Genetics for Management (marine mammals)

 

Certain kinds of genetic information are particularly well suited to assist in designing strategies to protect human-impacted marine mammals. What sort of genetic information is 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 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 limits on the species and numbers of marine mammals that can be incidentally killed during certain 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 un-supportable burden on fishermen by restricting their fishing options. Obviously, the kind and quality of biological data, genetic or otherwise, informing this choice are critical. Decisions have to be based on the current scientific information available or will be challenged in the courts. While 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, inappropriate decisions can be 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 die conservation effort. To ensure that genetic studies proposed are relevant for management needs requires an understanding of the policy (the conservation goals) before doing the science (the information gathering).

Currently, management-oriented genetic studies use primarily (1) microsatellite loci within the 3 X 108 or so base pairs (bp) of the mammalian nuclear genome or (2) DNA sequence data from a portion of the 1.6 X 104 bp of the mitochondrial genome; the sub-sequence is also known as a haplotype (Fig. 1). Mitochondrial (mt)DNA is a multicopy, circular, cytoplasmic DNA that, in marine 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. In addition, gender-specific markers have been developed, and sex can be determined genetically. Finally, there is potential of measuring a variety of condition-related parameters (e.g., stress, maturity, pregnancy, spectral sensitivity, age) by examining the DNA or an expressed product of the DNA (i.e., the protein) in a small piece of skin.

Data for management genetic studies primarily consist of microsatellite DNA, mitochondrial DNA, or both. Microsatel-lites are short tandem repeats (two, three, or four base repeats) of base pairs, e.g., CACACACACA .... ATGATGATB . . . , or GATAGATAGATA .... Microsatellite data consist of n pairs of alleles at 111 number of microsatellite loci within the 3 X 10s or so base pairs (bp) of the mammalian nuclear genome. There is estimated to be a microsatellite region every 3000 or so base pairs. Mi-crosatellites are part of the nuclear genome and are inherited bi-parentally. Mitochondrial data consist of 11 subsequences of base pairs (haplotypes) at some locus within the 1.6 X 10* bp of the mitochondrial DNA genome. mtDNA is a multicopy cijtoplasmic DNA that, in vertebrates, is inherited intact from the mother. Each mitochondria may have 5-10 DNA molecides, and there may be from 100 to 1000 mitochondria per cell The three pairs of paired lines within the nucleus represent chromosomes; the 10 and 18 bp represent two alleles at a microsatellite locus located on the long arms of chromosome 3. For mitochondrial DNA, the arrow leading out of mitochondria shoivs a sequenced portion of 12 bp of the 16,000-bp molecide. For example, sample 1 is heterozygous at microsatellite locus A having a pair of alleles that have five and nine CA repeats. Sample 1 also possesses an "A"-type mitochondrial haplotype that, for example, differs by 2 bp from the "C'-type. For actual studies, the number of microsatellite loci examined might range from 4 to 10, and the size of the mitochondrial sequence examined might range from 350 to 1200 bp.

Figure 1 Data for management genetic studies primarily consist of microsatellite DNA, mitochondrial DNA, or both. Microsatel-lites are short tandem repeats (two, three, or four base repeats) of base pairs, e.g., CACACACACA …. ATGATGATB . . . , or GATAGATAGATA …. Microsatellite data consist of n pairs of alleles at 111 number of microsatellite loci within the 3 X 10s or so base pairs (bp) of the mammalian nuclear genome. There is estimated to be a microsatellite region every 3000 or so base pairs. Mi-crosatellites are part of the nuclear genome and are inherited bi-parentally. Mitochondrial data consist of 11 subsequences of base pairs (haplotypes) at some locus within the 1.6 X 10* bp of the mitochondrial DNA genome. mtDNA is a multicopy cijtoplasmic DNA that, in vertebrates, is inherited intact from the mother. Each mitochondria may have 5-10 DNA molecides, and there may be from 100 to 1000 mitochondria per cell The three pairs of paired lines within the nucleus represent chromosomes; the 10 and 18 bp represent two alleles at a microsatellite locus located on the long arms of chromosome 3. For mitochondrial DNA, the arrow leading out of mitochondria shoivs a sequenced portion of 12 bp of the 16,000-bp molecide. For example, sample 1 is heterozygous at microsatellite locus A having a pair of alleles that have five and nine CA repeats. Sample 1 also possesses an “A”-type mitochondrial haplotype that, for example, differs by 2 bp from the “C’-type. For actual studies, the number of microsatellite loci examined might range from 4 to 10, and the size of the mitochondrial sequence examined might range from 350 to 1200 bp.

