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Baker, C.S., and Palumbi, S.R. (1994). Which whales are hunted? A molecular genetic approach to monitoring whaling. Science 265, 1538–1539. Baker, C.S., Steel, D., Choi, Y., Lee, H., Kim, K.S., Choi, S.-K., et al. (2010). Genetic evidence of illegal trade in protected whales links Japan with the U.S. and South Korea. Biol. Lett. 6, 647–650. Cipriano, F., and Palumbi, S.R. (1999). Genetic tracking of a protected whale. Nature 397, 307–308. Clapham, P.J., and Van Waerebeek, K. (2007). Bushmeat and bycatch: the sum of the parts. Mol. Ecol. 16, 2607–2609. Committee on Taxonomy. (2016). List of marine mammal species and subspecies, Society for Marine Mammalogy, World Wide Web electronic publication. http://www.marinemammalscience.org, Accessed on May 5, 2016. Costello, M.J., and Baker, C.S. (2011). Who eats sea meat? Expanding human consumption of marine mammals. Biol. Conserv. 144, 2745–2746. Dalebout, M.L., Baker, C.S., Cockroft, V.G., Mead, J.G., and Yamada, T.K. (2004). A comprehensive molecular taxonomy of beaked whales (Cetacea: Ziphiidae) using a validated mitochondrial and nuclear DNA database. J. Hered. 95, 459–473. Dalebout, M.L., Baker, C.S., Steel, D., Robertson, K.M., Chivers, S.J., Perrin, W.F., et al. (2007). A divergent mtDNA lineage among Mesoplodon beaked whales: molecular evidence for a new species in the tropical Pacific? Mar. Mamm. Sci. 23, 954–966. Dalebout, M.L., Lento, G.M., Cipriano, F., Funahashi, N., and Baker, C.S. (2002). How many protected minke whales are sold in Japan and Korea? A census by DNA profiling. Anim. Conserv. 5, 143–152. Dalebout, M.L., Van Helden, A., Van Waerebeek, K., and Baker, C.S. (1998). Molecular genetic identification of Southern Hemisphere beaked whales (Cetacea: Ziphiidae). Mol. Ecol. 7, 687–695. DeSalle, R., and Birstein, V.J. (1996). PRC identification of black caviar. Nature 381, 197–198. Endo, T., Haraguchi, K., Hotta, Y., Hisamichi, Y., Lavery, S., Dalebout, M.L., et al. (2005). Total mercury, methyl mercury and selenium levels in the red meat of small cetaceans sold for human consumption in Japan. Environ. Sci. Tech. 39, 5703–5708. Gales, N.J., Kasuya, T., Clapham, P.J., and Brownell, R.L. (2005). Japan’s whaling plan under scrutiny. Useful science or unregulated commercial whaling? Nature 435, 883–884. Gravena, W., Hrbek, T., da Silva, V.M.F., and Farias, I.P. (2008). Amazon River dolphin love fetishes: From folklore to molecular forensics. Mar. Mamm. Sci. 24, 969–978. Hebert, P.D.N., Cywinska, A., Ball, S.L., and DeWaard, J.R. (2003). Biological identifications through DNA barcodes. Proc. Roy. Soc. (Lond.) Ser. B, Biol. Sci. 270, 313–321. IWC, 1998. Report of the Scientific Committee, Annex Q. Report of the working group on proposed specifications for a Norwegian DNA database register for minke whales. Rep. Intern. Whal. Comm. 49, 287–289. IWC, 2004. Annex T: Report of the data availability working group. J. Cet. Res. Manage. 6(Suppl), 406–408. IWC. (2005). Report of the Specialist Group on the DNA Register/ Market Sampling Scheme Approach (SGDNA). available from the Secretariat, International Whaling Commission, The Red House, 135 Station Road, Impington, Cambridge, CB4 9NP UK IWC/ M05/RMSWG 5. IWC, 2006. Report of the Scientific Committee, Annex J: Working Group on Estimation of Bycatch and other Human-induced Mortality (BC). J. Cet. Res. Manage. 8(Suppl), 177–184. IWC, 2012. Report of the Scientific Committee. Annex D1. Report of the Working Group on the Implementation Review for western North Pacific common minke whales. J. Cet. Res. Manage. 13(Suppl), 102–129. Kang, S., and Phipps, M. (2000). A survey of whale meat markets along South Korea’s coast. TRAFFIC, East Asia. Leeney, R.H., Dia, I.M., and Dia, M. (2015). Food, Pharmacy, Friend? Bycatch, Direct Take and Consumption of Dolphins in West Africa. Human Ecol. 43, 105–118.
Lukoschek, V., Funahashi, N., Lavery, S., Dalebout, M.L., Cipriano, F., and Baker, C.S. (2009). High proportion of protected minke whales sold on Japanese markets is due to illegal, unreported or unregulated exploitation. Anim. Conserv. 12, 385–395. Malik, S., Wilson, P.J., Smith, R.J., Lavigne, D.M., and White, B.N. (1997). Pinniped penises in trade: a molecular genetic investigation. Conserv. Biol. 11, 1365–1374. May-Collado, L., and Agnarsson, I. (2006). Cytochrome b and Bayesian inference of whale phylogeny. Mol. Phylogenet. Evol. 38, 344–354. NOAA. (2015). Presidential Task Force on Combating IUU Fishing and Seafood Fraud. Action Plan for Implementing the Task Force Recommendations, March, World Wide Web electronic publication. http://www.nmfs.noaa.gov/ia/iuu/noaa_taskforce_report_final.pdf, Accessed on October 26, 2015. Oremus, M., Leqata, J., and Baker, C.S. (2015). Resumption of traditional drive hunting of dolphins in the Solomon Islands in 2013. Open Sci. 2, 140524. Palsbøll, P.J., Berube, M., Skaug, H.J., and Raymakers, C. (2006). DNA registers of legally obtained wildlife and derived products as means to identify illegal takes. Conserv. Biol. 20, 1284–1293. Perrin, W.F., Rosel, P.E., and Cipriano, F. (2013). How to contend with paraphyly in the taxonomy of the delphinine cetaceans? Mar. Mamm. Sci. 29, 567–588. Ratnasingham, S., and Hebert, P.D.N. (2007). The Barcode of Life Data System. Mol. Ecol. Notes 7, 355–364. Reeves, R.R., McClellan, K., and Werner, T.B. (2013). Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011. Endang. Spec. Res. 20, 71–97. Robards, M.D., and Reeves, R.R. (2011). The global extent and character of marine mammal consumption by humans: 1970–2009. Biol. Conserv. 144, 2770–2786. Rosenbaum, H.C., Brownell, R.L., Brown, M.W., Schaeff, C., Portway, V., White, B.N., et al. (2000). World-wide genetic differentiation of Eubalaena: questioning the number of right whale species. Mol. Ecol. 9, 1793–1802. Ross, H.A., Lento, G.M., Dalebout, M.L., Goode, M., McLaren, P., Rodrigo, A.G., et al. (2003). DNA surveillance: Web-based molecular identification of whales, dolphins and porpoises. J. Hered. 94, 111–114. Ross, H.A., and Murugan, S. (2006). Using phylogenetic analyses and reference datasets to validate the species identities of cetacean sequences in GenBank. Mol. Phylogenet. Evol. 40, 866–871. Song, K.-J., Kim, Z.G., Zhang, C.I., and Kim, Y.H. (2010). Fishing gears involved in entanglements of minke whales (Balaenoptera acutorostrata) in the East Sea of Korea. Mar. Mamm. Sci. 26, 282–295. Thompson, K., Baker, C.S., van Helden, A., Patel, S., Millar, C., and Constantine, R. (2012). The world’s rarest whale. Current Biol. 22, R905–R906. Tzika, A., D’Amico, E., Alfaro-Shigueto, J., Mangel, J.C., Waerebeek, K., and Milinkovitch, M.C. (2010). Molecular identification of small cetacean samples from Peruvian fish markets. Conserv. Genet. 11, 2207–2218. Wada, S., Oishi, M., and Yamada, T.K. (2003). A newly discovered species of living baleen whale. Nature 426, 278–281.
GENETICS, MANAGEMENT Phillip A. Morin and Andrew E. Dizon Certain kinds of genetic information are particularly well suited to assist in designing strategies to protect human-impacted marine mammals. The type of genetic information required depends on the particular conservation goals wildlife managers seek to achieve when protecting specific species, or populations within species. For example, is the goal to prevent extinction of the species as a whole
GENETICS FOR MANAGEMENT or to prevent extirpation of local, but not necessarily genetically unique, populations? For most developed nations, these goals are codified in laws presumably reflecting, at least in democratic societies, the will of the public. To achieve these goals, managers often choose between controversial and conflicting strategies, such as various limits on the species and numbers of marine mammals that can be incidentally killed during fishing operations. Relaxed limits favor the fishermen, but may put a population of marine mammals at risk; stringent limits are less risky but may put an unsupportable burden on fishermen by restricting their fishing options. Obviously, the kind and the quality of biological data, genetic or otherwise, informing this choice are critical. Decisions have to be based on the best available scientific information, or they will be challenged in the courts. Although most scientific information on impacted populations is of value, certain kinds of information are much more important for the management process. If only limited data are available (molecular or other), biased or misleading conclusions can result in inappropriate decisions being made, eventually imperiling the population needing protection in the first place. Biological data on marine mammals, especially cetaceans, are difficult and consequently expensive to obtain. By consuming limited conservation funds, even good but irrelevant studies can impede conservation efforts. An understanding of policy is needed to ensure that proposed genetic studies are relevant for management needs (the conservation goals) before doing the science (the information gathering) (Taylor and Dizon, 1999). One advantage that genetic analyses have over “whole animal” studies is that data are easier to collect and few constraints are put on the quality of a sample or its origin. DNA is a relatively tough molecule, and adequate samples can be obtained from tiny amounts of a variety of tissues such as skin, blood or blood stains, hair follicles, placenta, excrement, baleen, modern or ancient bone, or, in some circumstances, formalin-preserved tissues. For instance, adequate amounts of mitochondrial DNA (mtDNA) from ca. 1000-year-old bowhead whale (Balaena mysticetus) bones have been obtained, and whole genomes are now regularly sequenced from historical and ancient specimens. The field of ancient DNA (aDNA) holds promise for understanding past and contemporary patterns and processes for marine mammals (Foote et al., 2012a). For live animals, projectile biopsying (crossbow, firearm, or lance) has been used successfully for all but the smallest and shyest cetaceans (see chapter on Genetics, and Genomics), and even water samples containing environmental DNA (eDNA) are potential sources of DNA for abundance, distribution, and population studies (Foote et al., 2012b).
