Estimating animal abundance using noninvasive DNA sampling: Promise and pitfalls

Citation
Ls. Mills et al., Estimating animal abundance using noninvasive DNA sampling: Promise and pitfalls, ECOL APPL, 10(1), 2000, pp. 283-294
Citations number
64
Categorie Soggetti
Environment/Ecology
Journal title
ECOLOGICAL APPLICATIONS
ISSN journal
10510761 → ACNP
Volume
10
Issue
1
Year of publication
2000
Pages
283 - 294
Database
ISI
SICI code
1051-0761(200002)10:1<283:EAAUND>2.0.ZU;2-5
Abstract
Advances in molecular biology offer promise to the study of demographic cha racteristics of rare or hard-re-capture species, because individuals can no w be identified through noninvasive sampling such as fecal collection or ha ir snags. However, individual genotyping using such methods currently leads to a novel problem that we call a "shadow effect," because some animals no t captured previously are believed to be recaptures due to their DNA profil e being an indistinguishable shadow of previously captured animals. We eval uate the impact of the shadow effect on the two methods most commonly used in applied population ecology to estimate the size of closed populations: L incoln-Petersen and multiple-recapture estimators in program CAPTURE. We fi nd that the shadow effect can cause a negative bias in the estimates of bot h the number of different animals and the number of different genotypes. Fu rthermore, with Lincoln-Petersen estimators, the shadow effect can cause es timated confidence intervals to decrease even as bias increases. Because th e bias arises from heterogeneity in apparent "capture" probabilities for an imals with genetic shadows vs. those without, a model in program CAPTURE th at is robust to capture heterogeneity (Mh-jackknife) does not underestimate the number of genotypes in the population and only slightly underestimates the rotal number of individuals As the shadow effect increases, CAPTURE is better able to correctly identify heterogeneity in capture probability and to pick Mh-jackknife, so that the higher levels of shadow effect have less bias than medium levels. The shadow effect will occur in all estimates of demographic rates (including survival) that use DNA sampling to determine i ndividual identity, but it can be minimized by increasing the number of ind ividual loci sampled.