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Gene Flow and Isolation among
Populations of Marine Animals
Michael E. Hellberg
Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana 70803;
email: [email protected]
Annu. Rev. Ecol. Evol. Syst. 2009. 40:291–310
Key Words
The Annual Review of Ecology, Evolution, and
Systematics is online at ecolsys.annualreviews.org
connectivity, dispersal, phylogeography, heterozygosity
This article’s doi:
10.1146/annurev.ecolsys.110308.120223
Abstract
c 2009 by Annual Reviews.
Copyright All rights reserved
1543-592X/09/1201-0291$20.00
Successful dispersal between populations leaves a genetic wake that can reveal historical and contemporary patterns of connectivity. Genetic studies of
differentiation in the sea suggest the role of larval dispersal is often tempered
by adult ecology, that changes in differentiation with geographic distance are
limited by disequilibrium between drift and migration, and that phylogeographic breaks reflect shared barriers to movement in the present more than
common historical divisions. Recurring complications include the presence
of cryptic species, selection on markers, and a failure to account for differences in heterozygosity among markers and species. A better understanding
of effective population sizes is needed. Studies that infer parentage or kinship and coalescent analyses employing more markers are both likely to spur
progress, with analyses based on linkage disequilibrium potentially bridging
results from these studies and reconciling patterns that vary at ecological and
evolutionary timescales.
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INTRODUCTION
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At sea. The very expression suggests that being tossed about is an inevitable consequence of marine
life. Most marine animals spend part of their life in the plankton, and the Greek origins of plankton
convey wandering. Even sessile adults can disperse in the sea, rafted by pumice or kelp holdfasts
and nourished by nutrients in the fluid that carries them. Given the speeds of oceanic currents,
marine propagules can conceivably disperse hundreds or even thousands of kilometers in a single
generation. As a result, marine populations were once seen as demographically open, with genetic
isolation over the long term hard to come by.
Accumulating evidence suggests otherwise (Swearer et al. 2002). Over the past decade, markrecapture studies, chemical tagging studies, and detailed modeling of realistic currents and larval
survival have all reinforced a view that successful dispersers may travel far less than their apparent
potential, even for species with quite long pelagic development. Genetic data have contributed to
this view (Hellberg et al. 2002). The use of genetic markers should, under certain conditions, be
able to empirically estimate levels of exchange between marine populations. Such genetic markers
can provide general answers to questions about marine connectivity, but much gets lost in the
specifics. Even the words gene flow can confuse.
On the one hand, gene flow may refer to the genetic realization of ongoing patterns of dispersal
between populations. This is what ecologists are after when trying to assess the demographic
independence or interdependence of populations. Essentially, they seek results similar to those
provided by direct mark-recapture experiments: What is the pattern of successful movement
among populations in the present day? From this demographic perspective, gene flow only matters
if the dispersal levels it heralds contribute significantly to the persistence of populations. The
movements of a few odd larvae may homogenize populations genetically, but will fail to rescue a
heavily harvested population with no other demographic inputs.
At the other end of the spectrum, evolutionary biologists may be interested in genetic exchange
between populations that may have been otherwise isolated for thousands of generations. Rare
genetic exchange between populations that are (practically speaking) demographically isolated can
still introduce foreign alleles that can spread adaptive change, alter modes of speciation, and cloud
our ability to discern historical changes in population size and connectivity.
If these different views of gene flow were all best addressed in the same way, then there would
be limited scope for misunderstanding, but they are addressed differently, and thus the potential
for confusion is great. The kinds of markers and analyses used, the sample sizes necessary to have
statistical power, whether to emphasize collecting more loci or more individuals—all of these may
differ depending on which gene flow one is trying to understand.
In this review, I will outline problems with employing genetic markers to infer movements
between marine populations, review what’s been learned from existing studies, and suggest remaining problems and emerging approaches that seem likely to yield new insights in the near
future. First, I will review the limitations of the most common analysis (FST ) and most common
markers (mtDNA sequences and microsatellites) used to address gene flow in the sea. Next, I will
review generalities drawn from the large amount of work that has been done applying genetic
markers of marine animals. Finally, I will look toward the horizon to try to discern emerging patterns and approaches whose study may give us a deeper understanding of the connections between
marine populations.
BEFORE CASTING OFF
V I E
Before starting, we pause to review some safety matters; ignore the lifeboat drill at
your own risk. Briefly, know where you are going, don’t invest too heavily in a single
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navigation tool, and beware of the treacherous hand of selection lest it guide you to the
rocks.
The vessels we use to sample marine animals vary from small pirogues winding through narrow
estuarine channels to huge circumnavigating research ships. So which is best? The answer depends
upon your goals. Either can help you gather a lifetime’s worth of data, but neither alone is ideal
for every journal. Sticking to just one ensures that there will be places you cannot get to.
So it is with genetic markers: No single marker is best for every question. The choices for inferring population connectivity and isolation can be grouped into two categories: frequency markers
and sequence markers. Frequency markers derive their power from frequency arguments: Alleles
that are relatively rare overall but common in a few populations suggest these populations are
connected by gene flow. Associations between alleles at physically unlinked loci (linkage disequilibrium) can also be used to infer recent exchange and isolation (Pritchard et al. 2000). In the
extreme, parent-offspring ( Jones et al. 2005) and sibling (Selkoe et al. 2006) relationships can be
ascertained with high probability. Microsatellites are the primary codominant frequency markers
used today. Sequence markers, in contrast, derive their power from the ability to infer relationship
between alleles. MtDNA sequences have usually served as sequence markers to date. Single-copy
nuclear sequences are emerging as another form of these markers.
