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irtually all species of marine organisms, whether pelagic,
planktonic, or benthic, are patchily distributed, consisting of
local populations linked to a greater or lesser extent by dis-
persal. Ecologists are primarily concerned with characteriz-
ing this geographic structure in terms of the distributions and
movements of individual organisms within and among local
populations. Evolutionary biologists, on the other hand, often
think in terms of the spatial distribution of genetic variation
within and among local populations. Ecological and evolu-
tionary approaches thus differ in their emphasis on the spa-
tial distribution of individuals versus genes; where they con-
verge is in their attempts to understand the processes that
cause geographic variation in a species. In other words, ecol-
ogy shapes the distribution of genetic variation in a species
(Endler 1992), whereas genetic structure provides the evolu-
tionary context in which to interpret a species’ ecological in-
teractions with its environment.
Genetic structure in marine populations reflects the histor-
ical and contemporary interplay among a complex set of eco-
logical, demographic, behavioral, genetic, oceanographic, cli-
matic and tectonic processes (reviewed in Hedgecock 1986;
Palumbi 1995; Benzie 1999; Bohonak 1999). The combined ef-
fects of these mechanisms, acting across a range of spatial
and temporal scales, determine rates and patterns of disper-
sal of gametes, zygotes, larvae, and adults. It is these move-
ments, along with the survival and successful reproduction
of immigrants, that, in turn, control the scale and rate at
which random (i.e., genetic drift) and deterministic (i.e., nat-
ural selection) processes build or erode structure within and
among groups of individuals.
A number of recent papers have explored the population-
level ramifications of genetic structure in marine systems,
highlighting the relationships among developmental mode,
dispersal, gene flow (i.e., migration of gametes or individuals
that leads to the incorporation of their genes into a recipient
population), and speciation (e.g., Burton 1983, 1996; Hedge-
cock 1986; Palumbi 1994, 1995; Benzie 1999; Bohonak 1999).
In keeping with the theme of this book, we expand this pop-
ulation-level perspective to the community, and explore four
facets of the following basic question: What can information
about the spatial distribution of genetic variation within a
species, and the processes that generate these patterns, tell
ecologists about the nature and outcomes of species’ interac-
tions with their biotic and physical environment?
The first aspect of the question relates to the scale at which
a species’ predators, competitors, parasites, pathogens, and
symbionts regulate its population dynamics (Roughgarden
and Iwasa 1986; Roughgarden et al. 1988; Possingham and
Roughgarden 1990; Gaines and Lafferty 1995). The spatial
scales over which marine populations exhibit “open” versus
“closed” dynamics are generally not obvious, and they vary
according to the magnitude of migration among subpopula-
tions. Genetic structure can be used to place bounds on the
spatial scales over which species are likely to be demographi-
cally closed.
The second facet concerns the scales over which species
exhibit genetic structure, and how these patterns influence
responses to spatially varying selection (see papers in Mop-
per and Strauss 1998 for a terrestrial perspective; for a marine
perspective, see Warner 1997). The spatial distribution of neu-
tral genetic variation determines the degree and scale over
which subpopulations have been (or are) evolutionarily inde-
pendent, and consequently free to evolve in response to local
variation in selection. With high levels of gene flow among
selective regimes, the selective costs of local adaptation in-
crease, perhaps prohibitively. For example, if a polymorphic
61
V
Genetic Structure in the Sea
From Populations to Communities
Rick Grosberg and C. W. Cunningham
chapter 3
predator (or parasite or herbivore) population genetically
varies over a finer spatial scale than its prey (or host), it is un-
likely that the prey population will evolve local adaptations
in response to geographic variation in its predator (e.g.,
Menge 1976; Vermeij 1978, 1987; Palmer 1984).
Third, a number of recent genetic studies have revealed
the existence of sibling species complexes in what were once
thought to be a single polymorphic species (Knowlton 1993).
Such discoveries suggest that many marine species may have
more limited distributions than previously thought and may
be far more ecologically specialized as well.
Finally, when set into a phylogenetic context, genetic
structure can reveal a great deal about a population’s history
of subdivision and gene flow, in particular the history of its
interactions with other species (Avise et al. 1987; Brooks and
McLennan 1993; Avise 1994). Are strong interactions between
competitors, predators and prey, or parasites and their hosts
the result of a long, shared selective history (Brooks and
McLennan 1993), or are these associations the result of recent
introductions (e.g., Vermeij 1991)? Do traits that appear to be
adaptive in their current selective contexts reflect a genetical-
ly based response to selection in a particular place, or were
these traits shared by sister taxa prior to the evolutionary and
geographic divergence of these taxa? For example, if we can
determine that taxa on both sides of the North Atlantic lack
geographic subdivision as a result of ongoing gene flow, then
we might predict that differences in their morphology, physi-
ology, behavior, or ecology result from environmentally me-
diated phenotypic, rather than cumulative genetic, responses
to different environments. Conversely, if the lack of genetic
structure is due to recent colonization with low levels of on-
going gene flow, then cumulative genetic change becomes a
more plausible explanation for phenotypic divergence.
The process of characterizing genetic structure, and using
this information to estimate gene flow, effective population
size, and other ecologically relevant parameters has a long
and complex history. Different methods make different as-
sumptions about the equilibrium status of populations, mi-
gration patterns, population structure, and the attributes of
genetic markers (Gillespie 1998). Not surprisingly, the differ-
ent approaches can lead to very different conclusions about
the distribution of genetic variation, and the historic and con-
temporary processes that underlie these distributions (Slatkin
1994; Neigel 1997). For this reason, we begin by summarizing
the basic methods used to detect genetic structure and esti-
mate gene flow. Because genetically based inferences of pop-
ulation subdivision and gene flow depend on a host of as-
sumptions about migration patterns, mutation rates, selective
neutrality, and equilibrium status (Gillespie 1998), we next
consider problems associated with developing a mechanistic
interpretation of patterns of genetic structure in natural pop-
ulations. With this foundation in place, we finally return to
the basic ecological questions and use a series of case studies
to explore how genetic structure can provide crucial insights
into the nature and outcomes of species’ interactions with
their biotic and physical environments.
CHARACTERIZING GENETIC STRUCTURE
AND ESTIMATING GENE FLOW
The first quantitative attempts to depict genetic structure
originated with Wahlund’s (1928) observation that there
should be heterozygote deficiencies in structured popula-
tions and Wright’s (1951) analyses of the expected distribu-
tion of neutral genetic variation in spatially subdivided pop-
ulations. Wright’s approach built on the premise that species
can be reduced to a set of freely interbreeding (panmictic)
units, and that these units will resemble each other depend-
ing on the degree of gene flow between them (Slatkin 1994).
Wright developed statistics that summarize the distribution
of genetic variation within and among sampling units based
on the spatial (and sometimes temporal) distribution of gene
and genotypic frequencies. However, it was not until the ad-
vent of allozyme electrophoresis in the 1960s that population
geneticists could build an extensive multilocus database from
which to characterize the spatial distribution of gene frequen-
cies in natural populations.
There is no reliable way to infer the genealogical relation-
ships among allozyme variants, because there is no obvious
connection between the migration patterns of proteins on an
electrophoretic gel and the degree of divergence of the under-
lying DNA sequences. The development in the 1980s of indi-
rect and direct methods for detecting nucleotide sequence
variation made it possible for the first time to reconstruct the
ancestral–descendant relationships among genes, popula-
tions, and species. With this information, it became possible
for the first time to interpret the geographic distribution of al-
leles, haplotypes, or sequences in terms of their phylogenetic
relationships (phylogeography, sensu Avise et al. 1987).
Both the gene frequency and genealogical approaches can
be used to estimate the degree of geographic subdivision, the
amount of gene flow between populations, and effective pop-
ulation size, all critical elements in forging a synthesis be-
tween population genetics and community ecology. Gene-fre-
quency approaches have the advantage of using data that are
relatively easy to generate, often for multiple loci; but they
lack an explicit historical component. In contrast, the main ad-
vantage of genealogical methods is their ability to reconstruct
the spatial histories of populations. This includes distinguish-
ing between populations whose size has remained constant
and populations that have experienced a recent population
expansion. In a geographic context, genealogical data can be
used to detect introgression between species, and to identify
previously unrecognized cryptic species. Finally, when ex-
tended to the members of a community, a genealogical ap-
proach makes it possible to test hypotheses about the com-
mon processes that generate genetic structure, and the
geographic histories of interactions among species.
FST -Based Approaches
CHARACTERIZING GENETIC STRUCTURE USING F
ST .Wright (1931,
1951, 1965) developed the classic theory for portraying genet-
62 Chapter 3 / Grosberg and Cunningham
ic structure by partitioning the population-wide inbreeding
coefficient into contributions from population substructure
(FST, the fixation index) and nonrandoming mating within
subpopulations (FIS). The basic idea is simple: In a subdivided
population, individuals are more likely to mate with members
of their own subpopulation than with members of other sub-
populations. Thus, at the level of the entire population, a sub-
divided population will have a higher than expected (under
completely random mating) frequency of homozygotes.
Wright initially limited his analysis to two levels of struc-
ture: subpopulations and the total population, where sub-
populations represent panmictic units within the total popu-
lation. He further assumed that observed genetic structure
was a stable feature of a population. However, populations
often exhibit structure at a variety of spatial scales (reviewed
in Neigel 1997), and genetic structure may only be evident
during specific periods of the life cycle (McCauley and Goff
1998). For this reason, more complex hierarchical models of
genetic structure have been developed (Nei 1973; Weir and
Cockerham 1984).
F
ST measures the magnitude of population subdivision in
terms of deviations from heterozygosities expected under
Hardy-Weinberg equilibrium:
where HTis the probability that two alleles drawn at random
(with replacement) from the entire population differ in state
(i.e., the probability with no population structure), and H
—
Sis
the probability that two alleles drawn at random from a sub-
population differ in state (which, for a two-allele system, will
always be 2piqi, where piis the observed allelic frequency in
subpopulation i), averaged over subpopulations. In a com-
pletely randomly mating population, HT= H
—
S, andF
ST = 0. If
that population were to become subdivided, with limited mi-
gration among subpopulations, then genetic drift would
eventually lead to differences in allelic frequencies among
subpopulations, and HT> H
—
S. As the magnitude of this dif-
ference increases,F
ST will approach its maximum value of 1.
It is also possible to define F
ST as the standardized variance
in allelic frequencies among subpopulations (or any sam-
pling unit). From this perspective, F
ST reflects the amount of
genetic variance among subpopulations (usually the smallest
sampling units) relative to the total variance expected if all
subpopulations actually represented a single panmictic unit
(Wright 1943, 1951). It is then a small step to think of F-statis-
tics in terms of a nested analysis of variance, in which the dis-
tribution of variance of allelic and genotypic frequencies is
partitioned within and among sampling units (Weir 1990;
Neigel 1997).
With the rapid growth of a database on DNAsequences,
techniques have been developed to use this information to
characterize genetic structure. For example, the ratio of se-
quence divergences within and among subpopulations can
be used to estimate F
ST (reviewed in Hudson et al. 1992).
Other methods explicitly incorporate the nested ANOVA ap-
proach by partitioning sequence divergence between alleles
into the relative contributions of population subsets from spa-
tially nested samples (AMOVA, Excoffier et al. 1992). It is im-
portant to remember that all methods for calculating F
ST as-
sume that subpopulations are the same sizes and have equal
variances in allelic frequencies (discussed in Whitlock and
McCauley 1999). When these assumptions are violated, high-
ly biased inferences about genetic structure and, consequent-
ly, gene flow can result (e.g., Palumbi et al. 1997).
INFERRING LEVELS OF GENE FLOW.There are two basic ways
that population geneticists estimate gene flow. Direct mea-
surements (sensu Slatkin 1985a) involve the tracking of indi-
vidual or group movements (e.g., Gerrodette 1981; Burton
and Swisher 1984; Olson 1985; Grosberg 1987, 1991; Stoner
1990; Willis and Oliver 1990; Jones et al. 1999; Swearer et al.
