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21st Century Directions in Biology
Molecular Markers, Natural History,
and Conservation of Marine Animals
RONALD S. BURTON
Molecular genetic techniques have found broad utility in modern marine ecology, and applications continue to grow. Databases of DNA sequences
now permit nonexperts to identify eggs and larval stages of many marine animals that were previously mysteries. Molecular identifications of fieldcollected organisms and tissues are used to help assess population connectivity, investigate marine food webs, and identify marketed commodities.
Advances in technology already include prototype development of in situ robotic instrumentation for sampling and molecular identification of
animal larvae. Studies of population connectivity, once limited to a few gene loci, are slowly giving way to new genomic arrays of markers and
high-throughput methodologies for scoring genotypes. Population genetic theory is providing new computational techniques to assess patterns of
population structure, estimate effective population sizes, and infer aspects of demographic history. In this article I review a subset of recent work in
this growing area of molecular marine ecology.
Keywords: molecular ecology, population structure, species identification, marine ecology, historical demography
T
wo constraints impede our understanding of the biology
of marine animals: First, because we routinely probe only
a tiny fraction of the ocean’s biosphere, the diversity and
abundance of animals in much of the world’s oceans are extremely undersampled. Second, in many cases even familiar
species are observed only during portions of their life cycles.
Intertidal barnacles and sea urchins do not seem very
mysterious, yet a crucial portion of their life histories—when
millions of fertilized eggs develop and disperse in the water
column—remains poorly understood. Eggs and larvae of
even common species are frequently difficult or impossible
to identify on the basis of morphology. These facts make
already difficult questions all the more intractable. What
habitats are critical to the complete life cycle of a given
species? What is the geographic range of individual planktonic
larva? Do apparently isolated populations exchange migrants?
We can answer these questions for remarkably few marine
organisms.
This lack of knowledge cripples efforts to manage ocean
resources. Fisheries typically target adult populations of organisms, often without knowledge of the source of recruits
to the harvested stocks. Offshore fish stocks can be destroyed
if management policies for coastal wetlands unwittingly
permit unidentified key nursery areas to be disturbed. Without knowledge of spawning grounds and dispersal patterns
within and between populations, the role of many habitats in
fisheries production remains unknown, and, consequently,
at risk.
Exciting new tools are continually providing novel insights into previously inaccessible aspects of the natural
history of marine organisms. Examples include the use of trace
elements, isotopes, and satellite tracking to monitor the
movements of animals in order to better understand their
geographic ranges for feeding and reproduction (Thorrold et
al. 2002, Levin 2006, Teo et al. 2007). Although the capabilities of these methodologies continue to expand, the focus here
will be limited to DNA- and RNA-based molecular genetic
technologies. The goal of this article is to provide an overview
of the rapidly expanding applications of molecular genetic approaches for understanding the ecology of marine animals.
The application of molecular tools in marine ecology
spans a much broader range of topics than considered here.
For example, Hofmann and Place (2007) discussed the use of
microarrays in assessing the role of gene expression in adaptation to environmental change, and Edwards and Dinsdale
(2007) discussed the use of metagenomics in analyses of
marine microbial diversity and biogeochemical processes.
Here the focus will be conceptually quite simple—DNA (and
RNA) analyses permit estimates of genetic similarity among
organisms. At the coarsest level, we can identify species. At a
finer level, we can examine differences among populations
within species. Levels of diversity within and between populations can also provide insights into historical population
sizes and demography. Each level of inquiry presents challenges, and the molecular and analytical tools available for
meeting these challenges are evolving rapidly.
BioScience 59: 831–840. ISSN 0006-3568, electronic ISSN 1525-3244. © 2009 by American Institute of Biological Sciences. All rights reserved. Request
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November 2009 / Vol. 59 No. 10 • BioScience 831
21st Century Directions in Biology
Identification of species
By definition, species differ in their DNA sequences. Evolutionary analyses have clearly shown that the extent of
sequence differentiation is correlated with the time since the
taxa of interest shared a common ancestor. Consequently,
DNA sequences can be used both to identify organisms and
to reveal their evolutionary relationships. Although conceptually simple, the use of DNA sequencing for species identification has had a great impact in marine ecology. A large
proportion of marine organisms have complex life histories,
including larval forms that occupy different habitats and
neither resemble nor behave like their respective adult forms
(figure 1). In the absence of laborious (and frequently
unsuccessful) efforts to culture organisms through their
respective life cycles, we cannot identify larval forms to
the species level for a vast majority of marine organisms.
This limitation not only hinders our understanding of natural histories but also may result in erroneous assessments of
commercial fisheries stocks, where larval surveys are used to
predict future stock abundance.
