INTRODUCTION

An individual's fitness can be largely influenced by its genetic diversity. Low survival and poor fecundity are just few examples of the consequences of the loss of genetic variation (Mitton 1993; Falconer and Mackay 1996). Matings between closely related parents can result in progeny with lowered fitness, a phenomenon generally known as inbreeding depression (Lynch and Walsh 1998). However, genetic drift in small and isolated populations can also result in increased homozygosity, even in the absence of matings between close kin (Shields 1993). On the other hand, matings between genetically differentiated populations or strains often produce individuals with higher fitness during the first generation; a phenomenon called heterosis (Mitton 1993).

The earliest observations of the strong influence of an individual's genetic diversity on its fitness came from domestic and laboratory animals (reviewed by Falconer and Mackay 1996), and from zoos (Ralls and Ballou 1983) where inbreeding was seen to cause harmful effects on survival, fecundity and growth (direct evidence). In wild populations, where pedigrees have been known, the negative effects of inbreeding have also been observed (Kruuk et al. 2002; Reid et al. 2003). However, pedigree information is rarely available in wild populations. The majority of information on the genetic diversity of natural populations has actually been obtained using molecular markers believed to be generally neutral, namely allozyme and DNA markers (indirect evidence) (Frankham 1995; Haig and Avise 1996). Positive associations have observed between protein heterozygosity as assessed by allozymes and several fitness-related traits such as growth, metabolic efficiency, body size, fecundity and survival (Mitton and Grant 1984; Charlesworth and Charlesworth 1987; Danzmann et al. 1987, 1989; Allendorf and Leary 1988; Britten 1996; David 1998; Wang et al. 2002). With the development of the polymerase chain reaction (PCR), nuclear DNA molecular markers have become popular in heterozygosity–fitness studies. Microsatellites have been used extensively as genetic markers in a wide range of biological studies, not least in conservation genetics (Beaumont and Bruford 1999).

INTRODUCTION

The availability and application of molecular tools for biodiversity conservation has advanced considerably over the last 15 years, as has been documented in a series of books (e.g. Loeschcke et al. 1994; Avise and Hamrick 1996; Smith and Wayne 1996; Frankham et al. 2002, 2004), the journal Conservation Genetics (in production since 2000) and a series of reviews, all of which give the impression of a maturing discipline (e.g. Hedrick 2001; DeSalle and Amato 2004). Dramatic advances in data analysis have occurred over the last five to seven years and these are welldocumented elsewhere in this volume. However, in other respects, it could be argued that the field has been dominated by the use of a few tried-andtested marker types and has not been as quick as it could have been to adopt new laboratory methodologies. For instance, the quantum leaps in high throughput molecular protocols for detecting and analysing DNA polymorphisms, applicable for example to rapid community-level biodiversity assessment using DNA barcoding methods (such as developments in rapid sequencing and large-scale SNP genotyping), are yet to make a significant appearance in the conservation genetics literature. In addition, a framework for the routine translation of conservation genetic data into population management and specific actions in the field remains in its infancy. Conservation genetics has largely remained a field where routinely, a relatively small number of molecular markers are isolated and applied to a few populations of a single species, some of which may be threatened. The data from such studies are then published in reports and peer-reviewed scientific publications such as Molecular Ecology and Conservation Genetics, wherein management recommendations may be made. However, it is not clear whether these suggestions are ever incorporated into species on habitat action plans; in fact, many conservation geneticists complain that their results are, on the whole, completely ignored by management authorities.

Restocking is a common procedure for artificially increasing the population size of fish and game species in a particular geographical area. A similar intervention, which entails the (re)introduction of individuals from a source population (natural or captive) to a target area, is an important tool for ecosystem restoration, and is often essential to the recovery or rescue of endangered species or populations (Griffith et al. 1989; Frankham et al. 2002). In both cases, the principal aim of these so-called translocations is to establish stable and self-sustaining populations, taking care to preserve the original genetic structure and ecosystem dynamics of the particular species, while avoiding interference with natural evolutionary processes. But how can this goal be achieved in practice?

From an evolutionary and genetic perspective, these primary goals can be said to be attained when the introduced animals are successfully reproducing in the target environment, when negative selection pressures due to the effects of inbreeding or out-breeding depression are negligible, and when evolutionary potential is maintained (Moritz 1999; Frankham et al. 2002; Hufford and Mazer 2003; Tallmon et al. 2004). The challenge is to develop specific translocation plans which guarantee the achievement of all these objectives, and monitor the success of their implementation. For example, selecting animals or groups of animals appropriately adapted to a target environment is only possible by conducting a costly and long-term preliminary phase of fitness analysis. Similarly, the effects of inbreeding or out-breeding depression on the fitness of individuals in a translocated population can go undetected for extended periods of time. As this chapter will discuss, one solution to this dilemma is offered by the analysis of genetic markers; in fact, theoretical population and evolutionary genetics, together with empirical evidence suggest that levels and patterns of genetic variation within and between groups can be used, integrated with ecological studies, to plan and monitor translocations.