I. The “Conservation Unit” Defining the population segment on which to focus conservation efforts is the primary use of genetic information. The US Marine Mammal Protection Act (MMPA) of 1972, the US Endangered Species Act (ESA) of 1973, the relevant legislation of some other nations, and the revised management procedure of the International Whaling Commission (IWC) all suggest that management efforts must be focused on populations. Although many countries have not established laws codifying the conservation unit, biologists are generally in agreement that species comprise a collection of semiisolated populations (i.e., species-wide panmixia is the exception) and that those semiisolated populations should be the focus of management. However, the devil is in the details, and there is much controversy on the precise definition of these units. Besides having obvious biological consequences for getting the groupings correct, there can be economic ones as well. For instance, quotas on harvest
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or incidental take are calculated as some allowable fraction of the overall abundance within the chosen conservation unit. A small conservation unit is the most biologically risk-averse because quotas are then necessarily small, and there is a greater likelihood that removals will be equally distributed over the whole unit. However, a large conservation unit is the most economically risk-averse because the quotas are larger, and there is the potential that excessive removals in one part of the range (the sink) will be compensated for by immigration from outside of the exploited region (the source). Policy tries to provide managers with guidance to balance conservation and economic issues by defining the management unit (MU). For instance, the US ESA seeks to prevent the extinction of distinct population segments that are evolutionarily unique. The policy addresses last-ditch efforts to rescue populations whose abundances are so low, or whose abundances will become so low in the near future, that if something is not done immediately, they will likely go extinct. These so-called evolutionarily significant unit (ESUs) are defined as (1) being “substantially” reproductively isolated from other population segments of the same species and (2) representing an important component in the evolutionary legacy of the species. The first criterion speaks to the rate of exchange between the population segment and other segments. The second speaks to the time the population segment has been isolated. In contrast, the US MMPA seeks to maintain viable populations across their historical ranges at (at least) 50% of their historical population size. This act addresses maintenance of abundance. The MMPA conservation unit could be characterized as demographically independent population (DIPs) to contrast them with ESUs. Some use the term “MU” to refer to a DIP, but because both DIPs and ESUs are MUs in the strict sense, it is important to distinguish them. Genetic data are useful for defining both. However, the policy goals are different and, consequently, the details of genetic studies directed toward either must take slightly different approaches.
A. The Evolutionarily Significant Unit Because the ESA is concerned with conservation units that are characterized as being “evolutionarily” different, the genetic methodology employed must be sensitive to evolutionary distances between taxa. Indeed, the traditional academic use of genetic data is employed to reconstruct common ancestry and to group taxa based on common ancestry. No restriction is based on the taxon level examined (subspecies, species, genus, family, etc.), except that the taxa are assumed to be reproductively isolated and that sufficient time has passed so that measurable genetic differences have accrued between every individual in one taxon and every individual in another. For higher-level taxonomic relationships, the grouping derives a priori from a particular classification based on morphological distinctiveness. For groupings below the species level, the grouping often derives a priori from geographical clustering; some have termed this phylogeography to contrast it to traditional phylogenetics. Regardless, the key to ESU status is still reproductive isolation and time. Using DNA sequence data to test these a priori groupings to see if they are genetically accurate, an investigator demonstrates that all the individuals of each stratum fall into exclusive genetic clusters (Waples, 1991; Hillis et al., 1996; Ross et al., 2003). If so, ESU status can be presumed for the groupings. The evidence addresses the policy that protection should be offered to a population segment that is “substantially” reproductively isolated. If they are not isolated, it is not possible to demonstrate exclusive genetic clustering. The genetic evidence is usually presented in the form of
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a branching diagram representing the evolutionary pathways leading to mutually exclusive genetic clusters (Fig. 1A). In cetaceans, several species have been defined almost exclusively on the basis of genetic evidence for reproductive isolation, in the form of substantial genetic differentiation from other animals previously thought to be of the same species (Kingston and Rosel, 2004; Dalebout et al., 2007; Morin et al., 2016). If animals are commonly moving between groups and interbreeding, the groups will not be reproductively isolated from one another and they will share genetic material. As a result, the genetic analysis would not find unique groupings of individuals corresponding to each population, and no ESUs could be defined. It is important to remember, however, that the type of genetic marker used to infer genetic groupings may limit this interpretation to
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Figure 1 Hypothetical genetic evidence representing two different evolutionary histories presented in the form of branching diagrams representing the evolutionary pathways leading to mtDNA haplotypes observed in a sample of marine mammals. The size of circles is proportional to the number of individuals in the sample exhibiting the particular haplotype, and each haplotype differs from a connected neighbor by a 1-bp difference. (A) North Atlantic and South Atlantic stocks have been isolated for a sufficient amount of time so that there are no haplotypes common to both. Geographic strata are concordant with genetic ones. (B) The isolation of the two stocks is (1) recent so that common haplotypes (C, F, G, and I) have not yet been purged via genetic drift from the North Atlantic, the South Atlantic, or both or (2) the isolation is incomplete, and there is a degree of continual interchange between the stocks. Even though the geography and the genetics are not strictly concordant, the distribution of haplotypes within each of the two stocks in this example is modally different.
sex-specific gene-flow. Mitochondrial DNA is often used for phylogenetic analyses due to its relatively rapid rate of mutation and drift (Avise, 1986), but the maternal mode of inheritance limits inference of genetic isolation to the female lineage. Confirmation of genetic isolation (no male-mediated gene flow) requires analysis of the nuclear genome.
B. The Demographically Independent Population Consider, however, if the individuals in the sample fail to fall into exclusive genetic clusters that are congruent with the a priori classification. For example, what is happening if some of the individuals sampled in the Northern Hemisphere cluster genetically with those in the south (Fig. 1B)? This situation can be the result of (1) insufficient time having elapsed from when the populations split to purge ancestral shared alleles or haplotypes from the populations, (2) a degree of gene flow exists or has existed recently (e.g., a few adventure some northern individuals immigrated to the south or vice versa), or (3) a combination of the two. Whatever the case, it means that the populations under consideration do not meet ESU criteria based on this data set. Nevertheless, the populations may be genetically distinguishable if there are significant frequency differences in alleles or haplotypes between the groups. These populations would be characterized as DIPs and the definition would pertain to an intermediate situation between complete, long-term isolation of the ESUs and free gene flow between geographically distinct populations (panmixia). It is in the range of dispersal rates between the virtual isolation of the ESU and complete panmixia where the interpretation of genetic information requires an understanding of policy. The logical thread goes as follows: e.g., the US MMPA establishes, albeit somewhat obliquely, that populations be maintained at 50% of their historical capacity as functioning elements of their ecosystems. This is interpreted to mean that adequate population levels are being maintained across their historical ranges. It would forbid management action that resulted in extirpation in one portion of the range, even if such extirpation would not reduce the overall species abundance to below 50% of historical levels. What happens if anthropogenic mortality occurs at different levels in different parts of the range, e.g., there is heavy incidental take in the southern part of the range because it overlaps with a gill net fishery, but none at all in the central and the northern part of the range? For example, consider a temperate, coastal species that inhabits waters from northern California through Canada, the Aleutian Peninsula, to Japan. Due to the large distances involved, distinct habitat differences, and the coastal behavior of this species, complete panmixia is not very likely and some population structure, i.e., dispersal between certain population segments, is reduced. Say samples are available from each of five putative population groupings (defined a priori) in US Pacific northwest waters. An extensive genetic analysis using both mtDNA and microsatellites is performed, and initial analyses using phylogenetic methods demonstrate no striking genetic clustering concordant with the geographic groupings. However, proximal populations are observed to share haplotypes and microsatellite alleles, and statistical analysis shows significant frequency differences for the mtDNA haplotypes and for many of the microsatellite loci. The inference here is that dispersal is sufficiently limited among the five populations so that some genetic differentiation has occurred among them. The populations are isolated but cannot be considered ESUs because the “evolutionary legacy” criterion is not met. They should be considered DIPs because dispersal between them is sufficiently reduced
GENETICS FOR MANAGEMENT to warrant managing them separately [e.g., establishing individual quotas for incidental kills (take) for each population]. Moritz (1994) described such populations as MUs, representing “populations connected by such low levels of gene flow that they are functionally independent.” This recommendation can be made with confidence because of the shape of the curve that relates genetic differentiation and dispersal (Fig. 2). The strength of the result is reflected in the left-hand portion of the graph; genetic differentiation is detectable only when exchange rates between the putative populations are virtually nonexistent from a demographic or management point of view. This is in the range of a few dispersers per generation. However, the weakness of genetic analyses comes from how rapidly genetic differentiation declines when dispersal increases only slightly. Genetic differentiation disappears at dispersal rates that still might be considered insignificant from a demographic point of view (i.e., a few percentage per year). In other words, it is very difficult to demonstrate statistically significant genetic differentiation if dispersal between strata is more than a few dispersers per year. By demonstrating genetic differentiation, the geneticist has confidently demonstrated demographically insignificant exchange rates. The management consequences are that any anthropogenic mortality within the strata must be compensated for by production from within, rather than dispersal from adjacent, perhaps less impacted, units. Under this circumstance, which is actually common in coastal populations, mistakenly assuming that adjacent populations will serve as a source for the losses within the impacted population can result in destruction of the impacted population and failure to maintain it as a functioning element of its ecosystem.
