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98 results for “Capture recapture”
Data from: Using partial aggregation in Spatial Capture Recapture
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Data from: Separating mortality and emigration: modelling space use, dispersal and survival with robust-design spatial-capture-recapture data
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Data from: Estimating population density and connectivity of American mink using spatial capture-recapture
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Complete tag loss in capture-recapture studies affects abundance estimates: an elephant seal case study
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Capture-recapture data with partially known birth date in four populations of yellow-bellied toads
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Data from: Simulation-based validation of spatial capture-recapture models: a case study using mountain lions
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Application of photographic ‘capture-recapture’ modelling to estimate recruitment and apparent survival in a long-lived territorial raptor
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Data from: Comparison of photo-matching algorithms commonly used for photographic capture-recapture studies
Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost-effectiveness. Recently, several computer-aided photo-matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state-of-the-art photo-matching algorithms prior to implementation in capture–recapture studies involving possibly thousands of images. Here, we compared the performance of four photo-matching algorithms; Wild-ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel-based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match "by eye" can be easily translated to accurate individual capture histories necessary for robust demographic estimates.
F I G U R E 2 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 2 Schematic representation of the different capture–mark–recapture (CMR) events occurring in the Restigouche catchment in a given year. The letters C, M, and R correspond to the captured, marked, and recaptured fish, respectively. The total nonmarked smolt population Nmtot comprises four components: Kedgwick, Upsaquitch, and Matapedia abundance Nm, and the smolt abundance originating from the rest t 1:3,t of the catchment Nmrest. Note that the "rest" component can vary between years because not all rotary screw traps (RST) are operated annually. t Each year, each operating upstream RST is associated with a CMR experiment. Marked smolts in the upstream RSTs (Mt,1:3) can also be recaptured at the downstream RSTs (Rt,1:5). Each downstream RST samples an unknown fraction of the total population of smolts migrating out of the catchment. Due to the low number of recaptures at the downstream RSTs, it is not possible to estimate this fraction, and it is confounded with the catchabilities at the downstream RSTs. Variables in square boxes are known, and variables in round boxes are unknown.
Praomys delectorum capture-mark-recapture dataset from fieldwork on the Ukaguru Mountains, Tanzania.
<p><i>Praomys delectorum</i> occurs abundantly in both disturbed and intact forests in the Ukaguru Mountains within the Eastern Arc Mountains (EAM), Morogoro, Tanzania. While previous studies have reported that anthropogenic disturbance such as grazing, wood cutting and harvesting has a positive effect on the population density of <i>P. delectorum</i>, the impact of habitat disturbance on its demographic traits is still unknown. We performed a capture-mark-recapture study in both disturbed and intact forest from June 2018 to February 2020 in order to investigate the effects of habitat disturbance on abundance as well as two demographic traits: survival and maturation of <i>P. delectorum </i>in the Ukaguru Mountains. We found no variation in abundance or maturation between intact and disturbed forests, but habitat type did affect survival. However, this effect was sex-dependent since female survival was higher in disturbed forests while male survival remained similar across the two forest types potentially due to differences in predation pressure or food availability between the two habitats. Continuous demographic monitoring of <i>P. delectorum</i> in EAM is necessary given that the increasing human population surrounding the landscape is leading to higher deforestation rates, and expansion of the pine plantation in the forest reserve.</p>
Development of a site fidelity index based on capture-recapture data at population level
<p>R codes and dataset supporting the results of this article</p>
Development of a site fidelity index based on population capture-recapture data
<p>Raw data supporting the results of the scientific article</p>
A General Framework for Spatial Capture-Recapture with Unknown Identities
<p>Data to accompany examples in the manuscript.</p>
Data from: Comparison of photo-matching algorithms commonly used for photographic capture-recapture studies
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Praomys delectorum capture-mark-recapture dataset from fieldwork on the Ukaguru Mountains, Tanzania.
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Upper Columbia River Steelhead Capture-Recapture-Recovery data (2008-2018)
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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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International Brain Laboratory public data
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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.