Population genetic analyses often require the estimation of parameters such as population size and migration rates. In the 1960s, enzyme electrophoresis was developed; it was the first method to gather co-dominant data from many individuals in many populations relatively easily. Summary statistics methods, such as allele-frequency based F-statistics (Wright 1951), were used to estimate population genetics parameters from these data sets. These methods matured and expanded into many variants that were enthusiastically accepted by many researchers. F-statistics are still a hallmark of any population genetic study, especially in conservation genetics, although over the years, limitations have become evident (Neigel 2002). Many of these methods use restrictive assumptions, for example, disallowing mutation. F-statistics, such as FST methods, are often employed on pairs of populations; this can lead to biased parameter estimates (see Beerli 2004; Slatkin 2005) and the reuse of data in these pairwise methods is undesirable from a statistical viewpoint.

In 1982, Sir John Kingman developed the coalescence theory (Kingman 1982a, b). His overview of the developments of this theory (Kingman 2000) gives an interesting insight into the development of new ideas. This new development opened the door to methods in population genetics that go beyond the F-statistics methods and have led to several theoretical breakthroughs (Hein et al. 2005; although inferences based on coalescence theory were not practicable until about 1995 because of computational constraints). In recent years, computer-intensive programs that can estimate parameters using genetic data under various coalescent models have been developed; for example, programs that estimate gene flow (Beerli and Felsenstein 1999, 2001; Bahlo and Griffiths 2000; Wilson et al. 2003; De Iorio and Griffiths 2004; Hey and Nielsen 2004; Beerli 2006; Ewing and Rodrigo 2006; Kuhner 2006). These programs use different models and different approaches, but in all of them, the quantities of interest are difficult to calculate. Very generally, the goal of these applications is to calculate the probability of the parameters of the chosen model given the data.

In phylogeography or population genetic studies, evolutionary relationships among DNA haplotypes can be depicted either as a graph, called a ‘network’, with cycles (or ‘loops’), or as a set of phylogenetic trees (i.e. connected graphs with no circuits), possibly with multifurcation(s) and/or ancestral haplotype(s) (both represented by collapsing zero-length branches). For example, several equally optimal trees inferred under the maximum parsimony (MP) criterion display alternative relationships among haplotypes (Fig. 5.1a, b). A strict consensus tree can be used to summarize this set of trees (Fig. 5.1c), but this approach discards much of the historical information. Indeed, a strict consensus tree is typically compatible with many more alternative trees than those used to build it: e.g. the consensus in Fig. 5.1c is compatible with 105 different strictly bifurcating topologies although only two haplotypic trees have been used to build it. Furthermore, the consensus tree cannot easily summarize branch length information (e.g. in Fig. 5.1, taxon 4 is at the tip of a 0 step-long or a 1 steplong branch in trees (a) and (b), respectively). On the contrary, a network graph allows display much of the information contained in the data in a single figure (Fig. 5.1d). Therefore, the major advantage of such graphs over traditional phylogenetic trees is the possibility of using cycles (loops) to represent either ambiguities in the data or genuine reticulate evolution (due to e.g. recombination or horizontal gene transfer). In parsimony networks, sampled and unsampled haplotypes (white circles and black dots, respectively, in Fig. 5.1d) are symbolized by nodes (vertices) that are connected by edges, where each edge represents a single nucleotide substitution. Unsampled haplotypes are inferred to connect sampled haplotypes when the latter are separated by more than a single substitution. The so-called ‘degree’ of a node corresponds to the number of edges to which it is connected (e.g. in Fig. 5.1d, haplotype 2 is a node of degree 4).

INTRODUCTION

Since its inception, the concept of ‘evolutionarily significant unit’ (ESU) has had several theoretical definitions differing mainly on the emphasis given to neutral versus adaptive genetic diversity (Ryder 1986; Waples 1991; Moritz 1994; Crandall et al. 2000; reviewed in Fraser and Bernatchez 2001). The ‘neutral’ definition highlights a genetic background shaped over a long-term evolutionary time scale by evolutionary forces such as genetic drift and migration (Moritz 2002). In contrast, the ‘adaptive’ definition underlines the existing, adaptively significant phenotypic variation resulting from the ongoing action of natural selection (McKay and Latta 2002; van Tienderen et al. 2002). Thus, neutral and adaptive diversities depict distinct temporal realms (Fig. 6.1) and should be assessed separately and differently (Bowen 1999; Moritz 2002).