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Disregarding the geneticist’s recommendation may mean that the manager will have failed to meet a policy goal stipulated in the US MMPA. However, it is not a “symmetrical” situation. What happens when genetic evidence fails to establish significant demographic isolation between units? Because there was no evidence of population subdivision and hence restricted dispersal, a manager may be tempted to use this negative evidence to infer that the putative populations could be coalesced into one larger MU. Coalescence of two or more small populations into one larger MU would allow the manager to establish a larger incidental take quota and avoid the inevitable economic and political consequences of restricting fishing effort to reduce the incidental fishing mortality. The manager argues that high levels of take in one localized portion of the range (the sink) will be compensated for by production in and dispersal from less exploited portions of the range (the source). This would turn out to be an appropriate decision if the failure to find evidence of population subdivision was due to demographically high levels of exchange between the exploited and the unexploited regions. However, the decision may have serious biological consequences if the failure to find genetic differences was simply because the experimental design of the genetic study lacked statistical power to discriminate subdivisions (e.g., too few samples tested, too little portion of the genome tested, or an insufficiently variable portion of the genome tested), or if genetic isolation of the populations is recent. In reality, although undetected, in this case the populations were demographically isolated, and it would be unlikely that adjacent populations could replenish losses due to incidental take in the exploited region. Because exchange between populations may be high enough to prevent detection genetically but not high enough for demographic replenishment, failure to discriminate the subdivision genetically should not at present be used as a scientific rationale for coalescing smaller populations into larger MUs in the absence of sufficient evidence for statistical power to detect such subdivision. Such evidence can be obtained from simulations of the populations and genetic data, using POWSIM (Ryman and Palm, 2006).
II. Molecular Markers
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Figure 2 The idealized relationship between the degree of genetic differentiation (fixation index), dispersal rate expressed as the average dispersal rate per year, and population size expressed as the number of breeding animals, or breeding females in the case of mtDNA analyses (effective population size). The fixation index ranges between 1 (no common alleles or haplotypes) to 0 (no differences in allelic or haplotypic distribution) for bi-allelic loci. Demographically insignificant rates of exchange (e.g., 1% per year) in anything but the smallest effective population sizes probably result in an inability to subdivide populations with any degree of statistical confidence. Perhaps more importantly, because the curve is so flat at this point and higher, genetic data have little resolution to estimate accurately dispersal rate in this range.
Traditionally, management-oriented genetic studies use primarily (1) genotypes from microsatellite loci within the 3 × 109 or so base pairs (bp) of the mammalian nuclear genome or (2) DNA sequence data from a portion or, more recently, all of the 1.6 × 104 bp of the mitochondrial genome (Fig. 3). mtDNA is a multicopy, circular, cytoplasmic DNA that in mammals is inherited intact from the mother. In contrast, microsatellites are part of the nuclear genome and are inherited biparentally. They are short stretches of repeated DNA that are distributed abundantly in the nuclear genome and show exceptional variability in most species. Newer markers, which are rapidly gaining ground in molecular ecology studies of marine mammals, include single nucleotide poly morphisms (SNPs) (Morin et al., 2004; Elshire et al., 2011) and sequencing of whole mitochondrial genomes (Archer et al., 2013; Morin et al., 2015), nuclear genes (Palumbi and Cipriano, 1998; Dalebout et al., 2008), and even complete genomes on a population scale (Foote et al., 2016). These are methods for assessing sequence variation primarily in nuclear DNA, and primarily at the level of individual nucleotide changes (though insertions/deletions may also be assessed). Because single nucleotide changes are the most common type of variation in the genome, methods for assessing large numbers of polymorphic sites, such as SNP genotyping and
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Figure 3 Currently, data for most management genetic studies primarily consist of microsatellite DNA, mtDNA, or both, but SNPs are becoming more common. (A) Microsatellites are short tandem repeats (two, three, or four base repeats) of nucleotides, e.g., CACACACACA …, ATGATGATG …, or GATAGATAGATA …. Microsatellite data consist of n pairs of alleles for each individual at m number of microsatellite loci within the 3 × 109 or so base pairs of the mammalian nuclear genome. There is estimated to be a microsatellite region every 3000 or so base pairs. Microsatellites are part of the nuclear genome and are inherited biparentally. Mitochondrial data consist of DNA subsequences (haplotypes) of portions or all of the 1.6 × 104 bp mtDNA genome. mtDNA is a multicopy cytoplasmic DNA that, in vertebrates, is inherited intact from the mother. Each mitochondrion may have 5–10 DNA molecules, and there may be from 100 to 1000 mitochondria per cell. For mtDNA, a sequenced portion of 12 bp of the 16,000-bp molecule is shown. (B) Sample 1 is heterozygous at microsatellite locus A having a pair of alleles that have five and six CA repeats, and nucleotides C and T at SNP locus A. Sample 1 also possesses an “A”-type mitochondrial haplotype that, e.g., differs by 2 bp from the “C”-type. For actual studies, the number of microsatellite loci examined might typically range from 8 to 30, and the size of the mitochondrial sequence examined might range from 350 bp to the complete mitogenome.
RADseq (Restriction site Associated DNA Sequencing) (Davey and Blaxter, 2010), provide good statistical power for genetic assessment of populations, while also assaying more of the genome. With SNPs, the possibility for looking at patterns of variation
in individual genes opens the possibility of directly or indirectly assessing genetic variation under selection, or variation associated with known phenotypes. One often-used example of this is genetic determination of sex.
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III. Focusing on the Individual In the previous sections, the focus was on a population of animals united by some characteristic, e.g., geographic locale. In this section, the focus is on the individual and what information genetic studies can provide to management.
A. Illegal Traffic and Trade Two sorts of questions are usually asked: 1. Did sample X come from the same individual as sample Y? Nuclear marker analysis is used to establish an individual’s genetic fingerprint; this is also known as genotyping. 2. What is the provenance of sample X, i.e., what species or geographic population characterizes the sample? For this, sequence analyses are generally employed at higher levels of differentiation, and genotypes are used for assignment to a group or population. Question 1 is much like placing crime suspects at the crime scene via something the suspect has left behind (e.g., fingerprints, hair, DNA), and genotyping is a highly reliable means of answering it. The genetic profile of a piece of meat in a market of unknown provenance could be compared with the genetic profiles in a database of “legally” harvested whales or, alternatively, the sample could be compared with the genetic profiles in a database of biopsied, protected ones (see chapter on Genetics, Forensics). Question 2 is more general and deals with establishing that the sample came from an animal that belonged to a certain group or taxon. Genetic analyses can help determine whether a given market sample came from a proscribed or a permitted taxon. For example, a particular market sample is humpback whale (Megaptera novaeangliae). The unknown sample is compared genetically with samples whose taxon identity is known. Because the genetic differences between taxa above the species level are so large, assignment analyses are almost infallible (e.g., did the sample come from a whale or a cow?). In most situations, assignment is accurate at the species level (e.g., did the sample come from a minke whale Balaenoptera acutorostrata/bonaerensis, or a blue whale B. musculus?). However, there are exceptions, such as discriminating species among the genera Delphinus, Stenella, and Tursiops using only mtDNA control-region sequence. Accurate assignment of an individual sample to its geographic origin is more difficult [e.g., did the sample come from a gray whale (Eschrichtius robustus) harvested off the eastern Pacific Ocean or from the Okhotsk Sea?]. Although there are exceptions to this rule, in general the lower the taxonomic division the greater the difficulty in distinguishing provenance of an individual sample. At these lower levels, relatively large numbers of microsatellites or SNPs may provide sufficient power for statistical assignment tests. An example of this was the use by Kingston and Rosel (2004) of hundreds of variable AFLP (amplified fragment length polymorphism) loci to identify clear genetic differentiation of coastal and pelagic Tursiops truncatus populations in the western North Atlantic and differentiation between two sympatric species of Delphinus with relatively low mtDNA sequence divergence.