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 mtDNA from ca. 1000-year-old bowhead whale (Balaena mysticetus) bones from the Chukchi Peninsula have been obtained. For live animals, projectile biopsying (crossbow, firearm, or lance) has been used successfully for all but the smallest and shyest cetaceans. Harbor porpoises (Phocoena phocoena) have proven particularly elusive. However, Fig. 2 is a photograph of a crossbow biopsy being taken of a highly endangered North Pacific right whale (Eubalaena japonica) at a range of 70 m.

I. The “Conservation Unit”

Today, defining the population segment on which to focus conservation efforts is the primary use of genetic information. The U.S. Marine Mammal Protection Act of 1972 (MMPA), the Endangered Species Act of 1973 (ESA), and the Revised Management Procedure of the International Whaling Commission (IWC) all direct that management efforts must be focused on populations below the species level. Although other countries have not necessarily established laws codifying the conservation unit, biologists are generally in agreement that species comprise a collection of semi-isolated populations (i.e., species-wide panmixia is the exception) and that those semi-isolated 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 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 the greatest 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 the exploited region (the source).

Policy tries to provide managers with guidance to balance conservation and economic issues by defining the management unit. For instance, the 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 units (ESU) are defined in the statues 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 MMPA seeks to maintain viable populations across their historical ranges at 50% of their historical population size. This act addresses maintenance of abundance. The

Crossbow biopsy being taken of a highly endangered North Pacific right whale at a range of 70 in. Visible at about 40% of the distance from the bow of the boat and the whale is the cross-bow projectile. A genetic sample was obtained and analyzed successfully.

Figure 2 Crossbow biopsy being taken of a highly endangered North Pacific right whale at a range of 70 in. Visible at about 40% of the distance from the bow of the boat and the whale is the cross-bow projectile. A genetic sample was obtained and analyzed successfully.

MMPA conservation units could be characterized as demo-graphically significant units (DSUs) to contrast them with ESUs. Some use the term “management unit” to refer to a DSU, but because both DSUs and ESUs are management units 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 “evolutionarilv” 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.) save 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 taxa 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 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 a priori stratum fall into exclusive genetic clusters. 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 first of all “substantially” reproductively isolated. If they were not isolated, it would be impossible to demonstrate the presence of exclusive genetic clustering.

If animals are commonly moving between groups and interbreeding, the groups would not be reproductively isolated from one another and would share genetic material. As a result, die genetic analysis would not find unique groupings of individuals corresponding to each population, and no ESUs could be defined. However, if the individuals of a priori defined groups do cluster into exclusive genetic groups, that indicates they have been reproductively isolated from one another for a significant period of time and do represent at ESU. As such, they are likely following unique evolutionary pathways and each must be conserved independently. The genetic evidence is usually presented in the form of a branching diagram representing the evolutionary pathways leading to mutually exclusive genetic clusters (Fig. 3A).

B. The Demographically Significant Unit

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. 3B)? This situation can be the result of (1) insufficient time having elapsed from when the populations were split to purge ancestral genes from the populations, (2) a degree of gene flow exists or has existed recently (e.g., a few adventuresome individuals immigrated to the south or vice versa to breed), or (3) a combination of the two. It also means that the populations under consideration do not meet ESU criteria. 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 DSUs and the definition would pertain to an intermediate situation between complete, long-term isolation of the ESU and free gene flow between geographically distinct populations (panmixia).

Hypothetical genetic evidence representing two different evolutionary histories presented in the form of branching diagrams representing the evolutionary pathways leading to 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 haploti/pe, and each hap-lotype differs from a connected neighbor by a 1-bp difference. (A) North Atlantic and South Atlantic stock has 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.

