Data and code from: Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments

Obtaining robust estimates of population abundance is a central challenge
hindering the conservation and management of many threatened and exploited
species. Close-kin mark-recapture (CKMR) is a genetics-based approach that
has strong potential to improve monitoring of data-limited species by
enabling estimates of abundance, survival, and other parameters for
populations that are challenging to assess. However, CKMR models have
received limited sensitivity testing under realistic population dynamics
and sampling scenarios, impeding application of the method in population
monitoring programs and stock assessments. Here, we use individual-based
simulation to examine how unmodeled population dynamics and aging
uncertainty affect the accuracy and precision of CKMR parameter estimates
under different sampling strategies. We then present adapted models that
correct the biases that arise from model misspecification. Our results
demonstrate that a simple base-case CKMR model produces robust estimates
of population abundance with stable populations that breed annually;
however, if a population trend or non-annual breeding dynamics are
present, or if year-specific estimates of abundance are desired, a more
complex CKMR model must be constructed. In addition, we show that CKMR can
generate reliable abundance estimates for adults from a variety of
sampling strategies, including juvenile-focused sampling where adults are
never directly observed (and aging error is minimal). Finally, we apply a
CKMR model that has been adapted for population growth and intermittent
breeding to two decades of genetic data from juvenile lemon sharks
(Negaprion brevirostris) in Bimini, Bahamas, to demonstrate how
application of CKMR to samples drawn solely from juveniles can contribute
to monitoring efforts for highly mobile populations. Overall, this study
expands our understanding of the biological factors and sampling decisions
that cause bias in CKMR models, identifies key areas for future inquiry,
and provides recommendations that can aid biologists in planning and
implementing an effective CKMR study, particularly for long-lived
data-limited species.