Sévêque, A., R. C. Lonsinger, L. P. Waits, K. E. Brzeski, L. M. Komoroske, C. N. Ott-Conn, S. L. Mayhew, D. C. Norton, T. R. Petroelje, J. D. Swenson, and D. J. Morin. In Review. Sources of bias in applying close-kin mark–recapture to terrestrial game species with different life histories. Ecology. https://doi.org/10.1002/ecy.4244
Abstract
1. Close-kin mark–recapture (CKMR) is a method analogous to traditional mark–recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify proportions of related individuals detected in a single sampling occasion. An advantage of this novel method is that it enables the opportunistic use of samples from harvested wildlife. While the CKMR framework is mathematically straightforward, meeting key model assumptions is required to yield reliable results. Thus, it is important to explore the strengths and limitations of this emerging method under a range of scenarios to reveal potential obstacles and evaluate implications of violating model assumptions.
2. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among long-lived game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. We conclude with a simulated example using a harvested Michigan black bear Ursus americanus population.
3. Our simulations show that CKMR can yield non-trivial biases in both survival and abundance estimates if deviations from the core assumptions are not explicitly incorporated in the modeling framework. The number of related kin pairs observed in the sample, in combination with the type of close kin estimator used (parent-offspring pairs or half-sibling pairs), can affect the precision and accuracy of the estimates. The Michigan black bear population exemplifies our findings, with biases in estimated population abundance ranging from -12% to +99%.
4. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analyses continue to decline. However, our work highlights the importance of evaluating and accounting for all relevant parameters linked to the species of interest and the protocol through which individuals were sampled. Population simulations will be key to developing and validating increasingly complex models and making CKMR applicable to a greater number of species and systems.
2. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among long-lived game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. We conclude with a simulated example using a harvested Michigan black bear Ursus americanus population.
3. Our simulations show that CKMR can yield non-trivial biases in both survival and abundance estimates if deviations from the core assumptions are not explicitly incorporated in the modeling framework. The number of related kin pairs observed in the sample, in combination with the type of close kin estimator used (parent-offspring pairs or half-sibling pairs), can affect the precision and accuracy of the estimates. The Michigan black bear population exemplifies our findings, with biases in estimated population abundance ranging from -12% to +99%.
4. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analyses continue to decline. However, our work highlights the importance of evaluating and accounting for all relevant parameters linked to the species of interest and the protocol through which individuals were sampled. Population simulations will be key to developing and validating increasingly complex models and making CKMR applicable to a greater number of species and systems.