B. Other Uses of Individual-Oriented Genetic Information Genetic mark-recapture and monitoring methods based on genotyping can be substituted for traditional tagging methods, i.e., discovery tags, for estimating population size, dispersal rate, and migration pathways (Palsbøll, 1999; Schwartz et al., 2007). The management value of such data is obvious. However, if populations
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are large, the number of “recaptures” is likely to be small, and the cost of genetic analysis of many samples can be high. Nevertheless, such methods have been used to estimate population size and level of reproductive isolation (Garrigue et al., 2004) and to complement ongoing photographic identification projects. Besides reidentification of individuals, genotyping can be used to reliably identify parent-offspring relationships, although large numbers of microsatellite loci or even larger numbers of SNPs (>100) must be examined to do this accurately. It is probably worth the effort because by doing so, dispersal can be measured over two generations rather than over the lifetime of single individuals. For conservation decisions, intergenerational rather than intragenerational movement (i.e., gene flow) is probably a more important parameter than movements of a single individual. Another important demographic parameter that emerges from a study of parent-offspring relationships is the fraction of mature animals enjoying reproductive success. In other words, what is the breeding structure of the population, and how does that influence effective population size, inbreeding, and gene flow between populations (e.g., if a small proportion of males actually reproduce, dispersal does not accurately reflect gene flow)? Finally, determining sex provides a means to examine geographical segregation by sex and whether males or females are the dispersers. In many marine mammal species, females tend to be strongly philopatric, returning year after year to specific feeding or breeding sites. Female philopatry can be demonstrated by examining genetic population subdivision separately in males and in females. If only females are strongly philopatric, mtDNA subdivision should be apparent among the females but not the males. When males are the dispersers but not females, nuclear marker subdivision should be nonexistent because the males of breeding age serve as a “conduit” to homogenize the alleles between populations. If there are data on age, it is sometimes possible to demonstrate that the likelihood of dispersal increases with age of the males. There are policy implications in demonstrating female philopatry. Although this sort of population structuring would not qualify the population as an ESU, it does qualify it as a DIP worthy of management. If the animals from a particular feeding or breeding area are extirpated (males and females), recolonization will not likely take place. The strongly philopatric females from other breeding or feeding grounds would not recolonize the depopulated region, and the dispersing males would not likely return to an area with no females. Thus, if policy deliberately excluded populations based on female philopatry, a situation could easily arise where take could reduce or fragment ranges.
C. The Hidden Power of Molecular Genetics In addition to providing answers to population subdivision, dispersal, individual identities, and breeding behavior, molecular genetic analyses present a previously unexploited opportunity for gaining understanding of marine mammals via remote, nonlethal sampling. Some of these data can have direct relevance for management. Consider that a skin sample contains the entirety of the individual’s genetic blueprint. The ability to read this blueprint is progressing at an astounding rate, and although most of the progress is within the human genome, around 70% of cetacean genes are homologous, and tools developed for medical research can be utilized for marine mammals. For example, DNA sequence information extracted from the genes of skin cells can provide data about expressed characteristics of other tissues or organs. Sequencing visual pigment genes from skin is a good example. Levenson et al.
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(2006) have shown that, with collateral data about visual performance of particular photoreceptors via behavioral or physiological testing, it is possible to extrapolate from the DNA sequence to the spectral sensitivity. Newer next-generation sequencing (NGS) methods have been used to investigate the loss of function in a group of genes involved in vision (Springer et al., 2016). Understanding the visual abilities of cetaceans could aid in the design of fishing nets with increased color contrast, making them more visible to marine mammals, thereby reducing entanglement rates while sustaining the catch rate of the target species.
IV. Conclusion
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Although examination of genetic material offers unparalleled insights into many biological aspects of an animal’s life, certain sorts of genetic information provide data that are directly relevant to the management process. The most important is the definition of the conservation unit. By common sense and by law in many countries, this unit is created out of the understanding that the vast majority of species (marine mammal or otherwise) are not panmictic. Species are subdivided geographically into isolated and semiisolated groups. Genetic analyses can measure this directly and provide information to facilitate management of decision-making. This chapter has provided some examples of useful applications of various molecular genetics methods. Regardless of the sort of genetic information collected, to ensure that genetic studies and information will be useful for management it requires a clear understanding of the conservation policy that the studies are designed to help implement.
See Also the Following Articles Conservation n Genetics and Genomics n Genetics, Forensics Molecular Ecology n Stock Assessment n Stock Identity
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References Archer, F.I., Morin, P.A., Hancock-Hanser, B.L., Robertson, K.M., Leslie, M.S., Berube, M., et al. (2013). Mitogenomic phylogenetics of fin whales (Balaenoptera physalus spp.): Genetic evidence for revision of subspecies. PLoS One 8, e63396. Avise, J.C. (1986). Mitochondrial DNA and the evolutionary genetics of higher animals. Philos. Trans. R. Soc. (Lond.) B Biol. Sci. 312, 325–342. Dalebout, M.L., Baker, C.S., Robertson, K.M., Chivers, S.J., Perrin, W.F., Mead, J.G., et al. (2007). A divergent mtDNA lineage among Mesoplodon beaked whales: Molecular evidence for a new species in the tropical pacific? Mar. Mamm. Sci. 23, 954–966. Dalebout, M.L., Steel, D., and Baker, C.S. (2008). Phylogeny of the beaked whale genus Mesoplodon (Ziphiidae: Cetacea) revealed by nuclear introns: Implications for the evolution of male tusks. System. Biol. 57, 857–875. Davey, J.W., and Blaxter, M.L. (2010). RADSeq: Next-generation population genetics. Brief Funct. Genomics 9, 416–423. Elshire, R.J., Glaubitz, J.C., Sun, Q., Poland, J.A., Kawamoto, K., Buckler, E.S., et al. (2011). A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species. PLoS One 6, e19379. Foote, A.D., Hofreiter, M., and Morin, P.A. (2012a). Ancient DNA from marine mammals: studying long-lived species over ecological and evolutionary timescales. Ann. Anat. 194, 112–120. Foote, A.D., Thomsen, P.F., Sveegaard, S., Wahlberg, M., Kielgast, J., Kyhn, L.A., et al. (2012b). Investigating the potential use of environmental DNA (eDNA) for genetic monitoring of marine mammals. PLoS One 7, e41781. Foote, A.D., Vijay, N., Ávila-Arcos, M.C., Baird, R.W., Durban, J.W., Fumagalli, M., et al. (2016). Genome-culture coevolution promotes rapid divergence in the killer whale. Nature Comm. 7
Garrigue, C., Dodemont, R., Steel, D., and Baker, C.S. (2004). Organismal and “gametic” capture-recapture using microsatellite genotyping confirm low abundance and reproductive autonomy of humpback whales on the wintering grounds of New Caledonia. Mar. Ecol. Prog. Ser. 274, 251–262. Hillis, D.M., Moritz, C., and Mable, B.K. (1996). Molecular Systematics, 2nd ed. Sinauer Associates, Sunderland, MA. Kingston, S.E., and Rosel, P.E. (2004). Genetic differentiation among recently diverged delphinid taxa determined using AFLP markers. J. Hered. 95, 1–10. Levenson, D.H., Ponganis, P.J., Crognale, M.A., Deegan (2nd), J.F., Dizon, A., and Jacobs, G.H. (2006). Visual pigments of marine carnivores: Pinnipeds, polar bear, and sea otter. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 192, 833–843. Morin, P.A., Baker, C.S., Brewer, R.S., Burdin, A.M., Dalebout, M.L., Dines, J.P., et al. (2016). Genetic structure of the beaked whale genus Berardius in the North Pacific, with genetic evidence for a new species. Mar. Mamm. Sci. Morin, P.A., Luikart, G., Wayne, R.K., and the SNP Workshop Group, (2004) SNPs in ecology, evolution and conservation. Trends Ecol. Evol. 19, 208–216. Morin, P.A., Parsons, K.M., Archer, F.I., Ávila-Arcos, M.C., BarrettLennard, L.G., Dalla Rosa, L., et al. (2015). Geographic and temporal dynamics of a global radiation and diversification in the killer whale. Mol. Ecol. 24, 3964–3979. Moritz, C. (1994). Defining “Evolutionarily Significant Units” for conservation. Trends Ecol. Evol. 9, 373–375. Palsbøll, P. (1999). Genetic tagging: Contemporary molecular ecology. Biol. J. Linn. Soc. 68, 3–22. Palumbi, S.R., and Cipriano, F. (1998). Species identification using genetic tools: The value of nuclear and mitochondrial gene sequences in whale conservation. J. Hered. 89, 459–464. Ross, H.A., Lento, G.M., Dalebout, M.L., Goode, M., Ewing, G., McLaren, P., et al. (2003). DNA surveillance: Web-based molecular identification of whales, dolphins, and porpoises. J. Hered. 94, 111–114. Ryman, N., and Palm, S. (2006). POWSIM: A computer program for assessing statistical power when testing for genetic differentiation. Mol. Ecol. Notes 6, 600–6002. Schwartz, M.K., Luikart, G., and Waples, R.S. (2007). Genetic monitoring as a promising tool for conservation and management. Trends Ecol. Evol. 22, 25–33. Springer, M.S., Emerling, C.A., Fugate, N., Patel, R., Starrett, J., Morin, P.A., et al. (2016). Inactivation of cone-specific phototransduction genes in rod monochromatic cetaceans. Front. Ecol. Evol. Taylor, B.L., and Dizon, A.E. (1999). First policy then science: Why a management unit based solely on genetic criteria cannot work. Mol. Ecol. 8, S11–S16. Waples, R.S. (1991). Pacific salmon, Oncorhynchus spp., and the definition of “species” under the endangered species act. Mar. Fish. Rev. 53, 11–22.