The genetic changes that frequency and sequence markers can reveal occur at different
timescales, thus matching the markers to different types of questions about population connections. Frequency markers are suited to more ecological timescales. Parent-offspring analyses draw
on the conclusions of Mendelian inheritance and necessarily apply across a single generation. Some
multilocus clustering algorithms (e.g., Pritchard et al. 2000), which are based on the breakdown of
allelic associations across loci by recombination, can also approach this short timescale. Changes
in frequencies themselves can take longer (see below), potentially many thousands of generations
depending on the population size and migration rate. These longest timescales for frequency data
overlap the shortest ones for sequence data, which involve the time for isolated populations to
sort to reciprocal monophyly. Longer still would be the times required for sequence differences
to build up via mutation.
The question of concern, then, should dictate the choice of a genetic marker and analysis,
which in turn will determine the required sample size. Given the different sources of power
for frequency and sequence markers, an ample sample size for studies employing different types
of markers can be wildly different. For sequence analyses focused on resolving the history of
divergence among populations, the number of loci sampled may matter most, and sample size of
10 or even 3 individuals may be adequate (Pluzhnikov & Donnelly 1996). In contrast, frequency
markers require a good estimate of frequency differences. Whereas a sample size around 50 should
be adequate for assignment tests (depending on the number of loci and their power; see Paetkau
et al. 2004), an even larger number may be necessary when loci are highly variable (and thus
obtaining a reasonable estimate for many low-frequency alleles becomes hard).
This underscores another hard lesson: Choosing the right type of marker does not ensure
that it will provide the power needed to address the question at hand. The markers we seek
must meet a molecular Goldilocks test: enough variation or divergence to lend power, but not
so much that every individual is unique or that sequence alignments become ambiguous. Too
much variation can be especially crippling for frequency markers. If every individual carries two
unique alleles, one cannot expect any power to group by relatedness. Developing markers that
meet this criterion can be hard, but embracing weak markers and then spending time and money
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On Choosing Markers
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scoring hundreds of individuals for them will only end in tears when analyses eventually are
run.
Unflinching answers to a few questions can expose poor markers before the bonds of cohabitation create a tight but dysfunctional relationship. When using conserved primers, have they
ever provided resolution in similar studies? For a substantial number of primers, the answer is
no. Avoid these. Furthermore, are there clues that the primers will not inform in your taxon?
Sequences from the mtDNA gene region of cytochrome oxidase I amplified by universal primers
have proven fine markers for detecting phylogeographic structure and recognizing cryptic species
in an array of marine species, but extremely slow rates of nucleotide substitution in anthozoans
and sponges (Huang et al. 2008) limit their utility in these groups. Finally, are you turning away
from the clues you don’t want to see? Are microsatellites far from Hardy-Weinberg equilibrium,
or do three or more bands appear for some individuals. These might be due to null alleles or
a gene duplication, respectively. Inheritance studies can confirm whether microsatellite loci are
Mendelian, and it’s worth noting that the few marine studies that have bothered doing this work
have found a substantial proportion of non-Mendelian loci (Baums et al. 2005). Strange mtDNA
patterns may stem from insertions into the nuclear genome (Williams & Knowlton 2001). Genomic studies suggest high rates of gene duplication, which may go unnoticed against high levels
of variation found at many putatively single-copy nuclear gene regions. Heeding these cautions is
usually a thankless task, and an easy relationship may seem just the thing after lonely days in the
lab, but the siren call of bad markers but must be resisted.
FST and the Devil Within
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Wright’s FST is the most common way to summarize the degree of population differentiation
from genetic markers. FST potentially provides a common currency for comparing the degree of
differentiation found in different studies, but further offers a way (FST = 1/(1 + 4Ne m) to estimate
gene flow (Ne m, the product of the migration rate and the effective population size Ne ) given that
some simplifying assumptions are met. These assumptions, some of which will be outlined below,
are often not met (Whitlock & McCauley 1999), and fewer and fewer marine population genetic
studies present Ne m values. But what about the yardstick function of FST ? Can values from studies
of different species be fairly compared? What about values for the same species based on different
kinds of markers? The answer is: yes, but. . . .
The fundamental problem in comparing values of FST comes from heterozygosity found within
populations. In calculating FST , total variation across populations is apportioned into variation
arising within and between populations. As variation within populations increases, the remainder
left to be credited to differences among populations necessarily becomes lower. In mathematical
terms, maximum FST is less than 1 – HS (the average heterozygosity within populations) (Hedrick
1999). Thus, the oft-repeated notion that FST varies between 0 and 1 is true only for the extreme
case in which all populations are fixed for different alleles (HS = 0). But consider another case
in which populations share no alleles. Let the markers employed be highly heterozygous, say
HS = 0.95 (a value commonly seen in surveys of microsatellites and some mitochondrial and
nuclear sequence surveys; see Ne below). Now maximum FST can rise to only 0.05.
Does this make FST an unreliable yardstick for population differentiation? Wright originally
developed fixation indices to understand inbreeding (not an unreasonable interest for a man who
married his first cousin). Newly emerging metrics that aim to measure population differentiation
(e.g., Jost 2008) may prove more useful. Hedrick (2005) suggested standardizing FST by dividing
through by the theoretical maximum value. Using this approach in a survey of differentiation in
a tropical Australian sea snake, Lukoschek et al. (2008) were able to reconcile disparate values of
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FST produced by mtDNA and microsatellites owing to large differences in the heterozygosities
of these markers. Thus, FST can be modified such that it preserves its yardstick utility, albeit an
imperfect one.
