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126 results for “occupancy model”
Using machine learning to model nontraditional spatial dependence in occupancy data
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Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
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Data from: Distinguishing distribution dynamics from temporary emigration using dynamic occupancy models
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Data from: Integrating niche and occupancy models to infer the distribution of an endemic fossorial snake (Atractus lasallei)
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Multi-species occupancy model for estimating the probability of detecting amphibian species in Hungary
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Multi-species occupancy modeling provides novel insights into amphibian metacommunity structure and wetland restoration
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Data and code from: Addressing widespread detection heterogeneity in avian occupancy modeling using passive acoustic surveys
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Ignoring species availability biases occupancy estimates in single-scale occupancy models
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Multi‐species occupancy modeling reveals methodological and environmental effects on eDNA detection of amphibians in temporary ponds
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Can time-to-detection models with fewer survey replicates provide a robust alternative to traditional site-occupancy models?
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Supplementary information for: A continuous-score occupancy modeling framework for incorporating uncertain machine learning output in autonomous biodiversity surveys
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Data from: Sequential use of niche and occupancy models identifies conservation and research priority areas for two data-poor endemic birds from the Colombian Andes
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Data from: Disentangling elevational richness: a multi-scale hierarchical Bayesian occupancy model of Colorado ant communities
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A multi-state occupancy model to non-invasively monitor visible signs of wildlife health with camera traps that accounts for image quality
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Modeling and Monitoring Submerged Prehistoric Sites during Offshore Sand Dredging and Implications for the Study of Early Holocene Coastal Occupation of Southern California
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A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
<p>1. Given the public health, economic, and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues.</p> <p>2. We developed a multi-state occupancy modeling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained).</p> <p>3. We applied this model to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal, and genital mucosa of 510 feral swine (Sus scrofa) during the years 2014-2016 in Florida, USA.</p> <p>4. The constrained model was better supported by data. Estimated PrV prevalence varied among tissues, ranging from to 0.06 (CI: 0.02-0.14) in genital to 0.54 (CI: 0.14-0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06-0.18) in nasal to 0.51 (CI: 0.21-0.81) in genital tissue. Estimates of PrV prevalence after accounting for imperfect detection were higher than the naïve estimates for all four tissues.</p> <p>5. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased.</p> <p>6. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue-specific prevalence and detection probabilities similar to ours.</p> <p>7. We found that pathogen prevalence in multi-tissue disease systems can vary across tissues. Our results emphasize the importance of sampling multiple tissues, and the application of robust statistical models to account for imperfect detection in the surveillance of systemic diseases. The multi-state modeling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and where the infection status or detection of the pathogen in one tissue may depend on the infection status of the pathogen in other tissues). 29-Jul-2020</p>
Data: Using environmental DNA and occupancy modeling to estimate rangewide metapopulation dynamics
<p>We demonstrate the power of combining two emergent tools for resolving rangewide metapopulation dynamics. First, we employed environmental DNA (eDNA) surveys to efficiently generate multi-season rangewide site occupancy histories. Second, we developed a novel<i> </i>dynamic, spatial multiscale occupancy model to estimate metapopulation dynamics. The model incorporates spatial relationships, explicitly accounts for non-detection bias and allows direct evaluation of the drivers of extinction and colonization. We applied these tools to examine metapopulation dynamics of endangered tidewater goby, a species endemic to California estuarine habitats. We analyzed rangewide eDNA data from 190 geographically isolated sites (813 total water samples) surveyed from two years (2016 and 2017). Rangewide estimates of the proportion of sites that were occupied varied little between 2016 (0.52) and 2017 (0.51). However, there was evidence of extinction and colonization dynamics. The probability of extinction of an occupied site (0.106) and probability of colonization of an unoccupied site (0.085) were nearly equal. Stability in site occupancy proportions combined with nearly equal rates of extinction and colonization suggests a dynamic equilibrium between the two years surveyed. Assessment of covariate effects revealed that colonization probability increased as the number of occupied neighboring sites increased and as distance between occupied sites decreased. We show that eDNA surveys can rapidly provide a snapshot of a species distribution over a broad geographic range, and when these surveys are paired with occupancy modeling, can uncover metapopulation dynamics and their drivers.</p>