GEOGRAPHIC VARIATION William F. Perrin I. The Nature of Geographic Variation Mammals vary from place to place, in size, shape, coloration, osteology, and genetic features, including chromosomes, enzymes, and DNA sequences. They also vary in sounds produced, other behavior, life history, breeding system, parasites, contaminant loads, biochemical features such as fatty acids, and other characters. Mammal species tend to vary geographically most in those features that vary most within a population. If, as for most mammals, body size
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Baker, C.S., and Palumbi, S.R. (1994). Which whales are hunted? A molecular genetic approach to monitoring whaling. Science 265, 1538–1539. Baker, C.S., Steel, D., Choi, Y., Lee, H., Kim, K.S., Choi, S.-K., et al. (2010). Genetic evidence of illegal trade in protected whales links Japan with the U.S. and South Korea. Biol. Lett. 6, 647–650. Cipriano, F., and Palumbi, S.R. (1999). Genetic tracking of a protected whale. Nature 397, 307–308. Clapham, P.J., and Van Waerebeek, K. (2007). Bushmeat and bycatch: the sum of the parts. Mol. Ecol. 16, 2607–2609. Committee on Taxonomy. (2016). List of marine mammal species and subspecies, Society for Marine Mammalogy, World Wide Web electronic publication. http://www.marinemammalscience.org, Accessed on May 5, 2016. Costello, M.J., and Baker, C.S. (2011). Who eats sea meat? Expanding human consumption of marine mammals. Biol. Conserv. 144, 2745–2746. Dalebout, M.L., Baker, C.S., Cockroft, V.G., Mead, J.G., and Yamada, T.K. (2004). A comprehensive molecular taxonomy of beaked whales (Cetacea: Ziphiidae) using a validated mitochondrial and nuclear DNA database. J. Hered. 95, 459–473. Dalebout, M.L., Baker, C.S., Steel, D., Robertson, K.M., Chivers, S.J., Perrin, W.F., et al. (2007). A divergent mtDNA lineage among Mesoplodon beaked whales: molecular evidence for a new species in the tropical Pacific? Mar. Mamm. Sci. 23, 954–966. Dalebout, M.L., Lento, G.M., Cipriano, F., Funahashi, N., and Baker, C.S. (2002). How many protected minke whales are sold in Japan and Korea? A census by DNA profiling. Anim. Conserv. 5, 143–152. Dalebout, M.L., Van Helden, A., Van Waerebeek, K., and Baker, C.S. (1998). Molecular genetic identification of Southern Hemisphere beaked whales (Cetacea: Ziphiidae). Mol. Ecol. 7, 687–695. DeSalle, R., and Birstein, V.J. (1996). PRC identification of black caviar. Nature 381, 197–198. Endo, T., Haraguchi, K., Hotta, Y., Hisamichi, Y., Lavery, S., Dalebout, M.L., et al. (2005). Total mercury, methyl mercury and selenium levels in the red meat of small cetaceans sold for human consumption in Japan. Environ. Sci. Tech. 39, 5703–5708. Gales, N.J., Kasuya, T., Clapham, P.J., and Brownell, R.L. (2005). Japan’s whaling plan under scrutiny. Useful science or unregulated commercial whaling? Nature 435, 883–884. Gravena, W., Hrbek, T., da Silva, V.M.F., and Farias, I.P. (2008). Amazon River dolphin love fetishes: From folklore to molecular forensics. Mar. Mamm. Sci. 24, 969–978. Hebert, P.D.N., Cywinska, A., Ball, S.L., and DeWaard, J.R. (2003). Biological identifications through DNA barcodes. Proc. Roy. Soc. (Lond.) Ser. B, Biol. Sci. 270, 313–321. IWC, 1998. Report of the Scientific Committee, Annex Q. Report of the working group on proposed specifications for a Norwegian DNA database register for minke whales. Rep. Intern. Whal. Comm. 49, 287–289. IWC, 2004. Annex T: Report of the data availability working group. J. Cet. Res. Manage. 6(Suppl), 406–408. IWC. (2005). Report of the Specialist Group on the DNA Register/ Market Sampling Scheme Approach (SGDNA). available from the Secretariat, International Whaling Commission, The Red House, 135 Station Road, Impington, Cambridge, CB4 9NP UK IWC/ M05/RMSWG 5. IWC, 2006. Report of the Scientific Committee, Annex J: Working Group on Estimation of Bycatch and other Human-induced Mortality (BC). J. Cet. Res. Manage. 8(Suppl), 177–184. IWC, 2012. Report of the Scientific Committee. Annex D1. Report of the Working Group on the Implementation Review for western North Pacific common minke whales. J. Cet. Res. Manage. 13(Suppl), 102–129. Kang, S., and Phipps, M. (2000). A survey of whale meat markets along South Korea’s coast. TRAFFIC, East Asia. Leeney, R.H., Dia, I.M., and Dia, M. (2015). Food, Pharmacy, Friend? Bycatch, Direct Take and Consumption of Dolphins in West Africa. Human Ecol. 43, 105–118.
Lukoschek, V., Funahashi, N., Lavery, S., Dalebout, M.L., Cipriano, F., and Baker, C.S. (2009). High proportion of protected minke whales sold on Japanese markets is due to illegal, unreported or unregulated exploitation. Anim. Conserv. 12, 385–395. Malik, S., Wilson, P.J., Smith, R.J., Lavigne, D.M., and White, B.N. (1997). Pinniped penises in trade: a molecular genetic investigation. Conserv. Biol. 11, 1365–1374. May-Collado, L., and Agnarsson, I. (2006). Cytochrome b and Bayesian inference of whale phylogeny. Mol. Phylogenet. Evol. 38, 344–354. NOAA. (2015). Presidential Task Force on Combating IUU Fishing and Seafood Fraud. Action Plan for Implementing the Task Force Recommendations, March, World Wide Web electronic publication. http://www.nmfs.noaa.gov/ia/iuu/noaa_taskforce_report_final.pdf, Accessed on October 26, 2015. Oremus, M., Leqata, J., and Baker, C.S. (2015). Resumption of traditional drive hunting of dolphins in the Solomon Islands in 2013. Open Sci. 2, 140524. Palsbøll, P.J., Berube, M., Skaug, H.J., and Raymakers, C. (2006). DNA registers of legally obtained wildlife and derived products as means to identify illegal takes. Conserv. Biol. 20, 1284–1293. Perrin, W.F., Rosel, P.E., and Cipriano, F. (2013). How to contend with paraphyly in the taxonomy of the delphinine cetaceans? Mar. Mamm. Sci. 29, 567–588. Ratnasingham, S., and Hebert, P.D.N. (2007). The Barcode of Life Data System. Mol. Ecol. Notes 7, 355–364. Reeves, R.R., McClellan, K., and Werner, T.B. (2013). Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011. Endang. Spec. Res. 20, 71–97. Robards, M.D., and Reeves, R.R. (2011). The global extent and character of marine mammal consumption by humans: 1970–2009. Biol. Conserv. 144, 2770–2786. Rosenbaum, H.C., Brownell, R.L., Brown, M.W., Schaeff, C., Portway, V., White, B.N., et al. (2000). World-wide genetic differentiation of Eubalaena: questioning the number of right whale species. Mol. Ecol. 9, 1793–1802. Ross, H.A., Lento, G.M., Dalebout, M.L., Goode, M., McLaren, P., Rodrigo, A.G., et al. (2003). DNA surveillance: Web-based molecular identification of whales, dolphins and porpoises. J. Hered. 94, 111–114. Ross, H.A., and Murugan, S. (2006). Using phylogenetic analyses and reference datasets to validate the species identities of cetacean sequences in GenBank. Mol. Phylogenet. Evol. 40, 866–871. Song, K.-J., Kim, Z.G., Zhang, C.I., and Kim, Y.H. (2010). Fishing gears involved in entanglements of minke whales (Balaenoptera acutorostrata) in the East Sea of Korea. Mar. Mamm. Sci. 26, 282–295. Thompson, K., Baker, C.S., van Helden, A., Patel, S., Millar, C., and Constantine, R. (2012). The world’s rarest whale. Current Biol. 22, R905–R906. Tzika, A., D’Amico, E., Alfaro-Shigueto, J., Mangel, J.C., Waerebeek, K., and Milinkovitch, M.C. (2010). Molecular identification of small cetacean samples from Peruvian fish markets. Conserv. Genet. 11, 2207–2218. Wada, S., Oishi, M., and Yamada, T.K. (2003). A newly discovered species of living baleen whale. Nature 426, 278–281.
GENETICS, MANAGEMENT Phillip A. Morin and Andrew E. Dizon Certain kinds of genetic information are particularly well suited to assist in designing strategies to protect human-impacted marine mammals. The type of genetic information required depends on the particular conservation goals wildlife managers seek to achieve when protecting specific species, or populations within species. For example, is the goal to prevent extinction of the species as a whole
GENETICS FOR MANAGEMENT or to prevent extirpation of local, but not necessarily genetically unique, populations? For most developed nations, these goals are codified in laws presumably reflecting, at least in democratic societies, the will of the public. To achieve these goals, managers often choose between controversial and conflicting strategies, such as various limits on the species and numbers of marine mammals that can be incidentally killed during fishing operations. Relaxed limits favor the fishermen, but may put a population of marine mammals at risk; stringent limits are less risky but may put an unsupportable burden on fishermen by restricting their fishing options. Obviously, the kind and the quality of biological data, genetic or otherwise, informing this choice are critical. Decisions have to be based on the best available scientific information, or they will be challenged in the courts. Although most scientific information on impacted populations is of value, certain kinds of information are much more important for the management process. If only limited data are available (molecular or other), biased or misleading conclusions can result in inappropriate decisions being made, eventually imperiling the population needing protection in the first place. Biological data on marine mammals, especially cetaceans, are difficult and consequently expensive to obtain. By consuming limited conservation funds, even good but irrelevant studies can impede conservation efforts. An understanding of policy is needed to ensure that proposed genetic studies are relevant for management needs (the conservation goals) before doing the science (the information gathering) (Taylor and Dizon, 1999). One advantage that genetic analyses have over “whole animal” studies is that data are easier to collect and few constraints are put on the quality of a sample or its origin. DNA is a relatively tough molecule, and adequate samples can be obtained from tiny amounts of a variety of tissues such as skin, blood or blood stains, hair follicles, placenta, excrement, baleen, modern or ancient bone, or, in some circumstances, formalin-preserved tissues. For instance, adequate amounts of mitochondrial DNA (mtDNA) from ca. 1000-year-old bowhead whale (Balaena mysticetus) bones have been obtained, and whole genomes are now regularly sequenced from historical and ancient specimens. The field of ancient DNA (aDNA) holds promise for understanding past and contemporary patterns and processes for marine mammals (Foote et al., 2012a). For live animals, projectile biopsying (crossbow, firearm, or lance) has been used successfully for all but the smallest and shyest cetaceans (see chapter on Genetics, and Genomics), and even water samples containing environmental DNA (eDNA) are potential sources of DNA for abundance, distribution, and population studies (Foote et al., 2012b).