The technology involved in obtaining DNA sequence data
from even the smallest eggs and larval forms using the polymerase chain reaction (PCR) is now well developed (box 1).
The challenge in determining the species identity of a specimen lies in matching its DNA sequence to a conspecific
adult. To match larvae with adults requires a database with
appropriate DNA sequences from correctly identified adult
specimens. A fingerprint at a crime scene cannot be matched
to the FBI fingerprint database if the perpetrator has never
been fingerprinted. However, unlike the case with fingerprints, evolutionary relationships among DNA sequences
mean that even when there is no match on file, DNA
Figure 1. Adult barnacles and urchins are familiar sights, but they are less so when they start life as planktonic larvae, as
many marine animals do. (a) Adult barnacles on an exposed intertidal rock and (b) the planktonic nauplius stage of a
barnacle. (c) The pluteus-stage larva of a sea urchin and (d) adult urchins on the sea floor. Photographs: Ronald S. Burton
(a, b), Elisa Maldonato (c), and Dave Rudie (d).
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Box 1. A glimpse of the molecular genetic toolkit.
Molecular tools have long played a role in marine
ecological studies; protein polymorphisms were used to
study fish populations more than 50 years ago (e.g.,
Frydenberg et al. 1965; see Burton 1983). But protein
polymorphisms scored on electrophoresis gels (allozymes)
all derive from amino acid substitutions in nuclear genes.
This greatly limits the availability of genetic markers for
ecological studies. Much of the nuclear genome does not
encode proteins, and even in coding regions, many
mutations do not change amino acid sequences. Allozyme
studies also do not include any markers in the mitochondrial genome (mtDNA); since mtDNA is typically
maternally inherited without recombination, it can provide
a unique, sex-biased window into the genetic structure of
natural populations (Avise 2004).
The single technological breakthrough that allowed
widespread application of DNA-level studies to marine
ecology is the polymerase chain reaction (PCR). The PCR
makes it possible to amplify specific DNA fragments from
complex genomes and from vanishingly small and often
poorly preserved tissue samples; analyses of PCR-amplified
fragments are the primary data for most recent
population-level genetic analyses. Products from PCR
amplification from different individuals are analyzed by
determining either their sizes or their sequences. The most
popular methodology for the examination of intraspecific
population structure focuses on microsatellite loci, or
simple sequence repeats. Microsatellite polymorphisms
are variations in the number of simple di-, tri-, or tetranucleotide repeats found between two PCR primer sites
(see panel a in the figure). However, the PCR primers
required to amplify microsatellite loci must be derived
separately for each target species, a laborious task of
obtaining sequences of DNA that flank each microsatellite
locus; this startup investment can be substantial and
constrains the use of microsatellites. A contrasting
approach (amplified fragment length polymorphisms, or
AFLPs) permits rapid fingerprinting of entire genomes
without any prior sequence data. In brief, this PCR-based
method amplifies fragments from the DNA of a single
individual on the basis of the presence or absence of short
sequences (recognized by restriction endonucleases) at
hundreds of sites across the genome (see panel b; Vos et al.
1995). Variation in the presence or absence of a particular
band in the PCR products is scored across individuals.
Studies using AFLP do not require species-specific
primer sets.
Several approaches are available for directly assessing DNA
sequence variation among individuals within and between
species (see panel c). Direct sequencing of a DNA fragment
amplified by PCR is the most straightforward. Sequence
variation of mtDNA has become particularly accessible as
“universal primers” have become more available (e.g.,
Folmer et al. 1994); some similarly universal primers are
now available for nuclear genes as well (Jarman et al. 2002).
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(a) Microsatellite DNA polymorphisms consist of different numbers of simple
sequence repeats found between two PCR primers that are specific to the locus
under study. The figure shows three chromosomes (1, 2, and 3) that each carry
a different allele. The gel diagram shows expected bands for a homozygote (1/1)
and three heterozygous individuals; genotypes are scored by electrophoresis
with the smallest allele running farthest on the gel (allele a). (b) The amplified
fragment length polymorphism, or AFLP, protocol (Vos et al. 1995) identifies
many genomic DNA markers without prior sequence information using two
restriction endonucleases to assay sequence variability. Shown are three
chromosomes with differences in cut sites recognized as blue or red breaks
(representing the two enzymes) in the DNA strands. The blue cuts are
subsequently made into priming sites for the forward PCR primer (shown
as a blue arrow), and the red cuts are made into priming sites for the reverse
primer (red arrow). Wherever a blue cut and a red cut occur in order (without
intervening cuts), PCR amplification will occur and produce a band on the gel.
