Draft 29 Oct 01
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Chapter 4    Individuals, their interactions, and reproduction.
Bill Sherwin and Craig Moritz
Summary:
•	Even the simplest conservation actions such as a population census can be seriously hampered by incorrect diagnosis of individual sex and identity, family relationships or mating system. Many organisms have mixed reproductive strategies with varying ploidy and mixtures of sexual vs. clonal reproduction – this needs to be recognised in monitoring and management. More intensive actions such as translocations to wild or captive populations require detailed attention to these matters. Also, threatening processes have the potential to affect mating systems dramatically, or may affect certain family groups more than others.  These changes can, in turn, magnify other threats. 

•	Molecular markers can be used to identify genetically distinct individuals and their sex This, in turn, can be used for population censuses, monitoring of sex ratio and tracking individual movements. However physical recaptures remain necessary to test error rates and provide essential information such as condition, growth rates etc.

•	Highly variable markers are also used in natural populations to define parentage estimate relatedness among individuals and pedigrees and analyze the breeding system.

•	Some aspects of population structure, such as plant mating systems, are very responsive to certain threats.  This makes their molecular analysis a very good indicator of these threats, but the long-term importance of these changes is debatable, since the system itself is so labile. 

•	In most cases, molecular analyses are greatly aided by other ecological information, such as the ages of potential parents and offspring.

A manager faced with a group of organisms of the same species immediately needs to know their relationship to one another.  Are they all part of the same individual, or family, or subpopulation?  Do they coordinate their life history in some way, such as simultaneous release of gametes, or mass dispersal? What is the mating system – is mating at random or dominated by a small number of individuals?  How frequent is self-fertilisation or mating between relatives?  These questions can be summarised as "What is the substructure of the population?", and their answers are not trivial to the genetic and demographic management of the species.  Sometimes we can make guesses about these matters without genetic information, but usually it is necessary to have a mixture of ecological data and genetic observations.  Four examples show how genetic data can help us to manage a species.

In Australia , clusters of a rare tree Eucalyptus argutifolia which appear to be separate individuals, but have turned out to be shoots from the same root stock; these individual trees are therefore genetically a single individual ((Kennington, Waycott et al. 1996)).  These clusters of trees could lull the manager into a false sense of security, because each cluster contains no more genetic variation than a single tree, and recruitment of individual sexually produced trees to the area may actually be extremely poor when counted as the number of new clusters per decade, rather than as the number of new trees (which would be incorrect).  It could be debated that reproduction this way is not all bad, though – except for somaclonal variation (chapter 3), vegetative reproduction avoids the loss of variation that meiosis and fertilization can engender in a small population (chapter 5).  Clonal propagation of mitotic cell lineages, like the eucalypts above, is found frequently in microorganisms, and in over one-half the eukaryotic phyla (Fagerstrom, Briscoe et al. 1998) (Anderson and Kohn 1998).

Many species may show a mix of different reproductive systems, this may be seen as facultative variation within populations – in many plant populations, each year’s recruitment is composed of some sexual reproduction, some vegetative reproduction, and some sexual reproduction from the seed bank laid down some years before. Seed-banks are important for regeneration after fire in many ecosystems {Witkowski, 2001 #905; Valbuena, 2001 #904; Bradstock, }.   In desert mustard (Lesquerella fendleri) in a wildlife refuge in New Mexico, and in the rare annual plant Clarkia springvillensis, the seed-bank differs genetically from the adult population, so it is possible to identify the contribution from seed-banks by genetic methods (Cabin, Mitchell et al. 1998; McCue and Holtsford 1998).  The genetic difference also points to the seed-bank as an important reservoir of genetic variation because of either different selective processes in the seed-bank, or contributions of seeds from long ago and far away.  Resting phases are by no means restricted to plants - they occur in microorganisms and in many animal species, such as rotifers (Gomez and Carvalho 2000).

In the Nivelle river on the Iberian Peninsula, Atlantic salmon Salmo salar display the usual anadromous pattern of migrating to the seas for some years then returning to breeding fresh-water (Martinez, Moran et al. 2000).  The total number of breeding adults is important for estimating the demographic viability of the population, and its likely rate of loss of genetic variation (Chapter 5); in the Nivelle population, the effective number of breeders of both sexes appeared to be 205, but there was the possibility of breeding by some precocious juvenile males (“parr”) which had not yet left the freshwater.  Genetic studies showed that all egg-masses contained eggs fertilized by parr: 16-81% of eggs were fertilized by one to four parr, while only one or two anadromous males contributed the remainder of the successful sperm.  This nearly doubled the effective number of breeders, to 371, and also indicated that any dispersal from one river system to another by anadromous males would have a reduced genetic effect.  

Finally, even when biologists think that they are dealing with a single population, it may contain a separate populations which mix poorly or not at all.  Witch flounder Glyptocephalus cynoglossus from a single management area in the North Atlantic, were shown by genetic studies to contain a mix of three separate subpopulations (or "stocks" or “Mus” - chapter 6) with limited genetic exchange ((Fairbairn 1981)).  Subsequent studies showed that the three stocks had different recruitment rates, and that therefore the previous management based on the average recruitment rate was not appropriate.  Although the analysis of separate stocks is the topic of a later chapter (6), it is worth pointing out that not only will many of the studies discussed in this chapter be able to reveal such subdivision, but moreover, poor definition of population membership would undermine validity of conclusions about mating systems and resulting inferences for conservation management.

