Conservation Genetics

Conservation Genetics Katie Elizabeth Frith and A Rus Hoelzel, Durham University, Durham, UK r 2013 Elsevier Inc. All rights reserved.

Glossary Allele Variant of a gene at a given locus. Chain-termination sequencing Method for DNA sequencing that employs four reactions, one for each nucleotide, and terminates the copy at each occurrence of a given base. Coalescent Looking back in time through a genealogy to where two genetic lineages come together in a common ancestor. DNA slippage Polymerase error leading to a change in the length of a simple repetitive sequence. Genetic drift Stochastic changes in allele frequencies in a finite population.

Beginnings Charles Darwin understood that the motive force of evolution must be a process that reflected the diversity of individuals within populations of a given species. Inspired by the writings of economist Thomas Robert Malthus, Darwin saw that the exponential potential of reproductive growth would be kept in check by limited resources. As part of that process, some individuals would be better suited to survive and reproduce than others. These ideas are at the core of what we think of as conservation genetics today. An essential belief is that diversity in populations must be retained to allow adaptation to changing environments by the process Darwin called ‘‘natural selection.’’ Diversity at the population level is the raw material of evolution by natural selection, and the confirmation of this has been evident to animal and plant breeders for a long time. Even so, it was not until the mid-1960s that the reality of intraspecific diversity at the population level became widely appreciated. The turning point was a study by Hubby et al. (1966) in which they described the gel electrophoresis of enzyme variants, referred to as ‘‘allozymes.’’ Recognizing that proteins (including enzymes) have charge properties that affect their migration through an electrical field, and that through the manipulation of the catalytic properties of enzymes a dye can be produced to reveal the presence of a specific gene product (a ‘‘polypeptide’’), they provided a simple and reproducible method that could reveal molecular genetic diversity at the population level. In no time, keen population geneticists were grinding up pretty much anything they could get hold of and producing a bourgeoning record of diversity in natural populations for an expanding range of taxa. Soon the first studies that explicitly considered the importance of conserving genetic diversity were published. In the late 1960s and early 1970s, these studies were predominantly about conserving genetic resources for their economic value. For example, Orrewing (1969) wrote of developing a program of ‘‘genetic improvement’’ for

Encyclopedia of Biodiversity, Volume 2

Genetic load Extent to which an individual is inferior to the best possible individual in the population as a consequence of the genes it contains. Microarray Collection of microscopic DNA spots attached to a solid surface; used in the analysis of gene expression or genetic variation. Microsatellite locus Sequence of 2–4 base pairs repeated end on end. Neutral theory That the majority of nucleotide substitutions result from the random fixation of neutral or nearly neutral mutations. Phenotype Observable characteristics or traits of an organism.

commercial forests in British Columbia, and Turner (1971) wrote about the conservation of genetic resources in domestic animals. These studies extended to various aspects of plant and animal breeding and included studies associated with fisheries (e.g., Moller, 1969). However, even at this early stage, there were studies that concerned themselves primarily with the management of natural populations – for example, a study on the genetic diversity of declining peregrine falcon populations (White, 1969). A significant contributor to this early work was Otto Frankel, by then a septuagenarian. He had devoted his earlier career to plant breeding and initially built on that experience. But in 1974, Frankel published a paper that spelled out in the clearest of terms a dominant ethos of modern conservation genetics. In a paper titled ‘‘Genetic Conservation: Our Evolutionary Responsibility,’’ he wrote, ‘‘Wild species, increasingly endangered by loss of habitats, will depend on organized protection for their survival. On a longterm basis this is feasible only within communities in a natural state of continuing evolution, hence there is an urgent need for exploration and clarification of the genetic principles of conservation.’’ (Frankel, 1974). This set the stage and the tone for the next decade or so. The emphasis was very much on endangered species and on documenting levels of diversity toward a better understanding of the implications of lost diversity. Proponents of the neutral theory of evolution were describing not only the role of genetic drift in the loss of diversity but also the relationship between population size and the rate at which diversity was lost (Kimura, 1968; Nei et al., 1975). Small populations lose diversity quickly (at a rate of 1/2N each generation), so exploited populations were investigated for evidence of an anthropogenic impact. In 1974, Michael Bonnell and Robert Selander investigated genetic diversity at 24 allozyme loci for the northern elephant seal (Mirounga angustirostris) and found no diversity at all. Elephant seals were abundant off California in the 1970s and their populations were growing fast, but 100 years earlier the species had almost vanished. These seals had

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been hunted for their blubber to produce oil for lamps among other products, and their nature made them easy to approach and therefore easy prey. The last few were thought to have been taken by collectors from the Smithsonian Institution in 1892 when an expedition found eight on Guadalupe Island after no recorded sighting for the preceding 8 years. Most of those found there that day were killed, but one survived due to increasing inclement weather that forced the expedition to abandon the site. A later study calculated through simulation analyses that there had been about 20 individuals left at the species nadir, and it further demonstrated the extent of lost diversity at various genetic markers (Hoelzel et al., 1993, 2002). Bonnell and Selander (1974) and later studies showed the potential impact of human exploitation on genetic diversity (though it was not until later that the direct impact was demonstrated through the comparison of samples before and after bottleneck events using ancient DNA; e.g., Groombridge et al., 2000; Hoelzel et al., 2002). The next question was, what difference does it make? Are species or populations depauperate of variation really at a disadvantage? Apart from the need to retain adaptive variation, there was the possibility that low diversity could affect individual fitness. In the early 1980s, a very influential study by Ralls and Ballou (1983) illustrated the impact of inbreeding depression (sexual recombination of deleterious alleles during consanguineous matings, exposing homozygous recessive traits) on a range of mammalian species based on data from zoological collections. However, the potential harm from inbreeding depression when close relatives mate had been recognized for some time. For example, Darwin compared self- and cross-fertilization among 57 species of plants and found that self-fertilization reduced seed production by 41% and plant height by 13% on average (Darwin, 1876). Even so, the paper by Ralls and Ballou (1983) inspired a search for evidence of inbreeding depression in ‘‘natural’’ populations, and there was ample evidence reviewed by Crnokrak and Roff (1999). In the early 1980s, a number of papers illustrated the correlation between diversity at allozyme markers and various indicator measures of fitness. For example, Koehn and Shumway (1982) showed that the number of heterozygous loci correlated with O2 uptake in the American oyster (Crassostrea virginica) per unit time, such that greater heterozygosity correlated with lower uptake of O2 (greater efficiency). Around this time, the indirect measure of fitness based on fluctuating asymmetry (FA; van Valen, 1962) was also being assessed in this context, and a number of studies looked for correlations with genetic diversity. For example, Leary et al. (1983) assessed FA in the rainbow trout (Oncorhynchus mykiss) and showed that the number of asymmetric characters decreased with increasing number of heterozygous loci. To the extent that FA, the unbiased asymmetry of bilateral traits, reflects developmental stability (such that the expected symmetrical state has been disrupted), it could reflect the disruption of intergenic associations due to stochastic processes when populations are small and inbred. These and other measures could have reflected reduced fitness due to inbreeding, but the relative fitness of the heterozygote compared to the homozygote could also be a factor. The latter could come about by balancing selection, either through the advantage of the heterozygote,