All bands (marker loci) result from amplification with the same two primers.
The gel shows three polymorphic loci (b, c, and d) and one monomorphic
locus (a). (c) Species identification without sequencing. Based on DNA barcode
data, species-specific probe sequences (approximately 18 to 30 oligonucleotides)
are synthesized and attached to a support structure. Sample DNA (or RNA) is
fluorescently tagged and hybridized to the probeset. Microarrays such as the
“phylochip” have thousands of probes attached to a glass slide; species are
identified by location on the slide that hybridizes with the sample DNA
(DeSantis et al. 2007). Bead arrays (d) use probes attached to color-coded
microspheres; species are identified by flow cytometer, which determines the
color of the bead hybridized to sample DNA (Ellison and Burton 2005).
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Box 1. (continued)
If the goal is to distinguish among alternative DNA sequences (as in species identification using barcode information), DNA
hybridization methods are more efficient than direct sequencing. Short DNA probes can be synthesized that match species-specific
sequences in the barcode database. Sample DNA (or RNA) is fluorescently labeled; that which complements the probe sequence will
hybridize to the probe and can then be detected. For microbes, a phylochip containing thousands of taxon-specific probes can
determine which species are present in a field-collected sample (DeSantis et al. 2007); a suspension array approach, with probes attached
to color-coded microspheres, has been used to monitor marine phytoplankton (Ellison and Burton 2005). In a dramatic demonstration
of hybridization technology, a robotic adaptation of this approach (using only a small number of probes) has already been deployed in
Monterey Bay, California, to monitor marine invertebrate larvae (Jones et al. 2008).
sequence data can typically provide at least some taxonomic
information about the specimen (e.g., identification of a
related species).
The great value of DNA sequences for species identification has been widely embraced and is the basis of recent
global efforts for establishing databases of DNA “barcodes.”
These efforts focus on obtaining a short diagnostic DNA
sequence of the same gene from all species and placing the
sequences into a publicly accessible database; anyone who
wishes to identify a specimen can sequence its same gene and
compare the results with those in the database. Thanks to the
efforts of Paul Hebert at the University of Guelph in Canada,
a broad consensus has been reached regarding the gene that
is the primary focus of this effort: the cytochrome c oxidase
subunit1 (CO1) gene encoded in the mitochondrial DNA. This
gene has several attributes that make it an excellent choice for
species identification, including some highly conserved regions
that support the development of “universal”PCR primers, and
sufficient between-species variation to permit its use for discrimination of all but the most closely related taxa (see
www.barcoding.si.edu). As discussed below, barcoding databases are a great resource for marine ecology (Ratnasingham and Hebert 2007, Stoeckle and Hebert 2008); one such
database is found at the Web site for the Fish Barcodes of Life,
known as FISH-BOL (www.fishbol.org).
Identification of the early life stages of fish is of significant
value to management. For example, Robertson and colleagues
(2007) examined small juvenile scombrids in northwestern
Australia to determine which of nine morphologically similar species were breeding locally; mitochondrial DNA
(mtDNA) sequences of only six species were observed, suggesting that three species were spawning elsewhere. In cases
like this, in which one needs to discriminate among only a
small number of likely candidate species, DNA sequencing is
often unnecessary; methods such as multiplex PCR, in which
a single PCR includes multiple pairs of species-specific
primers, can cheaply and efficiently discriminate among alternative identifications. Such methods can be adapted to
shipboard use, giving the rapid results needed for more adaptive field sampling programs. Recently, Hyde and colleagues
(2005, 2006) applied this approach to billfish eggs and larvae,
which are difficult to distinguish morphologically. Shipboard
PCR and electrophoresis identified seven striped marlin larvae collected off the coast of Kona, Hawaii. Subsequent sequencing found that each larva had a unique mtDNA
834 BioScience • November 2009 / Vol. 59 No. 10
sequence. Because eggs receive the mtDNA of their mother,
this means that each larva had a different mother; hence,
numerous female marlin had spawned locally, suggesting
that the region is a spawning ground for that species.
Species identification is also important as a forensic tool for
conservation and enforcement. Using DNA sequences, the
source of marketed seafood or other traded commodities
can be determined to the species level. In probably the most
publicized example, Baker and Palumbi (1994) sequenced
DNA from samples of whale meat purchased in Japanese
markets and identified several samples of whale meats for sale
that were derived from protected species. This work has ultimately led to a recent suggestion that DNA samples can be
used to continuously monitor marketed cetacean products;
sample sequences could then be used to identify the species
composition of the products as well as to estimate the number of individuals taken for each species appearing in the market by using a clever adaptation of “mark-recapture” analysis
(Baker et al. 2007).