Even after allowing for problems caused by varying degrees of heterozygosity within populations, interpretation of FST still demands caution. The variance of FST among markers is expected
to be high. Thus, the biological meaning of FST values that do not significantly differ from zero
must be interpreted with care. Lack of statistical significance for FST among populations should
not be equated with high connectivity; it could be low power due to high variance. Waples et al.
(2008) showed further that distinguishing among critical levels of subdivision was theoretically
impossible when migration sat near levels required for demographic significance (m = 0.1) and
population size was modestly large (Ne > 104 ). Conversely, statistical significance of FST cannot
be equated with limited dispersal (see Hedrick 1999); given the high power of combining large
numbers of variable markers to detect subdivision, statistical significance may sometimes result
when biological significance is lacking.
And then there are the assumptions underlying FST (Whitlock & McCauley 1999), most significantly that Ne and m are constant among all populations, that all populations exchange migrants
with equal likelihood, that genetic drift and gene flow have had time to equilibrate, and that markers
are not under selection. As will be seen below, violations of all of these are likely common in the sea.
Finally, there are situations where the expected values of FST are so high or low as to make it
useless. At the low end, large effective population sizes leave little power to discriminate among
demographically meaningful levels of connectivity (Waples et al. 2008). At the other extreme, FST
makes a poor measure of differentiation between genetically isolated populations (such as newly
formed species) because the combination of large Ne and low migration (m = 0) result in very
long times to reach equilibrium.
Despite its many problems, FST still serves its yardstick function, facilitating comparisons
among studies. Better measures of population differentiation will probably emerge, but they will
take time to become accepted and for their foibles to become understood. Until then, FST will be
there.
Marker Mutiny: The Caveat of Selection
Population geneticists want the markers they employ to be well-behaved ciphers that allow themselves to be pushed around by gene drift and dispersal and passively record their experiences. But
some markers rebel against this neutral tedium, with selection producing patterns both fascinating
and confusing. On the one hand, strong disruptive selection can change gene frequencies over
spatial scales shorter than single-generation dispersal distances. In the mussel Mytilus edulis, a
cline at the Lap locus reestablished itself along the salinity gradient of Long Island Sound each
generation (Hilbish 1985). Juveniles along the Sound show few differences between frequencies
of oceanic and estuarine alleles, but by adulthood a cline becomes evident. Misinterpreting such
genetic differences as neutral would inflate measures of population differentiation, and thus underestimate larval dispersal. On the other hand, selection can also hold gene frequencies in check.
Populations of the splash zone copepod Tigriopus californicus are deeply subdivided, sometimes at
a scale of just kilometers between headlands. Despite irregular extinctions and recolonizations of
tidepool populations, allele frequencies at four allozyme loci varied little over nearly two decades
(Burton 1997), probably close to 200 copepod generations. Such constancy over time likely owes
to stabilizing selection on the allozyme markers themselves.
Across broad spatial scales, stabilizing selection could homogenize marker frequencies and
mask gene flow. This possibility rose to a condemnation of allozyme markers with the publication
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of analyses showing that restriction fragment length polymorphisms (RFLPs), both mitochondrial
and nuclear, revealed stronger breaks or higher FST than did more uniform allozymes, presumably
owing to strong stabilizing selection on the latter (Karl & Avise 1992). But delving further into
the patterns of individual loci and the mechanisms shaping their differentiation exposes a more
complicated picture than a simple distinction between good and bad classes of markers. Pogson
et al. (1995) contrasted an existing trans-Atlantic allozyme survey of cod (Gadus morhua) with a
matched population sampling using nuclear RFLPs. Relative to the allozyme data, the nuclear
RFLPs included more loci with significant FST s and showed a higher mean FST and a stronger
inverse relationship between genetic and geographic distances. Inspection of the 11 RFLP loci,
however, suggests that 2 (GM738 and GM798) are responsible for much of these differences. Subsequent investigations of one of these markers (GM798, recognized as a protein-coding region for
pantophysin) suggest this differentiation may be driven by selection (Pogson & Fevolden 2003).
The signature of selection has also been revealed by detailed analyses of other classes of presumably neutral markers, including mitochondrial DNA (Meiklejohn et al. 2007) and microsatellites
(Larsson et al. 2007).
The awesome potential for markers under selection to confound is underlined by a study
designed to identify loci associated with divergence between two forms of the periwinkle Littorina
saxatilis found at different intertidal heights (Wilding et al. 2001). The researchers collected
samples of both morphs (high and mid-intertidal) from sites all within 45 km of each other and
then scored them for >300 amplified fragment length polymorphisms (AFLPs). Simulations of
expected FST values grouped these markers into two classes: those behaving within the range of
neutral expectations and those falling outside this range (and presumably subject to some form
of selection). When the full data set was analyzed, populations grouped perfectly by geography:
High and mid-intertidal populations from the same sites grouped together, even to the exclusion
of populations just 5 km away. But when 15 outlier loci (candidate loci for selection) were removed,
populations grouped by morphology; all populations were closer to others of the same morph,
even when the other morph had been sampled just a few meters away. The lesson from these
disparate results is a simple one: Never turn your back on selection.
FAMILIAR SHORES
When aiming to explore the exotic, an understanding of the known not only makes for a safer
voyage, but can limit time wasted revisiting the same old ports. Here, I log some generalities about
genetic studies of marine population connectivity and isolation that have been well mapped, with
the hope that they can serve as bases for more exotic and fulfilling journeys rather than destinations
less worthy of our limited resources.
