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8 results for “multi-scale occupancy”
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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Long-term monitoring in endangered woodlands shows effects of multi-scale drivers on bird occupancy
<p>Occupancy predictor data, detection predictor data, and species detections from sites in remnant Box Gum Grassy Woodland patches in south-eastern Australia. Only sites, species, and predictors used in our statistical analysises included. For privacy, predictors have been standardised (mean = 0, standard deviation = 1) and latitude and longitude have been offset by random vectors.</p>
Physicochemical habitat data and multi-scale occupancy data for spring-associated fishes in Oklahoma streams
<p class="vC7TJ allowTextSelection">Spring-associated fishes occupy thermally unique habitats in groundwater-dominated streams that are often of high quality. However, outside of water temperature, little else is known about the physicochemical habitat requirements for many of these species. With human effects on streams increasing, it is important to conservation and management to characterize spring habitats and the species that occupy them. Our study objective was to determine the physicochemical factors related to occupancy of four spring-associated species in the Arbuckle Uplift and Ozark Highlands ecoregions, Oklahoma USA. We used a hierarchal approach to identify habitat relationships at multiple spatial scales. We collected detection and non-detection data using both snorkeling and seining methods. We examined the physicochemical relationships related to detection and occupancy for four spring-associated fishes. Data were analyzed using occupancy modeling in a Bayesian framework. Our results indicated water depth and water clarity were important factors affecting detection of spring-associated fishes. Occupancy of our target species differed by ecoregion, with least darter being less common in the Ozark Highlands ecoregion and subadult smallmouth bass being more common in the Ozark Highlands. Interestingly, we found water temperature occupancy relationship for only least darter and southern redbelly dace, whereas redspot chub and smallmouth bass were more likely to occur at sites with deeper pool habitats of larger streams. We documented both spatial and temporal differences in occurrence probabilities at ecoregion, reach, and riffle-run-pool complex scale. Furthermore, our results indicate snorkeling was a superior sampling method compared to seining for detecting most fishes in clear warmwater streams even at relatively low visibilities. Lastly, we demonstrate the importance of using multi-scale studies when developing conservation plans for warmwater fishes.</p>
Physicochemical habitat data and multi-scale occupancy data for spring-associated fishes in Oklahoma streams
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Long-term monitoring in endangered woodlands shows effects of multi-scale drivers on bird occupancy
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Predictive multi-scale occupancy models at range-wide extents: effects of habitat and human disturbance on distributions of wetland birds
<p><span><i>Aim:</i> Predicting distributions is fundamental to ecology, yet hindered by spatially-restricted sampling, scale-dependent relationships, and detection error associated with field surveys. Predictive species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi-scale data, and used it to develop range-wide breeding-season SDMs for 14 marsh bird species of concern.</span></p> <p><span><i>Location: </i>USA.</span></p> <p><span><i>Methods: </i>We built SDMs using data from range-wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out-of-sample prediction, range-wide sampling over broad conditions, multi-scale analyses and scale-optimization, and species-specific detectability for a suite of wide-ranging species. </span></p> <p><span><i>Results: </i>Distributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban, and hydrologic disturbances operating from local scales (100 – 500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at > 1 scale. Five species showed evidence for continental-scale range contraction during the study.</span></p> <p><span><i>Main conclusions: </i>We demonstrate how hierarchical occupancy models can be optimized for prediction across a species' range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data-driven, empirical SDMs built at the range-wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine-scale wetland vegetation missed important broad-scale drivers of occupancy for marsh birds. </span></p>
Predictive multi-scale occupancy models at range-wide extents: effects of habitat and human disturbance on distributions of wetland birds
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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.