A review of genomics methods and bioinformatics tools for the analysis of close-kin mark-recapture

Abstract

Stock assessments serve to monitor the condition of fish stocks and exploit them sustainably but require accurate data such as growth and mortality rates as input parameters. Most species fished worldwide lack the data needed to assess their status and even those closely assessed are often based on parameters that are known to contain uncertainty. This has resulted in an increased share of overfished stocks over the last half century, demanding urgently innovative methodologies that can provide novel means to reduce uncertainty of fish stocks assessments and expand the range of assessed species. CKMR has emerged recently attracting a great interest due to its potential to provide accurate demographic parameters of interest in stock assessments. The method is at the crossroads between fisheries science and genomics, requiring specialized knowledge that is usually outside of the experience of fisheries scientist and modellers, complicating the application of the method and its uptake in regular fisheries assessments. In this review, we provide useful information to perform the genomics and bioinformatics steps required to complete successfully a CKMR study. We discuss the most suitable genomics assays, considering the amount of information they provide, their easiness of use and cost of genotyping accurately the large number of individuals needed to assess most fish stocks. We provide an overview of methods of analysis and statistical methodologies that can be used to infer kinship with the accuracy required in a large population setting with sparse sampling, where most individuals are unrelated, determining a low probability of finding closely related individuals. We analyse potential sources of biases and errors and provide recommendations to facilitate the application of CKMR to a wider range of fish stocks.