Data from: Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys
False positive detections, such as species misidentifications, occur in ecological data, although many models do not account for them. Consequently, these models are expected to generate biased inference. The main challenge in an analysis of data with false positives is to distinguish false positive and false negative processes while modeling realistic levels of heterogeneity in occupancy and detection probabilities without restrictive assumptions about parameter spaces. Building on previous attempts to account for false positive and false negative detections in occupancy models, we present hierarchical Bayesian models that utilize a subset of data with either confirmed detections of a species' presence (CP model) or both confirmed presences and confirmed absences (CACP model). We demonstrate that our models overcome the challenges associated with false positive data by evaluating model performance in Monte Carlo simulations of a variety of scenarios. Our models also have the ability to improve inference by incorporating previous knowledge through informative priors. We describe an example application of the CP model to quantify the relationship between songbird occupancy and residential development, plus we provide instructions for ecologists to use the CACP and CP models in their own research. Monte Carlo simulation results indicated that, when data contained false positive detections, the CACP and CP models generated more accurate and precise posterior probability distributions than a model that assumed data did not have false positive errors. For the scenarios we expect to be most generally applicable, those with heterogeneity in occupancy and detection, the CACP and CP models generated essentially unbiased posterior occupancy probabilities. The CACP model with vague priors generated unbiased posterior distributions for covariate coefficients. The CP model generated unbiased posterior distributions for covariate coefficients with vague or informative priors, depending on the function relating covariates to occupancy probabilities. We conclude that the CACP and CP models generate accurate inference in situations with false positive data for which previous models were not suitable.
Data from: Modelling misclassification in multi-species acoustic data when estimating occupancy and relative activity
<p>1. Surveying wildlife communities provides data for informing conservation and management decisions that affect multiple species. Autonomous recording units (ARUs) can efficiently gather community data for a variety of taxa, but generally require software algorithms to classify each recorded call to a species. Species classification errors are possible during this process and result in both false negative and false positive detections. Available approaches for analysing ARU data do not model the species classification probabilities, meaning erroneous detections are attributed to an omnibus source instead of the presence of another species. Additionally, counts of call recordings for each species are often summarized to binary detection data for analyses. Expanding statistical models to capture these nuances of ARU data would allow for improved inferences about occupancy and relative activity. 2. Motivated by bat acoustic surveys, we developed a model to analyse counts of call recordings from multiple species simultaneously while accounting for species classification errors. Our model expands on previously developed false positive occupancy models to better describe acoustic data. We used simulations to compare our model to other false positive occupancy models for an example scenario with ARU data from two species. We also analyse acoustic data for eight bat species in Montana using our model. 3. In simulations, single-species models resulted in biased estimates of occupancy and relative activity because they failed to associate false positives with the presence of the second species. Models analysing binary observations ignored available information on relative activity and led to less precise estimates. Applying our model to bat acoustic data from Montana allowed for species-specific estimates of occupancy and relative activity. This analysis illustrates the flexibility in our model framework while also highlighting the assumptions and data requirements for implementation. Specifically, additional information on the species classification probabilities is needed and we discuss considerations for reliably estimating these parameters. 4. Directly modelling the species classification probabilities allows for improved ecological inferences for both occupancy and relative activity using community ARU data. Our statistical framework helps address the challenges posed by acoustic data, allowing ecologists to better utilize this technology to monitor wildlife communities.</p>
Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data
While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves (Canis lupus) that have been recolonizing France since the early 90's. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500m and positively influenced by the number of observed occupied sites at short and longdistances , forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decisionmakers to target sites where large carnivores are likely to occur and mitigate conflicts.
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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.