In this chapter, we will consider the ways that genetic analyses can shed light on the number, distribution and mating and reproduction of individuals within populations.  We will summarise the methods for each analysis, and the implications of each type of substructure to population viability.  Effects on viability can be either direct, through effects on recruitment, or indirect through alteration of genetic variation, which can then alter survival and reproduction.  The genetic effects are described further in the remainder of this book, but a brief description of demography and its application to conservation management is in order here.  Demography is the arithmetic of births and deaths, immigration and emigration, in a population, and the way this is affected by natural processes, threatening processes, and management intervention.  All the biological processes and concepts in this book can be judged by whether they can have a short- or long- term effect upon the demography, and therefore the size, growth rate, or probability of extinction, of the population (Table 3.1).  It is now well known that the demography of populations is affected not only by the average values of variables such as fecundity, but also by their variability (Table 3.1, (Lacy 1993; Lindenmayer, Lacy et al. 2000)).  Genetic methods are able to contribute much of the information needed for demographic forecasting, and in turn, the genetics of a population may be affected by both the overall demographic outcomes, and certain of the input variables (Table 3.1)  ((Milligan, Leebensmack et al. 1994)).  It is worth pointing out immediately, that often genetic estimates can have large errors, and design of studies to minimise this is critical if the results are to be useful; this is discussed throughout the chapter.

Table 3.1  Brief summary of demography of single populations, showing the major variables needed for demographic forecasts,  and a summary of the ways in which genetic data may be used to estimate these variables, and the ways in which these variables may affect genetic variation.  Details of genetic methods, and the necessary complementary ecological data, are given in this and other chapters; there are probably no cases where genetic information alosne is sufficient, but we highlight cases where genetic data can greatly improve the estimates.  Note that there is no column for “effect on demography”, since all variables are included only because they are vital input for the forecasting process.  In general, the demographic effect of altering any INPUT variable in a particular population, is assessed by what is called its “elasticity”, which estimates the relative importance of changes in different variables, using a criterion of its relative effect on population growth rate (Caswell).  The output of the demographic forecasts consists of future population growth rate, size, and probability of extinction, all of which secondarily effect genetic variation, aside from their obvious direct importance in population viability analysis

	Variable	Genetic estimates possible	Direct effect on maintenance of genetic variation	
	Reproductive rate - Mean	+	+	
	Reproductive rate – Variance between individuals	++	++	
	Reproduction -  +/- density dependence	?	+	
	Reproduction -  depression by genetic alteration (chapter 9,10)	+	++	
	Reproduction – contributions of different types	+	++	
	Survivorship - Mean	?	+	
	Survivorship - Variance	?	++	
	Immigration and emigration  (see chapter 6)	++	++	
	Past and present population size and variance  (both  total size and effective population size –see chapter 5)	+	++	
	Structure, eg ages, size of seed-bank, sex ratio, and their variation	+	+	
	Catastrophe	-	++	

4.1 Identification of individuals

Why identify individuals?
Demographic forecasting for conservation management and monitoring, and indeed for much of single-species ecology, relies upon estimates of numbers of individuals, or effects on individuals. Unfortunately, the individuals may be cryptic for one reason or another, and their identification by non-genetic methods may be too invasive for a threatened population, or simply too expensive.  The example of the eucalypts above shows us that it is not always straightforward to identify individuals, and that there are serious consequences for conservation management.  As well as the possibility that many apparently independent individuals are actually clones, there is also the possibility that what appears to be a single individuals is the result of fusion of several individuals.  Fusion can happen in plants such as strangler figs (Ficus sp.(Thomson, Herre et al. 1991)), and in animals such as hydrozoans where there can be both active fusion of different “individuals”, and active avoidance of fusion, possibly under genetic control ((Hart and Grosberg 1999; Grosberg and Hart 2000)).  Even when fusion and fission does not occur, identification and enumeration of individuals in the field is often no easy matter, and can be greatly assisted by non-invasive methods such as DNA-based identification from shed hairs, faeces, etc.  This section will look at the technical aspects of individual identification, and some of the ways that these have been, or could be, used to assist conservation.

The obvious way to identify separate individuals is to check whether they have the same genotypes.  This is the approach used in police investigations which are tracing the origin of particular blood–spots, and it is also used in cases such as the mallee eucalypts described above, where the aim is to define members of a "genet", or "clone" – organisms which have been produced by fragmentation of a single individual.  When we are trying to establish individual identity, it is important to use genetic markers with adequate variation for the task (Box 4.1).  The chosen suite of markers must be sufficiently variable that the chance of two different individuals showing the same genotype is vanishingly small.  In the eucalypt study, the initial identification of genets used allozymes, which are not highly variable; therefore the study could have been criticised for failing to distinguish between true genets, and clusters of sexually–produced offspring who happened to have similar allozyme genotypes, perhaps because of limited seed dispersal from a single parent.  In order to counter this criticism, Kennington et al (1996) repeated the analysis using hypervarible minisatellite DNA, and confirmed the allozyme results.  Further details of the sampling requirements for this type of work are in box 4.1. 

Population size estimation
The  ability to assign identities to the individuals, and recognise when the same individual has been encountered twice, allows application of a wide range of ecological and genetic analyses.  Most obviously, estimation of population size is an extremely important part of monitoring a threatened species.  Mills et al conclude that a 1% misdiagnosis rate is acceptable when identifying individuals as the basis for a mark-resighting population estimation; of course, failure to discriminate between individuals leads to underestimates of population size (Mills, Citta et al. 2000).  This method has been used successfully in a number of cases, such as the use of genotypes from sloughed skin to estimate population size of humpback whales ((Palsboll, Allen et al. 1997)).  Another study produced a believable estimate of population size in coyotes Canis latrans using a different statistical procedure called “rarefaction analysis”(Kohn, York et al. 1999).  This method relies upon the rate at which new genotypes appear when additional samples are analysed (Figure 4.1); the estimate of total population size was 38+8, which compared well to a population estimate of 37 from an intensive trapping survey.  

