Abstract1. The use of biomarkers (e.g., genetic, microchemical and morphometric characteristics) to discriminate among and assign individuals to a population can benefit species conservation and management by facilitating our ability to understand population structure and demography.2. Tools that can evaluate the reliability of large genomic datasets for population discrimination and assignment, as well as allow their integration with non-genetic markers for the same purpose, are lacking. Our r package, assignPOP, provides both functions in a supervised machine-learning framework.3. assignPOP uses Monte-Carlo and K-fold cross-validation procedures, as well as principal component analysis, to estimate assignment accuracy and membership probabilities, using training (i.e., baseline source population) and test (i.e., validation) datasets that are independent. A user then can build a specified predictive model based on the relative sizes of these datasets and classification functions, including linear discriminant analysis, support vector machine, naïve Bayes, decision tree and random forest.4. assignPOP can benefit any researcher who seeks to use genetic or non-genetic data to infer population structure and membership of individuals. assignPOP is a freely available r package under the GPL license, and can be downloaded from CRAN or at https://github.com/alexkychen/assignPOP. A comprehensive tutorial can also be found at https://alexkychen.github.io/assignPOP/.
K E Y W O R D Sassignment analysis, machine learning, population classification, quantitative genomics
Otolith microchemistry is a commonly used tool for stock discrimination in fisheries management. Two key questions remain with respect to its effectiveness in discriminating among river-spawning populations. First, do larvae remain in their natal river long enough for their otoliths to pick up that system’s characteristic chemical signature? Second, are larval otolith microchemical differences between natal rivers sufficiently large to overcome spatiotemporal variation in water chemistry? We quantified how larval age, the ratio of ambient strontium to calcium concentrations (Sr:Ca), and water temperature influence otolith Sr in both lab-reared and wild-collected Lake Erie walleye (Sander vitreus). Otolith microchemistry shows promise as a spawning stock discrimination tool, given that otolith Sr in larval walleye (i) is more strongly influenced by ambient Sr:Ca than by temperature; (ii) reflects Sr:Ca levels in the natal environment, even in larvae as young as 2 days old; and (iii) can effectively discriminate between larvae captured in two key Lake Erie spawning tributaries, even in the face of short larval river residence times and within-year and across-year variation in ambient Sr:Ca.
Delineating population structure helps fishery managers to maintain a diverse “portfolio” of local spawning populations (stocks), as well as facilitate stock‐specific management. In Lake Erie, commercial and recreational fisheries for Walleye Sander vitreus exploit numerous local spawning populations, which cannot be easily differentiated using traditional genetic data (e.g., microsatellites). Here, we used genomic information (12,264 polymorphic loci) generated using restriction site‐associated DNA sequencing to investigate stock structure in Lake Erie Walleye. We found low genetic divergence (genetic differentiation index FST = 0.0006–0.0019) among the four Lake Erie western basin stocks examined, which resulted in low classification accuracies for individual samples (40–60%). However, more structure existed between western and eastern Lake Erie basin stocks (FST = 0.0042–0.0064), resulting in greater than 95% classification accuracy of samples to a lake basin. Thus, our success in using genomics to identify stock structure varied with spatial scale. Based on our results, we offer suggestions to improve the efficacy of this new genetic tool for refining stock structure and eventually determining relative stock contributions in Lake Erie Walleye and other Great Lakes populations.
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