Sévêque, A., R. C. Lonsinger, L. P. Waits, and D. J. Morin. Accepted. Spatial close-kin mark-recapture models applied to terrestrial species with continuous natal dispersal. Methods in Ecology and Evolution 16: 1-11. https://doi.org/10.1111/2041-210X.14490
Abstract
1. Close-kin mark–recapture (CKMR) methods use information on genetic relatedness among individuals to estimate demographic parameters. In this case, an individual’s genotype can be considered a “recapture” of each of its parent’s genotype, and the frequency of kin-pair matches detected in a population sample can directly inform estimates of abundance. CKMR compares the “true” kinship between two individuals (confirmed via genotyping) to their prior probability of relatedness given a set of covariates. Among others, population structure can have a strong influence on the prior kinship probabilities. Many terrestrial species are philopatric or face barriers to dispersal, and not accounting for dispersal limitation in kinship probabilities can create substantial bias if sampling is also spatially structured (e.g., via harvest).
2. We present a spatially-explicit formulation of CKMR that corrects for incomplete mixing by incorporating natal dispersal distances and spatial distribution of individuals into the kinship probabilities. We used individual-based simulations to evaluate the accuracy of abundance estimates from four CKMR models with increasingly complex spatial components across six scenarios with distinct spatial patterns of relative abundance and sampling probability.
3. Estimates of abundance obtained with a CKMR model naïve to spatial structure were negatively biased when sampling was spatially biased. Incorporating patterns of natal dispersal in the kinship probabilities helped address this bias, but estimates were not always accurate depending on the model used and scenario considered. The most complex model was unbiased in all scenarios.
4. Incorporating natal dispersal into spatially structured CKMR models can address the bias created by population structure and heterogeneous sampling, but will often require additional assumptions and auxiliary data (e.g., relative abundance index). The models shown here were designed for terrestrial species with continuous patterns of natal dispersal and high year-to-year site fidelity, but could be extended to other systems.
2. We present a spatially-explicit formulation of CKMR that corrects for incomplete mixing by incorporating natal dispersal distances and spatial distribution of individuals into the kinship probabilities. We used individual-based simulations to evaluate the accuracy of abundance estimates from four CKMR models with increasingly complex spatial components across six scenarios with distinct spatial patterns of relative abundance and sampling probability.
3. Estimates of abundance obtained with a CKMR model naïve to spatial structure were negatively biased when sampling was spatially biased. Incorporating patterns of natal dispersal in the kinship probabilities helped address this bias, but estimates were not always accurate depending on the model used and scenario considered. The most complex model was unbiased in all scenarios.
4. Incorporating natal dispersal into spatially structured CKMR models can address the bias created by population structure and heterogeneous sampling, but will often require additional assumptions and auxiliary data (e.g., relative abundance index). The models shown here were designed for terrestrial species with continuous patterns of natal dispersal and high year-to-year site fidelity, but could be extended to other systems.