1999). In principle, these migration or dispersal patterns
could then be translated into estimates of gene flow by deter-
mining whether the movements lead to successful breeding
within the recipient population. In practice, the difficulties of
(1) detecting rare, long-distance movements that can dispro-
portionately contribute to gene flow, (2) tracking small, rare
propagules across the expanses of the oceans, and (3) deter-
mining whether immigrants successfully reproduce general-
ly doom this direct approach for the vast majority of marine
organisms (Hedgecock 1986; Grosberg 1987; Grosberg and
Levitan 1992; Johnson and Black 1995; Palumbi 1999).
Indirect methods use the spatial distribution of genetic
variation to estimate gene flow, under the assumption that
different patterns of dispersal and levels of gene flow will
generate distinctive genetic structures at evolutionary equi-
librium (Slatkin 1985a). Indirect approaches circumvent some
of the logistical problems of direct estimates of gene flow, in
part because indirect methods do not require the monitoring
of individuals through time. Furthermore, indirect methods
only incorporate movements that lead to successful establish-
ment of the genes carried by immigrants, and thus reflect the
cumulative effects of gene flow (and perhaps selection), aver-
aged over space and time (Slatkin 1985a). In one sense, this is
a virtue of indirect methods; however, indirect methods gen-
erally estimate gene flow averaged across subpopulations in
an idealized model of migration (Whitlock and McCauley
1999). As such, these estimates almost certainly do not repre-
sent actual rates of gene flow everywhere within the total
population at a given time and potentially sacrifice crucial
details of the processes that produce genetic structure.
Indirect measures of gene flow are expressed in terms of
two parameters that are extremely difficult to measure inde-
pendently: (1) the genetically effective migration rate, m(or
the fraction of individuals, on average, that migrate between
subpopulations each generation) and (2) the genetically effec-
tive population size, Ne(Slatkin 1993; see the section in this
chapter entitled Gene Flow, Selection, and Local Adaptation).
Roughly speaking, Neis the size of an idealized population
(i.e., stationary dynamics, equal sex ratios, binomial variance
FHH
H
ST TS
T
=–
Genetic Structure in the Sea 63
in reproductive success) that loses neutral genetic variation
due to drift at the same rate as the “real” population (re-
viewed in Crow and Kimura 1970; Ewens 1982; Whitlock and
Barton 1997). The product of Neand myields M, the average
number of migrants exchanged between subpopulations per
generation. As a general rule of thumb, when Mexceeds ap-
proximately one individual per generation, gene flow—given
sufficient time—will eventually offset the diversifying effects
of genetic drift (Slatkin 1994). As we discuss in the section
Scale of Population Regulation, the trivial levels of genetical-
ly effective migration necessary to homogenize allelic fre-
quencies at neutral loci limit the power of gene flow esti-
mates for reckoning demographically significant migration
or dispersal.
The literature devoted to inferring levels of gene flow from
allelic frequencies is massive, complex, and often arcane (re-
viewed by Slatkin 1985a; Slatkin and Barton 1989; Cockerham
and Weir 1993; Slatkin 1994; Neigel 1997; Bossart and Prowell
1998; Waples 1998; Hutchison and Templeton 1999; Whitlock
and McCauley 1999). By far the most popular methods are
based on F
ST. With the appropriate spatial model of migration
(a term we use interchangeably from here on with gene flow),
plus a variety of other assumptions we discuss below, F-statis-
tics can be used to estimate M(reviewed in Neigel 1997). The
basic and most widely used formula is based on Wright’s
(1931) island model of migration, and assumes equal likeli-
hood of migration throughout a species range. The relation-
ship between F
ST and gene flow (Nemor M) is
As values of FST increase from their theoretical minimum
of 0 (which implies that there is no detectable genetic struc-
ture and high levels of gene flow) to their maximum of 1
(which implies that subpopulations are fixed for different al-
leles and there is no gene flow), M declines to 0.
Slatkin (1985b) developed an alternative method for esti-
mating Nem, based on the distribution of alleles uniquely oc-
curring in single subpopulations. The approach builds on the
idea that the “lifespan” of such private alleles will depend on
the rate at which they arise by mutation (and are lost due to
drift) and the rate at which gene flow erases their uniqueness.
Thus, at an equilibrium between drift and migration, the dis-
tribution of private alleles can be related to migration rate.
ASSUMPTIONS UNDERLYING ESTIMATES OF GENE FLOW.The math-
ematically simple inverse relationship between F
ST and Nem
(= M) makes it easy to forget that this equation rests on many
unstated and often difficult to verify assumptions (reviewed
in Slatkin and Barton 1989; Neigel 1997; Bossart and Prowell
1998; Waples 1998; Whitlock and McCauley 1999). Popula-
tions of many marine species likely violate at least some of
these assumptions. Therefore, we explore briefly the nature
of these assumptions, and some of the effects of their viola-
tion on estimates of gene flow.
The first assumption is that sampling, in terms of (1) num-
ber of individuals, (2) number of loci, and (3) spatial scale,
can accurately reveal underlying genetic structure. Insuffi-
cient sampling of individuals, subpopulations, and loci can
introduce substantial error into estimates of F
ST (and analo-
gous estimators such as GST and q), and consequently M
(Weir and Cockerham 1984; Weir 1990; Neigel 1997; Waples
1998; Holsinger 1999; Whitlock and McCauley 1999). More-
over, most F
ST-based analyses require a priori specification of
hierarchical structure (but see Holsinger and Mason-Gramer
1996). When the scale of sampling (or data partitioning) does
not match the scale at which a population exhibits genetic
structure, structure may be exaggerated or obscured (Hus-
band and Barrett 1996; Rousset and Raymond 1997; Bohonak
1999). This potentially confounds comparisons between taxa
or studies that use different sample sizes, sampling regimes,
and loci. Additionally, because the relationship between F
ST
and Mis nonlinear, large values of Mare nearly impossible to
estimate within several orders of magnitude (Templeton
1998). For these reasons alone, estimates of gene flow based
on F
ST may have large associated errors.
The second assumption is that migration rates (m) must
greatly exceed mutation rates (m). This is because mutation re-
duces estimates of F
ST (Crow and Aoki 1984), and consequent-
ly upwardly biases inferred levels of gene flow (Neigel 1997).
For most allozyme markers with low mutation rates, this ef-
fect will be minimal with respect to other sources of error.
However, this assumption will almost certainly be violated
for more rapidly evolving markers such as microsatellites.
This prompted Slatkin (1995) to develop a statistic, RST, analo-
gous to F
ST, that incorporates a stepwise mutation process po-
tentially applicable to microsatellite loci. Neigel (1997) critical-
ly analyzed alternatives that putatively control for the effects
of mutation on F
ST-based estimates of gene flow inferred from
DNA sequence data. He concluded that when significant mu-
tation affects a marker, genealogy-based methods, based on
the ancestral-descendant relationships of alleles, may have
considerable advantages over gene-frequency approaches (see
the section entitled Genealogical Approaches).
Third, the model assumes that there is no selection on the
marker alleles (i.e., they are neutral). In theory, weak selection
should not greatly bias estimates of gene flow, as long as se-
lection on the marker alleles does not vary spatially (Slatkin
and Barton 1989). However, several empirical studies on ma-
rine systems suggest that selection on allozymes (and per-
haps other markers) may strongly influence the spatial distri-
bution of allelic variation (Koehn et al. 1980; Burton 1986;
Karl and Avise 1992; Johannesson et al. 1995; reviewed in
Avise 1994; Hilbish 1996; Bohonak 1999). Selection can either
increase or decrease estimates of F
ST (and consequently in-
ferred levels of gene flow). For example, undetected spatially
varying selection on marker loci can enhance differences
among subpopulations over that expected for neutral mark-
ers. On the other hand, balancing selection on genetic mark-
ers may spatially homogenize allelic frequencies, reducing
structure at the loci under selection and creating the illusion
FNm Nm F
ST
e
e
ST
≈+≈
1
14
1
4
11 or –
64 Chapter 3 / Grosberg and Cunningham
of high inferred levels of gene flow (Slatkin and Barton 1989;
Karl and Avise 1992).
Fourth, the simple formula that relates F
ST to Nembuilds
on a classic island model of dispersal in which a species con-
sists of a large number of equally sized subpopulations, all of
which have an equal probability of exchanging migrants. As
we discuss in the section Genetic Homogeneity, this may be a
realistic approximation of dispersal in those species for which
the scale of larval dispersal substantially exceeds the scale
over which there are suitable patches of habitat. However,
migration patterns for those species that live along coastlines
or hydrothermal vent systems, especially those whose propa-
gules have limited dispersal potential, may better fit a one-di-
mensional stepping-stone model of dispersal, in which mi-
grants only move between adjacent linearly arrayed patches
of habitat (Slatkin 1993; Vrijenhoek 1997). Similarly, for
species with limited dispersal that live in island groups, a
two-dimensional stepping-stone model may be a better ap-
proximation of actual migration patterns. In both classes of
stepping-stone migration model, genetic differentiation should
increase with the distance separating subpopulations and in-
ferred gene flow should attenuate (Kimura and Weiss 1964).
In theory, the exact migration model should minimally bias
estimates of gene flow based on F
ST (Slatkin and Barton 1989);
however, with more complex patterns of gene flow among
subpopulations, the failure to sample on a scale or pattern
corresponding to the existing genetic structure can substan-
tially bias F
ST-based estimates of gene flow (Husband and
Barrett 1996).
Finally, estimates of gene flow based on F
ST-like statistics
(and even coalescent methods, as described in the next sec-
tion) assume that populations are at a genetic equilibrium be-
tween the homogenizing effects of gene flow and the diversi-
fying effects of genetic drift (Felsenstein 1982). In other words,
historical effects no longer leave a signature on genetic struc-
ture. As we discuss in some detail in later sections, when pop-
ulations have not reached an equilibrium between gene flow
and drift, inferred gene flow may be highly biased (Whitlock
and McCauley 1999), and non-F
ST-based methods may be
more suitable (Slatkin and Barton 1989; Neigel 1997; Hutchi-
son and Templeton 1999). For example, a large difference in
allele frequencies between subpopulations can reflect either
restricted gene flow (the equilibrium inference, as assumed by
most F
ST-based methods), or a recent founding event or bottle-
neck (McCauley 1993; Hutchison and Templeton 1999). Con-
versely, subpopulations could be genetically similar either be-
cause they are linked by high levels of contemporary gene
flow (the equilibrium inference), or because they were rela-
tively recently isolated from each other (Felsenstein 1982).
THE APPROACH TO EQUILIBRIUM AND WHY IT MATTERS.Under an
island model of migration, the time, t(in generations), it
takes for F
ST to approach the equilibrium between the ho-
mogenizing effects of gene flow and the diversifying effects
of genetic drift is related to Neand mby the following expres-
sion (Crow and Aoki 1984):
For high migration rates and small effective population
sizes, this equilibrium may be approached quite rapidly.
However, for the large populations that may typify many
species of marine invertebrates, or for the low migration rates
characteristic of many clonal forms (Jackson 1986), this time
may be on the order of thousands, or even millions, of gener-
ations. In addition, a stepping-stone, as opposed to island,
pattern of migration can considerably lengthen the time it
takes to reach this equilibrium (Slatkin 1993).
This raises the important question of whether many ma-
rine invertebrate populations ever reach genetic equilibrium
throughout their ranges. Imagine under an island-migration
model a population with Ne= 105, m= 0.0001 (i.e., Nem= 10
migrants exchanged between subpopulations), and with a
generation time of one year. Following a perturbation to mi-
gration patterns, it would take F
ST roughly 5000 years to reach
its eventual genetic equilibrium (Whitlock and McCauley
1999). As we discuss later, many populations of marine or-
ganisms—particularly those more complex patterns of gene
flow, larger populations, or lower levels of gene flow—may
never have time to attain fully this equilibrium between gene
flow and drift before being perturbed again. To the extent
this is true, patterns of genetic structure in many species of
marine invertebrate may not reflect contemporary levels of
gene flow. For example, the last glacial period, approximately
10,000–12, 000 years ago, made many intertidal and near-
shore temperate and polar habitats unsuitable for numerous
species, and drove their distributions equatorially. It also low-
ered sea level in the tropics, potentially strengthening the iso-
lation between tropical island systems and basins (Paulay
1990; Benzie 1999). Depending upon the period of isolation of
these populations, the magnitude of isolation, and their prior
histories of isolation, the effect of these glacial periods may be
either to inflate or reduce observed F
ST’s over that expected at
equilibrium between contemporary gene flow and drift.
