Estimating population size with noninvasive capture-mark-recapture data.
Estimating population size of elusive and rare species is challenging. The difficulties in catching such species has triggered the use of samples collected noninvasively, such as feces or hair; from which genetic analysis yields data similar to capture-mark-recapture (CMR) data. There are, however two differences between classical CMR and noninvasive CMR. First, capture and recapture data are gathered over multiple sampling sessions in classical CMR, whereas in noninvasive CMR they can be obtained from a single sampling session. Second, because of genotyping errors and unlike classical CMR, there is no simple relationship between (genetic) marks and individuals in noninvasive CMR. We evaluated, through simulations, the reliability of population size estimates based on noninvasive CMR. For equal sampling efforts, we compared estimates of population size N obtained from accumulation curves, a maximum likelihood, and a Bayesian estimator. For a closed population and without sampling heterogeneity, estimates obtained from noninvasive CMR were as reliable as estimates from classical CMR. The sampling structure (single or multiple session) did not alter the results, the Bayesian estimator in the case of a single sampling session presented the best compromise between low mean squared error and a 95% confidence interval encompassing the parametric value of N in most simulations. Finally, when suitable field and lab protocols were used, genotyping errors did not substantially bias population size estimates (bias < 3.5% in all simulations). The ability to reliably estimate population size from noninvasive samples taken during a single session offers a new and useful technique for the management and conservation of elusive and rare species.
Petit
Eric
E
Laboratoire Ethologie Evolution Ecologie, UMR CNRS 6552, Universite Rennes I, Station Biologique, 35380 Paimpont, France. eric.petit@univ-rennes1.fr
Valiere
Nathaniel
N
D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D003247:Conservation of Natural Resources / Q000379:methods*; D004463:Ecology / Q000379:methods; D005838:Genotype; D016013:Likelihood Functions; D011156:Population Density; D011158:Population Growth