DNA identification is playing an ever-growing role in the
conservation and management of marine resources. Strong
demand in Asian markets for shark fins has led to the practice of cutting fins from captured sharks and discarding the
carcasses at sea. Shivji and colleagues (2002) devised a multiplex PCR approach to identify the species composition of marketed fins. Another obvious application is checking retail
fish markets for proper labeling. Marko and colleagues (2004)
found that 75% of the fish sold as red snapper in the eastern
United States were other species. Consumers are not getting
what they intended to purchase, and widespread mislabeling
of seafood may also lead to the perception that red snapper
(or other species) is abundant when, in fact, stocks are
depleted.
Gut contents and marine food webs
Given the difficulty of directly observing the behavior of
many species, researchers glean important ecological insights
and information about food-web relationships by examining
the gut contents of rare specimens. Blankenship and Yayanos
(2005) extracted DNA from the guts of deep-sea amphipods
and used universal PCR primers to amplify, clone, and sequence mtDNA (CO1) and 18S rRNA genes from the extract.
The results suggested that the amphipods have a broad diet;
however, as might be expected, few sequences could be
identified since the DNA database for deep-sea organisms—
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21st Century Directions in Biology
including possible amphipod prey—is quite limited. Still,
the general approach is quite straightforward and has been
applied in diverse settings. For example, the gut contents of
a rarely collected giant squid (Architeuthis sp.) were found
to include a common, commercially harvested fish (the
blue grenadier, Macruronus novaezelandiae), as well as conspecific DNA, which was interpreted as evidence for cannibalism (Deagle et al. 2005).
In addition to demonstrating links in the food web, new
approaches promise to elucidate the actual rates of consumption of specific prey. Durbin and colleagues (2008)
used quantitative PCR to estimate rates of predation by
adult copepods (Centropages typicus females) on nauplii of
another copepod species (Acartia tonsa). The general
approach was to determine the mitochondrial COI gene copy
number of an individual prey organism in the stomachs of
predatory copepods collected in the field. By measuring DNA
disappearance rates in the stomachs under lab conditions,
researchers could estimate field ingestion rates.
Cryptic species
The use of DNA in the identification of species has had rather
surprising consequences. Many marine taxa appear to have
global distributions, and given the apparent lack of barriers
to dispersal in the open ocean and the dispersal capacities of
planktonic larval stages, this is not unexpected. However,
molecular analyses of many broadly distributed species have
revealed that they are, in fact, groups of morphologically indistinguishable species (although criteria defining “species”
are inconsistent across studies). Knowlton (2000) suggested
that there has been an excessive tendency to lump taxa together
as single species. For example, Goetze (2003) examined a
large number of populations of 20 broadly distributed calanoid
copepods in the family Eucalanidae. Not only were populations within what was identified as a single species sharply
divergent genetically (and most likely cryptic species), but
neighboring populations were frequently not even the most
closely related taxa. Unfortunately, it is difficult to predict the
existence of cryptic taxa; Provan and colleagues (2009) found
no evidence for population differentiation across the entire
North Atlantic range of populations of a related copepod,
Calanus finmarchicus.
Identification of cryptic species is not just an academic exercise. Geller (1999) showed that the invasive European blue
mussel Mytilus galloprovincialis replaced the native Mytilus
trossulus in southern California over the past century essentially unnoticed because of the morphological similarity of the
two species. Since Mytilus is used as an indicator species in
marine toxicology studies, inadvertent use of cryptic species
with potentially different physiologies can jeopardize the
validity of monitoring programs. Similarly, taxonomic ambiguity could result in misguided management decisions.
Recent genetic studies have shown that the globally distributed bonefish (Albula vulpes), a prized tropical sport fish, consists of eight or more genetic lineages, with sympatric cryptic
species occurring in several parts of the world, including the
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Florida Keys (Colborn et al. 2001). Surprisingly, the juvenile
bonefish observed off the Keys belong not to the A. vulpes
species caught in the recreational fishery but to a different
one, Albula sp. B. The juvenile habitat of A. vulpes remains unknown (Adams et al. 2008).