The fact that allelic frequencies and, consequently, genetic
parameters such as F
ST may vary even over ecological time
scales further cautions against taking estimates of gene flow
at face value. For example, a growing number of studies
show that allelic frequencies vary from generation-to-genera-
tion at a particular site, even in species with extensive disper-
sal potential (e.g., Johnson and Black 1982, 1984a,b; Watts et
al. 1990; Kordos and Burton 1993; Lessios et al. 1994; Ed-
mands et al. 1996; Li and Hedgecock 1998; Ruckelshaus
1998). Thus, historical (e.g., vicariance, founder events/range
expansions, rare bouts of dispersal) and demographic (e.g.,
asexual propagation, low recruitment rates or long genera-
tion times, overlapping generations, and temporal variance in
reproductive or recruitment rates) processes may be as im-
portant as levels of contemporary gene flow in determining
the current genetic structure of some marine species (Felsen-
stein 1982; Hedgecock 1986; Ayre 1990; Cunningham and
1
21
2
mNe
+
Genetic Structure in the Sea 65
Collins 1994, 1998; Palumbi 1995; Hilbish 1996; Benzie 1999;
Hutchison and Templeton 1999).
EFFECTIVE POPULATION SIZE.Ne, or effective population size, is a
crucial element of any analysis of the relationship between ge-
netic structure and ecology. Neis one of the two parameters
(the other being m, the per generation migration rate) that to-
gether determine gene flow. Moreover, the value of Nedeter-
mines the relative importance of selection, drift, and mutation,
and especially a population’s potential for long-term adaptive
genetic response to changing selective regimes (Hill 1985; also
discussed here in the next section). Selection and mutation
generally play a relatively less important role than drift and
gene flow in determining gene frequencies in populations
with a small effective population size. In addition, popula-
tions with a small Netypically reach an equilibrium between
gene flow and genetic drift relatively quickly. Such popula-
tions may be far more prone to extinction due to demograph-
ic stochasticity, reduction in gene diversity, or accumulation of
deleterious mutations (Lynch and Gabriel 1990; Lande 1993,
1994; Hastings and Harrison 1994; Lynch et al. 1995).
In species that exhibit some combination of high individ-
ual variance in reproductive success, large changes in popula-
tion size, or local extinctions and recolonizations, Necan be
much smaller than the censused number of individuals in a
population (Nei and Tajima 1981; Waples 1989; Gilpin 1991;
Nunney 1996, 1999; reviewed in Luikart and England 1999;
also see the later section Range Expansion and Population
Growth). It is notoriously difficult to obtain the demographic
data necessary to estimate effective population size. Howev-
er, temporal and spatial variation in allelic frequencies (Nei
and Tajima 1981; Pollack 1983; Waples 1989; Li and Hedge-
cock 1998), as well as linkage disequilibrium (Hill 1981) and
heterozygote excess (Pudovkin et al. 1996) can also be used to
estimate Ne(given many of the assumptions cited above).
The few genetically based estimates of Nefor marine pop-
ulations have so far yielded some unexpected results. Analy-
sis of temporal variation in allelic frequencies in oysters indi-
cates that the effective numbers of breeders in a population
may be only a few percent, or less, of the censused popula-
tion (Hedgecock et al. 1992; Li and Hedgecock 1998). Other
high fecundity free-spawners, such as sea urchins, may also
have surprisingly small effective population sizes (e.g., Ed-
mands et al. 1996). Nunney (1996) contended that there is no
theoretical reason to expect that high fecundity predisposes a
population to low Nerelative to its census population size,
and that reported differences may be largely methodological.
However, Li and Hedgecock (1998) countered that the dis-
crepancy between Neand census population size in free-
spawning marine invertebrates is not necessarily an artifact
of the way Neis inferred, but instead results from enormous
among-individual variation in their genetic contributions to
the next generation. It remains to be seen how general this
pattern is. If small Neis widespread in numerically large ma-
rine populations, then it has profound implications for how
they will respond to selection.
GENE FLOW, SELECTION, AND LOCAL ADAPTATION.Gene flow has-
tens the spread of alleles across a species’ range. In so doing,
gene flow can either augment or counteract the effects of se-
lection, depending on the spatial scale over which selection
varies (Slatkin 1985a; 1994). What follows is a tremendous
oversimplification of how gene flow and selection interact;
the aim is to give a basic picture. For neutral alleles, in an is-
land model of population structure, if Nemsubstantially ex-
ceeds one migrant per generation, then gene flow will ulti-
mately prevent local genetic differentiation at neutral loci.
Adding the effects of selection to those of gene flow gets
complicated fast, especially when populations exhibit com-
plex genetic structure and extinction–recolonization dynam-
ics (Nunney 1999). In the simplest form, if different alleles are
favored in different subpopulations, then the equilibrium fre-
quency (p) of an allele favored in a subpopulation will be
, where sis the selection coefficient in favor of that al-
lele and mis the immigration rate of the alternative, unfa-
vored allele, when s> m(Haldane 1930; Nagylaki 1975).
(When s< m, p= 0.) The time it takes to reach this equilibri-
um is approximately.
So, the details aside, gene flow can offset some or all of the
differentiating effects of drift and spatially variable selection.
When gene flow among subpopulations inhabiting distinct
selective regimes is high, local adaptation is less likely to
occur (Endler 1977; Hedgecock 1986; Johnson and Black
1995). Selection should favor either a reduction in dispersal
potential or the evolution of phenotypic plasticity. Converse-
ly, when gene flow among subpopulations is low, small and
persistent differences in selective regimes among subpopula-
tions can eventually lead to their adaptive genetic divergence
(but see Holt and Gomulkiewicz 1997).
Genealogical Approaches
FST,GENE GENEALOGIES, AND COALESCENCE.Many evolutionary
biologists are familiar with using DNAsequence information
to reconstruct species-level phylogenetic relationships. Our
concern in this chapter is with depicting patterns of genetic
structure (and gene flow) within species. F-statistics and their
analogues use the spatial distribution of allelic frequencies,
usually at multiple loci, to portray genetic structure and to es-
timate rates of migration. F
ST-based approaches explicitly as-
sume that the primary determinant of genetic structure is a
balance between gene flow and drift, and do not consider that
every pair of alleles in a species has descended from a com-
mon ancestor sometime in the past. Ignoring the relationships
between alleles is a necessary evil for some kinds of data, such
as studies based on allozyme variation. However, for the last
two decades, restriction enzyme analysis and direct sequenc-
ing have made it possible to use DNA sequences to character-
ize genetic structure. Sequence information has two major
advantages over allozymes. First, allozyme studies reveal dif-
ferences only at the amino acid level, whereas DNA studies
can detect substitutions that do not affect the amino acid se-
quence. Second, allozyme studies do not identify which amino
1– sm
66 Chapter 3 / Grosberg and Cunningham
acid has changed in a protein, making it impossible to infer re-
lationships between alleles. Sequence information, on the
other hand, can be used to deduce genealogical relationships
between alleles by any of a number of standard phylogenetic
approaches (Hudson 1990; Avise 1994).
The development of coalescence theory has provided a
powerful framework for interpreting these gene genealogies
in a population genetic framework. This framework has gen-
erated novel insights into patterns of genetic structure and
the historical mechanisms that can generate genetic structure
(e.g., Hudson 1990; Barton and Wilson 1995; Templeton 1998).
Coalescent theory analyzes the structure of the tree moving
backward in time (i.e., from the top of the tree to its base),
with time usually measured in number of generations. Each
of the nodes represents the point in time at which two allelic
lineages “coalesce” or merge in their most recent common
ancestor.
In this section, we first introduce a coalescent approach for
estimating gene flow from gene genealogies. We then consid-
er how genealogical approaches are useful for diagnosing es-
pecially deep divergences (reciprocal monophyly) between
geographically separated populations, and the importance of
reciprocal monophyly for reconstructing the history of genet-
ic subdivision. We conclude this section by analyzing some of
the crucial differences between intraspecific versus interspe-
cific phylogenies, and between gene genealogies and popula-
tion phylogenies.
CHARACTERIZING GEOGRAPHIC STRUCTURE AND GENE FLOW USING
GENE GENEALOGIES.In DNA-based studies, alleles are defined
as sets of homologous sequences that differ by at least one
substitution. If the sequences are from mitochondrial genes,
alleles are referred to as haplotypes. The first step in a phylo-
genetically based analysis of intraspecific genetic structure is
to reconstruct the genealogical relationships among alleles
using parsimony or some alternative method (reviewed in
Swofford et al. 1996). These gene genealogies can also be re-
ferred to as gene trees. The geographic distribution of the
sampled alleles can then be mapped onto a genealogy, and
the pattern and degree of association between genealogical
and geographic structure assessed (see the section Genetic
Homogeneity later in this chapter). For example, if groups of
closely related alleles are consistently found in the same geo-
graphically restricted areas, then limited gene flow over long
periods of time is the simplest explanation.
Slatkin and Maddison (1989, 1990) were the first to devel-
op a coalescent approach to estimating migration rates from
gene genealogies. Their method considers the collection lo-
cality of an allele as a character state. If closely related alleles
are collected in different locations, then parsimony will infer
a migration event between the locations. Slatkin and Maddi-
son’s method uses parsimony to calculate the minimum
number of migration events across the entire gene genealo-
gy. This method is easiest to employ if phylogenetic methods
find a single gene genealogy; however, it is possible to calcu-
late the mean number of migration events across alternative
trees using MacClade 3.0 (Maddison and Maddison 1992).
Slatkin and Maddison (1989, 1990) used a simulation ap-
proach that assumes both evolutionary equilibrium and con-
stant population size to relate the minimum number of in-
ferred migration events on a gene genealogy to M (defined
above). The method is easy to apply, and recombination has
surprisingly little effect on estimates of M(Hudson et al.
1992).
Slatkin and Maddison’s parsimony approach has one con-
spicuous limitation: it does not consider variation in branch
lengths when reconstructing migration events. In other
words, the method implicitly assumes that the probability of
a migration event is independent of branch length. This is
unrealistic because longer branch lengths indicate that more
time has passed between nodes. Recently developed maxi-
mum likelihood methods are more realistic because they as-
sume that migration events are more likely on long branches
than on short ones (Nath and Griffiths 1996; Beerli and
Felsenstein 1999). However, it remains to be seen how well
these methods perform when applied to real data.
As with F
ST-based measures, coalescent approaches can
use randomization methods to test whether a reduced level
of gene flow between populations represents significant geo-
graphical subdivision. For example, if the minimum number
of migration events between two populations is significantly
lower than expected from a distribution of random trees,
then the degree of population subdivision is considered sig-
nificant (Maddison and Slatkin 1991).
THE HISTORY OF POPULATION SUBDIVISION AND THE IMPORTANCE OF
RECIPROCAL MONOPHYLY.Significant geographic subdivision
can occur between populations that still occasionally ex-
change individuals, but are at an equilibrium between gene
flow and drift. However, significant genetic structure can also
result when an historical event has permanently interrupted
gene flow between the populations being considered. These
historical interruptions of gene flow violate the assumption
of equilibrium that are at the heart of traditional population
genetic theory. Although allele-frequency approaches can be
used to detect nonequilibrium situations (e.g., Slatkin 1993),
these approaches offer few insights into the historical pro-
cesses that produce genetic structure.
Genealogical methods, are particularly well-suited to de-
tecting ancient interruption of gene flow. Whenever gene
flow between populations ceases, a combination of mutation
and random extinction of lineages (lineage sorting) will even-
tually generate reciprocal monophyly, where alleles from
each location form a monophyletic group relative to the alle-
les collected from the other location (Figure 3.1; Neigel and
Avise 1986; Avise 1994; see the section Range Expansion and
Population Growth, later in this chapter). In terms of coales-
cence theory, reciprocal monophyly occurs when the alleles
from each locality each have a unique common ancestor,
meaning that they coalesce with each other before they coa-
lesce with alleles in the other subpopulation. This process
takes on the order of 4Negenerations (Neigel and Avise 1986).
