Abstract
Accurately detecting steps in genetic diversity across landscapes is
important for locating barriers to gene flow, identifying selectively
important loci, and defining management units. However, there are many
metrics that researchers could use to detect steps and little
information on which might be the most robust. Our study aimed to
determine the best measure/s for genetic step detection along linear
gradients using biallelic single nucleotide polymorphism (SNP) data. We
tested the ability to differentiate between linear and step-like
gradients in genetic diversity, using a range of diversity measures
derived from the q-profile, including allelic richness, Shannon
Information, GST, and Jost-D, as well as Bray-Curtis dissimilarity. To
determine the properties of each measure, we repeated simulations of
different intensities of step and allele proportion ranges, with varying
genome sample size, number of loci, and number of localities. We found
that alpha diversity (within-locality) based measures were ineffective
at detecting steps. Further, allelic richness-based beta
(between-locality) measures (e.g., Jaccard and Sørensen dissimilarity)
were not reliable for detecting steps, but instead detected departures
from fixation. The beta diversity measures best able to detect steps
were: Shannon Information based measures, GST based measures, a Jost-D
related measure, and Bray-Curtis dissimilarity. No one measure was best
overall, with a trade-off between those measures with high step
detection sensitivity (GST and Bray-Curtis) and those that minimised
false positives (a variant of Shannon Information). Therefore, when
detecting steps, we recommend understanding the differences between
measures and using a combination of approaches.