assignPOP: An r package for population assignment using genetic, non‐genetic, or integrated data in a machine‐learning framework

Abstract

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

Spawning time of Atlantic herring ( Clupea harengus ) populations within a restricted area reflects their otolith growth at the larval stage

“…Likewise, Brophy et al [65] suggested that spawning season and location of Atlantic herring could be predetermined and not learnt from repeated spawning [66]. Support for natal homing and adaptations of Landvik herring to environmental conditions of its marginal habitat also originates from a recent genetic study using 20 microsatellite markers, where Landvikvannet differed from other local herring in Lindåspollene, Lusterfjord and Trondheimsfjord as well as from other herring populations surrounding the Norwegian Sea [67]. Unpublished results on the microsatellite locus Cpa112, which is non-neutral to salinity variability with allele frequencies varying from 45% in the Baltic to 2-4% in the North Sea [27], have shown that Landvik herring is obvious with a frequency of 15% (Carl André, pers.…”

“…This baseline can be further used in combination with machinelearning techniques [67] to assign individuals from mixed fisheries to populations.…”

RAD‐Seq Refines Previous Estimates of Genetic Structure in Lake Erie Walleye

“…Thus, the number of individuals used in stock reassignment training data sets needs to be high (Chen et al. ) and any population variance identified should be tested on an independent data set to avoid overfitting and biased results (Waples ).…”

“…Therefore, delineation may be possible using a combination of environmental markers (e.g., parasites and otolith microchemistry) and molecular markers (MacKenzie and Abaunza ; Chen et al. , ).…”

“…To do so, we used the R package assignPOP (Chen et al. ) to assign randomly chosen individuals from each local spawning site to training groups in one of three proportions (50, 70, and 90%), with the remaining individuals being used as test (i.e., holdout) individuals. All of the loci (with up to 20% missing data) from the randomly chosen training individuals were used to build a training model ( N = 3 in total, using 50, 70, or 90% of individuals), which we subsequently tested by using all of the loci from independent holdout individuals.…”