The genetic structure of natural
populations—a starting point
For most organisms, habitat is patchy. Stretches of sandy
beach are interrupted irregularly by rocky headlands, and
the fauna on rocky intertidal habitat differs from that on adjacent sandy beaches. But many of the rocky habitat organisms have larval stages that are planktonic and may be
transported among the rocky habitat patches. How frequently
are the larvae dispersed between rocky patches? Why is it
important? Consider a harvestable resource, such as a sea
urchin. If one harvests the urchins at one rocky site, will the
population be reseeded by larvae drifting in from neighboring sites? This is a question of demographic connectivity: Is
interpopulation dispersal sufficiently frequent that it contributes significantly to local population sizes? We might also
be interested in another level of connectivity—evolutionary
connectivity—in which low levels of dispersal lead to just
enough interbreeding (gene flow) to prevent the isolated
populations from achieving genetic differentiation. Although
they do not directly affect population sizes, low levels of gene
flow determine the extent to which populations can adapt to
local environments; such adaptation may limit the success of
migrants between populations (Hauser and Carvalho 2008).
Gametes and early larval stages are frequently microscopic
in size and not easily tracked. But connectivity can be inferred
from patterns of genetic variation across populations (e.g.,
Hellberg et al. 2002). An example reveals the power of the general approach. The copepod Tigriopus californicus lives in
high, intertidal rock pools along the Pacific coast of North
America. Because it is a tiny, free-swimming organism, one
would expect that T. californicus could be frequently transported by coastal currents among neighboring rocky sites. But
when protein polymorphism and later DNA sequence analyses were used to assess the connectivity of T. californicus populations, the results were startling—populations 500 meters
apart were sharply differentiated. In fact, levels of DNA sequence divergence between some populations indicate that
they have been isolated for at least many thousands of generations (Burton 1997, 1998). Surprisingly high levels of population differentiation have been observed in a diversity of
marine species, and it has become clear that life-history characteristics, such as the length of planktonic larval life, are
not always good predictors of genetic structure (Hellberg et
al. 2002).
Identification of stocks for
management and conservation
Management of fish stocks requires understanding connectivity patterns, and efforts to characterize population structure using genetic markers have been extensive. One major
November 2009 / Vol. 59 No. 10 • BioScience 835
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conservation challenge involves species that reproduce as
discrete populations, but migrate to common feeding grounds
where populations of mixed origin may be subject to significant mortality. Sea turtles exhibit such a life history. For
example, hawksbill turtles are endangered in every ocean
basin as a result of exploitation of “tortoiseshell” scutes for
artisan products. To determine the potential impact of
harvest on nesting populations, Bowen and colleagues (2007)
used mtDNA sequence variation to determine the relationships between nesting and feeding populations. Most nesting
populations were found to be strongly differentiated; in
the most extreme example, a sample of 69 turtles from the
Yucatan included 64 individuals with a haplotype that was not
observed at any other sampled nesting site. Yet that haplotype
was recovered in all the feeding areas, suggesting that the
Yucatan nesting population is an important contributor to the
entire Caribbean hawksbill metapopulation. All feeding
grounds are mixtures of multiple nesting populations, so
any local harvesting on the feeding grounds will affect nesting populations in multiple countries. These data clearly
indicate the essential need for international cooperation if
conservation efforts for this endangered species are to be
successful.
The sharp differentiation of hawksbills enabled a simple
genetic analysis of population mixing on the feeding grounds.
Other species of great importance for conservation and fisheries present greater challenges. The evolutionarily recent
origin of populations or large population sizes often result in
limited genetic divergence among marine stocks. Recent work
has shown that approaches relying on large numbers of loci
with low divergence are sometimes capable of teasing apart
subtle genetic signals. Nikolic and colleagues (2009) nicely
demonstrated how increasing the number of microsatellite
markers improves the genetic resolution of populations of
Atlantic salmon, Salmo salar (figure 2). Of course the ability
of salmon to return to their natal streams to spawn results
in the gradual genetic divergence of local populations, but
the relatively recent origin of many populations reduces
observed levels of genetic divergence. Efforts to use increasing numbers of loci to discriminate relatively young (postglaciation) populations of chinook salmon (Oncorhynchus
tshawytscha) require careful cross-calibration of methods
among laboratories. Seeb and colleagues (2007) reported
on a large international collaboration to standardize microsatellite DNA data needed to identify the stock composition
of chinook salmon caught during their oceanic migrations.
Nine laboratories identified 13 microsatellite loci and produced
a database of more than 16,000 salmon representing 110
putative populations, ranging from southeastern Alaska to the
Sacramento River in California. This data set now provides
a key tool for genetic stock identification of samples collected from mixed fisheries.
Genetic identification of stocks has proven important for
stock enhancement through fish hatchery activities. Banks and
colleagues (2000) noted that broodstock used in a hatchery
supplementation program for winter-run chinook salmon
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were unwittingly hybridized with spring-run salmon; such
hybridization could have detrimental effects on the very population targeted for conservation efforts. Appropriate broodstock are now identified using a panel of 10 microsatellite loci
that distinguish winter and spring runs; only fish classified as
having a high probability of belonging to the desired population (see below) are used as broodstock.