spatial, or temporal variation in adaptive advantage or through frequency-dependent selection (well-established examples include resistance to sickle cell anemia and selection favoring diversity at immune system genes; e.g., Penn et al., 2002). Therefore, within a decade or so, a principle had been established that recognized threats to population size (and therefore to genetic diversity), together with a good foundation of work indicating a negative impact on fitness (and by implication species survival) due to the loss of genetic diversity. In this context, Franklin (1980) proposed the ‘‘50, 500 rule.’’ The ‘‘50’’ and ‘‘500’’ referred to effective population sizes (Ne; an evolutionarily relevant measure of population size that represents the size of an idealized population that would show the same rate of decay of heterozygosity as the observed population). The essential idea was that an Ne of B50 should be enough to avoid the short-term effects of inbreeding depression, whereas an Ne of B500 would be required to avoid the long-term erosion of genetic variation in quantitative traits (phenotypic traits involving multiple genes) with high heritability. Franklin chose to consider quantitative traits because it is a phenotypic variation that is exposed to selection, and so this type of variation could allow a population to respond to a changing environment. The genetic variance at quantitative traits can be divided among ‘‘additive’’ and ‘‘nonadditive’’ components, but most quantifiable phenotypic variation will be determined by additive variance (VA), a correlated effect seen at multiple genes, and so he focused on this aspect. In small populations, the rate of change in VA should be determined by the rate of lost diversity through genetic drift and by the gain through mutation. Since the rate of loss in diversity is related to Ne, and at equilibrium (between genetic drift and mutation) the change in VA becomes zero, an approximate value for a minimum Ne in this context could be calculated from data available at the time. This figure was Ne ¼ 500, though various higher and more recent estimates have been made (some as high as Ne ¼ 5000). In the 1980s the 50, 500 rule was used quite extensively, especially by managers promoting the protection of populations under the US Endangered Species Act (ESA; enacted in 1973). Two quite high-profile occasions involved bird species in protected habitats. The northern spotted owl (Strix occidentalis) is found especially in old growth forest in western North America from California to Canada. In 1984, an estimated 2500 breeding pairs remained, and at that time the US Forest Service instituted a management plan based on the preservation of at least 500 pairs to maintain genetic diversity. This species was eventually listed as threatened in the states of California, Oregon, and Washington under the ESA in 1990. The red-cockaded woodpecker (Picoides borealis) lives in colonies in pinewood forests that are at least 80 years old. It was listed under the ESA in 1973 when there were an estimated 6000 birds. In 1985, the US Fish and Wildlife Service proposed a recovery plan under which a minimum of 500 birds would be retained to maintain genetic diversity. In each case, it was the number of individual birds that was to be managed (41000 for the owl and 4500 for the woodpecker), not Ne, which can be an order of magnitude or more smaller than the census number (see Low Diversity and Small Populations).

Conservation Genetics

Largely in response to the use of this shorthand measure in the ongoing development of management policy (though not specifically in response to the mistaken focus on census number rather than Ne), Russ Lande published a highly influential paper in 1988 titled ‘‘Genetics and Demography in Biological Conservation,’’ in which he focused on a number of key points. First was the concern that a convenient metric (e.g., Ne4500 to conserve genetic diversity) could lead to management plans that neglect essential factors, indicating the need for protecting a larger population. His focus was primarily on demographic factors such as Allee effects (when small populations experience low viability and reproduction for nongenetic reasons, such as difficulty in finding a mate, in addition to genetic reasons such as inbreeding), demographic stochasticity, edge effects, and local extinctions. The issue of Ne compared to the census size is again important here since an Ne of 500 could mean a census population size of 5000, 50,000, or more, and it is the census population size that is relevant to processes like demographic stochasticity. Lande made an example of both the northern spotted owl and the red-cockaded woodpecker. In the case of the former, he pointed out that models based on stochastic demography and habitat occupancy suggest a trajectory toward extinction if only 500 pairs were preserved due to the sparseness of the habitat. He cited habitat fragmentation as the particular problem with the red-cockaded woodpecker as well, and he predicted population decline under the proposed management plan. At the same time, if the managers working with the northern spotted owl, for example, had planned to preserve an Ne of 500 (perhaps 5000 birds) rather than a census number of 1000, this would have made a difference. Another aspect of Lande’s argument was the suggestion that a gradual reduction in population size, which may often be the case, would result in relatively little inbreeding depression since selection would purge deleterious recessive alleles when they become homozygous. However, more recent studies suggest that purging is not a very efficient process and that low diversity does correlate to greater extinction risk (see Frankham, 2005; and see Low Diversity and Small Populations). Lande’s conclusion – that demography may be more important in assessing minimum viable population sizes than population genetics – formed the basis for a long discussion on the subject and raised questions for some at the time about the utility of conservation genetics. The idea that species are typically driven to extinction before genetic factors could have an impact (referred to as the ‘‘Lande effect’’) is addressed in a meta-analysis by Spielman et al. (2004), which suggests that this is not the case. However, the broader issue about the utility of conservation genetics has been addressed in other ways as well. As pointed out in the editorial of the first issue of the journal Conservation Genetics, the potential applications of genetic methods in support of more-effective conservation are diverse. Beyond the simple identification of levels of diversity or the estimation of effective population size, genetics can provide taxonomic identification (e.g., in support of wildlife forensics), assess introgression (e.g., between introduced and native species), identify management units for stock management (and the rate and direction of gene flow among stocks), assess behavior relevant to management (e.g., kinship and reproductive

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behavior), track demographic histories, assist the practical management of captive populations, and aid in the assessment of local adaptation, among numerous other aspects. The remainder of this essay will focus on modern approaches to these various aspects and begin with our current understanding of the earlier question about the impact of lost diversity on everything from organismal health to species survival.

Low Diversity and Small Populations The effect of genetic drift on diversity in small populations, confirmed in various laboratory and natural experiments, focused minds on the urgency associated with reduced population size. As Lande (1988) reminded us, conservation biology is a ‘‘crisis discipline’’ with negative impacts (especially anthropogenic impacts) mounting all the time. Although he was more concerned with the extinction of small populations for demographic reasons, there is growing evidence that the loss of genetic diversity also plays a crucial role (e.g., Spielman et al., 2004). However, the loss of genetic diversity is related to the effective population size (Ne) in particular. Therefore, one of the most useful roles for conservation genetics in addressing this problem has been to provide methods for the estimation of Ne. At the same time, Ne is difficult to measure using genetics because of the stochastic nature of the key relevant processes (inbreeding and genetic drift) and the various factors that influence those processes (e.g., mutation, selection and migration). Given full information about population demographic histories, reproductive behavior (especially sex-biased skew in reproductive success), variance in family size, and various other factors, it would be possible to calculate Ne without genetic methods, but these details are rarely fully known. Instead, genetic markers can be used to estimate current Ne if the ‘‘systemic’’ forces of mutation, migration, and selection can be ruled out. In that case, changes in allele frequency over time will be due to the stochastic process of genetic drift, and since there is a known relationship between genetic drift and Ne, Ne can be estimated. The systemic forces can be controlled for by making some basic assumptions. First, for estimates of current Ne we assume a closed population (and so no migration). We also assume a short time frame (so no mutation, since mutation is a very slow process) and base analysis on neutral markers (so no selection). The first assumption is often most problematic, but the implications of violating this assumption can be assessed and the results interpreted accordingly. Two common methods for estimating current Ne are the ‘‘temporal method’’ and linkage disequilibrium (LD). The temporal method further assumes random mating and discrete generations (though some models allow overlapping generations), and it considers the change in allele frequencies between generations. Allele frequency changes will be due to genetic drift and sampling effects, so accounting for the latter allows for an estimate of the effects of genetic drift and therefore Ne. LD (a nonrandom association of alleles at different loci in gametes) can be generated in finite populations by migration, selection. and genetic drift (even without physical linkage on the chromosomes). Therefore, the same assumptions again allow us to focus on genetic drift and