Awash in Cryptic Species
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The oceans are filled with species that are difficult or even impossible to distinguish by means
other than genetic (Knowlton 1993). For example, what by some estimates is the most abundant
vertebrate in the world (Cyclothone alba, a small deep-sea fish) is composed of a complex of geographically and genetically isolated cryptic species (Miya & Nishida 1997). Even some species that have
been examined genetically break into cryptic species upon closer inspection. The crown-of-thorns
seastar Acanthaster planci was long held up as an example of large-scale population homogeneity
based on allozyme work, but closer inspection using mtDNA sequences revealed four distinct
allopatric lineages that had diverged no later than the early Pleistocene (Vogler et al. 2008). Some
cryptic species can be reasonably easy to spot and keep separate, as for allopatric color morphs of
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reef fish (Drew et al. 2008). Trickier to catch are instances where cryptic species with simplified
morphologies co-occur, as for bonefish in Brazil (Colborn et al. 2001).
Ecologically, failing to recognize these differences means that species with subtly different
niches are lumped into misinformative nominal species (Knowlton & Jackson 1994). In population genetic studies, cryptic species can inflate estimates of variation within lumped populations
and consequently mask variation among populations. Prada et al. (2008) used a combination of reciprocal transplants and genetic data to determine that deep and shallow water morphotypes of the
nominal gorgonian species Eunicea flexuosa were genetically isolated. These cryptic species showed
further geographic subdivision among populations when analyzed separately, but pooling morphs
from each site inflated within-population variation, leaving differences among populations near
zero. Careful notes on where genetic samples came from (depth, microhabitat) and photographic
vouchers of fresh samples (which may reveal colors that fade under ethanol preservation) can help
sort through cryptic species problems.
Species with simple morphologies create the most problems. These problems are compounded
in lineages where rates of mtDNA evolution are slow (such as sponges and anthozoans; Hellberg
2006, Huang et al. 2008) and thus, the easiest-to-use marker for flagging cryptic species is uninformative. The growing availability of single-copy gene nuclear gene markers should help in these
instances, along with recent analytical developments in how to recognize and delineate closely
related species (Knowles & Carstens 2007). Many broad questions about cryptic diversity remain.
Are there latitudinal or habitat trends in the incidence of cryptic species? Why do some taxa have
many cryptic species and others few? Systematic answers to these questions await.
Larvae Matter. . .Some
Because so many benthic marine animals move little in adulthood, movements by larvae have
been expected to be responsible for most dispersal and gene flow between populations. Larvae
are expected to vary greatly in their dispersal potential. First, some larvae (planktotrophs) can
feed themselves, and these should spend longer in the plankton, potentially dispersed by currents,
than larvae that rely on maternal provisions of yolk (lecithotrophs). For species with shared larval
feeding modes, dispersal might be tied to the duration of pelagic larval dispersal, which can be
determined by rearing larvae or (for fish) counting otolith rings. At a coarse level, predictions of a
link between larval dispersal and gene flow hold true: Bohonak (1999) found levels of subdivision
were correlated with dispersal, although much of this pattern was driven by species with no
planktonic development. Exceptions abound in which the length of pelagic larval duration (Bowen
et al. 2006), or even reproductive mode (Miller & Ayre 2008), correlate poorly with genetic
subdivision.
Why is this? For one, the assumption that limited larval dispersal limits dispersal is sometimes
wrong. Limited larval dispersal may actually improve chances of colonizing empty habitat patches
by keeping offspring together (and thus near potential mates). Faunal surveys of newly emerged
islands gave rise to this idea ( Johannesson 1988), and invasive populations of a spionid polychaete
lend further support. Whereas this worm (Streblospio benedicti) possesses a curious polymorphism
in its native range (including both planktotrophic and lecithotrophic populations), invasive populations are all lecithotrophic (Schulze et al. 2000). Another compounding factor may be the
sampling sites employed for the species to be compared. Because habitat distribution can have a
marked effect on subdivision ( Johnson & Black 2006), interspecific comparisons that sample from
different sites may confound differences in dispersal potential with differences in the physical isolation of sampling locales (e.g., McMillan et al. 1992). However, some studies that sample closely
related species with similar life histories from the same range still encounter strong interspecific
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differences in subdivision (e.g., Severence & Karl 2006). In some cases, the habitat specificity of
organisms can have a greater impact on population differentiation than does life history (Ayre
et al. 2009). Such results caution against hopes for predictions of population connectivity based
on shared larval characteristics.
Shared Phylogeographic Breaks: Place but Not Time
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A phylogeographic break occurs where a genealogical discontinuity within a species coincides
with a geographical feature. As for levels of genetic subdivision, larvae can play a determining role
in whether particular species show phylogeographic breaks. Collin (2001) found that Crepidula
limpets with directly developing larvae showed greater phylogeographic differentiation (more
breaks, more reciprocal monophyly between populations) than did planktonically developing congeners. Exceptions are again rife. Some of these may be tied to differences in ecology, which in
turn can affect available habitat (Hickerson & Cunningham 2005, Reid et al. 2006, Rocha et al.
2002) or recruitment success (Bird et al. 2007) such that a barrier for one species does not bar
crossing by another. Recent work by Hickey et al. (2009) exemplifies the range of patterns that
can be found even among closely related and co-occurring species. Their work focused on eight
species of triplefin fishes from an endemic New Zealand fauna, each with pelagic larval durations
on the order of three weeks. Heterozygosity within populations varied from unity (all haplotypes
unique) to zero (fixation) and subdivision from three geographically restricted cryptic species
(within the nominal Grahamina capito) to panmixia (in Ruanoho whero). Whereas differences in
heterozygosity among species to some extent drive differences in subdivision as measured by FST ,
species inhabiting deeper waters (>5 m) harbored more variation (and thus less subdivision) than
did shallow-water species. For those species showing subdivision, the number of regional groups
within species usually varied, as did the place where these groups were separated.
