4 | DISCUSSION
4.1 | Geographic
Variation
We detected very little geographic heterogeneity in the distribution of
genetic variation among North American blue crabs despite extensive
sampling and considerable statistical power. Our estimate of overallFST was
0.0002; without the sample from JAC the estimate would have been 0.0001
and not significantly above zero. In two previous studies, estimates ofFST were on the order of 0.01, 50 times higher
than ours. McMillen-Jackson and co-workers
(1994)
estimated Nm (population size times migration rate) fromFST to be between 11.1 and 19.0, from which their
unreported FST estimates can be inferred to have
been between 0.013 and 0.022. From RAD sequence data, Plough
(2017)
estimated FST between a sample of blue crabs from
the North American Atlantic and a sample from the Gulf of Mexico at
0.01. It is possible that this discrepancy in the magnitude ofFST estimates is due to accidents of sampling.
McMillen-Jackson and co-workers
(1994)
used only nine polymorphic allozymes, a small sample of loci.
Furthermore, they estimated FST with the software
BIOSYS-1
(Swofford
& Selander, 1981), which doesn’t use the now widely adopted Weir and
Cockerham
(1984)
correction for sampling variance in allele frequency estimates, and
therefore is expected to produce upwardly biased estimates ofFST . Plough
(2017)
surveyed only two North American locations, one may have been unusually
divergent like our sample from JAC. Differences in estimates ofFST can also reflect differences in statistical
methodology. A survey of microsatellite variation in North American blue
crabs yielded average pairwise FST estimates that
ranged between 0.003 and 0.008 depending on methodological details
(Macedo
et al., 2019). None of the pairwise FSTestimates in this study were significantly different from zero when the
full set of putatively neutral loci was analyzed by the permutation test
in Arlequin (Schneider et al., 2000).
Our study encountered several potential sources of error that could lead
to the false appearance of genetic structure in blue crabs or other
species. The first is misidentification of specimens. Megalopae of
several congeners of C. sapidus are found in the northern Gulf of
Mexico and are difficult to distinguish by morphology. This could
explain some of the anomalous findings of an allozyme survey of blue
crabs from the coast of Texas, which included large temporal differences
in allele frequencies among cohorts of settling blue crab megalopae over
a span of several months
(Kordos
& Burton, 1993). The observed pattern closely matches seasonal changes
in abundance of the megalopae of a related species, Callinectes
similis , and the megalopae of these species are difficult to
distinguish
(Sullivan
& Neigel, 2017). Sullivan and Neigel
(2017)
also identified the megalopae of C. rathbunae and C. danaeco‑settling with C. sapidus . In one of our samples, 6 out of 41
megalopae that we had identified as C. sapidus by morphology
matched the distinctive pattern of homozygous and unscorable loci seen
in a specimen of C. rathbunae . Had we not removed these megalopae
from our analysis we would have concluded that the sample was
genetically differentiated from others and included closely related
individuals. Our laboratory’s previous survey of sequence variation in
protein-coding genes of blue crabs found nuclear gene haplotypes in blue
crabs from Venezuela that were not found in samples from North America
(Yednock
& Neigel, 2014). These individuals from Venezuela appear to represent
either a divergent population of C. sapidus or a different
species. They were automatically excluded from the present study because
of their unusually low heterozygosity (which also suggests a different
species), but if we had included them our estimate ofFST among locations would have been much higher.
Another potential source of error is null alleles. Yednock and Neigel
(2014)
suspected null alleles at the ant locus in blue crabs because of
departures from Hardy-Weinberg proportions, and we confirmed this in the
present study. In their preliminary analysis, inclusion of data for this
locus created the appearance of significant population differentiation
not seen with other loci, even after a correction for null alleles was
applied (Yednock and Neigel, unpublished). From the above
considerations, it appears that known sources of error in genetic marker
data tend to inflate estimates of FST and create
false appearances of population structure. Large numbers of marker loci
are often viewed as providing “high resolution” analysis of population
structure
(e.g.