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estimate Ne. A rather different approach is to assume a state of equilibrium between mutation and genetic drift and estimate long-term, average effective population size. This is based essentially on a simple relationship between diversity (designated as y) and Ne, such that y ¼ 4Nem, where m in the mutation rate. A challenge is to estimate the mutation rate accurately. In general, estimates have shown that Ne can be much smaller than the census population size, and this is important especially if the apparent census size is large (giving the appearance of a population not at risk). Although in a review Frankham (1995) suggested a typical value of about 10%, in some cases Ne can be several orders of magnitude smaller than N (common for pelagic marine fish species; see White et al., 2011) or quite close to N (as in some natural populations of Drosophila; see Shapiro et al., 2007). Some populations apparently have naturally low levels of Ne; for example, Bourke et al. (2010) proposed that golden eagles in the British Isles have always had a low Ne despite once being more widely geographically distributed. A more extreme case would be naturally inbreeding populations, including selfing species of plants and some eusocial species such as some species of Hymenopteran insects (especially bees and ants). This raises the question of how such populations can survive the concomitant loss of diversity, especially the impact of inbreeding depression. Because inbreeding exposes deleterious recessive alleles and expresses the associated phenotype, natural selection should purge them from the population with the loss or reduced fitness of those individuals. However, research into purging (which has largely focused on laboratory species such as Drosophila) suggests that the positive effects are variable and often small (see Boakes et al., 2007). For example, Fox et al. (2008) performed serial inbreeding experiments (full-sibling mating over a few generations) in laboratory populations of a beetle (Stator limbatus). They found that the genetic load of recessive alleles was rapidly and efficiently purged but that despite this, 61.5% of the populations became extinct due to the stochastic fixation of deleterious alleles via genetic drift. Purging is likely to be more efficient in populations where alleles have a large deleterious effect (because selection quickly removes individuals expressing such alleles) when alleles are completely recessive (so they are not masking other deleterious alleles), when inbreeding is slow (so the population does not become extinct before purging can occur), when the population is large (so selection is more effective), when the population is naturally inbreeding (because long-term inbreeding will have reduced the genetic load), and where the population is sufficiently isolated such that immigration does not reintroduce purged deleterious mutations. For a review, see Charlesworth and Willis (2009). Therefore, in spite of the potential for purging to reduce the risk of inbreeding, there remains strong evidence that low diversity and inbreeding constitute a significant conservation risk. For example, a meta-analysis of the effect of inbreeding depression on extinction risk in 30 mammal and bird species found that inbreeding depression decreases the time to extinction by an average of 37% (O’Grady et al., 2006). Inbreeding depression has the potential to reduce any component of reproductive fitness, including birth weight, growth rate,

survival, parasite resistance, and tolerance to environmental stresses (Keller and Waller, 2002). It is also likely to be more severe in harsh and stressful environments; for example, Keller et al. (2002) found that inbreeding depression in Galapagos finches was five times more severe in years with low food availability and high population densities. As described earlier, factors such as frequency-dependent selection or heterozygote advantage are also important in some cases and best described for the evolution of immune system genes (e.g., Nei and Rooney, 2005). In this case, individual heterozygosity can itself be positively correlated to fitness. Assessments of heterozygosity and fitness correlations (HFCs) have revealed variable results, although there is general agreement that genetic diversity does correlate positively with fitness (Chapman et al., 2009). However, a correlation may be supported in several ways. There may be some direct association between the locus and fitness, as may have been the case for some of the allozyme studies showing HFCs, since diversity at these loci reflects function. It is also possible that there are relevant functional genes linked to the neutral markers under investigation. Perhaps more common, though, is the assumption that neutral markers will reflect the general diversity of the genome and so imply genomic diversity by inference. The extent to which this may be true is not yet fully known. In a meta-analysis by Reed and Frankham (2001), there was a weak correlation between molecular and quantitative measures of genetic variation but no significant relationship between genetic diversity and life-history traits. This could be because selection acts to maintain diversity at functionally important loci or conversely to purge deleterious alleles at those loci or because of the sometimes complex interactions among genes associated with phenotypic traits. For example, Olano-Marin et al. (2011) tested HFCs in blue tit populations using markers located at both neutral and functional loci. For neutral markers, they found a positive relationship between heterozygosity and fitness (measured as female hatching success and recruitment), but for markers located in functional genomic regions the relationship was negative. They attributed this to ‘‘outbreeding depression’’ at the functional genes. This can happen when local adaptation has generated complexes of genes that are not well-adapted elsewhere. In the blue tit study, there may have been a local effect such that diversity associated with these functional genes was negatively associated with fitness, whereas the neutral markers showed a positive correlation due to the effects of inbreeding. In general, there are data in support of the perceived risks associated with small population size and lost diversity and therefore an interest in strategies for mitigation. This is often associated with controlling patterns of connectivity. For example, populations of conservation concern are often found in highly fragmented habitats. Small habitat fragments may only support a relatively small population; depending on the species and nature of the habitat, fragmentation may inhibit gene flow. The usual mitigation policy involves either the establishment of corridors among fragments or the translocation of individuals. The former may be more likely to succeed since differentiation between neighboring populations may be relatively low (avoiding outbreeding depression). Translocations need to be carefully managed with respect to levels of

Genealogy

Conservation Genetics

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Therefore genetic methods can facilitate conservation through the identification of populations that have small effective sizes by tracking the dynamics of populations – for example, by allowing mitigation for populations in decline – and by quantifying the impact of low diversity and the potential contribution that may have on extinction risk. This has been a central component of the growing field of conservation genetics since its inception. However, in recent years the dominant body of work has instead been on the next step up in the hierarchical structure of diversity: differentiation among populations.

Population Structure

Time before present Figure 1 A ‘‘skyline plot’’ showing how coalescent intervals from a genealogy can be used to estimate changes in effective population size over time. The number of lineages represented at each coalescent, interval is given as k. Reproduced from illustrations in Pybus OG, Rambaut A, and Harvey PH (2000) An integrated framework for the inference of viral population history from reconstructed genealogies. Genetics 155: 1429–1437.

differentiation (or adaptation to a captive environment if relevant). The idea is to ‘‘rescue’’ genetic diversity. Laboratory studies of genetic rescue in inbred Drosophila lines found that the short-term rescued populations increased in fitness, but that this effect may break down after a number of generations, especially if the Ne of the population remains low (Bijlsma et al., 2010). Genetic rescue has been practiced in managed populations with mixed results. For example, cross-lineage breeding between captive Mexican wolves (an endangered subspecies of the gray wolf) increased fitness (measured by an increase in offspring number) in cross-lineage wolves (Hedrick and Fredrickson, 2010). However, for the Scandinavian wolf, genetic rescue resulted in an initial increase in fitness but then an increase of inbreeding depression in subsequent generations (Liberg et al., 2005). Another important consideration is the demographic trajectory of populations, and genetic methods can help to assess these trends as well. Wright (1931) noted that the probability that two gene copies come from the same gene copy in the previous generation is 1/2Ne, so every generation there is a 1/2Ne chance of coalescence (the lineage coming together as you look backward in time). John Kingman later generalized this idea to consider multiple gene copies and showed that the interval between coalescent points is related to Ne (see Kingman, 2000), and so changes in Ne over time can be estimated based on gene genealogies. This method of tracking the changes in Ne was initially referred to as a ‘‘skyline plot’’ (Figure 1; Pybus et al., 2000), and it has since been developed into quite a powerful tool that can track demography even over relatively short timescales, given the application of multiple genetic markers (see Ho and Shapiro, 2011 for a review). The method is greatly enhanced when data from ancient DNA are incorporated, providing a fuller representation of historical coalescent points.