Since DNA is an extremely tough molecule, there has been considerable interest in remote, non-invasive sensing from non-invasive samples like hair, feathers, scales, faeces or anything else which may bear traces of an organism’s DNA. This can provide valuable data on abundance, distribution, mating patterns or dispersal, and the samples can often be taken with minimal disturbance , such as a tiny snip of skin or leaf-tip, or shed tissues such as hairs feathers or scales left in nests or on adhesive tapes (Woodruff, 199*,(Taberlet, Camarra et al. 1997); (Sloane, Sunnucks et al. 2000)) or sloughed whale-skin (Valsecchi, GlocknerFerrari et al. 1998).  Tissue can also be used from recently or long-dead samples: mammal bones in owl pellets ((Taberlet and Fumagalli 1996)), or museum samples (oak - (Dumolin-Lapegue, Pemonge et al. 1999); wombats - (Taylor 1994)).   Non-invasive sampling has many advantages and disadvantages over direct sampling of the organism (Taberlet, Waits et al. 1999).  One obvious advantage is that non-invasive studies limit disturbance to the organism, such as the weight-loss seen after trapping of endangered northern hairy-nosed wombats (Hoyle et al ****). Disturbance can be bad for reasons of ethics, interference with behavioural study, or damage to a threatened population.  Another advantage of remote and non-invasive sampling is that the number of samples is not limited so severely by field time, equipment, or the behaviour of the organism.  For example, trapping programs have biases against trap-shy individuals, or double-capture if two individuals are present simultaneously; a non-invasive sample will not have these biases, although it may have others.  The chief challenge of remote sensing is the use of small amounts of degraded DNA; this has many attendant difficulties, which are discussed further in Box 4.2.  Secondly, there may be biases such as inability to detect mixed samples that derive from more than one individual.  Finally, there are cricial data which cannot be obtained without sampling the organism itself, such as weight and reproductive condition, unless the genetic methods are combined with non-genetic remote sensing, such as weigh-stations on runways to hair-traps. 

Figure 4.1  Coyote population size estimation based on a faecal genotype rarefaction curve.  The projected asymptote (38 individuals) is used as a population size estimate (Kohn, York et al. 1999).

BOX4.1 :  Sampling and technical precision

It must be recognised that the sampling, and quality of technical work,  necessary to perform many of the analyses in this chapter is quite exacting.  The tissue sampling may be easier than some other ecological studies, but there are other issues.  The details of sample requirements vary between sections of this chapter, but some general points are evident.  The number of individuals sampled is important, but also the number of loci, and their characteristics.  First, the loci should have high variability.  High variability, and larger number of loci, decrease the chance that samples left by two different individuals will have the same genotype, or that the progeny of two different fathers will have the same genotype.  For example, if there is one locus with two alleles at proportions p=q=0.5, then  the chance that two unrelated individuals would have the same genotype is p4 + 4p2q2 + q4.= 0.375  This 37.5% chance of failed discrimination is unacceptably high, and could be reduced by using a locus with more alleles (which would make the algebra more unruly) or by adding a second locus.  Another locus with p=q=0.5 would reduce the chance of failed discrimination to 0.375 x 0.375 = 0.140 – a 14% chance of failure.  Adding more loci reduces the chance still further.

Codominant markers such as microsatellites provide best power, but a tenfold larger sample of dominant markers can provide the same discriminating power ((Waits, Luikart et al. 2001)).  Which is better will depend upon the cost and speed of analysis of the two types of markers, and Krauss and Peakalll have demonstrated that codominant AFLP markers can give useful discrimination of parentage in plants ((Krauss and Peakall 1998)) .  The necessary sample of loci will also depend upon the exact relationships which it may be necessary to discriminate, and there are published tabulations available (Brookfield and Parkin 1993).  Distinguishing a sib-sib relationship from parent-offspring is one of the most difficult tasks, since both relationships have an expectation of 50% allele sharing, and they only differ in their between-locus variances.  The power of the loci will be lowered if they significantly depart from random mating expectations:  random associations of alleles within loci, of Hardy-Weinberg equilibrium, and random associations between alleles at different loci – genotypic disequilibrium (Hartl and Clark 1997; Hedrick 2000).  Note that even if there is not random mating, many loci may nevertheless remain in apparent equilibrium and therefore be useful in the analyses in this chapter.  The trade-off between increasing the number of individuals sampled and increasing the number of loci analysed depends on the question and the nature of the data; in parentage analysis, if the sample does not contain all the parents, then better results will be obtained by sampling more loci than sampling more offspring from the same nest (Neff, Repka et al. 2000; Neff, Repka et al. 2000).

For any particular data set, and any type of analysis, it is possible to check whether enough loci have been analysed to give stable relatedness estimates.  This checking procedure is called a rarefaction analysis: the relatedness or other calculation is repeated with increasing numbers of loci used, until the values stop changing with the addition of extra loci (Altmann, Alberts et al. 1996)

Often, the actual occurrence of identical genotypes is one to three orders of magnitude higher than expected from allele proportions ((Waits, Luikart et al. 2001)).  This is likely to be because of population substructure, such as the presence of high numbers of relatives, especially sibs which have a high chance of sharing alleles (Box 4.1).  Thus when planning individual identification programs, it is best to estimate the chance of discriminating between sibs, from the allele proportions at the markers to be employed ((Waits, Luikart et al. 2001)).  The acceptable level of failure in discrimination will depend upon the application.  Calculation of the chance of failed discrimination is based on the allele proportions in the "base population" – the population from which the individuals were drawn.  Definition of this population is a major challenge in conservation biology (or in police work, where a life may depend upon it), and is discussed further in 4.5 and chapter 6.  Even if the population is geographically well-defined, there can be great scope for errors in estimating base allele proportions.  For example, in the case of the eucalypts, the low dispersal of seeds means that the base population is fairly limited, so that addition or loss of even a few genotypes could mean that estimates of current allele proportions were quite different from proportions at the time when the studied trees were being recruited.  

