maintenance close to an equilibrium state during the disturbance, and resilience, i.e., ability to return to the original state following the disturbance, of the bird community. We used spatiotemporal hierarchical Bayesian models to assess the effects of reservoir filling on five community parameters: abundance, richness, phylogenetic diversity, functional diversity and species composition.</li> <li>In the period subsequent to reservoir filling, there was (i) a marked reduction in bird abundance, richness, phylogenetic diversity and functional diversity, and (ii) a reduction in the proportion of forest species, coupled with an increase in the proportion of savanna species. Except for bird abundance, none of the other community attributes returned to their original levels, even after six years. Our findings indicate that Cerrado bird communities have both low resistance and low resilience to habitat loss associated with the establishment of hydroelectric reservoirs.</li> <li> <i>Synthesis and applications.</i> The environmental costs of hydroelectric dams are still underestimated or neglected in Brazil. A new paradigm in the assessment of their environmental impacts is warranted, incorporating (i) models of spatiotemporal variations based on long-term monitoring with surveys initiated before disturbances and (ii) measures of functional and phylogenetic diversity, such that society can understand the costs and benefits of the establishment of new hydroelectric dams and make informed decisions. Biodiversity loss could be minimized by ensuring the preservation and connectivity of alluvial habitats, capable of maintaining the supply of resources and the functional and phylogenetic attributes of bird communities associated with such habitats.</li> </ol>
Bayesian hierarchical model gridded solar-induced fluorescence (BHM gridded SIF) data product
<p>This archive provides the solar-induced fluorescence (SIF) data product documented in "Estimation of solar-induced chlorophyll fluorescence using Bayesian hierarchical regression". The archive includes daily NetCDF files with the global gridded SIF estimates and associated uncertainties.</p>
Dataset for "Bayesian hierarchical models for combining misaligned two-resolution metrology data"
<p>This file contains the datasets used in the paper, Xia, Ding, and Mallick, 2011, “Bayesian hierarchical models for combining misaligned two-resolution metrology data,” <em>IIE Transactions</em>, Vol. 43, pp. 242 – 258.</p>
zigzag: A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes
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River dams and the stability of bird communities: A hierarchical Bayesian analysis in a tropical hydroelectric power plant
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Data from: Disentangling elevational richness: a multi-scale hierarchical Bayesian occupancy model of Colorado ant communities
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Data from: Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals
Aim: Our aim involved developing a method to analyze spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs' distributions and to analyze whether their distributions have shifted along with environmental changes. Location: Arctic shelf area. The Kara Sea. Methods: Our literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyze species' densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals. Results: The most important covariates explaining AMMs' relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17-yearlong study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea. Main conclusions: Point process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species' distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs' distributions in the marginal Arctic region.
Data from: A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error
Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes towards conservation. Importantly, few approaches account for bias arising from reporting errors; that is, reporting a positive attitude towards conservation when the respondent actually does not have one (a false positive error), or not reporting a positive attitude when the respondent is positive towards conservation (a false negative error). We borrow from developments in applied conservation science to use a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude towards wildlife, notionally (or in abstract terms) and at localized scales. The model allows us to assess stakeholder attitudes, and factors influencing these attitudes, while accounting for false negative and false positive reporting errors. We show through simulations that this method has lower bias than naïve estimates of the proportion of respondents who are positive towards wildlife, or Likert‐scores. We demonstrate the utility of the model by applying it to questionnaire surveys on Asian elephants Elephas maximus in the Kaziranga–Karbi Anglong landscape, Northeast India. After accounting for reporting errors, we estimated the probability of being positive towards elephants notionally as 0.85; at a localized scale, however, the proportion of respondents that were positive towards elephants was 50%. In comparison, without accounting for reporting errors, the proportion of respondents professing positive attitudes towards elephants in at least one of the certain questions, was 0.69 and 0.23, notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently non‐zero (0.22–0.68). We submit that regular and reliable assessment of stakeholder attitudes––combined with an understanding of factors contributing to variation in attitudes––can feed into participatory conservation monitoring programs, help assess the success of initiatives aimed at facilitating human behavioral change, and inform conservation decision‐making.
Data files for the article "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region"
<p>This repository contains R data files required for reproducing the results from the article by Räty et al (2021) "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region", submitted to Nat. Hazards Earth Syst. Sci. See the README file for more details on the content of the files.</p>
Data from: A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error
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Data from: Bayesian hierarchical models suggest oldest known plant-visiting bat was omnivorous
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Data from: Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals
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Data from: A hierarchical Bayesian approach for handling missing classification data
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Dataset and codes for "BaHSYM: parsimonious Bayesian Hierarchical Model to predict river Sediment Yield"
<p>This folder contains:</p> <ul> <li>R project file</li> <li>R code for Best Fit model</li> <li>R code for temporal cross-validation</li> <li>R code for spatial cross-validation</li> <li>R code for cluster analysis</li> <li>dataset containing all input variables for the river gauges (and catchments) used for the development and testing of the BaHSYM model in Austria</li> </ul> <p>It also contains the same codes and datasets adapted to reproduce the model by de Vente et al. (2011), i.e. with the same structure but with the variables used in such model.</p>
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets
<p>The model weights, chains, simulated datasets, and BNN samples used to produce the results shown in LSST DESC Collaboration paper "Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing." All files presented here are meant for use in tandem with the python package "ovejero" (<a href="https://github.com/swagnercarena/ovejero">https://github.com/swagnercarena/ovejero</a>).</p>
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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)
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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.