The great commercial value of salmon stocks has made
the large investment in salmon genetics feasible. But the
considerable effort required for the startup of microsatellite studies has made this method less accessible to investigators lacking the resources to construct and screen genomic
libraries. In recent years, the AFLP (amplified fragment
length polymorphism) approach (box 1, figure 2) has seen
increasing application. This method produces a genetic fingerprint that can be used to assess population connectivity.
Amplified fragment length polymorphisms have recently
proven useful in a variety of ecological genetic studies
(Campbell et al. 2003, Brazeau et al. 2005, Murray and Hare
2006). For example, using AFLPs, Shank and Halanych
(2007) found significant genetic divergence among populations of the deep-sea hydrothermal vent tubeworm Riftia
pachyptila; these results contrasted with earlier allozyme
and mtDNA studies. Similarly, AFLP studies revealed population differentiation in red abalone that was not evident
in allozyme, mtDNA, or microsatellite studies (Gruenthal et
al. 2007). These and other studies suggest that AFLPs may
provide enhanced sensitivity, presumably due to the large
number of markers scored. In addition to adding sensitivity to studies of population structure, the large number of
markers available in AFLP studies makes whole new types
of investigations possible. Campbell and Bernatchez (2004)
showed that AFLPs provide a “genomic scan” that permits
analyses of associations between specific markers and habitat parameters—associations that very likely reflect the role
of natural selection in population divergence.
Sex-biased dispersal
Differences in inheritance patterns of different genetic markers have permitted some remarkable inferences regarding
the ecology of marine animals. The logic is simple. Mitochondrial DNA is maternally inherited, so the relationships
revealed by marker similarity or identity reflect only the dispersal of females, whereas markers in the nuclear DNA, with
biparental inheritance, reflect migration of both sexes. Comparison of apparent gene flow using different markers can
therefore reveal sex-biased dispersal.
For example, analyses of mtDNA variation have revealed
significant population differentiation in sperm whales across
ocean basins; in contrast, microsatellite polymorphisms
(nuclear markers) did not reveal such differentiation. These
contrasting patterns suggest that males probably disperse
across ocean basins but females do not, an inference that is
consistent with observations of females being the philopatric
sex and having a more limited latitudinal distribution than
males (Lyrholm et al. 1999). The genetic composition of
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white shark populations in South Africa
and Australia led to a similar result;
mtDNA variation clearly distinguished
the populations and microsatellite variation did not. Again, the simplest explanation is that male white sharks disperse
across ocean basins significantly more
frequently than do females (Pardini et
al. 2001).
Equilibrium-based estimation
of population structure versus
assignment methods
Historically, estimation of interpopulation migration has relied almost exclusively on methods assuming that (a)
differences accumulate between populations as a result of mutation and genetic
drift, (b) any migration between the popFigure 2. Effect of number of microsatellite markers on population discrimination
ulations will homogenize their gene
in Atlantic salmon. The figures show the results of factorial correspondence analysis
pools, and (c) these processes jointly rein three dimensions of four Salmo salar populations using (a) 10 loci, (b) 20 loci,
sult in the observed level of population
(c) 28 loci, and (d) 37 loci (from Nikolic et al. 2009 [© 2009 The Fisheries Society
divergence (presumed to be at equilibof the British Isles]).
rium). The estimation of migration typically focuses on estimates of FST, the
times easier to use an exclusion approach: On the basis of
standardized variance in allelic frequencies among populasample allele frequencies, one can generate the probability that
tions; FST varies from 0 (no variation among populations) to
a certain genotype would be observed in that sample. If the
1 (all variation among populations). At equilibrium, FST can
probability is very low for a particular individual, the individual
be related to gene flow through the approximation FST ~ 1 /
is probably a migrant. Such analyses are used to discriminate
(4Nem + 1), where the product Nem (effective population size
between the winter and spring runs of chinook salmon
× proportion of migrants to a population per generation) is
discussed above.
