Expected improvements in precision when integrating opportunistic close-kin mark-recapture data into fisheries stock assessments

Close-Kin Mark-Recapture (CKMR) sampling, by providing information on abundance and survival rates (and potentially other quantities), offers a promising new data source for fisheries stock assessments. Sample design in order to achieve a desired precision is somewhat straightforward in simple CKMR models; however when integrated within a full stock assessment model with many other data sources, the value of the data (in terms of a reduction in uncertainty of model estimates) is less clear. Herein I demonstrate, using self-test simulations, the expected improvements in precision and accuracy of derived quantities and estimated parameters within statistical catch-at-age models when opportunistic CKMR sampling is conducted and the data integrated within the assessment. By opportunistic CKMR sampling I mean to describe the genetic sampling of individuals that comprise the age composition data, such that increases in CKMR sampling would also increase the age composition samples (and vice versa). I examine the expected improvements across three life history types (cod-like, flatfish-like, and sardine-like) and different amounts of data available to the assessment, including the uncertainty and inclusion of an abundance index and the sample size and time series length of CKMR and age composition samples. Results suggest CKMR data can provide considerable improvements in accuracy and precision of spawning stock biomass at the end of the time series and parameters defining natural mortality and scale of the population, provided an adequate annual sample size is collected relative to the spawning abundance of the stock during the period of CKMR inference. The time-series length of CKMR data and uncertainty or inclusion of an abundance index played a much more moderate role in how much improvement CKMR data provided over models fit without CKMR. This result was likely a function of the model being privy to an effectively known catch time series and known steepness, allowing it to estimate stock scale and trend reasonably well without CKMR data given informative composition data. I recommend simulation analyses including stock assessments as estimation models be carried out for those considering routinely collecting and integrating CKMR data into fisheries stock assessments.