Figure 3 Hypothetical genetic evidence representing two different evolutionary histories presented in the form of branching diagrams representing the evolutionary pathways leading to 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 haploti/pe, and each hap-lotype differs from a connected neighbor by a 1-bp difference. (A) North Atlantic and South Atlantic stock has 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.

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: The 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 shall be maintained across their historical ranges. It would forbid management action that resulted in extirpation in one portion of the range, although such extiipation 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, i.e., there is heavy incidental take in the southern part ol the range because it overlaps with a gill net fishery, but none at all in the central and 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 die large distances involved, distinct habitat differences, and the coastal behavior of this species, complete panmixia is not very likely and some population structure is, i.e., dispersal between certain population segments is reduced. Say samples are available from each of five putative population groupings (defined a priori) in the U.S. 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 were observed to share haplotypes and microsatellite, and a x” analysis showed that significant frequency differences for the mtDNA haplotypes and for many of the microsatellite loci distinguish the populations. 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 DSUs because dispersal between them is sufficiently reduced to warrant managing them separately (e.g., establishing individual quotas for incidental take for each population).

This recommendation can actually be made with confidence because of the shape of the curve that relates genetic differentiation and dispersal (Fig. 4). 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 falls as dispersal increases only slightly. Genetic differentiation disappears at dispersal rates that still would be considered nonsignificant from a demographic point of view, say a few percent 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 generation.

So 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. Disregarding the geneticist’s recommendation may mean that the manager will have failed to meet a policy goal stipulated in the MMPA.

The idealized relationship between the degree of genetic differentiation expressed as a fixation index, dispersal rate expressed as the average dispersal rate 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). 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 accurately estimate dispersal rate in this range.

Figure 4 The idealized relationship between the degree of genetic differentiation expressed as a fixation index, dispersal rate expressed as the average dispersal rate 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). 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 accurately estimate dispersal rate in this range.  

However, it is not a “symmetrical” situation. What happens when genetic evidence fails to establish significant demographic isolation between units? A manager may be tempted to use this negative evidence to infer, because there was no evidence of population subdivision and hence restricted dispersal, that the putative populations could be coalesced into one larger management unit. 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 subdivision (e.g., too few samples tested, too little portion of the genome tested, or an insufficiently variable portion of the genome tested). In reality, although undetected, 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 management units.

II. Focusing on the Individual

In the previous section, 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: Did sample X come from the same individual as sample Y? Microsatellite analysis is used to establish an individual’s genetic fingerprint: this is also known as genotyping. The second question is what is the provenance of sample X, i.e., what species or geographic population characterizes the sample? For this, sequence analyses are generally employed.

Question 1 is much like placing crime suspects at the crime scene via something the suspect has left behind (e.g., clothing fibers, 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.

Question 2 is more general and deals with establishing that the sample came from an animal that belonged to a certain group (taxa). 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. muscidus?). However, there are exceptions, such as discriminating species among the genera Dclphinus, Stenella, and Tursiops. Accurate assignment of an individual sample to its geographic origin is very difficult [e.g., Did the sample come from a gray whale (Eschrichtius ro-bustus) harvested off the eastern Pacific Ocean or from the Okhotsk Sea?]. While there are exceptions to this rule, in general there is an increasing level of difficulty in distinguishing provenance of an individual sample, the lower the taxonomic division.

B. Other Uses of Individual-Oriented Genetic Information

Genetic inark-recapture methods based on genotyping can be substituted for traditional tagging methods, i.e., Discovery tags, for estimating population size, dispersal rate, and migration pathways. The management value of such data is obvious. However, small population sizes are necessary to ensure a high frequency of “recaptures.” Large-scale mark-recapture studies based on molecular techniques are impractical because a re capture can only be recognized after a biopsy is taken and analyzed. As a result, a large number of expensive analyses would have to be done to ensure an adequate number of recaptures. Besides reidentification of individuals, genotyping can be used to reliably identify parent-offspring relationships, although large numbers of microsatellite loci 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, inter- rather than intragenerational movement 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 particular breeding structure of the population?

