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• W2912234025 abstract "Abstract Estimating abundance is one of the most fundamental and important aspects of population biology, with major implications on how the status of a population is perceived and thus on conservation and management efforts. Although typically based on one of two methods (distance sampling or mark-recapture), there are many individual identification methods that can be used for mark-recapture purposes. In recent years, the use of genetic data for individual identification and abundance estimation through mark-recapture analyses have increased, and in some situations such genetic identifications are more efficient than their field-based counterparts for population monitoring. One issue with mark-recapture analyses, regardless of which method of individual identification is used, is that the study area must provide adequate opportunities for “capturing” all individuals within a population. However, many populations are unevenly and widely distributed, making it unfeasible to adequately sample all necessary areas. Here we develop an analytical technique that accounts for unsampled locations, and provides a means to infer “missing” individuals from unsampled locations, and therefore obtain more accurate abundance estimates when it is not possible to sample all sites. This method is validated using simulations, and is used to estimate abundance of the Eastern Canada-West Greenland (EC-WG) bowhead whale population. Based on these analyses, the estimated size of this population is 9,089 individuals, with a 95% highest density interval of 5,107–17,079." @default.
• W2912234025 created "2019-02-21" @default.
• W2912234025 creator A5021247607 @default.
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• W2912234025 date "2019-02-14" @default.
• W2912234025 modified "2023-09-26" @default.
• W2912234025 title "Bayesian abundance estimation from genetic mark-recapture data when not all sites are sampled: an example with the bowhead whale" @default.
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• W2912234025 doi "https://doi.org/10.1101/549394" @default.
• W2912234025 hasPublicationYear "2019" @default.
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