The initial variation documented by allozyme studies at the population level was already a revelation, but the full details were appreciated only after DNA sequencing data became available. For example, Kreitman (1983) sequenced part of the alcohol dehydrogenase (ADH) locus in five natural Drosophila melanogaster populations and revealed 42 silent (i.e., no amino acid change) polymorphisms. A well-known two-allele allozyme system (comprising a ‘‘fast’’ and a ‘‘slow’’ allele) was thereby shown to reflect considerable neutral variation as well, and the importance of variation among populations has since become increasingly apparent. Species are subdivided into populations connected to one another to a greater or lesser degree by gene flow and differentiated by the processes of genetic drift and natural selection. This means that the preservation of the potential to evolve (retaining diversity as the raw material for evolution) entails conserving diversity both within and among populations. A rapidly expanding human population and increasing habitat degradation complicates this objective since these activities lead to populations becoming further subdivided and isolated (a process of ‘‘fragmentation’’). In addition, natural population structure can be cryptic (e.g., without obvious barriers to gene flow between them) representing diversity that may be important as populations adapt to changing environments over time but may not be intuitive from the physical structure of the environment. As a consequence of these and related factors, quantifying the level and patterns of differentiation between natural populations has become a major focus of conservation genetics. Genetically differentiated populations can evolve in isolation (‘‘allopatry’’), and this is the most obvious and easy to predict pattern of population structure. There can also be differentiation in sympatry (same geographic range) or parapatry (populations next to each other), though examples are less common and more controversial. Populations often show a pattern of isolation by distance (increasingly differentiated with greater geographic distance), though not always, and isolation by distance may also reflect a continuous pattern, not subdivided into populations. A useful definition of a population toward more effective conservation needs to incorporate the evolutionary forces of gene flow, mutation, drift, and selection. A commonly used definition is ‘‘a group of individuals living in sufficiently close proximity that any member of the group can potentially reproduce with any other member’’ (Waples and Gaggiotti, 2006). Such groups are ‘‘panmictic’’

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(free flowing) with respect to gene flow within the group, separate from other similar groups, and distinct from an ‘‘ideal’’ population (see Beginnings). Highly variable genetic markers are now readily available for essentially any nonmodel organism, and increasingly easy and inexpensive to screen (e.g., see Pearse and Crandall, 2004). In practice, there are two main approaches for identifying genetically distinct populations. The first is based on determining the degree of genetic differentiation between predefined putative populations (the statistical rejection of panmixia). By this method, putative populations are identified a priori based on factors such as geographic isolation or apparent barriers to gene flow. The second is based on using information from multi-allelic markers to cluster individuals into groups by some methods without a priori assumptions about putative population boundaries. Investigating population structure using the first approach has been largely based on F-statistics (or a derivative of F-statistics), which are ‘‘fixation indices’’ that describe the partitioning of diversity in a structured population at different hierarchical levels (e.g., for the individual within a subpopulation, as FIS, the inbreeding coefficient, and for subpopulations in the total population as FST; Wright, 1931). Population structure can be assessed using FST, which quantifies differentiation among populations (expressed as a value between 0 and 1, where 0 implies panmixia and 1 implies no gene flow). Although the magnitude of FST has some meaning, the primary initial question is typically about whether or not panmixia can be rejected (whether or not FST is significantly greater than zero). Wright’s (1943) original ‘‘island’’ model considered a complete set of subpopulations, however it is as common to see pairwise population comparisons as a single measure considering the structuring of all sampled populations, and the inference about significant structure remains valid. In some cases, the apparent level of structure will be small and the interpretation in the context of conservation strategy somewhat equivocal. For example, a study on population structure in sockeye salmon sampled from 11 different spawning sites and applied the criteria that a gene-flow rate between populations of less than 10% would be enough to render the populations independent. Because the Ne of each population was estimated to be approximately 1000, it was estimated that there would be panmixia if FST was below 0.0025 (thus reflecting gene-flow rates of less than 10%). However, FST was calculated to be 0.007, suggesting that even though differentiation was very low it was strong enough to reject panmixia and warrant the separate management of the 11 spawning-site populations (Ramstad et al., 2004). At the extreme there is some question about the meaning of these very low values of FST, a question addressed directly in a study that combined population genetic assessment with an extensive capture–mark–recapture study of Norwegian cod (Gadus morhua; Knutsen et al., 2011). The authors concluded that although the FST values were very low (average was 0.0037), they were nevertheless consistent (over 10-year replicate samples and among loci) and supported by the mark–recapture data, where individuals were most likely to be recaptured near their initial site of capture. For this example at least, the small but significant measure of genetic structure did appear to provide useful inference toward effective conservation and

management. More generally, the ‘‘rejection of panmixia’’ method has been criticized by some because low rates of gene flow (theoretically just one migrant per generation, though usually more than this in real populations) can be enough to prevent divergence, and conversely populations may be demographically independent even when gene flow is high (see Palsboll et al., 2007). White et al. (2011) modeled the latter question and showed that sufficient migration to make demographic trends match among populations may be too high to be detectable using genetic methods. The ‘‘individual assignment’’ approach can also be based on the initial identification of putative populations, in which case the relative likelihood of assignment to sample site compared to alternative populations is assessed based on individual genotype profiles and the distribution of allele frequencies in each putative population. However, assignment to populations does not require populations to be predefined and can instead use information contained within multiallelic markers to cluster individuals into populations based on either minimizing deviation from equilibrium assumptions (concerning the equilibrium between migration and genetic drift) or grouping populations based on similar allele frequencies (using one of several ‘‘principle components’’ clustering analyses). These methods are especially useful for populations that are difficult to define a priori (especially when individuals have a continuous geographic distribution) or when there is cryptic population structure between sympatric populations. For example, the European gray wolf is a highly mobile animal, with a relatively continuous distribution and no obvious geographic barriers to gene flow. However, investigation of the population structure in Eastern Europe using neutral microsatellites found two distinct population clusters, suggesting restricted gene flow and cryptic population structure (Pilot et al., 2006; Figure 2) and implying an important change in conservation strategy for this species. Clustering approaches that quantify the degree of assignment can also allow estimates of the degree of admixture (historical gene flow between different populations), which can help with the determination of the history and extent of isolation. Conservation managers are interested in the practical utility of population structure for defining the population segments that require management (coined management units, or MU, by Moritz, 1994). Although the exact definition of an MU varies (and has been refined over the years, see below), the idea is that genetically distinct populations should be managed separately to preserve locally adapted population genetic variation and independent evolutionary processes. A deeper level of divergence was defined as an evolutionarily significant unit (ESU) and contrasted with an MU by Moritz in his 1994 paper. The general idea has been officially recognized at the policy level for some time in the US as distinct population segments under the ESA, which are afforded similar protection as species. However, the application of DPSs has not been without controversy. One famous example is that of the red wolf. The red wolf was once widely distributed across the southern US, but hybridization with coyotes was thought to have resulted in the loss of almost all pure red wolf individuals. Consequently, the red wolf was afforded protection under the ESA. However, later genetic analyses suggested that

Conservation Genetics

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Figure 2 Assignment of individual wolves to subpopulations identified from microsatellite DNA clustering analyses. Note that distinct populations were identified despite the continuous range of wolves (dark gray area) in the region.