Another source of error in this work is spurious mismatches because of mutations or technical problems.  The choice of hypervariable loci for this work means that we are focusing upon loci with high mutation rates, often about 1 in 10,000 to 1 in 100 per gamete per generation. Thus it is possible that two samples from the same individual could be genetically different because of somatic mutation.  In the mallee eucalypt example, samples were taken from multiple stems of the same individual, to check for somatic mutation, and no variation was found.  A frequent technical problem is the occurrence of “null” alleles – alleles which do not amplify because of a mutation in their characteristic primer site used to initiate DNA amplification.  If family data are available, then these may be identified from odd segregation patterns in the offspring, with the appearance of unexpected homozygotes, as shown in thw figure below.  If family data are not available, then null allele frequencies can be estimated from an excess of homozygotes in the population, but since there are many other causes of heterozygote excess, this is not an ideal detection method ((Brookfield 1996)).

 
In this figure, the female parent in the top line has a null allele A3, which is not apparent to lab analysis.  Thus the two parents appear to be homozygous female A1 A1, and a homozygous male A2 A2.  However, they have produced one heterozygote A1 A2 as expected (the male offspring), but one apparent homozygote, A1 A1 (really A1 A3, but the A3 allele is not revealed by the lab analysis).  This second offspring should be impossible if the parents are A1 A1 x A2 A2., and such a result suggests the presence of a null allele.
In summary, loci should be screened for inheritance type, allelic variation, departure from random mating expectations, null alleles, and then the resulting baseline data used to assess the power of the set of loci for the question being posed.  This can result in considerable lead-times for studies, while a pilot investigation is being carried out.

END BOX 4.1

BOX 4.2:Remote sensing and Non- invasive sampling – special considerations  

All the discussions above have assumed that the sampling process easily provides large amounts of good-quality DNA.  In some cases, it is also necessary to assume that the DNA in each sample comes from a single individual, although this could be checked from the DNA of course.  In the case of non-invasive and remote sampling, the DNA will ususally be in very small amounts, especially if a number of loci need to be analysed in order to give statistical power.  Also, the DNA is often degraded, since tissues which fall off, such as hair, scales, feathers, buccal cells on food-wadges, or skulls in owl pellets have often not been in the protection of a living cell for sometime, and may also have been subject to attack by rain and bacteria on a shed hair, digestive enzymes and bacteria in a faecal sample, etc.

Because of the small amount of DNA, the usable marker loci are restricted to those which can be amplified by the polymerase-chain reaction (PCR - Chapter 2).  When tiny amounts of DNA are used in this procedure, there if frequent “alleleic dropout” – non amplification of alleles which are present in the individual – as well as appearance of spurious alleles  (Taberlet, Waits et al. 1999).  Repeatability is vital, and deos not come easily with small degreaded samples.   Thus extensive re-testing is required, as well as great care with contamination of samples, often increasing the workload by tenfold ((Taberlet, Waits et al. 1999)).

Despite these limitations, non-invasive methods have been successfully used.  One of the earliest studies was to estimate social structure in Chimpanzees, Pan troglodytes (Morin, Moore et al. 1994) (although this suffered from repeatability problems).  The method has also been used to estimate population size in Canadian brown bears  and coyotes  (Woods, Paetkau et al. 1999)  (Kohn, York et al. 1999), dispersal and population size in whales (Palsboll 1999), and population size and home range in Pyrenean brown bears (Taberlet, Camarra et al. 1997).
END BOX 4.2

4.2  Other attributes of individuals:

Molecular sexing  
Sex ratio is a critical factor in many aspects of population biology, including demographic forecasting of small populations (Table 4.1), and detecting the effects of inbreeding (chapter 10).    However, in dioecious species, which have separate sexes, it is often difficult to tell the sexes apart in the field, especially if we are dealing with remote samples.  In species where sex is genetically determined, we can sex individuals by using genetic analyses such as chromosome preparations or DNA tests.  Many dioecious species have one sex which is called "heterogametic", having two different sex chromosomes (eg X and Y in male mammals or W and Z in female birds); the other sex has two copies of one of the sex chromosomes  (XX in female mammals, ZZ in male birds), so the sexes can be separated by making chromosomal preparations.  However, chromosomal preparations, and the necessary sampling, are not always feasible, so a more generally applicable method is to develop a PCR–assay for the presence of genes that are only found in the heterogametic sex (eg, on the Y in mammals or the W in birds).  Molecular sexing has been used in whales, as a component of population size estimation and tracking ((Berube and Palsboll 1996; Berube and Palsboll 1996) (Palsboll 1999)).  Markers have been found in other mammals ((Wardell, Sudweeks et al. 1993)), and generic sex-diagnostic markers have been found which can be used in most birds (Griffiths, Double et al. 1998).  Molecular methods can also be used to identify mating-type in fungi, and investigate levels of selfing and outcrossing ((Marra and Milgroom 1999; Marra and Milgroom 2001))

Although generic sex-identification primers can be developed in some groups, as for the whales and birds above, in many cases there are reasons to develop them afresh for each species.  The same piece of DNA may simply not be sex-linked in every species, because of chromosomal differences, and a number of procedures have been established for relatively rapidly screening for sex-diagnostic markers ((Griffiths and Tiwari 1993); (Wardell, Sudweeks et al. 1993)).  Additionally, a number of species have genetic sex-determination systems which are more complex than those listed above, and may vary within the species.  Theory suggests that if there is variation of the sex determination system in time or space, this can significantly alter the probability of extinction of a small population, so estimation of the system may be important for conservation biology ((Leberg 1998)).