When phylogeographic breaks are shared among disparate taxa with varying life histories, they
provide a rational basis for guesses about where effective genetic exchange is lacking and thus
should be considered critical information for the management of fisheries and the placement of
marine protected areas. Where these breaks occur also suggests the kinds of physical features
that act as important barriers. An abrupt change in water masses, coincident with offshore flow,
underlies the marine break at Cape Canaveral in eastern Florida (Avise 2000). Strong currents
between islands divide some Caribbean species at the Mona Passage between Hispaniola and
Puerto Rico (Baums et al. 2005). In other places, barriers sit where sea level changes during the
Pleistocene exposed shallow shelves (Crandall et al. 2008). Whereas such shared phylogeographic
breaks generally coincide with places where many species’ distributions end, the converse is not
necessarily true, as evidenced by the varying placements, or total lack, of phylogeographic breaks
around Point Conception in California (Burton 1998).
Sharing a phylogeographic break in the present day, however, does not imply that multiple
species had populations sundered by a common isolating event in the past. Shared geographical
breaks often include species where lineages to either side of the break were isolated at different
times (see Schulze et al. 2000 for Cape Canaveral, Taylor & Hellberg 2006 for Mona Passage). The
underlying causes for divergence may thus vary among species with a common phylogeographic
break; what maintains the break is more likely to be shared. Figuring out this mechanism starts
with more detailed geographical sampling ( Jennings et al. 2009), but results from such analyses
are not always straightforward (Hare & Avise 1996). Detailed biophysical modeling based on
known currents, reproductive seasons, and larval durations can test the adequacy of post hoc
explanations for breaks (e.g., Baums et al. 2006), but finer sampling over time and across life
history stages would also be useful. Do larvae make it across phylogeographic barriers? If so,
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what’s their fate? Coalescent analyses that allow detailed inspection of the history of population
isolation (see Current Affairs below) may also lend insights.
Genetic measures of population differentiation do not always reflect ongoing levels of dispersal.
They tend to be stuck in the past, which can both create evidence for movements and obscure
real isolation. On the positive side, the imprint of past movements can be clearly preserved. In
the northern hemisphere, genetic diversity commonly steps to near zero toward high latitudes
(e.g., Hellberg et al. 2001), where inferred effective population sizes are also lower (Duvernell
et al. 2008). These observations are consistent with recent poleward recolonizations following
extinctions brought on by Pleistocene cooling. Just where the step in variation occurs varies
among species (e.g., Edmands 2001), and the step can disappear altogether in species buffered
from the effects of cold (Marko 2004).
On the negative side, genetic drift and gene flow take a long time to equilibrate. Specifically,
Crow & Aoki (1984) showed the time required for FST to go half way to equilibrium values was near
(ln 2)/(2m + 1/2Ne ). Thus, following changes in connectivity, measures of differentiation based on
allele frequencies will mislead. For the reexpansion just mentioned, differentiation could remain
low between resettled areas and the source of their settlers for thousands of generations. This is the
situation for many high-latitude species, especially those with limited larval dispersal capabilities
(Hellberg 1994). Measures based on linkage disequilibrium detect population isolation far faster,
such that populations isolated for just dozens of generations can sometimes be distinguished
(Rosenberg et al. 2001).
Genetic Differentiation Increases with Distance, but Not in the Wright Way
The larval dispersal distances of many marine species may be small relative to their total geographic
ranges. In such cases, distant populations should be connected demographically and genetically via
intervening stepping-stone populations. Assuming drift and gene flow have equilibrated, gene flow
restricted to immediately neighboring populations leaves a characteristic signature in the form
of an inverse relationship between the logs of gene flow and geographic distance (Slatkin 1993).
This analysis presents a useful opportunity to infer a precise model of dispersal from commonly
gathered genetic data. However, there are important caveats.
First, as we have seen, marine populations are often not at drift or migration equilibrium.
From the Crow and Aoki relationship above, we would expect populations experiencing higher
levels of gene flow (under a stepping-stone model, those closer together) to reach equilibrium
before distant populations experiencing less gene flow. Simulations by Slatkin (1993) confirm
this. Empirically, the relationship between gene flow and distance varies at different spatial scales
(Hellberg 1995, Planes & Fauvelot 2002). At small enough spatial scales, dispersal is equally likely
among all populations, and there will be no relationship between gene flow and distance. At large
enough spatial scales, ongoing dispersal will be essentially zero, so historical patterns will dominate
as inferred levels of gene flow slowly settle to zero. The tight relationship between gene flow and
drift predicted by a stepping-stone model occurs at intermediate scales, if at all. Simulations and
meta-analytical studies of marine and anadromous fish have borne out these generalities (Bradbury
& Bentzen 2007).
Second, Slatkin’s (1993) analysis relates a particular result (specific slopes of a log-log regression
of many pairwise comparisons) to a specific model of dispersal (movement restricted to nearest
neighbors). A significant relationship between genetic differentiation and geographic distance
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can arise via migration patterns other than the neighborhood restrictions of Wright’s (1943)
original isolation-by-distance model or a true stepping stone (Kimura & Weiss 1964). Terming
such patterns as isolation-by-distance is imprecise and potentially misleading, although these
weaker relationships can still indicate relatively localized dispersal (Palumbi 2003). Care must also
be taken to avoid a too-literal interpretation of dispersal inferred from particular pairwise FST or
gene flow values (Whitlock & McCauley 1999): Populations can be genetically connected through
a common intermediary, whether an intermediate population under a stepping-stone model or a
common source in a metapopulation arrangement, even though no migrants move between them.