Genetic Structure in the Sea 67
The establishment of reciprocal monophyly has several
important implications for population and community ecolo-
gists. First, reciprocal monophyly between spatially disjunct
subpopulations demonstrates a lack of genetically effective
migration between the subpopulations. Second, because reci-
procal monophyly takes a considerable time to evolve, its ex-
istence indicates that both subpopulations have resisted local
extinction for at least that much time (Cunningham and
Collins 1998; discussed further in the section entitled Com-
munity Assembly and the History of Species Interactions).
Finally, when populations isolated for a long period of time
come into secondary contact, F
ST-based approaches should
generally reveal significant subdivision if the secondary con-
tact is recent, or along a sharply defined hybrid zone. One of
the best-known marine examples is the zone of contact be-
tween Gulf and Atlantic populations of numerous distantly
related marine species at Cape Canaveral in Eastern Florida
(reviewed in Avise 1992). In this case , F
ST-based approaches re-
veal significant geographic subdivision, but cannot distinguish
whether the genetic subdivision is due to an extended histori-
cal interruption of gene flow, or an ongoing but significant re-
duction of gene flow. Gene genealogies, however, reveal that
the contacting populations are reciprocally monophyletic, and
clarify the status of the populations as evolutionarily indepen-
dent units (Cunningham and Collins 1994).
GENE GENEALOGIES VERSUS SPECIES-LEVEL PHYLOGENIES.Like
species-level phylogenies, intraspecific gene genealogies can
be inferred from DNAsequences or restriction fragment pro-
files using conventional search methods implemented in
such widely used software packages as PAUP* 4.0 (Swofford
1999) or PHYLIP (Felsenstein 1999). There are, however, two
important differences between species-level phylogenies and
intraspecific gene genealogies that are crucial to constructing
and interpreting gene genealogies (reviewed by Crandall and
Templeton 1996).
First and most importantly, the nodes in inter-specific phy-
logenetic trees represent ancestral populations that have gone
extinct. In contrast, truly ancestral alleles are almost always
found in intraspecific gene genealogies. Although counterin-
tuitive, this situation is expected because not every actual bi-
furcation in an intraspecific gene genealogy is reflected by a
mutation. Consider a mother who passes on her mitochon-
dria to two daughters. Only the first daughter’s mtDNA ex-
periences a mutation, whereas the other daughter’s does not.
The first daughter’s offspring will inherit a derived allele,
while the second daughter’s offspring will inherit the ances-
tral allele. In this way, alleles that are identical to the true an-
cestor remain in the population. These ancestral alleles are
easily identified in parsimony analyses because they (1) have
no unique substitutions (i.e., autapomorphies), (2) are deeply
nested, and (3) are often quite common in the population
(Figure 3.2). When one searches for gene genealogies using
parsimony or other method, zero-length branches should be
collapsed to reflect the existence of actual ancestors in the
population (Figure 3.2B).
Second, the traditional outgroup method for rooting inter-
specific phylogenies is not reliable for intraspecific gene ge-
nealogies. This is because the distance to the outgroup for an
intraspecific gene genealogy is vastly greater than the dis-
tance between individuals in the population (Castelloe and
68 Chapter 3 / Grosberg and Cunningham
12 1 212
Figure 3.1 The establishment of reciprocal monophyly in subdi-
vided populations. Reciprocal monophyly takes on the order of
4Negenerations, and results when a combination of mutation
and lineage sorting in a pair of isolated subpopulations eventual-
ly causes all of the alleles in each subpopulation to be more
closely related to each other than they are to any alleles in the
other subpopulation.
AB
G
F
IJ
C
D
E
H
ABC I J
DFGH
E
(A) Unrooted allele network (B) Midpoint-rooted
parsimony phenogram
Figure 3.2 (A) An unrooted allele (or haplotype) genealogy.
The letters denote a unqiue haplotype, the size of the circles cor-
responds to the relative frequency of that haplotype, and the
lines connecting the circles represent a single base pair substitu-
tion. (B) Midpoint-rooted parsimony phenogram of the same
genealogy shown in (A) from PAUP* (Swofford 1999), with zero-
length branches collapsed. As expected from the retention of
ancestral haplotypes in population-level genealogies, haplotypes
D, E, and H have no branch length of their own.
Genetic Structure in the Sea 69
Templeton 1994; Crandall and Templeton 1996). This means
that any site along the outgroup branch may have experi-
enced multiple substitutions, thereby erasing the historical
signal [similar to “long branch attraction” in interspecific
phylogenies (Felsenstein 1978)]. One option is to present a
gene genealogy as an unrooted allele network (Avise 1994;
Crandall 1994; Smouse 1998). Alternatively, there are several
methods that use coalescent theory to root the network (Cas-
telloe and Templeton 1994; Griffiths and Tavaré 1994). These
methods build on the premise that the oldest alleles should
be the most deeply nested in the network, because they will
have had more time to generate descendants.
GENE GENEALOGIES VERSUS POPULATION PHYLOGENIES AND FST-
BASED APPROACHES.The preceding discussion focused on
gene genealogies based on alleles sampled from individuals.
There are, however, many cases in which populations, not in-
dividuals, represent the taxonomic units in a phylogeny. For
allozyme data, the genealogical relationships between indi-
vidual alleles are unknown. Instead, allele frequencies are
used to generate distances between populations (e.g., Caval-
li-Sforza and Edwards 1967; Nei 1972), and population phy-
logenies are inferred from these distances. Population phylo-
genies can be built from almost any kind of frequency data,
such as DNA allele frequencies, microsatellite allelic frequen-
cies, or—of course—allozymes.
Unlike gene genealogies, population phylogenies have the
benefit of being estimated from multiple loci, and usually
from relatively large samples. In contrast, most gene genealo-
gies are inferred from relatively small samples of individuals
at a single locus. It is important, however, to keep in mind
that a population phylogeny does not necessarily predict the
relationship of any two individuals from distinct popula-
tions. If there has been recent migration between long-isolat-
ed subpopulations, the populations will exhibit sharp differ-
ences in allele frequencies, even though some individuals in
the two populations share closely related alleles due to the re-
cent migration (e.g., Van Syoc 1994). Moroever, because pop-
ulation phylogenies are based on distances inferred from al-
lele frequencies, they are subject to many of the same pitfalls
as pairwise F
ST’s calculated from gene frequencies (see F
ST-
Based Approaches).
On the other hand, gene genealogies represent the history
of only a single locus, and are not necessarily equivalent to or-
ganismal or population-level phylogenies. For several reasons,
the genealogy inferred from one gene may be contradicted by
other loci collected from the same individual. For example, in-
terspecific hybridization can lead to mitochondrial introgres-
sion from one species into another, leading nuclear and mito-
chondrial genes from the same individual to have very
different histories (e.g., Lamb and Avise 1986; Quesada et al.
1995). Similarly, in recombining loci, different parts of the
same gene may have distinct histories (Slatkin 1994). This dif-
ficulty can be overcome by generating gene genealogies from
multiple loci. When multiple loci yield congruent patterns,
then confidence in the inferred history is greatly increased.
Conclusions
With the appropriate significance tests (reviewed in Weir
1996; Neigel 1997), F
ST and analogues [e.g., GST (Nei 1973), qST
(Weir and Cockerham 1984), NST (Lynch and Crease 1990),
and RST (Slatkin 1993)] provide simple indices of the magni-
tude and scale over which populations exhibit significant ge-
netic structure, based on readily obtainable data. F
ST-based
approaches are best-suited to large samples, with data from
multiple, independent loci (Slatkin and Barton 1989; Neigel
1997). F
ST-based approaches have the virtue (and evil) of re-
ducing potentially very complex genetic structures into sim-
ple metrics, facilitating comparisons on the one hand, and ob-
scuring differences on the other (Gillespie 1998). The fact that
F-statistics and inferences of gene flow may be time-, locus-,
and scale-dependent is also both a blessing and a curse. This
sort of variation can provide critical insights into the ways
that mating patterns, dispersal, and selection influence genet-
ic structure. However, this variation cautions against taking
estimates of gene flow at face value, especially when they in-
volve different spatial and temporal scales, different loci, and
different species.
Perhaps the most important limitation of approaches
based on the spatial distribution of allelic and haplotypic fre-
quencies is that estimates of gene flow assume that a popula-
tion is in evolutionary equilibrium with respect to gene flow
and genetic drift, and that historical effects no longer persist.
Some genealogical methods for estimating migration rates
and the magnitude of genetic subdivision make similar equi-
librium assumptions, and the number of loci and individuals
that are usually sampled limits their power. However, to the
extent that populations of marine organisms deviate from
migration-drift equilibria, genealogical approaches at the
level of the individual and populations may be essential for
deciphering the imprint of history on genetic structure and
the nature and outcomes of species interactions.
INTERPRETING PATTERNS OF GENETIC
STRUCTURE IN MARINE POPULATIONS
The main problem with interpreting genetic patterns in nat-
ural populations arises because a particular pattern may be
generated by both historical and contemporary processes,
acting singly or combined, at different spatial and temporal
scales. Many of the methods for detecting patterns of genetic
structure and inferring gene flow assume that the genetic sig-
nature of past historical events has been erased by a combina-
tion of migration and drift. The assumption of an equilibrium
between migration and genetic drift also underlies the most
important generalization in the evolutionary genetics of ben-
thic marine organisms. Larval dispersal ability should be the
primary determinant of genetic structure (Palumbi 1995; Bo-
honak 1999), and ultimately rates and patterns of speciation
and extinction (Jablonski and Lutz 1983; Palumbi 1994; cf.
Hedgecock 1986).
Of course, the pattern of genetic structure at equilibrium
depends on many factors, including developmental mode,
larval behavior, circulation patterns, distribution of suitable
habitats, and geographical scale of sampling (reviewed in
Lessios et al. 1998; Benzie 1999; Bohonak 1999). At one ex-
treme, a species with exceptionally broadly dispersing larvae
should be panmictic over all but perhaps the largest spatial
scales. Species with somewhat more limited dispersal poten-
tial may show panmixia over fine and moderate spatial
scales, and isolation by distance (sensu Wright 1943; Malécot
1968) at much larger scales. At the other extreme, species
with effectively nondispersing larvae should show a pattern
of isolation by distance at all but the very finest spatial scales.
In the most recent review of the subject, Bohonak (1999)
found a statistically significant relationship between larval
dispersal ability and degree of geographic subdivision (mea-
sured by F
ST). However, like his predecessors (e.g., Burton
1983; Hedgecock 1986; Ayre 1990; Palumbi 1994, 1995; Hilbish
1996; Cunningham and Collins 1998; Ruckelshaus 1998), Bo-
honak (1999) identified numerous cases in which dispersal
potential only weakly predicted genetic structure (also see
Cunningham and Collins 1994, 1998; Hellberg 1994; Marko
1998; Benzie 1999). These exceptions emphasize that our un-
derstanding of how contemporary processes generate genetic
structure in the sea is, not surprisingly, incomplete. Moreover,
several lines of evidence suggest that the imprint of historical
processes on contemporary genetic structure may be both per-
vasive and persistent. For example, genetic breaks may not
correspond to known barriers to dispersal (reviewed in Cun-
ningham and Collins 1994, 1998; Shulman and Bermingham
1995; Palumbi 1997; Benzie 1999), genetic continuity may oc-
cur where there are no obvious contemporary dispersal path-
ways (e.g., Palumbi et al. 1997), and phylogeographic analysis
may reveal that sister taxa are nonadjacent (e.g., Marko 1998).
One of the best-studied marine examples of this kind con-
cerns the distribution of genetic variation among benthic in-
vertebrates along the coast of the southeastern United States
(also see examples from the Indo-Pacific in Benzie 1999).