Species identification
The first step in most biological investigations is to identify the species.  Even when the organism is held as a lab-stock, species identification can be erroneous, and when relying on tiny non-invasive samples, it requires special attention.  Hairs can often be examined microscopically to identify species, or at least genus (Coman, 19**).  Another way of confirming species identity is to use diagnostic DNA markers.  For example, microscopic analysis of hair–samples from South–eastern Australian forests often produce evidence for one or other "Potoroo species" (a small kangaroo–like animal).  This result is inadequate because the necessary conservation action is quite different, depending upon whether the relatively common long–nosed potoroo Potorous tridactylus is present, or the endangered long–footed potoroo Potorous longipes.  Hairs of these species can now be distinguished by amplification of mitchondrial cytochrome–b sequence, then cutting with diagnostic restriction enzymes (Sherwin et al, in prep).  Molecular species identification is becming more widespread in cases where there is noe other way of diagnosing members of a natural community, such as planktonic larvae ((Hare, Palumbi et al. 2000)), or there is a need to identify wildlife products ((Innes, Grewe et al. 1998);(Baker, Lento et al. 2000)).  Cryptic species identification will be dealt with in more detail in chapter 8.

Even in closely-managed populations, taxon-identification can be important.  The world's zoos are attempting to assist tigers by using their existing stocks for captive breeding.  This effort is hampered by the twin problems of lack of space, and the desire to keep only stocks with defined "provenance", meaning that their geographic origin is known.  Unfortunately, much of the space for tigers in the world's zoos is taken up by animals of uncertain provenance, so the dilemma to be faced is "Do we shoot existing unprovenanced cative tigers and exploit the small wild populations to obtain fully provenanced stock, or do we proceed with the use of unprovenanced stock?".  To address this problem, Wentzel et al ((Wentzel, Claiborne Stevens et al. 1997)) used 20 microsatellite loci to characterise the five living subspecies of tigers,  then assigned captive tigers of unknown origin to their subspecies of origin, using their genotypes for these microsatellites.  This has apparently solved the provenance question, but in chapters 8 and 11, we will discuss further whether there is actually a need to be quite so fussy about provenance.

 

4.3  Patterns of mating and reproduction

Varying strategies and conservation
A truly dazzling array of patterns of mating and reproduction are encountered in managed organisms.  Mating and reproduction are decoupled in many organisms, so must be analysed separately.  Clonal propagation of mitotic cell lineages, like the eucalypts above, is found frequently in microorganisms, and in over one-half the eukaryotic phyla (Fagerstrom, Briscoe et al. 1998).  Where there is transfer of genetic material between individuals, this does not always take the form of a 1:1 sex-ratio with random mating.  Micro-organisms have a variety of gene-transfer methods, some involving transfer between two mating types, others involving virus-mediated transfer of genes, or even uptake of naked DNA.  In the Dutch-Elm disease agent Ophiostoma ulmi it has been suggested the drastic variation in pathogenicity results from the interplay between chance effects, clonal propagation, and viral infection (Brasier 1988).  In plants we have already seen the importance of sexual and vegetative reproduction and seed-banks.

Many species of plants and animals are dioecious, having both sexes on the one individual and thus being able to self-fertilise, with more or less involvement of meiosis beforehand.  Some plants actively self-fertilise, but others have self-incompatibility genes (SI - chapter 3) to prevent fertilization by self or genetically similar individuals.  One of the conservation implications of an SI system is that in small populations, random loss of SI allele diversity can result in an absence of compatible mates, as in the endangered daisy Rutidosis leptorrhynchoides (chapter 10) (Brown and Young 2000).  Animals also often avoid mating with relatives, as seen in the tiny marsupial Antechinus ((Cockburn, Scott et al. 1985)), but in other cases live in inbred colonies such as those of the naked mole-rat (Reeve, Westneat et al. 1990), or complex groups of highly-related individuals such as many species of hymenopterans.  Genetic analysis is often required to reveal such behaviours, and their alteration due to threats or management may sharply increase the occurrence of inbreeding depression (lowered survival and fertility in inbred individuals – chapter 10). 

Geographic variation in reproductive strategies also occurs, such as the cline of sexuality associated with ploidy (chapter 2) differences in Daphnia pulex (Ward, Bickerton et al. 1994).  Where genomes of different ploidy are involved, careless translocation by managers could severely reduce viability of populations because of incompatibility of the genomes.  Finally, some species are really a symbiosis of two species with different reproductive strategies, and each species can be considered as a heritable character for the other:  lichens are a symbiosis of fungi and algae, and many arthropods carry intracellular parasites which distort their sex-ratio.  Different levels of infection can alter this ratio, and therefore the demography of the population; often genetic studies can be used to detect the infections ((Malloch, Fenton et al. 2000)).

In species capable of outbreeding, the frequency and method of gamete exchange is an important component of the population biology.  These are determined by a whole suite of factors, ranging from dispersal patterns of whole individuals or gametes, through to the mechanisms by which pollination or mate choice occurs. If a threatening process disrupts any of these mechanisms, there can be very adverse conservation consequences, including complete failure of reproduction, or changes of the natural inbreeding level, which can lead to problems of high homozygosity and poor fitness (see chapter **). 

Detailed observations of the species are vital to define mating patterns, but when these are compared with genetic studies, the results are sometimes quite surprising – it is often found that the actual reproductive success of individuals is not well correlated with the field observations.  The reasons for this mismatch of results are cryptic behaviour such as extra–harem mating in deer (), or that short–term observations are an insufficient description of lifetime reproductive success ((Altmann, Alberts et al. 1996)).  Thus, both observations and genetic data are needed. 

Clearly all these different mating options have very different genetic consequences.  Relative to random-mating expectations, the results could range between great elevation or reduction in either homozygosity or the association between alleles at different loci (genotypic disequilibrium).  Box 4.3 shows these effects in a little more detail, and indicates how they can be analysed in a population that mixes different mating systems.  What effect will the mating system have on the population?  The adverse consequences of homozygosity will be discussed in more detail in chapters 9 and 10, but here it is interesting to note that many authors have proposed that there may be an optimum level of inbreeding for each species.  Ecological data on North American pikas suggested that these small mammals only move to the closest habitat patch in their fragmented environment; therefore, increasing fragmentation could severely affect the ability of pikas to find unrelated mates.  However, genetic data suggest that there must be undetected long-distance movement (Peacock and Smith 1997), and that the Pikas choose mates who have the full range of relatedness available in all patches (Figure 4.2) (Peacock and Smith 1997).  Other mammals make genetically based mating decisions, such as the effects of MHC on mate choice discussed in the last chapter.  Such a system could be sensitive to changes in levels of genetic variation between individuals.