Chaotic Larval Cohorts
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Given that many marine species are composed of multitudes of females that each cast millions of
larvae into a roiling ocean, the homogenization of these planktonic offspring before they return to
settle would seem inevitable. But this is not the case. Instead, cohorts of larvae are often genetically
differentiated both from each other and from the adult populations near where they were sampled,
a pattern termed chaotic genetic patchiness (Edmands et al. 1996, Johnson & Black 1982). Three
mechanisms potentially account for this pattern. First, larvae may be subjected to selection while
in the plankton ( Johnson & Black 1984). Second, seasonal or interannual changes in currents
might deliver larvae from alternative differentiated sources (Kordos & Burton 1993, Selkoe et al.
2006). Finally, the large census populations of marine animals may be misleading; thanks to high
fecundity and massive larval mortality, only a handful of adults may be successfully contributing
to each cohort. This sweepstakes hypothesis (Hedgecock 1994) also may explain the reduced
variation within cohorts relative to adult populations. Evidence for a sweepstakes effect has been
equivocal. Whereas an mtDNA study by Flowers et al. (2002) found no evidence for a strong
sweepstakes effect in urchins, some recent microsatellite work has found not only evidence for
reduced variation within cohorts, but even evidence that siblings may stay together during larval
dispersal (Hedgecock et al. 2007, Selkoe et al. 2006).
ON THE HORIZON
Although the above subjects still afford room for discovery, all have been explored for 15 years
or more, and thus refinements seem more likely than revolutions. In this section, I outline some
questions and approaches that have more recently emerged from murky depths and thus should
be more likely to yield novelty.
Population Size Matters
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The effective population size (Ne ) is defined as the size of the size of an idealized population that
would show the same level of genetic drift as the real population being considered. In its essence, Ne
is a measure of how many individuals in a population matter in evolutionary genetic terms. Inferred
levels of gene flow, mechanisms of selection, the role of genetic drift—all of these hinge critically on
how large the effective population is and has been. Presently, however, there is little consensus on
whether effective population sizes in the sea are large or small. Standing levels of genetic variation
in many marine species are consistent with very large population sizes: Heterozygosities can
approach unity for many markers (e.g., Haney et al. 2007), with SNP data from oysters suggesting
polymorphisms segregating about once every 60 base pairs across the genome (Sauvage et al.
2007). Assembling the urchin genome required accounting for high heterozygosity and extensive
sequence divergence between alleles (Sodergren et al. 2006). In contrast, some studies using
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repeated sampling to follow drift directly across generations suggest low Ne (Laurent & Planes
2007, Palstra & Ruzzante 2008, Turner et al. 2002), as do some aforementioned sweepstakes
studies. Notably, these different patterns both appear in species with long pelagic larval durations,
so the explanation for such a difference will not be trivial. A comparative approach seems in order:
Do closely related species differ in inferred Ne ? What are the life history or ecological attributes
of this variation?
Variation in population size is also evident in studies that infer historical changes in Ne . The
distribution of mismatches between nonrecombining sequences can be used to infer and roughly
date past population expansions. Lessios et al. (2001) used this approach to reject a human cause for
population expansion in the Caribbean echinoid Diadema antillarum: Mismatch data suggested its
numbers had expanded long before those of two congeners with far smaller census sizes. Mismatch
patterns also suggest that co-occurring reef species may respond similarly to sea level changes.
Fauvelot et al. (2003), for example, found similar mismatch distributions for seven reef fish cooccurring in French Polynesia, even though reproductive strategies and microhabitats varied
among these species.
Recent advances should sharpen the focus of this demographic hindsight. Hickerson et al.
(2006) developed formal tests of whether splits shared by multiple taxa were simultaneous, and
these should expand to include tests for coincident population expansions. New analyses also offer
the promise of looking behind the most recent population bottleneck to demographic changes
more distant in the past (Heled & Drummond 2008). Implementation of these analyses requires
multiple independent sequence markers, and rapidly expanding banks of genomic data will reduce
our reliance on mtDNA as our sole witness to the long ago. Together, these should allow more
robust exploration of community assembly and the history of interspecific interactions (à la Wares
& Cunningham 2001).
Alas, as we free ourselves from a mitochondrial dependency, another codependency is exposed:
the Isthmus of Panama. Coalescent models require estimates of the substitution rate, and getting
time into that rate requires calibrating a molecular clock. Calibration points should fall in the same
time frame as the dates of concern, lest they pull inferred dates toward them (Arbogast et al. 2002).
The rise of the Isthmus of Panama has served ably in this regard for Plio-Pleistocene studies, but
closer inspection of the 100+ studies of geminates split by this barrier (Lessios 2008) shows cause
for caution. Most trans-Isthmian divergencies appear to predate the rise of the Isthmus (Lessios
2008), whereas others occur demonstrably later (Hickerson et al. 2006). Still, dozens of lineages
appear to have been separated by the rise itself, affording multiple calibrations for gastropods,
crustaceans, echinoids, and teleosts. Calibrated rate estimates for a greater diversity of taxa and
for additional geographic calibration points would be welcomed (see Hickerson & Cunningham
2005 for a trans-Arctic example).