Here, many species, regardless of their dispersal potential,
are subdivided into two reciprocally monophyletic popula-
tions in the Atlantic and the Gulf of Mexico, with a narrow
hybrid zone at Cape Canaveral (reviewed in Avise 1992,
1994). It is not certain whether local adaptation or oceano-
graphic barriers maintain the genetic distinctions between
these populations (see Hare and Avise 1996). What is certain
is that a massive vicariant event interrupted gene flow for
many co-occurring taxa, regardless of their dispersal ability,
and that larval dispersal has yet to restore genetic homogene-
ity between the Gulf and Atlantic populations, even in those
species with broadly dispersing larvae.
In this section, we analyze some of the problems of distin-
guishing the contributions that historical and contemporary
processes make to genetic structure. We first evaluate from
equilibrium and nonequilibrium perspectives the two sim-
plest and most extreme patterns expected at genetic equilibri-
um: genetic homogeneity and isolation by distance. We note
that true uniformity may be difficult to detect, and that ap-
parent examples of both homogeneity and isolation by dis-
tance may reflect the operation of a mixture of historical, as
well as contemporary, processes. We then review some statis-
tical methods that can be used to identify historical contribu-
tions to genetic structure, especially the effects of range ex-
pansions (and contractions) and population growth.
Genetic Homogeneity
The lack of obvious physical barriers to dispersal in many of
the world’s oceans led early marine population geneticists to
predict that species with broadly dispersing larvae should ex-
hibit little genetic structure across their ranges (reviewed in
Burton 1983; Hedgecock 1986; Benzie 1999). The presence of
larvae from near-shore species in the middle of the Atlantic
and Pacific Oceans reinforced this expectation (Scheltema
1986), and several recent genetic studies on fish (reviewed in
Graves 1998; Waples 1998) and invertebrates suggest that
broadly dispersing larvae (and adults) can maintain genetic
cohesiveness over large distances. For example, in an allo-
zyme study of a solitary coral (Paracyathus stearnsii) with
planktonic larvae, Hellberg (1996) found no significant geo-
graphic subdivision over thousands of kilometers along the
West Coast of the United States. Similarly, both solitary and
clonal forms of the freely spawning sea anemone Anthopleura
elegantissima lack significant genetic structure over the same
geographic range (McFadden et al. 1997). Perhaps the most
spectacular and best-documented example of genetic homo-
geneity concerns the sea urchin Echinothrix diadema (Lessios
et al. 1998). E. diadema is one of the few species whose distrib-
ution spans the Eastern Pacific Barrier (EPB), 5400 km of abys-
sal water without any shallow habitats that could serve as
stepping stones for dispersal. Neither nuclear (allozymes)
nor mitochondrial markers reveal any geographic structure
reflecting restricted gene flow across the Eastern Pacific Barri-
er, leading Lessios et al. (1998) to propose that El Niño events
propel larvae across the EPB sufficiently often to homogenize
genetic structure at this vast scale.
Equilibrium explanations invoking panmixia over broad
geographic expanses should, however, be interpreted cau-
tiously for several reasons. First, in several cases, extension of
a sampling regime to include the entire range of a species
changed the initial inference of panmixia and extensive gene
flow. For instance, allozyme studies of genetic structure in the
free-spawning giant clam Tridacna gigas (Benzie and Williams
1992) and the starfish Linckia laevigata (Williams and Benzie
1996) revealed little genetic differentiation over thousands of
kilometers in Australia. In both cases, expanded sampling re-
vealed significant geographic subdivision (Benzie and Wil-
liams 1995; Williams and Benzie 1998). Similarly, Palumbi
and Wilson (1990) reported that mitochondrial DNA diversi-
ty was homogeneous over a range of 1,500 km in the sea
urchin Strongylocentrotus purpuratus. Wider sampling, how-
ever, revealed subtle but significant genetic differentiation be-
tween two California locations south of Point Conception
(Edmands et al. 1996).
Second, some early reports of broad genetic uniformity
based on allozymes have been contradicted by subsequent
70 Chapter 3 / Grosberg and Cunningham
analysis using DNA markers (reviewed in Hilbish 1996).
Buroker’s (1983) allozyme study of the American oyster Cras-
sostrea virginica is a classic example. Buroker (1983) reported
panmixia from Georgia to Texas; however, subsequent analy-
sis using mitochondrial (Reeb and Avise 1990) and nuclear
DNA markers (Karl and Avise 1992) showed reciprocal
monophyly between the Atlantic and the Gulf of Mexico.
Whether there is geographic structure in the allozyme dataset
is debatable (Cunningham and Collins 1994); however, there
is no doubt that in the absence of migration allozyme fre-
quencies can fail to reach genetic equilibrium even after mil-
lions of years of isolation. For example, geminate pairs of the
sea urchin genus Diadema (Bermingham and Lessios 1993)
and the snapping shrimp Alpheus (Knowlton et al. 1993)
show no significant allozyme divergence on either side of the
Isthmus of Panama. In both cases, mitochondrial DNA
showed deep divergences whereas allozymes did not (re-
viewed in Cunningham and Collins 1994). Similarly, allo-
zymes failed to reveal any genetic structure across the Indo-
Pacific in the sea star Linckia laevigata (Williams and Benzie
1996), whereas a subsequent analysis using mtDNArevealed
significant differentiation among some populations (Williams
and Benzie 1997).
Finally, there are several surprising examples of genetic
uniformity that challenge equilibrium explanations, because
they occur in species with demersal larvae and limited dis-
persal potential. These include an allozyme study of North
Sea and Irish Sea populations of the sea anemone Urticina
equs (Solé-Cava et al. 1994), and mitochondrial DNAanalyses
of genetic structure in three benthic invertebrates found on
both sides of the North Atlantic: the gastropods Nucella lapil-
lus and Littorina obtusata, and the isopod Idotea baltica (Wares
et al., in press). These populations may currently be panmic-
tic over these regional scales; however, their dispersal poten-
tial suggests that they should exhibit isolation by distance
(see the next section). Consequently, nonequilibrium alterna-
tives should be seriously considered (Slatkin 1993). For exam-
ple, if a vicariant event recently subdivided the formerly pan-
mictic North and Irish Sea populations of U. equs, divergence
in allozymes due to drift may not have had time to accumu-
late to detectable levels (Solé-Cava et al. 1994).
Isolation by Distance
If the geographic range of a species is large relative to the dis-
persal potential of its propagules, then genetic drift will lead
to divergence between subpopulations, even at equilibrium
(isolation by distance, sensu Wright 1943; Malécot 1968).
Under this isolation-by-distance scenario, the relationship be-
tween genetic differentiation and spatial separation of sub-
populations can be described in terms the log10 of M
ˆ(the
amount of inferred gene flow between pairs of populations as
defined above) versus the log10 of geographic distance. At
equilibrium, this relationship should have a characteristic
slope, the value of which depends upon (1) whether gene flow
follows a one- or two-dimensional stepping-stone model
(Kimura and Weiss 1964) and (2) the spatial distribution and
separation of suitable habitats (Slatkin 1993; Hellberg 1996;
Hutchison and Templeton 1999). Values of M
ˆcan be estimat-
ed by any of the methods described earlier (in the sections In-
ferring Levels of Gene Flow and The History of Population
Subdivision and the Importance of Reciprocal Monophyly;
Slatkin 1993). Isolation by distance can also be detected using
spatial autocorrelation (e.g., McFadden 1996), which circum-
vents some of the scale-dependent biases of F-statistics, or by
nested clade analysis (Templeton 1994, 1998). Afull discussion
of these methods is beyond the scope of this paper.
The pattern of isolation by distance should be most ap-
parent in species that live in continuously distributed habi-
tats and whose propagules have limited dispersal potential
(e.g., Ayre and Dufty 1994; reviewed in Knowlton and Jack-
son 1993). For instance, the cup coral Balanophyllia elegans in-
ternally broods large, sexually produced, demersal larvae,
and lives in the shallow subtidal and low intertidal along the
West Coast of North America (Gerrodette 1981; Fadlallah
1983). Hellberg (1994, 1995, 1996) found a highly significant
negative relationship between genetic (M
ˆ, inferred from al-
lozymes) and geographic distance in B. elegans. Over scales
less than 50 km, the 95% confidence intervals of this slope in-
clude the predicted value of 1.0 for a one-dimensional step-
ping-stone model at equilibrium between limited larval dis-
persal and genetic drift (Hellberg 1995). However, beyond
approximately 50 km the magnitude of genetic differentia-
tion no longer increased (Hellberg 1994, 1995). Similar obser-
vations in marine systems of the genetic signal of isolation
by distance fading at larger regional scales occur in the inter-
tidal gastropod Nucella emarginata (Marko 1998), the splash-
zone harpacticoid copepod Tigriopus californicus (reviewed in
Burton 1998), and the mangrove littorine Littoraria cingulata
(Johnson and Black 1998).
The failure to find a pattern consistent with isolation by
distance at larger scales may be due to an historical disrup-
tion of genetic structure, caused by events such as the Pleis-
tocene glaciations in the Northern Hemisphere. Following
such a disruption, neighboring populations should achieve
equilibrium before more distant ones. The resulting pattern
of genetic structure could be quite complex, especially when
the historical events produce multiple barriers to dispersal,
across which reequilibration occurs at different rates. In fact,
Hellberg (1995) estimated that the time to reach an equilibri-
um between migration and gene flow in B. elegans is on the
order of 40,000 years. Because this exceeds the amount of
time between major climatically induced fluctuations in sea
level, temperate species such as B. elegans and N. emaginata,
whose larvae have a very limited capacity for dispersal, may
only rarely reach an equilibrium between migration and gene
flow throughout their ranges.
Taken together, these examples imply that even when
there is a significant relationship between the magnitude of
genetic subdivision and geographic distance at some spatial
scales, historical processes may contribute to the pattern at
other spatial scales. Furthermore, apparent isolation by dis-
tance can be generated by distinctly nonequilibrium process-
Genetic Structure in the Sea 71
es (Slatkin 1993; Barton and Wilson 1995). For example, in a
widely ranging species that consists of several reciprocally
monophyletic populations inhabiting geographically restrict-
ed areas, genetic distances between areas will tend to be
large, whereas distances within areas will tend to be small.
This can produce a regional pattern that resembles isolation
by distance at equilibrium, despite the absence of any ongo-
ing migration among areas.
Range Expansion and Population Growth
We have argued that, at least in temperate regions, the ranges
of many nearshore species of marine organisms are unlikely
to be static over the periods necessary to establish a range-
wide equilibrium between gene flow and drift. It is possible
to use information about genetic structure to reconstruct the
history of range expansions (and contractions). This is be-
cause, after a species expands its range—whether by way of
gradual expansion or long-distance colonization—the newly
colonized area will generally carry only a subset of the alleles
in the source population (Figure 3.3; Hewitt 1996; Templeton
1994, 1998). This leads to three straightforward predictions
about genetic structure following recent range expansions.
First, newly colonized areas should have significantly lower
genetic diversity than the parent population (Hewitt 1996;
Grant and Bowen 1998; Marko 1998). For DNA sequence
data, the significance of a difference in genetic diversity can
be assessed by simple permutation. Second, alleles in the col-
onized area should be phylogenetically nested within the di-
versity of alleles from the source area (Templeton 1994, 1998).
Third, rapid population growth is more likely in newly colo-
nized areas compared to source areas, and this should leave a
characteristic signature on a gene genealogy (see below).
Figure 3.4 illustrates the rationale for the first two predic-
tions. The figure shows an invasion of North America from
Europe in terms of geographical maps of allele genealogies.
Because we are considering mitochondrial data, alleles that
differ by at least one substitution will be referred to as haplo-
types. The invading species initially exists only in Europe,
with at least six unique haplotypes (Figure 3.4A). The species
subsequently colonizes North America (Figure 3.4B). At this
point, all haplotypes sampled from North America will be
identical to one another and to one of the European haplo-
types from which they descended. If there is no further mi-
gration from the European source, new haplotypes (from mu-
tation)—descended from the invading haplotype—will
appear in North America (Figure 3.4C).
