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Anthony Seveque

Publications and source records attributed to Anthony Seveque.

2 recordsLinked to original sources

Spatial close-kin mark-recapture models applied to terrestrial species with continuous natal dispersal

Close-kin mark–recapture (CKMR) methods use information on genetic relatedness among individuals to estimate demographic parameters. 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 inference procedures require analysts to define kinship probabilities in functional forms, which inevitably involve simplifying assumptions. Among others, population structure can have a strong influence on how kinship probabilities are formulated. 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). 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 obtained with one spatially naïve and two spatially explicit CKMR models across six scenarios with distinct spatial patterns of relative abundance and sampling probability. 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 the scenario considered. 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 indices). 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 species.

Methods in Ecology and Evolution

Sources of bias in applying close-kin mark–recapture to terrestrial game species with different life histories

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 related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species’ and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. 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 game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent–offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.

Ecology