Davey & Blaxter, 2010) because they increase the statistical power to
detect genetic differentiation, but they may also increase the
sensitivity of statistical tests to errors in data
(Chapuis
& Estoup, 2006).
4.2 | Accuracy of Illumina
Genotyping
SNP genotypes determined by the Illumina Infinium assay for our study
were highly reproducible and in aggregate, the data were consistent with
the low population differentiation expected for a species that
experiences very high gene flow. Differences between genotypes
determined by the Infinium assay and those determined by sequencing are
explained as null alleles in the sequencing data. Individuals with
anomalous genotypes can be readily explained as misidentified specimens.
Although a direct comparison between the accuracy of our data and those
based on other types of genetic markers in blue crabs is not feasible,
our comparatively low estimates of FST imply
greater accuracy. The biology of blue crabs suggestsFST should be very low, and errors in genetic
marker data are expected to inflate estimatesFST . The distinction between no genetic
differentiation and slight genetic differentiation is not trivial. As
Palumbi
(2003)
pointed out in discussing the relevance of apparent “slight geographic
differentiation” to the design of marine reserves “Interpreting the
significance of this slight genetic signal has been difficult because
even mild genetic structure implies very limited demographic exchange
between populations, but slight differentiation could also be due to
sampling error.” We hope that our study will serve to stimulate
interest in the use of Infinium genotyping for population genetic
surveys, and that the error assessment methods we developed will prove
useful.
4.3 | Temporal Variation without Sweepstakes
Reproductive
Success
Sweepstakes Reproductive Success (SRS) occurs when the progeny of a few
lucky spawners comprise the majority of a cohort of settling
individuals, drastically reducing effective population size and creating
immediate genetic drift
(Hedgecock
& Pudovkin, 2011;
Hedrick,
2005). SRS is often considered the de facto cause of temporal variation
in allele frequencies in marine populations, although it can be
difficult to rule out other mechanisms
(Cornwell
et al., 2016). We detected temporal variation in allele frequencies for
blue crabs across years: among samples from all locations as well as
among samples exclusively from Louisiana estuaries. We tested for two
spatiotemporal patterns predicted to result from SRS: 1) genetic
differentiation among cohorts (chaotic genetic patchiness), and 2) the
occurrence of siblings or half-siblings that are the progeny of the
lucky spawners
(Hedgecock
& Pudovkin, 2011;
Selkoe,
Gaines, Caselle, & Warner, 2006). We found no evidence of chaotic
genetic patchiness among 21 collections of settling megalopae. However,
we did not sample every settlement event so it is possible that we
missed those coinciding with SRS. We therefore also tested for genetic
differences among annual cohorts of juveniles. Juveniles represent all
the successful settlement cohorts of the previous year, and so would
include any from SRS events. However, because temporal variation in
juveniles could also be caused by post-settlement mechanisms unrelated
to SRS it is important to also test for the second prediction of SRS:
the presence of full or half siblings within the samples exhibiting
temporal variation. Although we did detect genetic heterogeneity among
juveniles from different years, we did not find any instances of
siblings or half-siblings among juveniles or indeed among any of the
individuals that we genotyped. This suggests that a mechanism other than
SRS caused temporal variation. We also uncovered a potential bias in
relatedness estimation that can lead to the false appearance of closely
related individuals. When we estimated coancestry with allele
frequencies from pooled samples it appeared that pairs of closely
related individuals occurred in some samples. However, this was an
artifact created by differences in allele frequencies among samples,
which causes individuals from the same sample to have more alleles in
common than expected for unrelated individuals drawn from pooled
samples. In this light, we consider the findings of Iacchei and
co-workers
(2013)
who used microsatellite allele frequencies pooled from 17 locations to
estimate kinship between lobsters (Panulirus interruptus ). They
reported average kinship varied among locations and that related
individuals were more often at the same location than expected by
chance. From this, they concluded that “siblings more likely settle
together than disperse across sites”. Our analysis suggests that
observations like this could be artifacts.