the red wolf was formed as a hybrid between the gray wolf and the coyote (neither of which were listed as endangered; Wayne and Jenks, 1991; Reich et al., 1999), and the protection status of the red wolf was subsequently hotly disputed (see Allendorf et al., 2001). The routine assessment of MUs has been common, exemplified by fisheries management. For example, it is recognized that many salmon fisheries are comprised of different population units that have either been historically isolated or have functionally significant adaptive differences. Consequently, the need to define and manage such populations as separate stocks has been effectively applied in salmon fisheries across the globe (e.g., Beacham et al., 2004). Later revisions to the concept of an MU have included the suggestion that adaptive variation and ecological specialization are more important than genetic differentiation at neutral markers (Crandall et al., 2000). The debate about the relative importance of historical isolation (reflected in neutral markers) compared to functional differences is likely to be resolved eventually by our increasing ability to address both (see below) but can also be addressed in part by protecting distinct populations across a range of different habitats, thus preserving both past evolutionary processes and selective pressures (Moritz, 2002). An essential aspect of understanding population structure is the further understanding of the pattern of connectivity among populations. Fixation indices and clustering methods allow us to understand population structure in terms of

population differentiation (which is a function of gene flow), but we can also use genetics to specifically quantify the level and direction of gene flow among populations. Traditional population genetic methods for estimating gene flow used Wright’s island model to calculate the effective number of genetic migrants per generation (Nm). Because of a simple relationship with FST, Nm is relatively straightforward to calculate. However, Wright’s island model makes some simplifying assumptions that may not be biologically realistic (such as equilibrium between migration and genetic drift, equal subpopulation sizes, equal migration rates between subpopulations, no mutation, and no selection), and it only gives us an estimate of the magnitude of gene flow between subpopulations, providing no information about the directionality (Whitlock and Macaulay, 1999). With increasingly powerful computers, we can now estimate migration rates much more effectively using modeling approaches based on coalescent theory (see above) to provide us with estimates of asymmetrical migration rates between subpopulations (directional gene flow). The basic principle is as follows. The coalescent models a gene genealogy for a population and works backward in time to find points on the genealogy where lineages come together (coalesce) and share a common ancestor. Separate populations, reflecting different lineages, reveal directional gene flow when the historical coalescent for an individual is in a population different from the one it was sampled in (Figure 3). The specific approach used depends on whether (1) the subject

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A

B

Figure 3 Illustration of an individual belonging to one lineage found in a population dominated by another lineage to show how coalescent analysis can provide information on directional migration (in this case, from population A to population B).

populations diverged from one another a long time ago such that they exchange migrants at a constant rate over evolutionary time (Beerli and Felsenstein, 2001) or (2) they diverged more recently and are still exchanging migrants with one another at a variable rate (Hey and Nielsen, 2004). Both methods have been applied successfully to determine directional migration rates, but the second class of methods is especially useful for looking at more recent patterns of gene flow. These latter methods employ an isolation with migration (IM) model, which assumed that two populations (N1 and N2) have diverged from a single ancestral population (NA) at some time point (t) in the past (though note that more recent versions allow for multiple populations; Hey, 2010). Hoelzel et al. (2007) used the IM model to determine that ongoing low-level gene flow was occurring between distinct populations of killer whales (Orcinus orca) despite there being considerable population structure and different resource specializations suggesting possible isolation. This type of inference is important as it indicates the need for continuing to facilitate connectivity, even when populations appear to be strongly differentiated. Although methods that assume divergence a long time ago also assume an equilibrium between genetic drift and migration, IM is in this sense a ‘‘nonequilibrium’’ model, since it simultaneously estimates a point of divergence and the migration rate after that point in time (although it is an average rate following the time of divergence). Contemporary dispersal between populations can also be estimated using assignment tests, though the relevant time frame is very short and may not reflect gene flow (unless historical admixture is being assessed). One complication is that migrant individuals are easier to detect when there is strong population structure (and hence different allele frequencies between populations), but strongly differentiated populations will not be exchanging many migrants. Conversely, migrants are difficult to detect when population structure is weak, despite migration occurring at a higher rate. Using genetics to assess contemporary population assignment is especially useful in the fight against wildlife crime – wildlife forensics. Before the widespread use of genetic methods it would be extremely difficult to identify the origin of products in trade or to determine whether specimens had been poached illegally. Genetic assignment tests can identify biological samples to species and in some cases populations. For example, Wasser et al. (2008) used assignment tests to determine that a large volume of elephant ivory traded in Asia and Africa originated from a common geographic area, suggesting that

elephant populations in some regions were being hit particularly hard by ivory poaching. Another factor with potential relevance to conservation is the possibility of sex bias in gene flow, in which one sex may disperse further or more frequently than the other (for a review see Lawson-Handley and Perrin, 2007). This can be assessed either by comparing biparentally inherited autosomal markers between the sexes (whereby postdispersal individuals of the dispersing sex should show weaker population assignment and population structure) or by using markers with sexspecific modes of inheritance, notably mitochondrial DNA or the W chromosome (for species like birds with a ZZ/ZW sex determination system) in females and the Y chromosome in males. For example, Kerth et al. (2002) investigated population structure in the Bechstein’s bat using both autosomal and mitochondrial markers and found mitochondrial differentiation to be more than 60 times higher than autosomal differentiation, suggesting female philopatry and male-biased dispersal in this species. Describing the structure of populations is useful one species at a time, but broader inference requires an appreciation of the underlying mechanisms. One approach to learning more about this is to consider how geographic and environmental variables may have differentially affected gene flow across a landscape (for a review, see Segelbacher et al., 2010). Most such landscape genetic studies have two key steps: first to identify genetic discontinuities, and second to correlate these discontinuities with landscape and environmental features. Methods that identify discontinuities have generally focused on global statistics that identify large-scale patterns within a landscape. These include matrix correlations such as Mantel tests, which test for a significant statistical association between genetic and geographic distance (isolation by distance); spatial autocorrelation analyses, which test whether individuals adjacent in space are more genetically similar than those farther apart; and clustering methods, which use genetic and spatial information to group individuals into populations. In the second step, local landscape or environmental features that may act as barriers to gene flow are assessed. Various methods have been applied but typically are individual-based analyses (like assignment tests), which makes them well-suited to investigating fine scale processes. For example, Perez-Espona et al. (2008) used least-cost analyses to investigate the impact of landscape features on gene flow in red deer populations in the Scottish highlands. They found that landscape features significantly affected gene flow (sea lochs, slopes, roads, and forests acted as barriers whereas inland lochs and rivers promoted gene flow), explaining more of the variation than geographic distance alone. Essential components of these analyses are an accurate assessment of environmental variables and an appropriate choice of geographic scale for the focal species. Population structure also has a temporal dimension, which may reflect historical processes or events. A common approach to investigating this has been ‘‘phylogeography,’’ a method described in detail by its main proponent, John Avise, in his book of the same title (Avise, 2000). Phylogenies based on samples from populations distributed across a geographic range (often constructed from mtDNA sequences) are interpreted in the context of both geography and lineage history.

Conservation Genetics

One system about which considerable insight was revealed from this approach was the dynamic population structure of various species during and since the last glacial maximum in Europe. The ice age forced species to relocate further south into three principal areas of refuge – Anatolia, Italy, and the Iberian peninsula. The signature of this period of isolation and patterns of mixing to various extents following the retreat of the ice and during migrations north were evident in the phylogenetic reconstructions. These processes have played an important role in the structuring of natural populations in Europe (see review in Hewitt, 2000). More recently, population dynamics and historical connectivity have been investigated using methods based on the coalescent and simulation modeling, providing even greater insight into the processes that have shaped population structure (see review in deBruyn et al., 2011). Investigating population structure using neutral markers has provided a wealth of information on demographic processes associated with genetic drift (especially related to gene flow and population size). These data on connectivity, population history, and the rate and direction of gene flow are central to the development of effective conservation strategies. However, the potential for local adaptation is also of conservation concern. Neutral markers are only informative about natural selection when linked to a functional gene. For example, White et al. (2010) investigated population structure in a deep sea fish (Coryphaenoides rupestris) in the North Atlantic and found significant population structure only at a locus that showed deviation from neutral expectations. Allele frequencies at this locus were strongly correlated with the depth at which the fish had been living, suggesting linkage to a functional gene associated with adaptation to depth. However, phenotypic traits often involve multiple genes; some effects may be associated with the control of other genes in the system, and the key differences in different environments may relate to gene expression rather than allelic variation. Therefore, although examples of linkage to neutral markers suggesting local adaptation exist, this is not a very reliable way to detect selection at functional genes. Given a ‘‘candidate’’ gene that may be under selection, various tests can be applied to look for relevant signals. Selection acts on phenotypes and genotypes, and it can increase the frequency of a favorable allele (or decrease the frequency of an unfavorable one) through ‘‘directional’’ selection. It can also favor diversity by frequency-dependent selection on different alleles, spatial or temporal pattering to selection, or selection for heterozygous genotypes (‘‘balancing’’ selection). Selection therefore affects allele frequencies, and so methods that detect departures from a neutral distribution of alleles can be used to detect selection, though departures from neutrality may also be due to demographic effects (e.g., Tajima, 1989). Another potential signature of selection can be assessed by investigating the pattern of change in codons (the three-base code that determines the amino acid sequence in a protein) within coding genes. Only some base pair changes result in a change of amino acid (‘‘nonsynonymous’’ changes), whereas the rest are silent’’ (synonymous changes). Because the rate of nonsynonymous substitution under neutrality is expected to be equal to the mutation rate, the ratio of nonsynonymous to synonymous change (dN/dS) should be 1. Under negative directional

