Steven T. Kalinowski

Revising how the computer program cervus accommodates genotyping error increases success in paternity assignment

Genotypes are frequently used to identify parentage. Such analysis is notoriously vulnerable to genotyping error, and there is ongoing debate regarding how to solve this problem. Many scientists have used the computer program cervus to estimate parentage, and have taken advantage of its option to allow for genotyping error. In this study, we show that the likelihood equations used by versions 1.0 and 2.0 of cervus to accommodate genotyping error miscalculate the probability of observing an erroneous genotype. Computer simulation and reanalysis of paternity in Rum red deer show that correcting this error increases success in paternity assignment, and that there is a clear benefit to accommodating genotyping errors when errors are present. A new version of cervus (3.0) implementing the corrected likelihood equations is available at http://www.fieldgenetics.com.

Counting alleles with rarefaction: Private alleles and hierarchical sampling designs

The number of alleles (allelic richness) in a population is a fundamental measure of genetic variation, and a useful statistic for identifying populations for conservation. Estimating allelic richness is complicated by the effects of sample size: large samples are expected to have more alleles. Rarefaction solves this problem. This communication extends the rarefaction procedure to count private alleles and to accommodate hierarchical sampling designs.

The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure.

One of the primary goals of population genetics is to succinctly describe genetic relationships among populations, and the computer program STRUCTURE is one of the most frequently used tools for doing so. The mathematical model used by STRUCTURE was designed to sort individuals into Hardy–Weinberg populations, but the program is also frequently used to group individuals from a large number of populations into a small number of clusters that are supposed to represent the main genetic divisions within species. In this study, I used computer simulations to examine how well STRUCTURE accomplishes this latter task. Simulations of populations that had a simple hierarchical history of fragmentation showed that when there were relatively long divergence times within evolutionary lineages, the clusters created by STRUCTURE were frequently not consistent with the evolutionary history of the populations. These difficulties can be attributed to forcing STRUCTURE to place individuals into too few clusters. Simulations also showed that the clusters produced by STRUCTURE can be strongly influenced by variation in sample size. In some circumstances, STRUCTURE simply put all of the individuals from the largest sample in the same cluster. A reanalysis of human population structure suggests that the problems I identified with STRUCTURE in simulations may have obscured relationships among human populations—particularly genetic similarity between Europeans and some African populations.

Population structure of Atlantic salmon (Salmo salar L.): a range‐wide perspective from microsatellite DNA variation

Atlantic salmon (n = 1682) from 27 anadromous river populations and two nonanadromous strains ranging from south‐central Maine, USA to northern Spain were genotyped at 12 microsatellite DNA loci. This suite of moderate to highly polymorphic loci revealed 266 alleles (5–37/locus) range‐wide. Statistically significant allelic and genotypic heterogeneity was observed across loci between all but one pairwise comparison. Significant isolation by distance was found within and between North American and European populations, indicating reduced gene flow at all geographical scales examined. North American Atlantic salmon populations had fewer alleles, fewer unique alleles (though at a higher frequency) and a shallower phylogenetic structure than European Atlantic salmon populations. We believe these characteristics result from the differing glacial histories of the two continents, as the North American range of Atlantic salmon was glaciated more recently and more uniformly than the European range. Genotypic assignment tests based on maximum‐likelihood provided 100% correct classification to continent of origin and averaged nearly 83% correct classification to province of origin across continents. This multilocus method, which may be enhanced with additional polymorphic loci, provides fishery managers the highest degree of correct assignment to management unit of any technique currently available.

Maximum likelihood estimation of the frequency of null alleles at microsatellite loci

We review three methods for estimating the frequency of null alleles at codominant loci (such as microsatellite loci) and present a new maximum likelihood approach. Computer simulations show that the maximum likelihood estimator has a smaller root mean squared error than previous estimators.

Hybridization rapidly reduces fitness of a native trout in the wild

Human-mediated hybridization is a leading cause of biodiversity loss worldwide. How hybridization affects fitness and what level of hybridization is permissible pose difficult conservation questions with little empirical information to guide policy and management decisions. This is particularly true for salmonids, where widespread introgression among non-native and native taxa has often created hybrid swarms over extensive geographical areas resulting in genomic extinction. Here, we used parentage analysis with multilocus microsatellite markers to measure how varying levels of genetic introgression with non-native rainbow trout (Oncorhynchus mykiss) affect reproductive success (number of offspring per adult) of native westslope cutthroat trout (Oncorhynchus clarkii lewisi) in the wild. Small amounts of hybridization markedly reduced fitness of male and female trout, with reproductive success sharply declining by approximately 50 per cent, with only 20 per cent admixture. Despite apparent fitness costs, our data suggest that hybridization may spread due to relatively high reproductive success of first-generation hybrids and high reproductive success of a few males with high levels of admixture. This outbreeding depression suggests that even low levels of admixture may have negative effects on fitness in the wild and that policies protecting hybridized populations may need reconsideration.

Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning.

How many alleles per locus should be used to estimate genetic distances

As more microsatellite loci become available for use in genetic surveys of population structure, population geneticists are able to select loci to use in population structure surveys. This study used computer simulations to investigate how the number of alleles at loci affects the precision of estimates of four common genetic distances. This showed that equivalent results could be achieved by examining either a few loci with many alleles or many loci with a few alleles. More specifically, the total number of independent alleles appears to be a good indicator of how precise estimates of genetic distance will be.

An improved method for predicting the accuracy of genetic stock identification

Estimating the accuracy of genetic stock identification (GSI) that can be expected given a previously collected baseline requires simulation. The conventional method involves repeatedly simulating mixtures by resampling from the baseline, simulating new baselines by resampling from the baseline, and analyzing the simulated mixtures with the simulated baselines. We show that this overestimates the predicted accuracy of GSI. The bias is profound for closely related populations and increases as more genetic data (loci and (or) alleles) are added to the analysis. We develop a new method based on leave-one-out cross validation and show that it yields essentially unbiased estimates of GSI accu- racy. Applying both our method and the conventional method to a coastwide baseline of 166 Chinook salmon (Onco- rhynchus tshawytscha) populations shows that the conventional method provides severely biased predictions of accuracy for some individual populations. The bias for reporting units (aggregations of closely related populations) is moderate, but still present.

Estimating relatedness and relationships using microsatellite loci with null alleles

Relatedness is often estimated from microsatellite genotypes that include null alleles. When null alleles are present, observed genotypes represent one of several possible true genotypes. If null alleles are detected, but analyses do not adjust for their presence (ie, observed genotypes are treated as true genotypes), then estimates of relatedness and relationship can be incorrect. The number of loci available in many wildlife studies is limited, and loci with null alleles are commonly a large proportion of data that cannot be discarded without substantial loss of power. To resolve this problem, we present a new approach for estimating relatedness and relationships from data sets that include null alleles. Once it is recognized that the probability of the observed genotypes is dependent on the probabilities of a limited number of possible true genotypes, the required adjustments are straightforward. The concept can be applied to any existing estimators of relatedness and relationships. We review established maximum likelihood estimators and apply the correction in that setting. In an application of the corrected method to data from striped hyenas, we demonstrate that correcting for the presence of null alleles affect results substantially. Finally, we use simulated data to confirm that this method works better than two common approaches, namely ignoring the presence of null alleles or discarding affected loci.