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6 results for “hierarchical occupancy modelling”
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 from: Large-scale eDNA sampling and hierarchical modeling elucidates the importance of stream habitat for eastern hellbender (<em>Cryptobranchus a. alleganiensis</em>) occupancy and eDNA detection
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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.
Data from: Disentangling elevational richness: a multi-scale hierarchical Bayesian occupancy model of Colorado ant communities
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Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community
Emerging conservation paradigms have shifted from single to multi-species approaches focused on sustaining biodiversity. Multi-species hierarchical occupancy modelling provides a method for assessing biodiversity while accounting for multiple sources of uncertainty. We analysed camera trapping data with multi-species models using a Bayesian approach to estimate the distributions of a terrestrial mammal community in northern Botswana and evaluate community, group, and species-specific responses to human disturbance and environmental variables. Groupings were based on two life-history traits: body size (small, medium, large and extra-large) and diet (carnivore, omnivore and herbivore). We photographed 44 species of mammals over 6607 trap nights. Camera station-specific estimates of species richness ranged from 8 to 27 unique species, and species had a mean occurrence probability of 0·32 (95% credible interval = 0·21–0·45). At the community level, our model revealed species richness was generally greatest in floodplains and grasslands and with increasing distances into protected wildlife areas. Variation among species' responses was explained in part by our species groupings. The positive influence of protected areas was strongest for extra-large species and herbivores, while medium-sized species actually increased in the non-protected areas. The positive effect of grassland/floodplain cover, alternatively, was strongest for large species and carnivores and weakest for small species and herbivores, suggesting herbivore diversity is promoted by habitat heterogeneity. Synthesis and applications. Our results highlight the importance of protected areas and grasslands in maintaining biodiversity in southern Africa. We demonstrate the utility of hierarchical Bayesian models for assessing community, group and individual species' responses to anthropogenic and environmental variables. This framework can be used to map areas of high conservation value and predict impacts of land-use change. Our approach is particularly applicable to the growing number of camera trap studies world-wide, and we suggest broader application globally will likely result in reduced costs, improved efficiency and increased knowledge of wildlife communities.
Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.