the number of migrants exchanged per generation. It must be
Such approaches classify individuals to likely source popuremembered that Nem is an equilibrium estimate of genetic
lations. But the rather continuous distributions of some
exchange; a high Nem (low FST) estimate can reflect a low level
marine organisms make population delineation itself a probof ongoing exchange or a high level of exchange in the
lem. This problem has been addressed by recent statistical
distant past, followed by a period of no exchange (but large
methods that assess the probabilities that the samples fall
population sizes). From an evolutionary point of view, the
into some unknown number of different clusters (or popudifference may not be important, since both scenarios prevent
population differentiation. But from a management point of
lations). These approaches rely on detecting the nonrandom
association of alleles across loci (termed “linkage disequilibview, low FST values (i.e., low levels of genetic differentiation,
rium”) generated when individuals from different populations
as commonly observed in marine systems) are difficult to
are mixed in a sample. Clustering methods break the sample
interpret.
into groups of individuals (populations) by minimizing disTo make inferences of contemporary levels of connectivequilibrium within the constructed groups (Pritchard et al.
ity among populations from genetic data, a variety of new
2000). This method has been applied in a number of recent
methods of analysis has been developed. Manel and colstudies; for example, Moller and colleagues (2007) applied the
leagues (2005) discussed the use of “assignment tests” that take
methods to analyze microsatellite variation among samples
advantage of the multilocus genotypes of individuals to, for
of bottlenose dolphins from southeastern Australia. Cluster
example, identify individual migrants within population
analysis verified that the five geographic samples are best
samples. In the simplest approach, potential source populasorted into only two biologically significant populations, an
tions are sampled, and expected multilocus genotypic probembayment population and a coastal population, representabilities are calculated using allele frequencies. Then, for any
ing recent differentiation between the two habitat types.
individual multilocus genotype, the probability that it came
from a particular source can be calculated, and the individEstimation of effective population size
ual is assigned to the source with the greatest probability. Of
Genetic data are increasingly being employed to give insight
course, there is also the assumption that all potential source
into the demography of populations of marine organisms.
populations have been sampled. Alternatively, it is somewww.biosciencemag.org
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Several methods have been proposed for the estimation of
present-day effective population sizes. For example, a temporal
approach is based on changes in allelic frequencies in samples
taken one or more generations apart; the magnitude of genetic drift (random sampling error resulting in changes in allele frequencies) is inversely dependent on effective population
size. Hence, if large changes are observed, Ne must be small
(assuming there is no natural selection favoring specific alleles). Although this method can estimate Ne, the relationship
between Ne and census population size in many marine organisms is difficult to infer. In fact, the level of genetic variation maintained in many marine species is orders of
magnitude below what census population sizes would lead one
to expect (Hauser and Carvalho 2008). Several factors may
contribute to this observation. Depending on the vagaries of
the environment, some females in highly fecund species may
be wildly successful, whereas others, by chance, may fail to leave
any offspring. This sweepstakes pattern of recruitment may
account at least in part for the discrepancy between census and
effective population sizes (Flowers et al. 2002, Hedgecock et
al. 2007, Palstra and Ruzzante 2008).
Alternative methods are currently being developed to
estimate Ne from single samples. Tallmon and colleagues
(2008) estimated Ne from microsatellite data from a single
sample; the calculation was based on simulations of many populations with the same number of individuals sampled, and
comparable microsatellite data (number of loci and repeat motif). Ultimately, the Ne estimate is determined by comparing
the observed data to the simulated data for a series of statistics relating genetic variation to Ne, including expected heterozygosity, number of alleles per locus, mean and variance of
multilocus homozygosity, and others. Although the idea of
calculating Ne from observed within-population genetic variation (or temporal change in allele frequencies) is attractive,
empirical results can be erratic; a recent study employed two
different methods for estimating Ne in the spiny spider crab
and obtained values that varied from 100 to 1 million for the
same population (Sotelo et al. 2008). Palstra and Ruzzante
(2008) have discussed some of the important complications
in estimating Ne, including importance of both proper sampling and consideration of population demographic factors.
Historical population sizes
A recurrent problem in marine conservation is that we frequently lack baseline information on species abundances
prior to historic exploitation. The problem of shifting baselines was first cited by Pauly (1995), who noted that successive generations of fishers have quite different personal
experiences and expectations; in many parts of the world, a
big catch today is probably substantially smaller than a big
catch was one or two generations ago. How do we know
when a depleted stock has recovered? If we have no knowledge of the size of the original, unexploited stock, how can we
set appropriate management goals? A 50% increase in the
number of whales might seem a great management success,
but if our frame of reference is one in which stocks have
838 BioScience • November 2009 / Vol. 59 No. 10
been reduced to less than 10% of their preexploitation
levels, the increase (to 15% of preexploitation levels) is not
so impressive.