Because the expected shape of the gene genealogy at equi-
librium is well known, coalescent theory can be used to dis-
tinguish between populations that have undergone recent
growth from those that have remained at a constant size. This
makes it possible to explore the third prediction of rapid pop-
ulation growth in the newly founded populations. The genet-
ic signatures of rapid population growth were originally ex-
plored using the simple distributions of pairwise distances
(Slatkin and Hudson 1991; Rogers and Harpending 1992).
Two recent advances now make it possible to use patterns of
genealogical relationships to characterize population dyan-
mics. The first approach predicts the number of lineages
through time under models assuming either constant popula-
tion size or exponential growth (e.g., Nee et al. 1995; Rambaut
et al. 1997), and then tests the fit of linear transformed empiri-
cal data to the models (software End-Epi, http://evolve.zps.
ox.ac.uk; Rambaut et al., 1997). These transformations can be
interpreted graphically so as to distinguish between exponen-
tial and linear growth, and whether a population has been
growing exponentially at a changing rate.
Kuhner et al. (1998) recently developed a maximum likeli-
hood method that simultaneously estimates q(effective pop-
ulation size X mutation rate), as well as a growth parameter.
A sampling procedure is used to estimate these parameters
across many alternative trees (software Fluctuate, http://www.
evolution.genetics.washington.edu/lamarc/fluctuate.html).
Given an independent estimate of mutation rate, a trajectory
of effective population size through time can be estimated
from this approach. An old population that has had no recent
bottleneck should have a shallow growth trajectory, whereas
a recently founded population should have experienced ex-
plosive growth from a few founding individuals. Although
this approach makes unrealistic assumptions, such as assum-
72 Chapter 3 / Grosberg and Cunningham
Subset of alleles takes
part in colonization
Figure 3.3 The effects of range expansions or long-distance dis-
persal on gene genealogies. When a source population (in black)
colonizes a new area (in white), the newly founded population
will likely contain a subset of the haplotypic diversity in the
source population. Thus, the newly founded population will be
less diverse than the source population, and the haplotypes in
the newly founded population will be phylogenetically nested
within haplotypes of the source population (bottom panel).
ing that the shape of the curve will always be exponential
(Kuhner et al., 1998), it may be useful to distinguish between
very different classes of histories.
Range expansions often occur naturally, especially fol-
lowing climatic or tectonic changes. However, human-medi-
ated introductions are becoming distressingly common in
the sea (Carlton and Geller 1993). With adequate genetic
sampling, it should be possible to rule out human-mediated
transport if there are at least a few unique alleles in the
newly colonized area (Ó Foighil and Jozefowicz 1999). The
evidence against human-mediated colonization is especially
strong if most or all of these unique alleles are descended
from one of the common, or “founding” alleles (as in Figure
3.4B). If, however, all alleles in the colonized area are shared
with the hypothetical source population, other information,
including historical records, must be incorporated into the
reconstruction of colonization.
Summary and Conclusions
As others have argued (e.g., Palumbi 1994; Hilbish 1996; Ben-
zie 1999), the evidence summarized in this section suggests
that there are relatively few marine species that fully satisfy
the expectations of equilibrium between genetic drift and mi-
gration across their entire ranges. At one extreme, the ab-
sence of significant genetic structure cannot always be equat-
ed with true equilibrial panmixia. It may take millions of
years following subdivision for allozyme markers to reveal
genetic structure. Although mtDNA-based markers may be
more sensitive to vicariance, migration models that assume
equilibrium will yield low but detectable levels of migration
between populations that have long since stopped exchang-
ing migrants. Similarly, recently founded populations and
their putative sources may not show significant genetic dif-
ferences; however, their levels of genetic diversity should sig-
nificantly differ. In all of these cases, the absence of detectable
genetic structure cannot necessarily be equated with signifi-
cant present-day gene flow.
At the other extreme, a simple correspondence between
geographical distance and genetic distance (or inferred mi-
gration rate) does not necessarily mean that populations are
at equilibrium. The details of scale, once again, count: If the
correspondence occurs only across small spatial scales, then it
suggests that subpopulations have yet to equilibrate at re-
gional scales (Hutchison and Templeton 1999). When a popu-
lation consists of an aggregate of more-or-less contiguous
local populations that are internally panmictic, but that do
not exchange migrants with the other subpopulations, an
analysis of genetic structure that includes both within- and
among-subpopulation comparisons can yield a significant re-
lationship between genetic and geographic distances, despite
the absence of gene flow among subpopulations.
We conclude that the present-day genetic structure of many
species of marine invertebrates often reflects the operation of
both contemporary gene flow and historical factors, and that
populations are often not in equilibrium throughout their
ranges. More specifically, in species with extensive dispersal
potential, and at local scales, the effects of gene flow and drift
may predominate; in species with less extensive dispersal po-
tential, or at relatively larger spatial scales, nonequilibrial pro-
cesses may prevail. With allele frequency approaches to the
analysis of genetic structure, the relative contributions of his-
toric and contemporary are often impossible to distinguish.
Phylogeographic approaches, on the other hand, now make it
possible to begin to assess the relative contributions that his-
torical factors and contemporary gene flow make to current
genetic structure. The few studies of marine organisms that
Genetic Structure in the Sea 73
(A) (B) (C)
Figure 3.4 An unrooted gene genealogy illustrating the process
of colonization of North America from Europe by a single mito-
chondrial haplotype. Each circle (or oval) represents a single hap-
lotype, and approximates the geographical range of that haplo-
type. (A) Prior to the colonization event, the species is endemic
to Europe. (B) A propagule (or propagules) carrying a single
European haplotype colonizes North America. (C) Mutation and
drift will eventually cause the North American haplotypes to
diverge from their European progenitors. In the absence of sub-
sequent colonization from European sources, all new haplotypes
in North America will be descended from from the founding
haplotype.
employ such methodologies suggest that many species of ma-
rine invertebrates consist of genetically differentiated subpop-
ulations whose evolutionary histories geographically vary. To
the extent that this is generally true, it raises a critical series of
questions for the community ecologist concerning the spatial
and temporal scales over which populations of marine organ-
isms interact with their competitors, predators, and pathogens.
FROM POPULATIONS TO COMMUNITIES
In this section, we return at last to the question posed at the
beginning of this chapter: What can information about pat-
terns of genetic variation within a species, and the historical
and recent processes that generate these patterns, tell us about
the ecological and evolutionary outcomes of species interac-
tions? We first consider the strengths and weaknesses of using
genetic structure to address questions concerning open and
closed ecological systems, and the scales over which interact-
ing species can influence each other’s population dynamics.
We then extend this analysis into predicting the nature of ge-
netic and phenotypic responses to spatially varying selection.
Third, we consider how genetic information can be used to
distinguish sibling species, and the importance of these dis-
tinctions for understanding the evolution of ecological special-
ization. Finally, we explore how genetic structure can be used
to reconstruct the history of species interactions, specifically to
distinguish long-term, locally adapted residents from recent
arrivals with less potential for local adaptation.
Scale of Population Regulation
The spatial scale and magnitude of demographic connection
among subpopulations of the species that compose a local
community depend on species-specific modes of develop-
ment, larval behavior, and local and regional patterns of water
movement (Gaines and Roughgarden 1985; Possingham and
Roughgarden 1990; Gaines and Bertness 1993; Todd 1998; re-
viewed in Booth and Brosnan 1995; Caley et al. 1996; Cowen
et al. 2000). The debate over whether populations of marine
organisms exhibit open or closed dynamics was ignited in the
early 1980s when marine ecologists rediscovered the impor-
tance of recruitment limitation to the demography of benthic
invertebrates and reef fish populations (Doherty 1981; Under-
wood and Denley 1984; Gaines and Roughgarden 1985;
Roughgarden et al. 1985; Young 1987; Hughes 1990; Grosberg
and Levitan 1992; Booth and Brosnan 1995; Caley et al. 1996;
Waples 1998). Recruitment levels can affect population dy-
namics and species interactions by regulating the intensity of
competition (both intra- and interspecific) and predation
(Menge and Sutherland 1987; Connolly and Roughgarden
1999). To the extent that the adults of many marine organisms
are relatively sedentary and their propagules are relatively
motile, the supply of recruits to a local population of adults
could be governed by processes occurring outside the local
population, instead of by local reproductive output (Menge
and Olson 1990). This is what is conventionally meant by
“open” population dynamics.
In most benthic marine communities, occupants of differ-
ent trophic levels, and competitors at the same trophic level,
often have different developmental modes. For example, two
of the keystone predator genera of the Northern Hemisphere
rocky intertidal have dramatically different dispersal poten-
tial. The major gastropod predator Nucella has direct develop-
ment, whereas the predatory seastars Asterias and Pisaster
have planktotrophic larvae. Since the major prey of all of
these predators are mussels and barnacles, both of which
have planktotrophic larvae with extensive dispersal potential,
the scales over which prey abundance regulates predator
population dynamics (or vice versa) should differ for interac-
tions with Nucella compared to those involving Asterias and
Pisaster. Gaines and Lafferty (1995) developed a series of
models exploring the dynamics of predators and prey, com-
petitors, and hosts and pathogens, when interacting species
exhibited different combinations of locally closed versus
open populations. These models nearly uniformly yield dra-
matically different dynamics than conventional models in
which interacting species are closed or open at matching spa-
tial scales.
From the perspective of community ecology, the critical
problem is therefore to determine not only the magnitude
and scale of demographic exchange among subpopulations
(i.e., how open or closed are they?) of each interacting spe-
cies, but also the degree to which the population dynamics of
interacting species spatially correspond (McLaughlin and
Roughgarden 1993; Holt 1993; Underwood and Petraitis
1993; Booth and Brosnan 1995; Gaines and Lafferty 1995; Con-
nolly and Roughgarden 1999). This problem has remained an
unanswered challenge in most marine systems because of the
difficulties of directly tracking migration of motile propag-
ules. One approach, widely used in agricultural systems, is to
breed “genetically engineered” stocks that carry rare mark-
ers, introduce them into natural or experimental arrays, and
sample offspring for the marker gene. Such genetic tags have
also been used in marine invertebrates (e.g., Grosberg and
Quinn 1986; Grosberg 1991) and fish (Wilson et al. 1997; Wil-
son and Donaldson 1998; Perez-Enrique and Tanigushi 1999).
It is now also possible to track fish larvae using natural or ar-
tificial chemical tags (Campana et al. 1995; Jones et al. 1999;
Swearer et al. 1999).
The value of naturally occurring genetic markers for char-
acterizing demographic units (and identify larval sources) re-
lies on the presence of detectable genetic differences among
subpopulations (Utter and Ryman 1993; Hedgecock 1994;
Burton 1994, 1996; Palumbi 1995; Waples 1998). In turn, the
existence of such differences depends upon the relationship
between the genetically effective migration rate, m, and the
mutation rate, m. In general, if m is greater than mfor a mark-
er, then at equilibrium, the marker will not reveal differences
among subpopulations. If, however, m is less than m, new al-
leles will arise within subpopulations more frequently than
they are exchanged with adjacent subpopulations, and
unique alleles characteristic of that specific subpopulation
should be detectable in at least some individuals.
74 Chapter 3 / Grosberg and Cunningham
If selectively neutral markers reveal genetic structure at a
particular spatial scale, and this structure is temporally sta-
ble, then the genetically distinct subpopulations cannot be
experiencing much present-day gene flow (e.g., Bulnheim
and Scholl 1981; Burton et al. 1979; Todd et al. 1988; Burton
and Lee 1994; Lessios et al. 1994; Lewis and Thorpe 1994; Bur-
ton 1997; Edmands et al. 1996; see Waples (1998) for caveats
when F
ST is small (< 0.05), but nonetheless, significant). Such
subpopulations ought to be independently regulated by their
parasites, pathogens, predators, and competitors, with sub-
stantially different population dynamics than would be the
case in demes connected by extensive migration (Antonovics
1994; Gaines and Lafferty 1995).
