4.2 Article

SOURCES AND RATES OF ERRORS IN METHODS OF INDIVIDUAL IDENTIFICATION FOR NORTH ATLANTIC RIGHT WHALES

期刊

JOURNAL OF MAMMALOGY
卷 90, 期 5, 页码 1246-1255

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ALLIANCE COMMUNICATIONS GROUP DIVISION ALLEN PRESS
DOI: 10.1644/08-MAMM-A-328.1

关键词

error rates; genotyping; individual identification; photo-identification; right whale

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资金

  1. National Marine Fisheries Service
  2. Natural Sciences and Engineering Research Council of Canada
  3. Canadian Whale Institute
  4. Canada Research Chair

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Many long-term studies of wildlife populations rely on individual identification based on natural markings or genetic profiling, or both. However, only rarely are these 2 independent data sets systematically compared with each other to estimate the error rates inherent in these studies. Here, >25 years of photo-identification data on the endangered North Atlantic right whale (Eubalaena glacialis) were compared with high-resolution genetic profiles, available for >75% of the individuals in the photo-identification catalog, in order to identify sources and rates of errors associated with both methods of individual identification. The resulting estimates were 0.0308 errors/identification for the photo-identification data, and 0.00121 errors/locus and 0.0327 errors/multilocus profile for the genetic data. These are among the lowest error rates yet reported, and indicate that the approaches used provide reliable means of individual identification for this species. However, despite these low error rates, the large size of the data sets results in a nonnegligible estimated number of errors, indicating that the potential for these errors needs to be incorporated into other analyses that are based on these data. A similar situation likely exists in other long-term studies where, although error rates are assumed to be low, the size of the data set results in a large number of errors that will influence subsequent analyses. Regularly conducting and reporting extensive database comparisons such as this is invaluable for maintaining the integrity of long-term data sets by identifying where sources of error are occurring and how protocols can be improved to lower error rates in the future.

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