Macbeth Gilbert M, Broderick Damien, Ovenden Jennifer R, Buckworth Rik C
Molecular Fisheries Laboratory, Queensland Primary Industries and Fisheries, Ritchie Building No. 64A, Research Road, University of Queensland, PO Box 6097, St Lucia, Queensland, 4072, Australia.
Theor Popul Biol. 2011 Nov;80(3):185-96. doi: 10.1016/j.tpb.2011.06.006. Epub 2011 Jul 6.
Genotypes produced from samples collected non-invasively in harsh field conditions often lack the full complement of data from the selected microsatellite loci. The application to genetic mark-recapture methodology in wildlife species can therefore be prone to misidentifications leading to both 'true non-recaptures' being falsely accepted as recaptures (Type I errors) and 'true recaptures' being undetected (Type II errors). Here we present a new likelihood method that allows every pairwise genotype comparison to be evaluated independently. We apply this method to determine the total number of recaptures by estimating and optimising the balance between Type I errors and Type II errors. We show through simulation that the standard error of recapture estimates can be minimised through our algorithms. Interestingly, the precision of our recapture estimates actually improved when we included individuals with missing genotypes, as this increased the number of pairwise comparisons potentially uncovering more recaptures. Simulations suggest that the method is tolerant to per locus error rates of up to 5% per locus and can theoretically work in datasets with as little as 60% of loci genotyped. Our methods can be implemented in datasets where standard mismatch analyses fail to distinguish recaptures. Finally, we show that by assigning a low Type I error rate to our matching algorithms we can generate a dataset of individuals of known capture histories that is suitable for the downstream analysis with traditional mark-recapture methods.
在恶劣野外条件下通过非侵入性采集样本所产生的基因型，往往缺乏所选微卫星位点的完整数据。因此，将其应用于野生动物物种的遗传标记重捕方法时，可能容易出现错误识别，导致“真正的未重捕个体”被错误地当作重捕个体接受（I型错误），以及“真正的重捕个体”未被检测到（II型错误）。在此，我们提出一种新的似然方法，该方法允许对每对基因型比较进行独立评估。我们应用此方法，通过估计和优化I型错误与II型错误之间的平衡来确定重捕个体的总数。我们通过模拟表明，通过我们的算法可以使重捕估计的标准误差最小化。有趣的是，当我们纳入具有缺失基因型的个体时，重捕估计的精度实际上得到了提高，因为这增加了成对比较的数量，有可能发现更多的重捕个体。模拟表明，该方法能够容忍每个位点高达5%的错误率，并且理论上可以在仅对60%的位点进行基因分型的数据集中工作。我们的方法可以应用于标准错配分析无法区分重捕个体的数据集中。最后，我们表明，通过为我们的匹配算法设定较低的I型错误率，我们可以生成一个已知捕获历史个体的数据集，该数据集适用于传统标记重捕方法的下游分析。