Parental Guidance
To this point, nearly all genetic studies of dispersal in the sea have used a combination of surveys of
standing population variation and different analyses to indirectly estimate connectivity, but genes
can also be used as markers for direct estimates of dispersal. Grosberg (1987) carried out the first
such larval mark and recapture study. He used laboratory crosses to create a colony homozygous
for a rare (frequency <0.005) allozyme allele. This colony was transplanted into the field. New
colonies subsequently settling near the colony were mapped and genotyped. Most (>80%) colonies
bearing the rare allele were very close (<25 cm) to the source colony.
This direct approach, less the crosses, has been scaled up to examine the extent to which some
larvae are retained in local populations. In the first such study, Jones et al. (2005) mapped the home
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anemones of all pairs of panda damselfish (Amphiprion polymnus) within five habitat patches that all
lay within 1 km of each other but were more distant from other populations. All paired adults were
genotyped for 11 microsatellite markers and then returned to their home anemone. Subsequently
recruiting larvae were then collected and genotyped. Almost one-third of these settlers were the
offspring of surveyed parents, a remarkable degree of local recruitment for a species with a 9–12
day pelagic larval duration. Stable isotope tagging studies suggest such high self-recruitment may
hold for other reef fish with even longer pelagic dispersal (Almany et al. 2007). Furthermore,
Planes et al. (2009) extended this study to a regional spatial scale, genetically screening recruits for
the offspring of parents from Kimbe Island (centrally located within a network of marine protected
areas in Kimbe Bay, Papua New Guinea) at populations dozens of kilometers away. Their results
provided direct evidence of larval dispersal to a population 35 km distant from Kimbe Island and
suggested that dispersers account for a sizeable proportion (about 10%) of recruitment to adjacent
reserve populations.
The exhaustive genotyping and mapping work of Planes, Jones and colleagues confers exceptional power and is more than a little daunting. Happily, parentage analyses employing more
limited sampling may still produce useful results. Peery et al. (2008) used simulations grounded
with demographic data to generate expectations for the number of parent-offspring dyads expected under alternative sink and closed population models. Using microsatellite data from about
one-third of an isolated population of seabirds (marbled murrelets, Brachyramphus marmotus), they
were able to reject a closed population model for one of low immigration (2–6% per year) into
a sink population. Whereas this approach is probably not suited to situations where the population size is large (say, >1000), it should be most powerful under high migration, where indirect
methods perform worst (Waples & Gaggiotti 2006).
As an alternative to genotyping individual parents and tracking their offspring, existing hybrid
zones can serve as readymade F1 markers for dispersal. Gilg et al. (2007) used a hybrid zone between
two mussel species in southwest England to assess larval retention. The hybridizing species, Mytilus
galloprovincialis and M. edulis, differ in the timing of reproduction and larval settlement as well
as at marker loci. Genotyping and comparing settlement time, they showed that populations of
M. edulis and hybrids were largely self-recruiting, but the M. galloprovincialis populations, as little
as 20 km distant, received more external input. Previous work around this hybrid zone (Gilg &
Hilbish 2003) had found larval dispersal distances of about 30 km, with patterns of connectivity
tied to local circulation patterns.
Current Affairs
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That the relevant distance between populations would not be purely geographic, but rather determined by current vectors seems a reasonable expectation for marine dispersal. Despite this
logic, the match between currents and genetically inferred patterns of connectivity is often poor.
In reviewing allozyme and mtDNA data available for marine organisms in the Indo-West Pacific
about a decade ago, Benzie (1999) suggested that their patterns owed more to past range contractions and expansions than to contemporary patterns of circulation. On the other hand, some
other studies reveal a close association between patterns of genetic subdivision and contemporary
currents (Baums et al. 2006, Dupont et al. 2007) or locations where eddies form (Banks et al.
2007).
Why the disagreement? Certainly, generalizations about larvae as passive particles and surface
currents as indicators of the water movements they experience have been taken too far. A stronger
appreciation of the abilities of larvae to affect their own dispersal (Dixson et al. 2008) along with
more realistic biophysical models (Cowen et al. 2006) should help remedy these oversights, but
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disagreements may also stem from studies based on markers where genetic estimators of gene flow
are expected to equilibrate slowly. As mentioned previously, drift-migration equilibrium can take
many thousands of generations for analyses based on changes in allele frequencies. In contrast,
several newly developed analyses are designed to function free of equilibrium assumptions and
are thus better suited to estimating contemporary connectivity (Manel et al. 2005). Moreover,
some of these approaches (e.g., Wilson & Rannala 2003) offer the possibility of pulling Ne from
gene flow (Ne m) and estimating the migration rate (m) among populations—exactly the parameter
that marine ecologists are after. These approaches are not without their problems. Unsampled
populations can conceivably twist results (Slatkin 2005), and the models implementing them may
have hidden assumptions that can lead to erroneous conclusions (e.g., the Bayesass program for
Wilson and Rannala’s approach imposes 70% self-recruitment). Analysis of randomized genotypes
is an important precaution, lest too much be made of the odd individual assigned to a distant
population of origin. Still, simulation studies have indicated the promise of these approaches in
delineating populations (Waples & Gaggiotti 2006), and at the least they offer the promise of
getting ecologists and population geneticists talking about the same temporal scales for dispersal.