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selection, dN/dS will be less than 1, whereas under positive directional selection and under balancing selection it should be greater than 1 (see Nielsen, 2005). Screens of many genes at once have identified outliers as anonymous, possible candidates for being under selection, though this approach is controversial. Another possibility is to screen genes at the population level and test the expectation that there should be a predictable relationship between diversity within populations and genetic differentiation between them (typically assessed using FST). If there is more differentiation than expected for a given level of diversity, then this suggests directional selection, whereas lower suggests balancing selection (see Foll and Gaggiotti, 2008). For example, the marine snail species Littorina saxitalis has two divergent ecotypes found in different rocky shore habitats, and there are phenotypic differences between the ecotypes. Galindo et al. (2009) compared multiple loci from across the genome to identify outlier loci associated with ecotype differences, and they compared population structure using outliers versus selectively neutral loci. They found population structure to be far stronger using the outliers, which suggested that selection was promoting divergence between the ecotypes despite ongoing gene flow. One set of genes stands out as having special conservation relevance, and those are the genes involved in immune response. There is ample evidence that diversity is selected for at these loci, in response to pressure from the diversity of environmental pathogens (e.g., Klein, 1987), and this extends over a broad range of studies involving natural populations (see Bernatchez and Landry, 2003). There was some initial enthusiasm for a focus on these genes in particular in support of conservation (e.g., Schreiber and Tichy, 1992), though also the realization that this is only one aspect of a more complicated storey (e.g., Miller and Hedrick, 1991). Assessing the patterns of genetic structure at neutral and selected loci in natural populations allows us to determine best strategies for conserving the natural state and to understand the processes that generate the observed patterns of structure and connectivity. However, another key objective is to help address problems that we generate as we exploit the natural world and construct roads and cities around it. Habitat fragmentation and degradation are particular concerns, because the consequent reduction in gene flow can result in increasingly isolated populations and the loss of genetic diversity (and therefore evolutionary potential) through genetic drift and inbreeding (see Beginnings). For example, Reed et al. (2009) compared historic and contemporary levels of gene flow between nine populations of a wolf spider (a species that disperses by a form of wind-driven dispersal called ‘‘ballooning’’). Historically, they found that gene flow between populations occurred at a rate of 1.5 migrants per generation but that this dropped to 0.2 migrants per generation in extant populations. The dramatic decline in gene flow coincided with an increase in habitat fragmentation in the area in which the spiders were sampled, suggesting that anthropogenically mediated habitat degradation was having a negative impact. However, some studies have found habitat fragmentation to have no effect or even to increase gene flow in some circumstances, likely a consequence of differences in life history. In a review on the genetic effects of fragmentation in plants, Young et al. (1999) found that factors such as longevity, pollination system, and mating system all

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had the potential to influence whether or not a population was adversely affected by fragmentation. Human disturbance can also work the opposite way and force hybridizations (the interbreeding of individuals from genetically distinct populations or species) that are maladaptive (see Allendorf et al., 2001). One well-known example is that of the white-headed duck, a globally threatened species that has been negatively affected by hybridization with the invasive ruddy duck. Using genetic markers, Munoz-Fuentez et al. (2007) were able to show that the hybridization process had begun in the last pure stronghold of the ruddy duck in Europe, but there was no evidence for widespread introgression (incorporation of foreign genes), suggesting that conservation measures could be implemented in time to save this population. Both of these examples of the potential impact of human influence show how understanding more about the behavior of individual organisms can be valuable toward the development of more-effective conservation strategies.

Individual Genotypes In the previous sections (see Beginnings, Low Diversity and Small Populations, and Population Structure), we describe how individual fitness is correlated with genetic diversity, how genotypes can facilitate individual assignment to populations, and how this can facilitate wildlife forensic investigations. Here we consider how knowing individual genotypes can provide information on behavior or inform about processes that can influence the level and distribution of diversity within populations relevant to the development of effective conservation strategies. For example, populations may show genetic substructuring around kin groups, or a skew in reproductive success may affect levels of diversity. Dispersal behavior that shows a directional or sex bias may provide important information about population trends or habitat requirements, and food resource requirements can be assessed through the molecular identification of food species. The identification of kin relationships is important due to the possible clustering of kin (e.g., in social groups) and because reproductive skew reduces effective population size. The most straightforward application is the identification of parent–offspring relationships. This involves subtracting the genotype of one diploid parent from the profile of the offspring in order to identify the other parent, though it is necessary to have a sufficient number of polymorphic loci to reduce the probability of chance identifications, and it is often necessary to compensate for possible genotyping errors (see Queller and Goodnight, 1989). Although parentage can sometimes be estimated by observing mating events between individuals, there are obvious limitations and logistical considerations (e.g., if there is a poor correlation between association and mating success, failed fertilization). For species such as mammals, where there is a dependent association between mother and offspring, the identification of the mother–offspring pair can serve as the basis for the identification of paternity. The apparent extent of skew can sometimes be very much at odds with the reality. For example, the reed bunting is a monogamous bird in which parental care is shared by the

sexes; however genetic paternity tests revealed that up to 55% of chicks were sired as the result of extra pair fertilizations (EPF; Dixon et al., 1994). That EPFs occur under monogamy is likely to be the rule rather than the exception, though the extent of EPFs and any consequent skew will vary among populations and species (Hughes, 1998). In addition, for some species in which reproductive skew was thought to be the norm, genetics has revealed that a significant portion of paternity can be assigned to sneaky copulations with subordinate or outsider males. Arguably the most extreme polygyny is displayed by the elephant seal. Single males can defend groups of hundreds of females in harems at annual breeding colonies. The males are several times as massive as the females and fight exhaustively with competing males for access. For the northern elephant seal, paternity testing indicated that some males showed a surprisingly poor correspondence between apparent success (based on observational behavioral data) and their lower actual success based on paternity testing (Hoelzel et al., 1999). For this species, it is possible that severe depletion by overhunting is in part to blame, as genetic diversity was largely lost (Bonnell and Selander, 1974), and in other species this has been shown to affect reproductive health (e.g., Wildt et al., 1987). At the same time, work with the southern elephant seal (Mirounga leonina) suggests that alpha male success may also be related to environment. Harem holders at the Falkland Islands where the tidal range is small were more successful than in Argentina where the tidal cycle is large, possible due to the greater access a large tidal range avails peripheral males as estrous females leave the beach (Fabiani et al., 2004). These differences matter toward effective conservation, since skew can affect the relationship between apparent abundance and the effective population size. Genetics can also be used to determine kinship beyond parentage based on shared alleles that are identical by descent (IBD) as measured by the coefficient of relatedness (r). For example, in a sexually reproducing diploid species, we would expect parents and offspring or full siblings to have an r ¼ 0.5, half siblings or cousins 0.25, and so on. However, based on genetic markers, unless the number of markers used is very large, these estimates provide a broad distribution of values around the expected mean and so provide approximate kinship with decreasing accuracy as the level of kinship diminishes. Expectations that social groups represent associated kin are sometimes shown to be correct (e.g., African elephants, Loxodonta africana; Wittemyer et al., 2009) and sometimes not, even when social structure is very similar (e.g., sperm whales, Physeter macrocephalus; Ortega-Ortiz et al., 2011). Each of these species shows a hierarchical social structure where the primary association is between mother and dependent calf, followed by stable female–subadult groupings, and then temporary associations of the stable groups. However, although the stable ‘‘level 2’’ elephant groups show average kinship at about the ‘‘cousin’’ level, this is more variable for the sperm whale, and average kinship among whales did not differ within compared to between groups. Dispersal behavior is the primary driver of population connectivity and inbreeding, and it can be determined in part by social or mating behavior. For example, a mating system such as promiscuity fosters outbreeding, and in some species it

