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SEA TURTLE STOCK ESTIMATION USING GENETIC MARKERS: ACCOUNTING FOR SAMPLING ERROR OF RARE GENOTYPES
Author(s) -
Bolker Benjamin,
Okuyama Toshinori,
Bjorndal Karen,
Bolten Alan
Publication year - 2003
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(2003)013[0763:stseug]2.0.co;2
Subject(s) - markov chain monte carlo , rookery , bootstrapping (finance) , statistics , econometrics , bayesian probability , stock (firearms) , sampling (signal processing) , computer science , geography , mathematics , population , demography , archaeology , filter (signal processing) , sociology , computer vision
The contributions of different sea turtle rookeries to mixed‐stock populations on foraging grounds can only be estimated by indirect methods such as analysis of mitochondrial DNA samples from the mixed stocks and rookery populations. We explain and evaluate methods for genetic stock estimation using simulations and data from previous studies. We focus on Markov Chain Monte Carlo (MCMC) estimation, a relatively new method. MCMC differs from older combinations of maximum likelihood (ML) with nonparametric bootstrapping in (1) using a Bayesian prior to quantify previous knowledge; (2) taking account of multiple modes in the probability distribution of contributions; and (3) incorporating sampling error more flexibly, allowing for the possibility that rare haplotypes actually present in a particular rookery were not detected in a small sample. In the context of sea turtle stock analysis, the differences in point estimates between ML and MCMC methods are relatively small, but MCMC gives wider and more accurate confidence limits than ML with bootstrapping, which tends to underestimate small contributions as zero. Corresponding Editor: H. B. Shaffer