Box 4.3 Mating systems – genetic consequences and detection.
How do molecular ecologists distinguish different types of mating and reproduction?  The table below shows the genetic consequences of several systems, for a pair of siblings, who have similar genotypes.  
Genotypes of two parents, who are siblings.	A1 A2	A2 A2	
Offspring genotype	A1 A1	A1 A2	A2 A2	A1 A1	A1 A2	A2 A2	
Vegetative reproduction	-	100%	-	-	-	100%	
Self-fertilisation
With meiosis	25%	50%	25%	25%	50%	25%	
Biparental inbreeding (the two siblings at the top of the table mate)		50%	50%		50%	50%	
Outcrossing	Other alleles should appear in the progeny, or other sets of genotype proportions, depending on the genetic composition of the population	

The results would vary depending upon the parent’s genotypes, but in general, if the parents were siblings, they  would share one or more alleles at the average locus, as shown in the table.  The methods of analysing mixed mating systems are based on extension of the above ideas to more individuals,  multiple loci and any array of allele and genotype proportions in the parent population.

Ritland (19**) and others have devised models for the expected genotypic arrays in progeny under various models of mating, such as the MMM, or mixed mating model, which includes self–fertilisation and oucrossing, and the ESR, or effective selfing model in which there can be outcrossing, selfing, and biparental inbreeding.  Although both selfing (uniparental inbreeding) and biparental inbreeding produce a reduction in homozygosity of the progeny, they can be distingiushed by comparison of selfing rates derived from single locus data and from multi–locus data ((Coutellec-Vreto, Jarne et al. 1998)).  These methods are especially applicable to plants, because of their reliance upon large progeny arrays, and their emphasis of detection of selfing.  They have also been used in snails, though ((Coutellec-Vreto, Jarne et al. 1998)).  In most animal species, often only a few, or a single, offspring is available for testing from each parent, so checking for reduction in homozygosity within individual progeny arrays is not possible, but by constructing pedigrees within the popualtion, mating systems can be inferred (Altmann, Alberts et al. 1996).
End Box 4.3
Figure 4.2 Band-sharing scores between potential mates and between mated pairs of Pikas.  The bands are autosomal dominant markers from minisatellite fingerprinting.  From (Peacock and Smith 1997).

Given the direct relationship between mating system and homozygosity, and the possibility that each species has a mating system which is adapted to produce an appropriate level of homozygosity in progeny arrays, it is disturbing to note that many threatening processes are capable of altering the mating system.  For example, habitat alteration can affect dispersal, so that the pool of potential mates is altered.  Also, loss of pollinators can alter the outcrossing rate; in a vulnerable Asian flower, Primula sieboldii, the loss of bumblebees has meant that flowers whose morphology encourages outcrossing have very poor fertility, while flowers with stigma and anther close together, which can readily self–fertilise, have up to ten–fold higher seed-set than the outcrossers (Washitani 1996).  Even when pollinators are abundant, the outcrossers appear to vulnerable to incompatibility in small populations (Matsumura and Washitani 2000).

In a landmark study of lions that had been isolated in a crater and subjected to a population crash in the early 1960s, ecological and genetic data revealed inbreeding in the crater population (Gilbert, Packer et al. 1991; Packer, Pusey et al. 1991).  The level of genetic variation was lower than the Serengeti population outside the crater.  This was coupled with signs that the loss of variation was affecting demography:  individual male’s inbreeding levels were correlated with sperm abnormalities which could affect fertility, and the number of young per females had declined slightly over the study period.

Sex–differences in dispersal will also have an effect on mating systems; this will be discussed further in chapter 6 on the definition of populations and subpopulations.

Using mating systems as an early-warning indicator in conservation
It has been suggested that mating systems are particularly sensitive to threatening processes, and serve as early warning systems for the genetic effects of threatening processes ((Brown 1997)).  It is certainly true that an alteration to mating systems can have immediately visible effects on the genotypes of progeny arrays, but as with anything so sensitive, it must be remembered that the effect could be rapidly reversed in the next generation, so that detection of an altered mating system would have to be evaluated to determine whether there was an obvious cause of the alteration.  There need to be baseline studies in non threatened populations, or before the threat, and correlation with other data on the threat and changes to viability.  If the threat was likely to persist, then the knowledge that it had resulted in rapid genetic changes, and might persist, would lead to a strong recommendation for intervention.  In a number of plant species, habitat disturbance is associated with reduced levels of outcrossing and sometimes those that are outcrossed show a strong correlation of paternity (ie, all the offspring of one mother have the same father) ((Coates and Hamley 1999) (Richardson, Ayre et al. 2000) (Ayre, Whelan et al. 1994) (Sampson, Coates et al. 1996)).  Often the habitat disturbance has been one which adversely affects some aspect of the pollinator species’ biology, so an obvious conservation recommendation is to increase the pollinator population.

Another possible case where assessment of the mating system could be used as an early-warning indicator is the larger mouse-eared bat Myotis myotis (Petri, Paabo et al. 1997).  Genetic studies of mating showed that offspring in one roost were mostly fathered by males from another roost, suggesting strong movement and mixing.  Dispersal is notoriously difficult to measure by ecological methods (chapter 6), but if it were to be reduced in this species, it would be expected to disrupt mating patterns severely.  One way of monitoring this would be to assess the proportion of young who were fathered by residents of another roost.  As pointed out above, any change in a single year could be a transitory fluctuation, but in combination with other information on threats to dispersal, it could be a useful monitoring tool.