Conservation Genetics

may have evolved as a mechanism to avoid mating with kin. In a harem or hierarchical system in which only a few males breed, daughters may need to disperse to avoid inbreeding. Although purging deleterious recessives is possible (see Low Diversity and Small Populations), inbreeding avoidance is a powerful force. One striking example involves killer whales: both males and females are known to remain in some social groups for life. This leads to fixed mtDNA lineages within regional groups and high levels of kinship within social groups. However, Pilot et al. (2010) found that male-mediated gene flow was occurring during temporary associations among social groups, including among social groups whose core home ranges were quite distinct. In this way, outcrossing was being promoted even without dispersal. Mechanisms for inbreeding avoidance in plants reflect the complexity of their breeding systems, where self-fertilization is in some cases common. Some plants are obligate outcrossers because they possess self-incompatible (S) alleles preventing self-fertilization or that from a plant with a similar S-allele genotype. However, although this promotes diversity, it can limit productivity, especially in small, isolated populations when the diversity of S alleles becomes low. The various implications of plant breeding systems in the context of conservation strategy are reviewed by Barrett (2003). The timing, direction and any sex bias associated with dispersal can also be assessed using individual genetic profiles in much the same way that this is done with tags or natural markings. Methods for estimating the extent and direction of gene flow based on population parameters were addressed in the previous section, but in some cases it is useful to use the permanent, unique nature of genetic identity to supplement those data by tracking individuals (or their propagules). For example, Riley et al. (2006) used a combination of radio tracking and genetic assignment tests (see Population Structure) to determine that freeways acted as significant barriers to dispersal for the bobcat and coyote, despite these species possessing strong dispersal abilities. In a study investigating sexbiased dispersal behavior, observational evidence suggested that both male and female bottlenose dolphins in southeastern Australia were philopatric. However, genetic assignment tests revealed that females were correctly assigned to their natal population more often than males (Moller and Beheregaray, 2004), suggesting male-biased dispersal. Finally, individual genotypes can be used as a tool to help understand aspects of animal ecology (and habitat requirements), for example with respect to foraging behavior. This is most commonly based on genetic analyses to determine the dietary content of stomachs or scats. For example, little information was known about the dietary ecology of the Barbastelle bat due to its nocturnal habits and the degradation of soft prey tissues during digestion. However, Zeale et al. (2011) used DNA barcoding to identify 37 different taxa in scats, revealing broad resource requirements. For a review of molecular dietary analysis, see Symondson (2002).

Ex Situ Conservation Sometimes habitats become so degraded or a species so rare in the wild that it becomes necessary to implement

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ex-situ conservation measures. Ex-situ measures entail the captive breeding of organisms, often with a view to one day reintroducing their descendants into the wild. There are two primary objective that should be met during these efforts: first to retain as much of the natural diversity as possible, and second to avoid adaptation to the captive environment (at the expense of fitness in the wild). Various methods are employed to minimize inbreeding (and thereby maximize Ne), including equalizing family size and establishing pedigrees so that matings can be arranged that minimize kinship for the mating pair. Genetic methods can help both through the choice of initial founders and in the establishment of mating programs when pedigree data are not available. A key challenge has often been the nature and size of the initial founder group. Especially for endangered species, there may be little option about which individuals are chosen to establish the breeding population. Genetic assessment of kinship can help early on so that the least-related pairs are bred (though the resolution is limited, see Individual Genotypes). Leaving them to their own devices can lead to substantial skew. For example, the golden lion tamarin (Leontopithecus rosalia) captive population was founded from 48 individuals and increased in size to 500, but pedigree analyses revealed that almost two-thirds of genes in the population had descended from a single breeding pair, revealing a need for managed breeding to reduce inbreeding (Ballou and Lacy, 1995). Subsequent managed breeding to minimize kinship between mated pairs greatly reduced the level of inbreeding for the golden lion tamarins. Another concern is the sampling effect that occurs when the founder group is chosen (or during the bottleneck that reduced the size of the native population), potentially distorting allele frequencies. In this way, a rare recessive trait could become quite common in the captive population just by chance. A well-known example is the California condor (Gymnogyps californianus), which declined dramatically in the wild to a population of just 14 individuals. All individuals were brought into captivity and entered into a breeding program. The breeding program was a success, and numbers recovered sufficiently to begin reintroducing birds to the wild. However, managers noticed that a few chicks had begun to hatch with lethal deformities. Further investigation revealed the deformities to be consistent with chondrodystrophy – a form of dwarfism with an autosomal recessive genetic basis in the condor (Ralls et al., 2000). Worryingly, the frequency of this recessive allele in the captive population was estimated to be B9% – probably a consequence of the initial founder effect. Selective breeding can be used to reduce the frequency of the deleterious allele in the captive population, though as in this case, such a strategy has to be balanced against the risks associated with removing too many individuals from the breeding pool. To avoid inbreeding and to minimize sampling effects, large founder sizes and large Ne in captive populations are key objectives. However, selection is a stronger force when Ne is large. Therefore, since adaptation to the captive environment has the potential to reduce fitness for organisms subsequently released into the wild, an intermediate effective population size may be most appropriate in captivity

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(see Frankham, 2008). Araki et al. (2007) compared lifetime reproductive success of steelhead trout bred in captivity and released into the wild over three generations. They found that reproductive success declined by B40% each generation for individuals from domestic stock, a process they attributed to the absence of selective pressures in captivity relevant to natural conditions. This has now been documented in a wide range of taxa, including plants, birds, amphibians, and insects (Frankham, 2008). Estimates of Ne using genetic markers may help breeders maintain an optimal captive population size. Experimental studies on populations of Drosophila (Margan et al., 1998) compared the efficacy of a single large effective population to several smaller subpopulations for maintaining diversity and reducing adaptation to captivity. They found that the smaller populations collectively had higher diversity, higher reproductive potential, higher fitness under competitive conditions, and lower inbreeding coefficients than the equivalent effective size managed as a single larger population. Although this strategy has yet to be tested on a large scale in captive populations of conservation concern, such experimental studies suggest it may be a viable management option (Frankham, 2008). More generally, it is important to assess the compatibility between the organisms to be reintroduced (or translocated) and the proposed destination population. Apart from concerns associated with habitat suitability, genetic compatibility is important to avoid outbreeding depression (a reduction in fitness when individuals with divergent adaptive or genomic characteristics interbreed). Sagvik et al. (2005) tested the effect of outbreeding depression on the common frog by crossing individuals from two different populations (one from a large pond and the other from a small, isolated pond). They found reduced body size and increased deformities in F1 tadpoles produced by crosses between females from the large and males from the small pond. The genetic component leading to reduced fitness may or may not be reflected in diversity at neutral markers, and ultimately it would be ideal to be able to investigate relevant functional gene systems directly. However, gross chromosomal compatibility can be assessed (e.g., with respect to chromosome number) and combining populations that are strongly divergent at nuclear markers can be avoided. For example, Piertney et al. (2005) assessed mitochondrial sequence variation among water vole populations in the UK and found divergent lineages between Scottish and English and Welsh regions, and consequently recommended against translocation among these sites. For the longer term, cryopreservation of animal gametes and seed banks can help preserve diversity and permit a controlled management of diversity and reintroductions based on individuals of known genotype. These collections have the potential to be substantial, given the reduced space requirements and ability to store materials in a suspended state (dormant seeds or cryopreserved eggs and sperm). For example, the Millennium seed bank project at Kew Gardens, London, currently houses seeds from 10% of the world’s wild plant species and aims to increase this figure to 25% by the year 2020. This will provide a valuable resource for promoting plant genetic diversity in the future, which is clearly important given the current rates of habitat destruction and climate change.