Ideally, indirect genetic measures will be reconciled with more direct measures of dispersal. Jones
et al. (2005), for example, were able to cross-check that recruiting fish inferred by genetic parentage
analyses to be returning to natal populations were indeed dispersing such small distances because
they had chemically tagged the demersal eggs from which these fish hatched. Saenz-Agudelo
et al. (2009) compared the results of genetic parentage analysis to those of assignment tests. Each
approach had its strengths and weaknesses. Assignment tests performed well when gene flow was
low, but not when gene flow was high. Parentage analysis proved complementary, overestimating
self-recruitment at small scales when assumptions of Hardy-Weinberg equilibrium were violated
but doing well under high gene-flow conditions where indirect approaches have, to this point,
failed.
Coalescent analyses can also be used to test how currents connect populations. Jennings et al.
(2009) found asymmetrical gene flow among populations around Cape Cod using the program
Migrate. Generally, however, these analyses are more useful for larger spatial and temporal scales.
Lessios & Robertson (2006) tested for connectivity across the 4000+-km Eastern Pacific Barrier
using isolation with migration (IM) (Hay & Nielsen 2004), which was designed to distinguish
low levels of ongoing gene flow from isolation in the recent past. Their mtDNA data indicate
that Central and Eastern Pacific populations of most of the 20 reef fish they examined diverged
less than 125,000 years ago, and more surprisingly that the most recent connection has often
been in an east-to-west direction, opposite that expected. Resolving such paradoxes will probably
require an integrated approach, in which multilocus analyses that delineate populations and infer
migration patterns between them are combined with coalescent analyses of deeper connections
between more differentiated regions.
Un(der)charted Waters
The cataloging of where isolated populations and phylogeographic breaks lie and what physical
features enforce them may allow for some (cautious) generalities. In some well-studied regions
breaks are shared commonly [Cape Canaveral (Avise 2000)], and in others hardly at all [Point
Concepcion (Burton 1998), Mediterranean/Atlantic transition (Paternello et al. 2007)]. Comparative studies in the hyperdiverse Coral Triangle are growing rapidly, as they are in other regions
situated near centers of research (e.g., East Asia and South Africa). However some areas that
support intensive fisheries and substantial biodiversity (e.g., West Africa, northwestern South
America) remain nearly unknown.
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Exploring new regions can also mean confronting new kinds of barriers to dispersal. In looking
at differentiation among nemertean populations inhabiting Antarctica, the tip of South America, and sub-Antarctic islands, Thornhill et al. (2008) found a strong barrier to intercontinental
movements at the Arctic Polar Front, where currents and water temperatures change abruptly
hundreds of kilometers from land. At a finer geographic scale, regions of upwelling and coastal
heterogeneity, long recognized as influencing the transport and settlement of larvae (Wing et al.
1995), are also proving notable barriers to population connectivity (Banks et al. 2007, Nicastro
et al. 2008). Examples from both large and small spatial scales thus suggest that oceanography
needs careful consideration before samples are gathered. Sampling regimes specifically designed
to test predicted barriers in novel systems should help refine our understanding of what curbs
dispersal between marine populations.
More and Different Markers
The power of genetic analyses has often been limited by the availability of appropriate markers.
This should no longer be the case. Existing databases can be mined for microsatellite repeat
regions (Wang et al. 2009) and should facilitate the design of degenerate PCR primers for protein
coding regions. Past efforts have focused on universally conserved exon priming, intron-crossing
primers ( Jarman et al. 2002, Palumbi & Baker 1994), but genomic surveys indicate that silent
sites within coding regions have even higher levels of variation and higher substitution rates
(Andolfato 2005). Nuclear exons also bypass problems with scoring indel heterozygotes, and
the use of many independent sequence markers should greatly enhance the precision of genetic
studies. Sequencing random inserts from cDNA libraries can generate nuclear markers capable of
differentiating populations and closely related species (e.g., Eytan et al. 2009).
In addition, studies of tightly associated symbionts may provide a new source of insight for
host dispersal. Blakeslee et al. (2008), for example, used mtDNA sequences from a host-specific
trematode parasite (Cryptocotyle lingua) of the periwinkle Littorina littorea as extra evidence supporting a recent trans-Atlantic introduction of the snail to North America. Vertically transmitted
microbes with high rates of substitution should prove more powerful, as suggested by recent work
on the colonization of Pacific island by humans (Moodley et al. 2009).
CONCLUSIONS
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With regard to understanding the isolation and connectivity of marine populations, then, we are
not entirely at sea. We have plied these waters long enough to learn some useful things. Larval
dispersal can help determine levels of exchange between populations, but other factors including
the ecology of adults can have as large an effect. Genetic similarity between populations tends
to attenuate with distance, but this relationship breaks down at the extremes, where drift and
migration never equilibrate. Abrupt genetic changes at phylogeographic breaks are sometimes
shared among species, but the timing of these splits vary, suggesting that these barriers say more
about contemporary limits to dispersal than grand historical sunderings of populations. Coalescent
studies suggest large changes in the past population sizes of many marine animals, and effective
population size also sits at the heart of efforts to explain the oft observed pattern of chaotic
patchiness, in which larval cohorts differ genetically from each other and nearby adults.
In addressing these questions, conflicts between ecological and evolutionary perspectives on
dispersal and gene flow have sometimes led to confusion. The parentage analysis of larval dispersal
by Jones et al. (2005) demonstrates why this is. One-third of recruiting larvae had a local origin,
a stunning degree of demographic impact for a planktonically dispersed species. Still, this means
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DISCLOSURE STATEMENT
The author is not aware of any affiliations, memberships, funding, or financial holdings that might
be perceived as affecting the objectivity of this review.
ACKNOWLEDGMENTS
This work was supported by a grant from the National Science Foundation (OCE-0550270
to M.E.H. and Iliana Baums). I thank J. Andras and D. Harvell for helpful comments on the
manuscript.
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