Assessing mating systems
The importance of mating systems to population biology in general has led to considerable interest in assessing them in a variety of species.  As a result of this feverish activity, many methods are available to conservation biologists who may wish to detect effects of threatening processes on mating systems.  In the study of lions mentioned above, it was shown that in all but one case, genetic data gave the same parentage assignments as extensive ecological data collected over many years.  This confirms that at least in this species, if field constraints prevent extensive and continuous observations, then genetic parentage assignment can be used (Gilbert, Packer et al. 1991; Packer, Pusey et al. 1991).  Conversely, in marine turtles, (Fitzsimmons, 1998) and many birds (Birkhead )  DNA fingerprinting produced results at odds with behavioural observation, so the use of genetic methods is an important component of assessing mating systems.

Genetic methods of assessing mating systems are best based upon genotyping of parents and offspring, preferably multiple offspring from each parent.  This is particularly easy to do in plants, where collection of leaf tissue and seeds from the same plant provides a sample parent and offspring.  Each combination of possible mating systems and population allele frequencies will lead to a particular expected array of genotypes in the offspring of a particular parent.  Often these arrays can be distinguished from one another, so the mating system can be inferred.  Any heritable information could possibly assist in parentage identification, but in practice, variants with simple inheritance are used.  Usually hypervariable codominant markers such as microsatellites are used, but information can be gained from other variants such as organelle DNA or chromosomal morphology, although the latter require samples of living tissue, then time-consuming culture ((Duffield, Wells et al. 1994))

The first and most obvious estimate of relatedness is parentage exclusion; this exclusion is based upon the knowledge that for an autosomal marker, a parent and offspring should share at least one allele at each locus.  Therefore, if a pair of individuals do not share alleles at any one locus, they could not be parent and offspring - we say we have "excluded" the possible parent.  Of course, this method assumes that the typing of the loci is perfect, as a single mistake could lead to erroneous exclusion of a parent; thus it is important to use loci that are easily scored, such as tetra–nucleotide microsatellites, or to use statistical methods which allow compensation for occasional mis–scoring (or mutations) ((Sancristobal and Chevalet 1997)).

Bilbies are a small marsupial with huge ears, which are being studied intensely because they are being translocated for conservation purposes.  There is a worry that in the small remaining populations, or in small new populations, inbreeding may occur, with adverse consequences described later in this book.  To check on the occurrence of inbreeding in wild bilbies, parentage exclusion was used to build pedigrees.  In a small remnnant wild population of bilbies, parental exclusions were made using nine microsatellite loci, as well as mitochondrial DNA variants and information such as relative age of individuals.  Possible parent–offspring pairs (dyads) were analysed, then dyads which were not excluded were assembled into all possible combinations of two parents and one offspring (triads); finally, triads were excluded if their genotypes were inconsistent with Mendelian inheritance of the markers.  This work resulted in construction of a pedigree containing 13 individuals (Fig 4.3)  ((Moritz, Heideman et al. 1997)); in this pedigree, the single father is obviously making a disproportionate contribution to the next generation.  If this pattern continued for many generations, it could result in inbreeding.

Fig 4.3  A pedigree of wild bilbies, based on parentage exclusion as described in text.  Four individuals could not be placed in the pedigree.  The dotted line connecting male no. 23 and female no. 13 indicates probable first-order relatedness (parent-offspring or full-sib), inferred from the high relatedness value (Figure 4.4) From (Moritz, Heideman et al. 1997)

Despite the obvious power of the parentage exclusion method, it is often the case that parentage cannot be decided by exclusion – firstly, there may be more than one non–excluded individual. Secondly, exclusion is not feasible for relationships other than parent–offspring which do not have such strict rules about allele sharing.  For example, the average pair of siblings are expected to share half their alleles, but it may happen that they share none or all their alleles, depending upon the chance of transmission of alleles to each of them from their parents.  So exclusion methods cannot be used to establish sib-sib relationships.  Where exclusion is not helpful for any reason, there are other methods for assessing genetic relationships, based on the probability that two individuals have a particular genetic relationship, given their genotypes. Box 4.4 gives some details of the procedures.  Each method has to be considerably adjusted to take account of “real life” data, where there may be misscored alleles (Boxes 3.1 and 3.2), and there may also not be complete sampling of parents (Neff, Repka et al. 2000; Neff, Repka et al. 2000).  Methods have been devised specifically to cope with incomplete sampling of parents ((Nielsen, Mattila et al. 2001)).  Additionally, parentage assignment is a more exacting task when siblings or other close relative compete for matings, because the genetic signatures of the two fathers will be so similar ((Brookfield 1995); (Nichols 1996);(Double, Cockburn et al. 1997)).  In a real situation, it will usually not be possible to know which of these potential biases afflict the study, so it is uncertain what is the best method for any data set.  The best solution is if a number of known parent-offspring relationships are used to validate the method before proceeding.  In tests on koalas and dolphins, neither the likelihood method (CERVUS) nor the NEWPAT method assigned all known parents correctly, even with relaxation of test criteria, which may lead to incorrect assignments in a wider sample (Sharkey, 2001 ms, Kruetzen, 2001, ms).  Rather than attempt to assign all parents correctly, it may be better to tests of other questions related to parentage, more tailored to the specific conservation question.  One such question is: “What is the chance that all the offspring in a particular group  are the progeny of the same male” (Amos, DADSHARE, unpubl).  Amos ((Amos 1993)) used molecular data to estimate the frequency with which pilot whales of different ages are accompanied by their mother, without assigning individual relationships.  Another question which arises often in plants is: “what has been the level of selfing and outcrossing in this plant population in the recent past”, and this may be addressed by within-population phylogenetic analysis of the time to common ancestry of alleles (Milligan, Leebensmack et al. 1994); this is an example of coalescent-based methods which are discussed further in Chapter 6.  A disadvantge of asking questions tailored to the particular species, is that a custom-made statistical analysis usually has to be developed.