I. The “Conservation Unit” Defining the population segment on which to focus conservation efforts is the primary use of genetic information. The US Marine Mammal Protection Act (MMPA) of 1972, the US Endangered Species Act (ESA) of 1973, the relevant legislation of some other nations, and the revised management procedure of the International Whaling Commission (IWC) all suggest that management efforts must be focused on populations. Although many countries have not established laws codifying the conservation unit, biologists are generally in agreement that species comprise a collection of semiisolated populations (i.e., species-wide panmixia is the exception) and that those semiisolated populations should be the focus of management. However, the devil is in the details, and there is much controversy on the precise definition of these units. Besides having obvious biological consequences for getting the groupings correct, there can be economic ones as well. For instance, quotas on harvest
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or incidental take are calculated as some allowable fraction of the overall abundance within the chosen conservation unit. A small conservation unit is the most biologically risk-averse because quotas are then necessarily small, and there is a greater likelihood that removals will be equally distributed over the whole unit. However, a large conservation unit is the most economically risk-averse because the quotas are larger, and there is the potential that excessive removals in one part of the range (the sink) will be compensated for by immigration from outside of the exploited region (the source). Policy tries to provide managers with guidance to balance conservation and economic issues by defining the management unit (MU). For instance, the US ESA seeks to prevent the extinction of distinct population segments that are evolutionarily unique. The policy addresses last-ditch efforts to rescue populations whose abundances are so low, or whose abundances will become so low in the near future, that if something is not done immediately, they will likely go extinct. These so-called evolutionarily significant unit (ESUs) are defined as (1) being “substantially” reproductively isolated from other population segments of the same species and (2) representing an important component in the evolutionary legacy of the species. The first criterion speaks to the rate of exchange between the population segment and other segments. The second speaks to the time the population segment has been isolated. In contrast, the US MMPA seeks to maintain viable populations across their historical ranges at (at least) 50% of their historical population size. This act addresses maintenance of abundance. The MMPA conservation unit could be characterized as demographically independent population (DIPs) to contrast them with ESUs. Some use the term “MU” to refer to a DIP, but because both DIPs and ESUs are MUs in the strict sense, it is important to distinguish them. Genetic data are useful for defining both. However, the policy goals are different and, consequently, the details of genetic studies directed toward either must take slightly different approaches.
A. The Evolutionarily Significant Unit Because the ESA is concerned with conservation units that are characterized as being “evolutionarily” different, the genetic methodology employed must be sensitive to evolutionary distances between taxa. Indeed, the traditional academic use of genetic data is employed to reconstruct common ancestry and to group taxa based on common ancestry. No restriction is based on the taxon level examined (subspecies, species, genus, family, etc.), except that the taxa are assumed to be reproductively isolated and that sufficient time has passed so that measurable genetic differences have accrued between every individual in one taxon and every individual in another. For higher-level taxonomic relationships, the grouping derives a priori from a particular classification based on morphological distinctiveness. For groupings below the species level, the grouping often derives a priori from geographical clustering; some have termed this phylogeography to contrast it to traditional phylogenetics. Regardless, the key to ESU status is still reproductive isolation and time. Using DNA sequence data to test these a priori groupings to see if they are genetically accurate, an investigator demonstrates that all the individuals of each stratum fall into exclusive genetic clusters (Waples, 1991; Hillis et al., 1996; Ross et al., 2003). If so, ESU status can be presumed for the groupings. The evidence addresses the policy that protection should be offered to a population segment that is “substantially” reproductively isolated. If they are not isolated, it is not possible to demonstrate exclusive genetic clustering. The genetic evidence is usually presented in the form of
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a branching diagram representing the evolutionary pathways leading to mutually exclusive genetic clusters (Fig. 1A). In cetaceans, several species have been defined almost exclusively on the basis of genetic evidence for reproductive isolation, in the form of substantial genetic differentiation from other animals previously thought to be of the same species (Kingston and Rosel, 2004; Dalebout et al., 2007; Morin et al., 2016). If animals are commonly moving between groups and interbreeding, the groups will not be reproductively isolated from one another and they will share genetic material. As a result, the genetic analysis would not find unique groupings of individuals corresponding to each population, and no ESUs could be defined. It is important to remember, however, that the type of genetic marker used to infer genetic groupings may limit this interpretation to
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Figure 1 Hypothetical genetic evidence representing two different evolutionary histories presented in the form of branching diagrams representing the evolutionary pathways leading to mtDNA haplotypes observed in a sample of marine mammals. The size of circles is proportional to the number of individuals in the sample exhibiting the particular haplotype, and each haplotype differs from a connected neighbor by a 1-bp difference. (A) North Atlantic and South Atlantic stocks have been isolated for a sufficient amount of time so that there are no haplotypes common to both. Geographic strata are concordant with genetic ones. (B) The isolation of the two stocks is (1) recent so that common haplotypes (C, F, G, and I) have not yet been purged via genetic drift from the North Atlantic, the South Atlantic, or both or (2) the isolation is incomplete, and there is a degree of continual interchange between the stocks. Even though the geography and the genetics are not strictly concordant, the distribution of haplotypes within each of the two stocks in this example is modally different.
sex-specific gene-flow. Mitochondrial DNA is often used for phylogenetic analyses due to its relatively rapid rate of mutation and drift (Avise, 1986), but the maternal mode of inheritance limits inference of genetic isolation to the female lineage. Confirmation of genetic isolation (no male-mediated gene flow) requires analysis of the nuclear genome.
B. The Demographically Independent Population Consider, however, if the individuals in the sample fail to fall into exclusive genetic clusters that are congruent with the a priori classification. For example, what is happening if some of the individuals sampled in the Northern Hemisphere cluster genetically with those in the south (Fig. 1B)? This situation can be the result of (1) insufficient time having elapsed from when the populations split to purge ancestral shared alleles or haplotypes from the populations, (2) a degree of gene flow exists or has existed recently (e.g., a few adventure some northern individuals immigrated to the south or vice versa), or (3) a combination of the two. Whatever the case, it means that the populations under consideration do not meet ESU criteria based on this data set. Nevertheless, the populations may be genetically distinguishable if there are significant frequency differences in alleles or haplotypes between the groups. These populations would be characterized as DIPs and the definition would pertain to an intermediate situation between complete, long-term isolation of the ESUs and free gene flow between geographically distinct populations (panmixia). It is in the range of dispersal rates between the virtual isolation of the ESU and complete panmixia where the interpretation of genetic information requires an understanding of policy. The logical thread goes as follows: e.g., the US MMPA establishes, albeit somewhat obliquely, that populations be maintained at 50% of their historical capacity as functioning elements of their ecosystems. This is interpreted to mean that adequate population levels are being maintained across their historical ranges. It would forbid management action that resulted in extirpation in one portion of the range, even if such extirpation would not reduce the overall species abundance to below 50% of historical levels. What happens if anthropogenic mortality occurs at different levels in different parts of the range, e.g., there is heavy incidental take in the southern part of the range because it overlaps with a gill net fishery, but none at all in the central and the northern part of the range? For example, consider a temperate, coastal species that inhabits waters from northern California through Canada, the Aleutian Peninsula, to Japan. Due to the large distances involved, distinct habitat differences, and the coastal behavior of this species, complete panmixia is not very likely and some population structure, i.e., dispersal between certain population segments, is reduced. Say samples are available from each of five putative population groupings (defined a priori) in US Pacific northwest waters. An extensive genetic analysis using both mtDNA and microsatellites is performed, and initial analyses using phylogenetic methods demonstrate no striking genetic clustering concordant with the geographic groupings. However, proximal populations are observed to share haplotypes and microsatellite alleles, and statistical analysis shows significant frequency differences for the mtDNA haplotypes and for many of the microsatellite loci. The inference here is that dispersal is sufficiently limited among the five populations so that some genetic differentiation has occurred among them. The populations are isolated but cannot be considered ESUs because the “evolutionary legacy” criterion is not met. They should be considered DIPs because dispersal between them is sufficiently reduced
GENETICS FOR MANAGEMENT to warrant managing them separately [e.g., establishing individual quotas for incidental kills (take) for each population]. Moritz (1994) described such populations as MUs, representing “populations connected by such low levels of gene flow that they are functionally independent.” This recommendation can be made with confidence because of the shape of the curve that relates genetic differentiation and dispersal (Fig. 2). The strength of the result is reflected in the left-hand portion of the graph; genetic differentiation is detectable only when exchange rates between the putative populations are virtually nonexistent from a demographic or management point of view. This is in the range of a few dispersers per generation. However, the weakness of genetic analyses comes from how rapidly genetic differentiation declines when dispersal increases only slightly. Genetic differentiation disappears at dispersal rates that still might be considered insignificant from a demographic point of view (i.e., a few percentage per year). In other words, it is very difficult to demonstrate statistically significant genetic differentiation if dispersal between strata is more than a few dispersers per year. By demonstrating genetic differentiation, the geneticist has confidently demonstrated demographically insignificant exchange rates. The management consequences are that any anthropogenic mortality within the strata must be compensated for by production from within, rather than dispersal from adjacent, perhaps less impacted, units. Under this circumstance, which is actually common in coastal populations, mistakenly assuming that adjacent populations will serve as a source for the losses within the impacted population can result in destruction of the impacted population and failure to maintain it as a functioning element of its ecosystem.
