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30 results for “spatial capture-recapture”
Spatial capture-recapture data of the Darwin's frog
<p>These data and code are intentended to serve as complementary information to allow the full reproducibility of the results presented in the study "Natal dispersal is facilitated by routine movements integrated along a straight path in the Darwin’s frog" submitted to Ecography.</p> <p>The first file titled "data and code used to run the sCJS.rar" contains capture-recapture data from three populations of the Darwin's frog (<em>Rhinoderma darwinii</em>) collected between March 2014 and December 2016. The age of the individuals, population of origin, and x- and y-coordinates of each capture are provided. The x- and y-coordinates are in meters. The sampling design follows the Pollock's robust design. The capture-history matrices (one for the x-coordinates and another for the y-coordinates) contain 48 columns representing twelve evenly distributed primary capture periods, each one composed of four consecutive days of capture (secondary capture ocassions). The time-period between primary capture periods is 3 months. However, note that we did not visited the populations during all these periods, therefore, some of collumns contain only NA's. The rows of the capture-history matrices represent the different individuals that were captured across the study. There are 370 rows, but for seven of these individuals we don't have spatial data (these individuals are removed in the code). Then we provide the R code used to run the sCJS of Schaub and Royle (2014). This code is almost identical to the provided by these authors, but a few modifications were made in order to fit our study design. Results of the model are also provided.</p> <p>The second file "Data required to run the simulation model.rar" contains the data required to run the movement path simulation model presented in the Supplementary material, Appendix 2 of the paper. Further details about these files and model can be found in the article.</p> <p>Any question can be adressed to andresvalenzuela.zoo@gmail.com</p> <p>Enjoy it!</p>
Data from: Examining disease prevalence for species of conservation concern using non-invasive spatial capture-recapture techniques
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Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
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Data from: Evaluating otter reintroduction outcomes using genetic spatial capture-recapture modified for dendritic networks
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Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus
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Data from: Estimating population density and connectivity of American mink using spatial capture-recapture
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Data from: Simulation-based validation of spatial capture-recapture models: a case study using mountain lions
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A General Framework for Spatial Capture-Recapture with Unknown Identities
<p>Data to accompany examples in the manuscript.</p>
Data from: Population closure and the bias-precision trade-off in Spatial Capture-Recapture
1. Spatial capture-recapture (SCR) is an increasingly popular method for estimating ecological parameters. This method often relies on data collected over relatively long sampling periods. While longer sampling periods can yield larger sample sizes and thus increase precision of estimates, they also increase the risk of violating the closure assumption, thereby potentially introducing bias. The sampling period characteristics are therefore likely to play an important role in this bias-precision tradeoff. Yet few studies have studied this tradeoff and none has done so for SCR models. 2. In this study, we explored the influence of the length and timing of the sampling period on the bias-precision tradeoff of SCR population size estimators. Using a continuous time-to-event approach, we simulated populations with a wide range of life histories and sampling periods before quantifying the bias and precision of population size estimates returned by SCR models. 3. While longer sampling periods benefit the study of slow-living species (increased precision and lower bias), they lead to pronounced over-estimation of population size for fast living species. In addition, we show that both bias and uncertainty increase when the sampling period overlaps the species' reproductive season. 4. Based on our findings, we encourage investigators to carefully consider the life history of their study species when contemplating the length and the timing of the sampling period. We argue that SCR (and non-spatial capture-recapture) studies can safely extend the sampling period to increase precision, as long as it is timed to avoid peak recruitment periods. The simulation framework we propose here can be used to guide decisions regarding the sampling period for a specific situation.
Data from: Population closure and the bias-precision trade-off in Spatial Capture-Recapture
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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.