Although it would seem that we cannot turn back the
clock to genetically sample the populations of the past, several studies have been able to use DNA extracted from shells
or bones recovered from archaeological middens and museum
samples to investigate genetic changes over ecologically
important time frames. For example, studies of mtDNA
extracted from ancient bones of the now endangered seabird,
the Chatham Island taiko, show that despite precipitous
population declines (from millions to less than 200 birds
over the past several hundred years), there has been no significant loss of genetic diversity (Lawrence et al. 2008). Analyses of ancient DNA promise to provide some important
insights into temporal changes in genetic composition in
response to human activities in the past several centuries.
Even in the absence of historic samples, genetic analyses can
be used to estimate historic population sizes. Standing levels
of neutral genetic diversity within populations are determined by a balance between loss of variation due to genetic
drift (a function of Ne) and introduction of variation due to
mutation. For maternally inherited mtDNA, for example,
the formula q = 2 [Ne (f)] μ, where q is a measure of genetic
diversity, and the effective female population size is Ne (f),
describes the expected relationship. Estimation of Ne requires
an estimate of the mutation rate (μ) in the studied sequence.
Roman and Palumbi (2003; also see Lubick 2003) used this
approach to estimate long-term population sizes of three
North Atlantic baleen whale species. They estimated genetic
diversity from mtDNA sequences, and estimated the rates of
mutation on the basis of fossil evidence suggesting that the
humpback genus Megaptera is at least 6 million to 10 million
years old. Once they had estimated Ne (f), they used three
factors to convert this to estimates of the long-term census
populations sizes: (1) sex ratio, (2) proportion of breeding
adults versus total adult numbers, and (3) proportion of
adults versus juveniles in the population. Their results were
surprising—the estimates of long-term population sizes of
humpbacks in the North Atlantic were on the order of 240,000
animals (current population estimates are 10,000), which
contrasts with historical estimates suggesting that only about
20,000 humpbacks existed in the North Atlantic before commercial exploitation began in the 19th century.
The conservation implications of such genetic estimates of
past population sizes are considerable, given the concept of
shifting baselines. The international moratorium on commercial whaling sets in place targets for recovery based on estimates of preexploitation population sizes. If the genetic
data are correct, those targets might be far too low. However,
the drop in abundances from those estimated to current levels could have happened rather far back in evolutionary time.
Has the carrying capacity actually changed over time, or have
anthropogenic actions been the primary cause of large-scale
changes in whale numbers?
www.biosciencemag.org
21st Century Directions in Biology
In another study, Jackson and colleagues (2008) have also
used measures of extant mtDNA variation in southern right
whale (Eubalaena australis) populations to estimate several
parameters of historical demography of this formerly heavily exploited species. On the basis of whaling records and
current abundance, the International Whaling Commission
(IWC) has estimated current rates of population growth. By
using the genetic data to inform historical reconstructions of
population dynamics, Jackson and colleagues (2008) found
that IWC estimates are likely to be inaccurate in important
ways. Existing mtDNA variation in the population suggests
that (a) preexploitation population sizes were probably substantially higher than the IWC estimates (as also suggested by
Roman and Palumbi [2003] for other whale species), (b) the
number of whales surviving through the bottleneck (when
population size was at its minimum) was most likely larger
than the IWC assessment, and (c) the current population
growth rate estimated by the IWC is probably too high. The
study again suggests the potentially key role genetics can play
in informing management decisions.
Conclusions
Despite decades of study, two issues continue to obscure our
understanding of populations of marine organisms: (1) We
routinely visit or monitor biological activities in an infinitesimally small fraction of the world’s oceans, and (2) the complex life histories of many marine species make it difficult or
impossible to track individuals from birth to death. Against
this difficult backdrop, molecular genetics has made some remarkable contributions. Strong intuitions regarding population connectivity in species with planktonic larvae have
been proven wrong. Geographic ranges of “cosmopolitan”
species have been corrected as cryptic species have been
uncovered. Larval forms have been matched to adults, and
spawning sites in the open ocean have been elucidated. Gut
contents analyzed by molecular tools provide new information about marine food webs. Sex-biased migration patterns
have been inferred in megafauna that are very difficult to
observe, including the great sharks and whales. Contributions
of different populations to mixed population fisheries have
been clarified. Estimates of contemporary and historical
effective population sizes have been made. Even this partial
list is impressive, but both the molecular genetic toolbox and
the analytical approaches available to marine ecologists have
advanced faster than their respective applications, suggesting
that molecular marine ecology is still far from mature.
Acknowledgments
This work has been supported by National Science Foundation grants DEB-0717178 and OCE-0526979. Xavier Mayali,
Jeffry Mitton, and two anonymous reviewers provided many
helpful comments on an earlier version of the manuscript.
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Ronald S. Burton ([email protected]) is with the Marine Biology Research
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