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Disregarding the geneticist’s recommendation may mean that the manager will have failed to meet a policy goal stipulated in the US MMPA. However, it is not a “symmetrical” situation. What happens when genetic evidence fails to establish significant demographic isolation between units? Because there was no evidence of population subdivision and hence restricted dispersal, a manager may be tempted to use this negative evidence to infer that the putative populations could be coalesced into one larger MU. Coalescence of two or more small populations into one larger MU would allow the manager to establish a larger incidental take quota and avoid the inevitable economic and political consequences of restricting fishing effort to reduce the incidental fishing mortality. The manager argues that high levels of take in one localized portion of the range (the sink) will be compensated for by production in and dispersal from less exploited portions of the range (the source). This would turn out to be an appropriate decision if the failure to find evidence of population subdivision was due to demographically high levels of exchange between the exploited and the unexploited regions. However, the decision may have serious biological consequences if the failure to find genetic differences was simply because the experimental design of the genetic study lacked statistical power to discriminate subdivisions (e.g., too few samples tested, too little portion of the genome tested, or an insufficiently variable portion of the genome tested), or if genetic isolation of the populations is recent. In reality, although undetected, in this case the populations were demographically isolated, and it would be unlikely that adjacent populations could replenish losses due to incidental take in the exploited region. Because exchange between populations may be high enough to prevent detection genetically but not high enough for demographic replenishment, failure to discriminate the subdivision genetically should not at present be used as a scientific rationale for coalescing smaller populations into larger MUs in the absence of sufficient evidence for statistical power to detect such subdivision. Such evidence can be obtained from simulations of the populations and genetic data, using POWSIM (Ryman and Palm, 2006).
II. Molecular Markers
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Figure 2 The idealized relationship between the degree of genetic differentiation (fixation index), dispersal rate expressed as the average dispersal rate per year, and population size expressed as the number of breeding animals, or breeding females in the case of mtDNA analyses (effective population size). The fixation index ranges between 1 (no common alleles or haplotypes) to 0 (no differences in allelic or haplotypic distribution) for bi-allelic loci. Demographically insignificant rates of exchange (e.g., 1% per year) in anything but the smallest effective population sizes probably result in an inability to subdivide populations with any degree of statistical confidence. Perhaps more importantly, because the curve is so flat at this point and higher, genetic data have little resolution to estimate accurately dispersal rate in this range.
Traditionally, management-oriented genetic studies use primarily (1) genotypes from microsatellite loci within the 3 × 109 or so base pairs (bp) of the mammalian nuclear genome or (2) DNA sequence data from a portion or, more recently, all of the 1.6 × 104 bp of the mitochondrial genome (Fig. 3). mtDNA is a multicopy, circular, cytoplasmic DNA that in mammals is inherited intact from the mother. In contrast, microsatellites are part of the nuclear genome and are inherited biparentally. They are short stretches of repeated DNA that are distributed abundantly in the nuclear genome and show exceptional variability in most species. Newer markers, which are rapidly gaining ground in molecular ecology studies of marine mammals, include single nucleotide poly morphisms (SNPs) (Morin et al., 2004; Elshire et al., 2011) and sequencing of whole mitochondrial genomes (Archer et al., 2013; Morin et al., 2015), nuclear genes (Palumbi and Cipriano, 1998; Dalebout et al., 2008), and even complete genomes on a population scale (Foote et al., 2016). These are methods for assessing sequence variation primarily in nuclear DNA, and primarily at the level of individual nucleotide changes (though insertions/deletions may also be assessed). Because single nucleotide changes are the most common type of variation in the genome, methods for assessing large numbers of polymorphic sites, such as SNP genotyping and
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Figure 3 Currently, data for most management genetic studies primarily consist of microsatellite DNA, mtDNA, or both, but SNPs are becoming more common. (A) Microsatellites are short tandem repeats (two, three, or four base repeats) of nucleotides, e.g., CACACACACA …, ATGATGATG …, or GATAGATAGATA …. Microsatellite data consist of n pairs of alleles for each individual at m number of microsatellite loci within the 3 × 109 or so base pairs of the mammalian nuclear genome. There is estimated to be a microsatellite region every 3000 or so base pairs. Microsatellites are part of the nuclear genome and are inherited biparentally. Mitochondrial data consist of DNA subsequences (haplotypes) of portions or all of the 1.6 × 104 bp mtDNA genome. mtDNA is a multicopy cytoplasmic DNA that, in vertebrates, is inherited intact from the mother. Each mitochondrion may have 5–10 DNA molecules, and there may be from 100 to 1000 mitochondria per cell. For mtDNA, a sequenced portion of 12 bp of the 16,000-bp molecule is shown. (B) Sample 1 is heterozygous at microsatellite locus A having a pair of alleles that have five and six CA repeats, and nucleotides C and T at SNP locus A. Sample 1 also possesses an “A”-type mitochondrial haplotype that, e.g., differs by 2 bp from the “C”-type. For actual studies, the number of microsatellite loci examined might typically range from 8 to 30, and the size of the mitochondrial sequence examined might range from 350 bp to the complete mitogenome.
RADseq (Restriction site Associated DNA Sequencing) (Davey and Blaxter, 2010), provide good statistical power for genetic assessment of populations, while also assaying more of the genome. With SNPs, the possibility for looking at patterns of variation
in individual genes opens the possibility of directly or indirectly assessing genetic variation under selection, or variation associated with known phenotypes. One often-used example of this is genetic determination of sex.
GENETICS FOR MANAGEMENT
III. Focusing on the Individual In the previous sections, the focus was on a population of animals united by some characteristic, e.g., geographic locale. In this section, the focus is on the individual and what information genetic studies can provide to management.
A. Illegal Traffic and Trade Two sorts of questions are usually asked: 1. Did sample X come from the same individual as sample Y? Nuclear marker analysis is used to establish an individual’s genetic fingerprint; this is also known as genotyping. 2. What is the provenance of sample X, i.e., what species or geographic population characterizes the sample? For this, sequence analyses are generally employed at higher levels of differentiation, and genotypes are used for assignment to a group or population. Question 1 is much like placing crime suspects at the crime scene via something the suspect has left behind (e.g., fingerprints, hair, DNA), and genotyping is a highly reliable means of answering it. The genetic profile of a piece of meat in a market of unknown provenance could be compared with the genetic profiles in a database of “legally” harvested whales or, alternatively, the sample could be compared with the genetic profiles in a database of biopsied, protected ones (see chapter on Genetics, Forensics). Question 2 is more general and deals with establishing that the sample came from an animal that belonged to a certain group or taxon. Genetic analyses can help determine whether a given market sample came from a proscribed or a permitted taxon. For example, a particular market sample is humpback whale (Megaptera novaeangliae). The unknown sample is compared genetically with samples whose taxon identity is known. Because the genetic differences between taxa above the species level are so large, assignment analyses are almost infallible (e.g., did the sample come from a whale or a cow?). In most situations, assignment is accurate at the species level (e.g., did the sample come from a minke whale Balaenoptera acutorostrata/bonaerensis, or a blue whale B. musculus?). However, there are exceptions, such as discriminating species among the genera Delphinus, Stenella, and Tursiops using only mtDNA control-region sequence. Accurate assignment of an individual sample to its geographic origin is more difficult [e.g., did the sample come from a gray whale (Eschrichtius robustus) harvested off the eastern Pacific Ocean or from the Okhotsk Sea?]. Although there are exceptions to this rule, in general the lower the taxonomic division the greater the difficulty in distinguishing provenance of an individual sample. At these lower levels, relatively large numbers of microsatellites or SNPs may provide sufficient power for statistical assignment tests. An example of this was the use by Kingston and Rosel (2004) of hundreds of variable AFLP (amplified fragment length polymorphism) loci to identify clear genetic differentiation of coastal and pelagic Tursiops truncatus populations in the western North Atlantic and differentiation between two sympatric species of Delphinus with relatively low mtDNA sequence divergence.