Unfortunately, the absence of genetic structure may say
relatively little about demographic interconnectedness, because
a demographically insignificant amount of gene flow among
subpopulations—on the order of one migrant per genera-
tion—will eventually homogenize allelic frequencies at neu-
tral loci. As we discussed earlier (in the section entitled The
Approach to Equilibrium and Why It Matters), the time it
takes to reach this equilibrium depends on the migration
rate, effective population size, and mutation rate (Takahata
1983). With low mutation rates, and demographically plausi-
ble migration rates, this equilibrium will be reached quickly,
even in relatively large populations, leaving no detectable ge-
netic signature. However, as the mutation rate, m, of a marker
increases with respect to the migration rate, m, the rate of ap-
proach to equilibrium will be slowed.
In this respect, the development of hypervariable markers
such as microsatellites has dramatically improved the power
to distinguish previously undetectable levels of genetically
effective migration among subpopulations. Coupled with a
variety of recent statistical innovations, hypervariable mark-
ers potentially allow more detailed inferences about the pat-
tern, scale, and history of gene flow than has been possible
with less variable markers (e.g., Bertorelle and Excoffier 1999;
Waser and Strobeck 1998; reviewed in Luikart and England
1999). For example, hypervariable markers such as some mi-
crosatellite loci can be used [with some caveats about muta-
tional models, estimating allelic frequencies, and so forth; see
papers in Goldstein and Schlötterer (1999)] to estimate gene
flow using the procedures described earlier for F
ST-like statis-
tics (e.g., RST). Such indirect estimates of gene flow can then
be compared to direct estimates based on genetic identifica-
tion of the sources of individual immigrants. One way to do
this is to “engineer” genetically or chemically migrants so
that they can be distinguished from residents upon resam-
pling. Alternatively, if populations are even slightly genetical-
ly differentiated, hypervariable markers dramatically im-
prove the prospects for using likelihood methods to assign
individual genotypes in a sample to their correct source pop-
ulation (reviewed in Waser and Strobeck 1998; also see
http://www.biology.ualberta.ca/jbrzusto/Doh.html). Simi-
larly, maximum-likelihood methods can be sometimes be
used to distinguish between sets of subpopulations with the
same F
ST’s that are linked by gene flow (i.e., in equilibrium)
from those that are partially or fully independent (Beaumont
and Bruford 1999).
To some, these advances promise to bridge the gap be-
tween the shortcomings of direct and indirect measures of
gene flow. A more precise understanding of the short- and
long-term outcomes of the interaction between spatially vary-
ing selection and gene flow should follow (see the next sec-
tion). Nonetheless, for all but the lowest rates of exchange
among subpopulations, it is unlikely that naturally occurring
genetic markers alone will ever be able to reveal fully the ge-
ographic sources of immigrants to a population and the mag-
nitude of demographic connections.
Responses to Spatially Varying Selection
When populations are at equilibrium, the scale and magni-
tude of genetic subdivision strongly reflects the extent to
which (1) individuals experience different selective regimes
over their lifetimes and (2) subpopulations can independent-
ly evolve in response to spatially varying selection. Thus, the
correspondence between the scale and magnitude of genetic
structure and the scale over which diversifying selection op-
erates determines the likelihood of cumulative genetic
change and local adaptation (Endler 1992). In general, species
with limited gene flow should be more likely than species
with extensive gene flow to exhibit local adaptation to spa-
tially varying selection (Holt and Gaines 1992). They should
also do so on finer spatial scales than species with more
widespread gene flow. Genetically differentiated subpopula-
tions may ultimately diverge to such an extent that they be-
come reproductively isolated.
At the other extreme, when the spatial scale of gene flow
exceeds the scale over which selection varies, cumulative
adaptive genetic changes are unlikely to occur. In other words,
spatially varying selection favoring a specific genotype in a
particular location can overcome the homogenizing effects of
gene flow within generations; however, a continuing flow of
recruits from neighboring populations limits the opportunity
for the accumulation of genetically based local specialization
to spatially varying selection (Ament 1979; Strathmann and
Branscomb 1979; Strathmann et al. 1981; Warner 1997). Selec-
tion should instead favor the evolution of generalist pheno-
types or reduced dispersal (Slatkin 1973; Gooch 1975; Endler
1979; Hedgecock 1986; Warner 1997). When the appropriate
environmental cues exist, phenotypic plasticity or habitat se-
lection may also evolve (Adler and Harvell 1990). Thus, there
should be a tradeoff between the expected degree of local ge-
netic adaptation and the magnitude of dispersal.
Does empirical reality in marine species match these pre-
dictions? Many of the classic marine studies of the scale of
genetic differentiation concern spatially varying selection im-
posed by the physical environment (reviewed in Janson and
Ward 1984; Hedgecock 1986; Behrens Yamada 1989; Ayre
1995; Warner 1997). Behrens Yamada (1989) contrasted geo-
graphic variation in life-history traits in two species of Littori-
na, an intertidal, herbivorous snail common in temperate wa-
ters of the eastern Pacific.L. sitkana embryos directly develop
Genetic Structure in the Sea 75
into crawl-away juveniles, whereas L. scutulata larvae devel-
op in the plankton. Both species exhibit significant geograph-
ic variation in the expression of growth rates and reproduc-
tive timing, attributed to selection imposed by variation in
desiccation stress (Behrens Yamada 1989). These life-history
differences persisted in common garden experiments as well
as reciprocal transplants, suggesting that at least some of the
demographic variation is heritable. Consistent with the pre-
dicted tradeoff between scale of local adaptation and disper-
sal potential, the scale of geographic differentiation for these
traits is on the order of tens of kilometers in the directly de-
veloping L. sitkana versus hundreds of kilometers in L. scutu-
lata. Unfortunately, the study lacked a genetic assessment
using neutral markers of geographic structure, making it im-
possible to reject the scenario that the regional pool of recruits
is genetically homogeneous, with post-recruitment selection
within generations producing the observed local pattern.
Other studies of the response to spatial variation in the
physical environment that explicitly consider genetic struc-
ture also support some of the basic predictions of the tradeoff
model. Tigriopus californicus is an intertidal and supralittoral
harpacticoid copepod common in tidepools along the West
Coast of North America. The life history of T. californicus im-
plies that it should have very limited dispersal potential, a
prediction verified by high levels of temporally stable genetic
differentiation at very fine spatial scales (reviewed in Burton
1998). Patterns of micro-geographic and regional variation at
several allozyme loci associated with osmoregulation strong-
ly correspond to spatial variation in salinity and temperature
along both intertidal and latitudinal gradients. Physiological
studies and transplant experiments confirm that these loci are
under selection by the thermal and salinity regime. Whether
selection favored the evolution of reduced dispersal in T. cali-
fornicus remains to be seen; nevertheless, it appears that limit-
ed gene flow in this species permits selection to cause cumu-
lative adaptive change at very fine spatial scales.
In species with extensive dispersal potential, such as bar-
nacles (Hedgecock 1986; Schmidt and Rand 1999) and mus-
sels (e.g., Hilbish and Koehn 1985), genetic data, either in the
form of allozymes or mtDNAdata, show that cohorts of new
recruits appear to be genetically well-mixed over local and
sometimes regional scales. However, at some loci (associated
with thermal, salinity, or desiccation tolerance), the genetic
composition of cohorts recurrently diverges following re-
cruitment, presumably due to diversifying selection imposed
by local variation in temperature or salinity. In these exam-
ples, the perhaps unexpected product of the interaction be-
tween high gene flow and fine-scale post-recruitment selec-
tion appears to be the short-term maintenance of a balanced
genetic polymorphism within populations for variation in
the physiological traits under selection, rather than pheno-
typic plasticity or reduced dispersal.
The effects of spatial variation in predation intensity on
phenotypic variation in prey populations are far better stud-
ied than the effects of other species interactions such as com-
petition or parasitism. What are the effects of genetic struc-
ture on the evolution of this phenotypic variation, and to
what extent does this variation represent local adaptation
versus predator-induced phenotypic plasticity? Conspecific
populations of many marine gastropods, including members
of the genera Nucella and Littorina, often exhibit site-specific
variation in shell thickness that corresponds to the intensity
of predation by crabs (Janson 1982, 1987; Palmer 1985, 1990;
Trussell 1996). Thin-shelled morphs are more resistant to dis-
lodgement by waves, and predominate on exposed shores
where crab predators are relatively rare; in adjacent protected
waters, where crab predation intensifies, thick-shelled morphs
predominate (reviewed in Trussell 1996).
The whelk genus Nucella consists entirely of directly de-
veloping species with demersal, crawl-away juveniles. Stud-
ies of genetic structure based on both allozymes (e.g., Day
1990; Day et al. 1993) and mtDNA sequences (Marko 1998)
show that populations exhibit extensive genetic structure at
spatial scales corresponding to phenotypic variation in shell
thickness and the abundance of predators. Unlike the previ-
ously cited barnacle and mussel examples, where it appears
that recruits are well mixed (at least at local scales), the exis-
tence of substantial genetic structure at a scale roughly corre-
sponding to that over which selection varies suggests that
phenotypic variation in shell thickness signifies true local
adaptation. However, several experimental studies indicate
that much of the phenotypic variation in shell thickness can
be induced by crab predators (Appleton and Palmer 1988;
Palmer 1990), and thus may not entirely represent local ge-
netic adaptation.
In the North Atlantic, two species of Littorina have been es-
pecially well characterized in terms of genetic structure and
geographic variation in shell structure. Like the North Pacific
species pair studied by Behrens Yamada (1989), L. saxatalis is
a direct developer, with crawl-away juveniles; L. littorea is
sympatric with L. saxatalis, but its larvae remain in the plank-
ton for 4–6 weeks (Janson 1987). Allozyme studies show the
expected genetic patterns, with L. saxatalis exhibiting fine-
scale genetic structure on the order of meters (Janson 1987)
and L. littorea lacking detectable genetic structure over hun-
dreds of kilometers (Berger 1973; Janson 1987). In both spe-
cies, the degree of variation in shell morphology corresponds
to the pattern of genetic structure. L. saxatalis displays varia-
tion in shell-thickness according to local variation in wave ex-
posure, and L. littorea lacks such phenotypic variation (Cur-
rey and Hughes 1982; but see Dudley 1980).
Once again, the question is, does the variation in the shell
morphology of the direct-developing L. saxatalis represent ge-
netic differentiation or phenotypic plasticity? As with Nucel-
la, the answer remains equivocal. Newkirk and Doyle (1975)
showed that variation in shell morphology is partially under
genetic control in L. saxatalis, a result consistent with the ex-
pectation of local adaptation. However, a recent study on L.
obtusata (also a direct developer) demonstrated that crab-pre-
dation can directly induce a substantial increase in shell
thickness (Trussell 1996), suggesting a role for phenotypic
plasticity.
76 Chapter 3 / Grosberg and Cunningham
To what extent can information about the genetic struc-
ture of marine populations be used to predict a species re-
sponse to spatially varying selection? The answer at this point
remains equivocal. Species with minimal structure and ex-
tensive gene flow that we might expect to exhibit phenotypic
plasticity often do not. Species with substantial structure and
minimal gene flow often exhibit phenotypic plasticity. Stud-
ies conducted at different scales and on different populations
can produce conflicting results.
Part of the failure to match expectations is almost certain-
ly due to our extremely limited knowledge of the nature of
spatial and temporal variation in selection. In addition, our
understanding of the genetics of adaptation remains in its in-
fancy (Orr 1998). But just as importantly, we still know re-
markably little about genetic structure and the history of
gene flow in natural populations of marine organisms. In
terms of the response to selection, the details of genetic struc-
ture and the equilibrium status of populations are critical:
Populations with identical inferred levels of gene flow may
differ in their actual degree of isolation from one another. For
instance, populations may lack significant genetic structure
for neutral markers either because they are currently ex-
changing migrants, or because they have been recently sub-
divided and not yet reached evolutionary equilibrium (e.g.,
Benzie 1999). Conversely, populations that appear to be
equally differentiated may actually represent a mosaic of for-
merly isolated subpopulations, some of which are now inter-
connected (but have yet to reach equilibrium), and others of
which remain unconnected. In both situations, subpopula-
tions with the same apparent genetic structure may differ in
their responses to spatially varying selection because some
subpopulations may be partially or fully evolutionarily inde-
pendent, whereas others may only appear to be.