The Future Substantial inference has been gained toward the more effective conservation of biodiversity based on the assessment of neutral genetic diversity (or diversity that was assumed to be neutral), greatly facilitated by advancing technologies for the more-efficient analysis of molecular markers. Allozymes reflected partial information because only coding regions were assessed and only some DNA changes were reflected in the different allozyme charge properties that could be detected as different alleles. ‘‘Chain-termination’’ sequencing allowed all base pairs to be analyzed for a given locus, but sequencing large regions by this method was time consuming. Something of a revolution in resolution was achieved through the analysis of repetitive DNA regions, which evolve by evolutionary processes (especially ‘‘DNA slippage’’) that are faster than point mutational change altering DNA sequence. These markers are also relatively easy to analyze, and ‘‘microsatellite DNA’’ loci especially remain widely used. However, a new era began once the first entire human nuclear genome was sequenced (Venter et al., 2001) based on traditional sequencing technology, and both the potential of having these data and the great expense and difficulty of generating it by existing technologies became evident (the Human Genome Project cost billions of dollars and took 10 years to complete). The new objective became the analysis of diversity across the genome, and at first this was facilitated by methods that looked at random loci defined by largely uncharacterized variation (amplified fragment length polymorphisms, or AFLP) and then by the more precise assessment of single nucleotide polymorphisms (SNP). Screening hundreds or thousands of SNP markers (e.g., using ‘‘microarray’’ technology; see Stoughton, 2005) allowed an exact assessment of genotype but still only represented a small proportion of the total genome. However, in the 21st century, the difficulty and cost associated with sequencing whole genomes was dramatically decreased by the advent of new technologies, referred to now as ‘‘next generation sequencing’’ (for a review, see Mardis, 2008). These methods typically sequence many fragments in parallel and use a new type of chemistry associated with the quantification of light released when DNA strands are extended (‘‘pyrosequencing’’; see Mardis, 2008). The applications of these genomic technologies in conservation genetics are largely in two categories (see review in Allendorf et al., 2010). The first simply takes advantage of the much greater power availed by the very large number of neutral polymorphic sites revealed (e.g., to obtain more accurate estimates of Ne, migration rate, population structure (units of management), and populations dynamics). It would also permit better assessment of introgression toward management against breeding hybrid organisms. Although these applications permit greatly improved resolution, the other category offers something new. Although work on candidate genes has allowed tests for selection at functional genes, sequences from whole genomes will eventually allow functional gene systems important to conservation to be specifically identified and assessed. However, single gene effects based on mutational differences in that gene are likely to be relatively rare. Instead, phenotypes are encoded by multiple genes, genes may play different roles in different genomic or environmental

Conservation Genetics

contexts, and phenotype is usually determined to some extent by environment. Further, some genes control the expression of other genes (so called nonadditive interactions), and these expression profiles may be the relevant measure for assessing local adaptation. In some cases, differences in phenotype will be due to the ability of organisms with a given genotype to express different phenotypes, known as ‘‘phenotypic plasticity’’. Genomic methods make the analysis of all of these aspects possible but not less complicated. The researcher investigating a whole genome is typically confronted with billions of bases of DNA, and one of the greatest challenges for the future will be finding the means to efficiently and effectively analyze those data. In spite of the limitations so far, the potential is great, and already significant advances are being made. Hundreds of nonhuman whole genome sequences are now available or are in progress at different levels of resolution, and a consortium of scientists is promoting the sequencing of 10,000 species (the Genome 10K project; Haussler et al., 2009). A major objective of the Genome 10K project is to promote biodiversity conservation. Work to promote the conservation of functional diversity will be able to employ methods that permit the identification of loci under selection on a comparatively large scale. As described previously, there is a predictable relationship between diversity within populations and genetic differentiation between them (see Foll and Gaggiotti, 2008). When thousands of loci can be investigated at the sample time, then an assessment of this relationship can be undertaken for all loci at once, and the significance of outliers assessed, indicating candidate genes that may be under selection (Figure 4). So, rather than starting with the gene based on known function, it is possible to search for genes showing a signal for selection and then consider their function in the context of quantifiable differences in environmental or ecological factors. One of the first studies to apply this approach using genomic technologies involved a study of marine and freshwater populations of the three-spine stickleback (Gasterosteus aculeatus; Hohenlohe et al., 2010). The authors used a technique that sequences a subset of DNA from across the genome (but still including millions of base pairs) to identify SNP loci that can be screened among individuals at the population level. Hohenlohe and co-authors identified more

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than 45,000 SNPs and screened them among 100 fish from both marine and freshwater populations. They identified numerous loci that showed evidence for selection and found parallel changes that suggest a common signature for adaption to freshwater, indicating that large random mating marine populations had repeatedly given rise to freshwater populations, and that the adaptive mechanisms permitting this were comparable for independent events. This and similar studies in future will provide conservation biologists with key information about how biodiversity can best be protected. It will likely be possible to identify genes’ relevant major processes such as climate change, and it is already clear that populations showing little differentiation at neutral genetic markers may be differentiated at loci that are under selection (e.g., White et al., 2010), reflecting functional diversity that should be conserved. Conservation genetics is a field that is especially well-served by advancing technologies and is of increasingly critical importance as anthropogenic impacts increase and natural populations decline.

Appendix List of Courses 1. 2. 3. 4. 5.

Conservation Biology Molecular Ecology Population Genetics Evolutionary Biology Biogeography

See also: Captive Breeding and Reintroduction. Captive Breeding and the Evolutionarily Significant Unit. Conservation Biology, Discipline of. Diversity, Molecular Level. Ecological Genetics. Evolution in Response to Climate Change. Evolution, Theory of. Genetic Diversity. Habitat Loss and Fragmentation. In Situ, Ex Situ Conservation. Inbreeding and Outbreeding. Loss of Biodiversity, Overview. Nucleic Acid Biodiversity: Rewriting DNA and RNA in Diverse Organisms. Population Genetics. Restoration of Biodiversity, Overview. Species Diversity, Overview. Threatened Species: Classification Systems and Their Applications

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References

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Allendorf FW, Hohenlohe PA, and Luikart G (2010) Genomics and the future of conservation genetics. Nature Reviews Genetics 11: 697–709. Allendorf FW, Leary RF, Spruell P, and Wenburg JK (2001) The problems with hybrids: Setting conservation guidelines. TREE 16: 613–622. Araki H, Cooper B, and Blouin MS (2007) Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318: 100–103. Avise JC (2000) Phylogeography: The History and Formation of Species. Harvard University Press. Ballou JD and Lacy RC (1995) Identifying genetically important individuals for management of genetic diversity in pedigreed populations. Population Management for Survival and Recovery: Analytical Methods and Strategies in Small Population Conservation. New York: Columbia University Press. pp 76–111. Barrett SCH (2003) Mating strategies in flowering plants: The outcrossing-selfing paradigm and beyond. Philosophical Transactions of the Royal Society Series B 358: 951–1004.

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Expected heterozygosity Figure 4 Under neutral expectations, there is a predicted relationship between the diversity within and the genetic distance between populations. This illustration shows how multiple loci can be screened for this relationship, where outliers outside 95% (light gray line) or 99% (dark gray line) confidence limits indicate loci likely under selection (ringed blue dots in the figure).

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