B. Other Uses of Individual-Oriented Genetic Information Genetic mark-recapture and monitoring methods based on genotyping can be substituted for traditional tagging methods, i.e., discovery tags, for estimating population size, dispersal rate, and migration pathways (Palsbøll, 1999; Schwartz et al., 2007). The management value of such data is obvious. However, if populations
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are large, the number of “recaptures” is likely to be small, and the cost of genetic analysis of many samples can be high. Nevertheless, such methods have been used to estimate population size and level of reproductive isolation (Garrigue et al., 2004) and to complement ongoing photographic identification projects. Besides reidentification of individuals, genotyping can be used to reliably identify parent-offspring relationships, although large numbers of microsatellite loci or even larger numbers of SNPs (>100) must be examined to do this accurately. It is probably worth the effort because by doing so, dispersal can be measured over two generations rather than over the lifetime of single individuals. For conservation decisions, intergenerational rather than intragenerational movement (i.e., gene flow) is probably a more important parameter than movements of a single individual. Another important demographic parameter that emerges from a study of parent-offspring relationships is the fraction of mature animals enjoying reproductive success. In other words, what is the breeding structure of the population, and how does that influence effective population size, inbreeding, and gene flow between populations (e.g., if a small proportion of males actually reproduce, dispersal does not accurately reflect gene flow)? Finally, determining sex provides a means to examine geographical segregation by sex and whether males or females are the dispersers. In many marine mammal species, females tend to be strongly philopatric, returning year after year to specific feeding or breeding sites. Female philopatry can be demonstrated by examining genetic population subdivision separately in males and in females. If only females are strongly philopatric, mtDNA subdivision should be apparent among the females but not the males. When males are the dispersers but not females, nuclear marker subdivision should be nonexistent because the males of breeding age serve as a “conduit” to homogenize the alleles between populations. If there are data on age, it is sometimes possible to demonstrate that the likelihood of dispersal increases with age of the males. There are policy implications in demonstrating female philopatry. Although this sort of population structuring would not qualify the population as an ESU, it does qualify it as a DIP worthy of management. If the animals from a particular feeding or breeding area are extirpated (males and females), recolonization will not likely take place. The strongly philopatric females from other breeding or feeding grounds would not recolonize the depopulated region, and the dispersing males would not likely return to an area with no females. Thus, if policy deliberately excluded populations based on female philopatry, a situation could easily arise where take could reduce or fragment ranges.
C. The Hidden Power of Molecular Genetics In addition to providing answers to population subdivision, dispersal, individual identities, and breeding behavior, molecular genetic analyses present a previously unexploited opportunity for gaining understanding of marine mammals via remote, nonlethal sampling. Some of these data can have direct relevance for management. Consider that a skin sample contains the entirety of the individual’s genetic blueprint. The ability to read this blueprint is progressing at an astounding rate, and although most of the progress is within the human genome, around 70% of cetacean genes are homologous, and tools developed for medical research can be utilized for marine mammals. For example, DNA sequence information extracted from the genes of skin cells can provide data about expressed characteristics of other tissues or organs. Sequencing visual pigment genes from skin is a good example. Levenson et al.
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(2006) have shown that, with collateral data about visual performance of particular photoreceptors via behavioral or physiological testing, it is possible to extrapolate from the DNA sequence to the spectral sensitivity. Newer next-generation sequencing (NGS) methods have been used to investigate the loss of function in a group of genes involved in vision (Springer et al., 2016). Understanding the visual abilities of cetaceans could aid in the design of fishing nets with increased color contrast, making them more visible to marine mammals, thereby reducing entanglement rates while sustaining the catch rate of the target species.
IV. Conclusion
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Although examination of genetic material offers unparalleled insights into many biological aspects of an animal’s life, certain sorts of genetic information provide data that are directly relevant to the management process. The most important is the definition of the conservation unit. By common sense and by law in many countries, this unit is created out of the understanding that the vast majority of species (marine mammal or otherwise) are not panmictic. Species are subdivided geographically into isolated and semiisolated groups. Genetic analyses can measure this directly and provide information to facilitate management of decision-making. This chapter has provided some examples of useful applications of various molecular genetics methods. Regardless of the sort of genetic information collected, to ensure that genetic studies and information will be useful for management it requires a clear understanding of the conservation policy that the studies are designed to help implement.
See Also the Following Articles Conservation n Genetics and Genomics n Genetics, Forensics Molecular Ecology n Stock Assessment n Stock Identity
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References Archer, F.I., Morin, P.A., Hancock-Hanser, B.L., Robertson, K.M., Leslie, M.S., Berube, M., et al. (2013). Mitogenomic phylogenetics of fin whales (Balaenoptera physalus spp.): Genetic evidence for revision of subspecies. PLoS One 8, e63396. Avise, J.C. (1986). Mitochondrial DNA and the evolutionary genetics of higher animals. Philos. Trans. R. Soc. (Lond.) B Biol. Sci. 312, 325–342. Dalebout, M.L., Baker, C.S., Robertson, K.M., Chivers, S.J., Perrin, W.F., Mead, J.G., et al. (2007). A divergent mtDNA lineage among Mesoplodon beaked whales: Molecular evidence for a new species in the tropical pacific? Mar. Mamm. Sci. 23, 954–966. Dalebout, M.L., Steel, D., and Baker, C.S. (2008). Phylogeny of the beaked whale genus Mesoplodon (Ziphiidae: Cetacea) revealed by nuclear introns: Implications for the evolution of male tusks. System. Biol. 57, 857–875. Davey, J.W., and Blaxter, M.L. (2010). RADSeq: Next-generation population genetics. Brief Funct. Genomics 9, 416–423. Elshire, R.J., Glaubitz, J.C., Sun, Q., Poland, J.A., Kawamoto, K., Buckler, E.S., et al. (2011). A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species. PLoS One 6, e19379. Foote, A.D., Hofreiter, M., and Morin, P.A. (2012a). Ancient DNA from marine mammals: studying long-lived species over ecological and evolutionary timescales. Ann. Anat. 194, 112–120. Foote, A.D., Thomsen, P.F., Sveegaard, S., Wahlberg, M., Kielgast, J., Kyhn, L.A., et al. (2012b). Investigating the potential use of environmental DNA (eDNA) for genetic monitoring of marine mammals. PLoS One 7, e41781. Foote, A.D., Vijay, N., Ávila-Arcos, M.C., Baird, R.W., Durban, J.W., Fumagalli, M., et al. (2016). Genome-culture coevolution promotes rapid divergence in the killer whale. Nature Comm. 7
Garrigue, C., Dodemont, R., Steel, D., and Baker, C.S. (2004). Organismal and “gametic” capture-recapture using microsatellite genotyping confirm low abundance and reproductive autonomy of humpback whales on the wintering grounds of New Caledonia. Mar. Ecol. Prog. Ser. 274, 251–262. Hillis, D.M., Moritz, C., and Mable, B.K. (1996). Molecular Systematics, 2nd ed. Sinauer Associates, Sunderland, MA. Kingston, S.E., and Rosel, P.E. (2004). Genetic differentiation among recently diverged delphinid taxa determined using AFLP markers. J. Hered. 95, 1–10. Levenson, D.H., Ponganis, P.J., Crognale, M.A., Deegan (2nd), J.F., Dizon, A., and Jacobs, G.H. (2006). Visual pigments of marine carnivores: Pinnipeds, polar bear, and sea otter. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 192, 833–843. Morin, P.A., Baker, C.S., Brewer, R.S., Burdin, A.M., Dalebout, M.L., Dines, J.P., et al. (2016). Genetic structure of the beaked whale genus Berardius in the North Pacific, with genetic evidence for a new species. Mar. Mamm. Sci. Morin, P.A., Luikart, G., Wayne, R.K., and the SNP Workshop Group, (2004) SNPs in ecology, evolution and conservation. Trends Ecol. Evol. 19, 208–216. Morin, P.A., Parsons, K.M., Archer, F.I., Ávila-Arcos, M.C., BarrettLennard, L.G., Dalla Rosa, L., et al. (2015). Geographic and temporal dynamics of a global radiation and diversification in the killer whale. Mol. Ecol. 24, 3964–3979. Moritz, C. (1994). Defining “Evolutionarily Significant Units” for conservation. Trends Ecol. Evol. 9, 373–375. Palsbøll, P. (1999). Genetic tagging: Contemporary molecular ecology. Biol. J. Linn. Soc. 68, 3–22. Palumbi, S.R., and Cipriano, F. (1998). Species identification using genetic tools: The value of nuclear and mitochondrial gene sequences in whale conservation. J. Hered. 89, 459–464. Ross, H.A., Lento, G.M., Dalebout, M.L., Goode, M., Ewing, G., McLaren, P., et al. (2003). DNA surveillance: Web-based molecular identification of whales, dolphins, and porpoises. J. Hered. 94, 111–114. Ryman, N., and Palm, S. (2006). POWSIM: A computer program for assessing statistical power when testing for genetic differentiation. Mol. Ecol. Notes 6, 600–6002. Schwartz, M.K., Luikart, G., and Waples, R.S. (2007). Genetic monitoring as a promising tool for conservation and management. Trends Ecol. Evol. 22, 25–33. Springer, M.S., Emerling, C.A., Fugate, N., Patel, R., Starrett, J., Morin, P.A., et al. (2016). Inactivation of cone-specific phototransduction genes in rod monochromatic cetaceans. Front. Ecol. Evol. Taylor, B.L., and Dizon, A.E. (1999). First policy then science: Why a management unit based solely on genetic criteria cannot work. Mol. Ecol. 8, S11–S16. Waples, R.S. (1991). Pacific salmon, Oncorhynchus spp., and the definition of “species” under the endangered species act. Mar. Fish. Rev. 53, 11–22.
GEOGRAPHIC VARIATION William F. Perrin I. The Nature of Geographic Variation Mammals vary from place to place, in size, shape, coloration, osteology, and genetic features, including chromosomes, enzymes, and DNA sequences. They also vary in sounds produced, other behavior, life history, breeding system, parasites, contaminant loads, biochemical features such as fatty acids, and other characters. Mammal species tend to vary geographically most in those features that vary most within a population. If, as for most mammals, body size