For this reason alone, future studies at the interface be-
tween ecology and genetics should incorporate an explicit
historical component that utilizes some combination of ge-
nealogical methods and high-resolution genetic markers ca-
pable of distinguishing low levels of ongoing gene flow
from the recent cessation of genetic exchange. In this re-
spect, intrapecific genealogical information can be used in
some situations to identify those species in an assemblage
with the greatest potential for local adaptation. If a species
can be recognized as a recent colonist (see the section enti-
tled Range Expansion and Population Growth), then it may
have had little opportunity to respond to local selection. If
populations in one area are reciprocally monophyletic with
respect to populations in other areas, then it is most parsi-
monious to infer that the species has survived in both areas
with little gene flow between them (see Genealogical Ap-
proaches). The length of time populations have persisted in
both areas corresponds roughly to the number of substitu-
tions along the internal branch dividing the populations
(Figure 3.5). Reciprocally monophyletic populations there-
fore satisfy two requirements for local adaptation: long-
term residence, and little or no genetic exchange with other
populations.
Cryptic Species: Intraspecific Polymorphism
versus Interspecific Diversification
In the previous section we concluded that recent and historic
patterns of gene flow are key determinants of the evolution-
ary response of populations to spatially varying selection.
Given sufficient time and limited gene flow, diversifying se-
lection and drift can eventually lead to the acquisition of ge-
netically based post- and pre-reproductive isolation between
populations (reviewed in Coyne and Orr 1998). In many
cases, there are few reliable morphological clues to this tran-
sition from a polymorphic species to interspecific diversifica-
tion, and lab tests of reproductive compatibility are notori-
ously difficult to implement and interpret. Yet this transition
from a state of intraspecific polymorphism to two or more
cryptic (“sibling”) species is critical to identify, because in
many organisms (plants and corals may be exceptions) it sig-
nifies the irreversible evolutionary independence of lineages.
The existence of numerous complexes of sibling marine
species is now well established (see review in Knowlton
1993). Although an unambiguous definition of cryptic or sib-
ling species is controversial (for a good discussion see Knowl-
ton and Weigt 1997), cryptic species can often be identified by
genetic data, especially when populations show fixed allelic
differences or reciprocal monophyly (see the section entitled
The History of Population Subdivision and the Importance of
Genetic Structure in the Sea 77
Figure 3.5 Establishment of reciprocal monophyly following the
colonization process shown in Figure 3.4. If there is no further
migration across the Atlantic, then mutations (represented by
hatch marks) will accumulate between European and North
American populations, producing a long internal branch in the
genealogy (see Templeton 1994). Such a pattern would indicate
the independence of resident populations on opposite coasts.
Reciprocal Monophyly). For this reason, Avise and Ball (1990)
used gene genealogies as the basis for their concordance
species concept. They argued that if multiple unlinked loci
show congruent patterns of reciprocal monophyly, the indi-
viduals so defined should be considered distinct species.
When they are not diagnosed, cryptic species that have
fully or partially sympatric distributions can be confused with
stable intraspecific polymorphisms. Such confusion can, in
turn, dramatically alter interpretations of outcomes of ecologi-
cal interactions. For example, the snail Acanthina angelica preys
on the barnacle Chthamalus anisopoma, and induces the produc-
tion of a hooded morph that is more resistant to predation
than the normal conical morph (Lively 1986; reviewed in Live-
ly et al. 2000). The conical morph of C. anisopoma occurs
throughout the Gulf of California, whereas the hooded morph
occurs primarily in the northern Gulf. The predator, A. angelica,
lives only in the northern Gulf of California, suggesting that
geographic variation in the prey’s phenotypic polymorphism
is the result of environmental induction by the predator. How-
ever, Lively et al. (2000) recently showed that barnacles collect-
ed from the northern Gulf differed in their inducibility by
Acanthina, raising the possibility that there is also an underly-
ing genetic polymorphism controlling the amount of pheno-
typic plasticity in the northern Gulf population.
Why are there noninducible morphs in the northern Gulf?
One option is that some sort of balancing selection maintains
an equilibrial genetic polymorphism within the northern Gulf
(Lively et al. 2000), as appears to be the case in the barnacles
and mussels discussed above. An unexplored nonequilibrial
alternative is that the inducible and uninducible forms repre-
sent genetically differentiated populations, or even cryptic
species. Uninducible southern Gulf populations or species
may be continually swept into the northern Gulf in such large
numbers that they persist in the northern Gulf, maintaining
apparent polymorphism for inducibility despite their selective
disadvantage in the face of Acanthina predation. If this sce-
nario were correct, then our ecological and historical interpre-
tation of the distribution of hooded morphs of C. anisopoma
would substantially differ from that based on a genetic or en-
vironmentally induced intraspecific polymorphism.
One of the most ecologically dramatic examples in marine
systems of the importance of distinguishing interspecific dif-
ferentiation among cryptic species from intraspecific pheno-
typic polymorphisms concerns the symbiosis between her-
matypic corals and their zooxanthellae. Until recently, this
symbiosis was thought to represent an association between a
diversity of host coral species and a single species of dinofla-
gellate in the genus Symbiodinium. However, Rowan and Pow-
ers (1991, 1992), following previous speculation (e.g., Kinzie
and Chee 1979; Jokiel and York 1982), challenged the long-
standing hypothesis that the Symbiodinium that inhabited all
hermatypic corals belonged to the same ecologically general-
ized “cultivar.” Using RFLPs of genes encoding small riboso-
mal RNAs, they showed that there are three very distinct taxa
of symbionts, designated A, B, and C. Later work revealed
that each of these three cultivars of Symbiodinium had different
irradiance optima. At least in the corals Montastrea annularis
and M. faveolata, symbionts Aand Bare common in shallow,
high irradiance habitats; Cpredominates in deeper, low irradi-
ance habitats (Rowan and Knowlton 1995; Rowan et al. 1997).
The mere discovery that Symbiodinium was not a mono-
typic species, but instead a species complex, consisting of at
least three very distinct members with different physiologies,
was in and of itself a major revolution in our understanding
of the natural history of coral symbioses. It also helped to
clarify why there is so much variation within and among
coral heads in intensity of bleaching. Rowan et al. (1997) sam-
pled tissue from low and high irradiance parts of individual
coral heads, and from corals living at different depths. Sam-
ples from a single coral head contained different relative
amounts of each symbiont, and the relative abundance of
each type of symbiont corresponded to predictions based on
irradiance optima (i.e., symbiont Cwas most common in low
irradiance positions on individual coral heads, and increased
in relative abundance in corals sampled from increasing
depth). In other words, there is zonation within and among
coral heads, and subsequent experimental manipulations
showed that irradiance plays a major role in controlling com-
munity composition of the symbionts (Rowan et al. 1997).
These findings do not exclude other important intrinsical-
ly driven effects on coral bleaching such as physiological ac-
climitization of hosts and symbionts, and genetically based
physiological differences among host corals. But the data do
highlight the existence of previously unknown genetically
based differences among the symbionts, and the role that
such variation may have in producing the distinct patterns of
bleaching so commonly observed throughout the Caribbean.
Without this genetic information about the taxonomy of Sym-
biodinium, and the fine-scale distribution of the symbiotic
taxa within and among coral heads, the evolutionary and
ecological relationships between corals and their algal sym-
bionts, not to mention the ecological interactions and mainte-
nance of diversity among symbionts, would at best be half
told stories (Rowan and Knowlton 1995).
Community Assembly and the
History of Species Interactions
Do the recurrent similarities and differences that characterize
modern species assemblages of marine organisms principally
reflect the outcomes of contemporary ecological interactions,
repeated in time or space, or do the members of similar as-
semblages also share a genealogical connection? The great
promise of “historical ecology” was that phylogenetic analy-
sis would bring two new perspectives to our understanding
of the historical and contemporary contributions to commu-
nity assembly (Brooks 1985; Brooks and McLennan 1991).
First, a phylogeny should allow identification of a species’
closest relatives, and thus allow one to reconstruct ancestral
and derived character states. With this knowledge, it would
be possible to infer which features evolved in situ, and which
were inherited from its ancestor. For example, ecological
character displacement can lead to differences in size be-
78 Chapter 3 / Grosberg and Cunningham
tween competing species (e.g., Schluter et al. 1985). If, howev-
er, the closest relatives of one or both species were the same
size as the competing species, then the size difference be-
tween the interacting species more likely represents a re-
tained ancestral difference, rather than the outcome of the on-
going competitive interaction.
Second, phylogeographic analysis should make it possible
to identify species that have had a long shared history with
one another. If two interacting species collected from the same
area have congruent phylogenies, then these species probably
shared a long history (reviewed in Cunningham and Collins
1994; Page and Hafner 1996). Conversely, if one of the mem-
bers of the interaction has been a long-term resident, and the
other arrived recently from elsewhere, then the species have
had relatively little time to evolve in response to one another.
Shared history can also be inferred if a number of species
share congruent patterns of reciprocal monophyly on either
side of a genetic break (reviewed in Avise 1994). In the case of
the North Atlantic fauna, the set of species that show recipro-
cal monophyly between Europe and North America must
have persisted on both coasts with little appreciable gene
flow, despite glacial fluctuations (Cunningham and Collins
1998). If, on the other hand, there is consistent genetic evi-
dence for recent colonization of a particular area by members
of an assemblage, then the newly colonizing species may
have had a long history in the source—but not the recipient—
area (e.g., Europe in Figure 3.5).
SUMMARY
Ecology has two fundamental goals. The first is to identify
the processes that regulate species distribution and abun-
dance, and the temporal and spatial scales over which these
processes operate. The second is to understand the nature
and outcomes of species’ interactions with their biotic and
physical environments and how these interactions regulate
community structure. It remains difficult to identify many of
these processes (and their scale of operation) and to predict
these outcomes, in part because there has been little concert-
ed effort by systematists and population geneticists to pro-
vide marine ecologists with the information necessary to de-
cipher the history of species distributions and the spatial
scales over which populations are genetically connected.
Promising beginnings have been made in the southeastern
United States (reviewed in Avise 1994; Cunningham and
Collins 1998), the Isthmus of Panama (Knowlton et al. 1993;
Collins 1996; Lessios 1998), the Indo-Pacific (reviewed in Ben-
zie 1999), the West Coast of the United States (e.g., Burton
1998), and hydrothermal vent systems (reviewed in Vrijen-
hoek 1997). These studies, along with the analyses of genetic
structure presented in this chapter, suggest that few, if any,
marine species are in equilibrium with respect to gene flow,
drift, and selection throughout their ranges. To the extent that
this proves to be correct, we should expect substantial geo-
graphic variation in the nature and outcomes of these interac-
tions, both as a result of ongoing spatial variation in selection,
as well as the different histories of selection, colonization, and
extinction experienced by different populations.
Understanding the history of species distributions in
terms of contemporary and historic patterns of selection, ex-
tinction, and colonization also underlies the development of a
tradition of “comparative marine ecology.” How do we inter-
pret ecological similarities and differences among communi-
ties? To what extent are these similarities the result of shared
histories or shared selective regimes? To what extent are dif-
ferences possible despite shared histories? Do the biogeo-
graphic boundaries that define major breaks in community
composition correspond to genetic discontinuities in their
constituent taxa (Burton 1998; cf. Avise 1994)? Are the species
that occupy adjacent biogeographic provinces closely relat-
ed? For example, the rocky intertidal community of New
England differs dramatically from that of the temperate Pa-
cific, yet the New England assemblage consists largely of
species that arrived from the Pacific in the past few million
years (Vermeij 1991). Answers to these ecological questions
fundamentally depend on growing collaboration among
ecologists, population geneticists, and systematists. This col-
laboration remains to be fully implemented, but with it will
come a much deeper understanding of the principles that
govern community structure and dynamics.
ACKNOWLEDGMENTS
We were both supported by grants from the National Science
Foundation while we wrote this chapter. R.K.G. was also
supported by a California Sea Grant.
Genetic Structure in the Sea 79
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