Finally, determining gender provides a means to examine geographical segregation by sex and whether males or females are the dispersers. It is a common situation with many marine mammal species that 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 females. If only females are strongly philopatric, mtDNA subdivision should be apparent among the females but not the males. If there are some data on age, it is sometimes possible to demonstrate that the likelihood of dispersal increases with age of the males. When males are the dispersers but not females, microsatellite subdivision should be nonexistent because the males of breeding age serve as a “conduit” to homogenize the alleles between populations. There are policy implications in demonstrating female philopatry. While this sort of population structuring would not qualify the population as an ESU, it does qualify them as a DSU worthy of management. If the animals from a particular feeding or breeding area are extirpated (males and females both), 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, there could likely arise a situation where harvest 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 individuals 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 the cetacean genes are homologous, and tools developed for medical research can be utilized for marine mammals. Two new approaches will be described briefly in which analyses of skin DNA and its expressed products can be used to gain understanding regarding the biological characteristics of populations and individuals.

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. 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. Understanding the visual abilities of cetacean could aid in the design of fishing nets with increased color contrast, making them more visible to marine mammals, thereby reducing entanglement rates. A second approach is to directly examine expressed proteins within the skin itself, asking what proteins are up- or downregulated in skin under certain conditions. A good example is stress. Changing environmental conditions can perturb homeostasis, causing cellular stress and triggering a molecular stress response. At our laboratory, using immunohistochemical staining of thin skin sections, to date, 15 stress-responsive proteins (SRPs) have been identified whose expression is increased by 10-fold in stressed dolphins and whales in comparison to unstressed control animals (Fig. 5). The SRPs examined are widespread in the proliferating epithelial cells of the epidermis, and their induction is conserved in different species, genders, ages, and stressors. The procedure employed is simple and can be used for screening the large numbers of skin specimens necessary for correlating the presence of cellular stress with various environmental and anthropogenic factors. The management value is clear: Monitoring cellular stress in representative components of the marine ecosystem could provide an early warning system, allowing timely intervention in the case of habitat alteration. Cetaceans are top predators sensitive to many forms of environmental and anthropogenic stress, making them highly suitable as stress-reporting marine species.

III. Conclusion

While examination of genetic material offers unparalleled insights into all 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 panniictic. Species are subdivided geographically into isolated and semi-isolated groupings. Genetic analyses can measure this directly and provide the main avenue whereby the geneticist can provide information to facilitate decision making. Other genetic information on impacted populations is certainly of high value. This article has provided some examples. Regardless of the sort of genetic information collected, to ensure that genetic studies and information will be useful for management requires a clear understanding of the conservation policy that die studies are designed to help implement.

Immunohistochemical staining of skin sections to examine stress in marine mammals. Rapidly proliferating epithelial cells in the basal portion of the epidermis, in dolphins and whales exposed to stressful conditions, have higher concentrations of stress-responsive proteins (shaded cells) than cells proliferating under nonstressful conditions. These higher concentrations of stress-responsive proteins are maintained as the epithelial cells are pushed to the surface of the skin and shed if new cell growth underneath. The process takes approximately 2 months. Thus the skin provides a "record" of the approximate intensity of stress that has occurred over that period. Only stress that has occurred continuously for about a half a day can be detected presently, making the test insensitive to acute stress such as short bouts of high-intensity exercise.

Figure 5 Immunohistochemical staining of skin sections to examine stress in marine mammals. Rapidly proliferating epithelial cells in the basal portion of the epidermis, in dolphins and whales exposed to stressful conditions, have higher concentrations of stress-responsive proteins (shaded cells) than cells proliferating under nonstressful conditions. These higher concentrations of stress-responsive proteins are maintained as the epithelial cells are pushed to the surface of the skin and shed if new cell growth underneath. The process takes approximately 2 months. Thus the skin provides a “record” of the approximate intensity of stress that has occurred over that period. Only stress that has occurred continuously for about a half a day can be detected presently, making the test insensitive to acute stress such as short bouts of high-intensity exercise.

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