The neutral component of genetic diversity was the first to be investigated, coinciding with the breakthrough in the development of molecular markers and new tools and concepts in phylogeography and population genetics. A wide range of methods is currently available to measure neutral genetic variability, which has resulted in crucial answers to several key conservation issues (see examples in Frankham et al. 2002). In contrast, characterizing adaptive genetic variation remains challenging (van Tienderen et al. 2002; Vasemägi and Primmer 2005), requiring the identification of a small number of adaptive loci scattered throughout the genome (Black et al. 2001). Moreover, molecular markers are generally assumed to be neutral and they have been shown to be poor indicators of adaptive genetic diversity (Merilä and Crnokrak 2001; Reed and Frankham 2001; McKay and Latta 2002). Given this background, we review here different strategies that can be adopted to reveal genetic polymorphisms with an adaptive role. The goal of this chapter is not to present an exhaustive and detailed list of existing methods, but rather to highlight the underlying principles of those we believe to be the most useful and/or readily applicable, as well as their advantages and drawbacks. Several examples relevant to conservation are also presented.

INTRODUCTION

Genetic sequence data have become widely used in evaluating the unique relationship between geography and evolutionary history for conservation of species. Traditional methods, such as bifurcating trees and Wright's F-statistics, often fall short in detailing past and contemporary events and contribute little intraspecific information (Posada and Crandall 2001; Pearse and Crandall 2004). Phylogenetic techniques, when applied in lower level systematic studies, show poor resolution, often resulting in polytomies and ambiguous connections (Crandall et al. 1994). This is particularly the case when species have recently diverged or have complicated metapopulation structure, in which case, bifurcating trees do not have the ability to accurately depict their evolutionary history (Posada and Crandall 2001). Despite this lack of resolution, broad geographic patterns can still be elucidated for older taxa using phylogenetic approaches. The field of phylogeography began by overlaying phylogenies onto geography and making broad inferences about evolutionary histories of species and populations (Avise 1989). This approach, however, does not provide the opportunity to (1) statistically test the null hypothesis of no geographic association between populations, (2) test whether samples (number of individuals and collection localities) are sufficient, or (3) infer historical and contemporary processes and patterns that dictate current genetic variation (Carbone and Kohn 2004). However, approaches such as Nested Clade Analysis (NCA: Templeton et al. 1995), also known as Nested Clade Phylogeographic Analysis or NCPA (Templeton 2004), provide a statistical framework in which to test hypotheses about historical events and current population structure within species.

Indeed, conservation of a species is highly dependent on understanding the processes and the patterns that gave rise to the current phylogeographic composition of each unique taxon. The NCPA approach also has important applications to species delimitation and diagnosis, as it can be used to test for exchangeability and genealogical ‘exclusivity’ (Crandall et al. 2000). In this chapter, we detail the methodology of the NCPA of haplotype trees in phylogeographic studies and its application to a wide range of issues in conservation biology.

INTRODUCTION

Marine mammals are a taxonomically diverse group of species with evolutionary roots right back to the earliest mammalian radiations. The smallest species is a mustelid, the sea otter (Enhydra lutris), and the largest the blue whale (Balaenoptera musculus). The only things marine mammals have in common are the facts that they are all mammals (and therefore dependent on breathing air and constrained by the necessities of live birth and maternal care), and they are all dependent on an aquatic, typically marine environment. These two common attributes have meant that they are constrained in similar ways, though the different groups have met these challenges in different ways. The mustelid, the carnivore (polar bear, Ursus maritimus) and the pinnipeds (seals, sea lions and walrus) all meet thermoregulatory challenges with dense pelage. Most of these also still give birth on land, and are to varying extents amphibious. The cetaceans (whales, dolphins and porpoises) and sirenians (manatees and dugongs) are fully aquatic, and have little or no pelage. Instead they have adjusted to the high thermal conductivity of water and the generally cold temperatures by developing thick layers of subcutaneous fat, and in many cases, by becoming large (which provides a high volume to surface area ratio and conserves heat). All of these species, with the exception of the polar bear, have adapted to more efficient locomotion in water by acquiring a relatively fusiform shape – most extensively developed in the delphinid cetaceans (the dolphins).

In this chapter my focus will be on those features among the marine mammals that help to explain common patterns of population structure, or differences in these patterns among taxa. One feature shared by many is their high trophic position in the ecosystems they occupy. One exception is the sirenians, which are herbivorous. However, most marine mammals are predators, though their trophic position can vary dramatically (from baleen whales feeding on krill to killer whales feeding on other marine mammals). Another typical feature is large size.