Box 4.4  Probabilistic  parentage analysis in wild populations
The LOD or likelihood method is widely used in human parentage testing; this method is based on the idea that if the two individuals share an allele which is extremely rare in the general population, then it would be more likely that they are members of the same family than that they would be unrelated ((Meagher and Thompson 1986)).  A program has been written to allow use of this method on data from wild populations (CERVUS, (Krauss and Peakall 1998)). 

The LOD method recognises that for any pair of individuals, there are two possible origins for their genotypes: either the two individuals are unrelated and the genotypes occurred by chance sampling of alleles from the general population, or else the two individuals are related in some specific way (eg, parent–offspring), and the genotypes originate from a combination of Mendelian inheritance within the family unit, and random sampling of alleles from outside the family unit.  

Suppose two individuals are sampled from a population, one juvenile, and one old enough to be its parent  Adult:  Genotype a1 a2;  Juvenile:  Genotype a1 a3.
Two possible relationships are
Hypothesis 1 (Unrelated): the two individuals are unrelated
Since both individuals have been sampled at random from the population, The probability of obtaining the pair of genotypes is based on the allele proportions in the general population:  2P(a1)P(a2) x 2P(a1)P(a3)

Hypotyhesis 2 (Related):  the adult is the parent of the juvenile
The probability of obtaining the adult’s genotype by random sampling from the population is still the same: 2P(a1)P(a2).  However, according to this hypothesis, one of the juvenile’s allees is not randomly sampled from the population – it came from the adult.  The other allele would still be a random sample.  Under this hypothesis, the probability of the juvenile’s  genotype is based on Mendelian inheritance of one allele from the adult, with the other allele being randomly sampled from the general population:  0.5 x P(a3).

The next step is to evaluate which hypothesis is the most likely –unrelated (UU) or related (QQ)?  Obviously the probabilites of obtaining the genotypes under each hypothesis is a guide here –  if the genotypes are extremely unlikely to occur unless the two are parent and offspring, we would favour this explanation.  To formalise this calculation,we examine the ratio of the two probabilities or its log (LOD, (Meagher and Thompson 1986)) or bets, convert it  converted to a chi–square value ((Kruglyak 1996) ).  This chisquare value indicates whether the hypothesis of relationship  is significantly more likely to be the explanation for the results than the hypothesis of "unrelated".  This method can be applied to comparisons of any levels of relatedness, and to triads.  It is important to note that other hypotheses ar epossible, such as sib-sib relationships, which we have not considered here.

The likelihood of achieving significance in LOD analyses of genetic relationships depends heavily on the number of loci analysed,  the distribution of allele frequencies (even allele proportions are better), and the possible relationships that could be present.  Brookfield and Parkin ((Brookfield and Parkin 1993)) calculated the number of loci necessary to give any desired chance of discriminating between particular types of relationships.  In general, distant relationships, such as second cousins, are difficult to discriminate, but close genetic relationships can be discriminated by the analysis of approximately ten loci, each having five alleles in equal proportions; the method can even discriminate between relationships with quite similar genetic expectations, such as parent–offspring and sibling–sibling.

The accuracy of this work also depends on good estimates of the base population from which random alleles could be drawn.  Nichols ((Nichols 1996)) and  points out that the estimation procedure is seriously undermined if there is substructure in the population, especially if most of the possible parents are relatives.    

Blouin et al ((Blouin, Parsons et al. 1996)) devised a method which is similar to the likelihood approach as described above, but does not rely upon a knowledge of allele proportions in the population, and has similar statistical power to detect relationships.

Amos  has written another method similar to the likelihood method, that uses resampling statistics to test parentage assignments, instead of the LOD criterion (Newpat (Wilmer, Overall et al. 2000; Amos, Wilmer et al. 2001))
End box 4.4

Genetic assessment of reproduction and its variation
Demographic forecasting, and estimation of genetically effective population size (chapter 5) are both strongly influenced by variation in reproductive output between individuals, although only the effective size is strongly influenced by male reproduction.  Estimation of variation in reproduction requires us to identify parent–offspring pairs and sibling–sibling pairs, which is often difficult to estimate from ecological observation alone.  Genetic studies showed that grey seal males make much lower contributions to their colony than expected from behavioural observation, suggesting that unobserved matings occur at sea ((Wilmer, Overall et al. 2000; Amos, Wilmer et al. 2001)).  The reproductive success of individual grizzly bear males aws assayed by molecular studies, and showed that  only about half the males bred in an eighteen-year period, suggesting that a reduction in random loss of genetic variation could be achieved by management aimed at increasing mating opportunites in this polyandrous and polygynous species (Craighead, Paetkau et al. 1995).   In polar bears, molecular analysis showed that females can adopt cubs of other females, which has the potential to result in an underestimate of reproductive variation, and would have to be considered in forecasting (Lunn, Paetkau et al. 2000).

As well as estimating the amount of reproduction, a knowledge of its mode is vital for conservation.  The use of molecular and other studies showed that a rare shrub Halogadendron lucasii had only a handful of genetically different “genets”, despite the occurrence of many apparent individuals, or “ramets” within these, and a total of hundreads of plants over four known populations (Sydes and Peakall 1998).  The knowledge that reproduction is almost entirely vegetative in this species suggested that it was a higher priority to maintain all populations, rather than every shrub within a population, and also to search for populations which may contain sexually reproducing individuals, and thus may be capapble of producing more variation by recombination.

 4.4 Relatedness
Like individual identity and parentage, other genetic relationships often cannot be discerned without genetic studies. Relatedness methods use data from hypervariable loci to investigate whether a pair (or group) of individuals are more genetically similar than a pair drawn from the population at random.  On average, we expect family members to resemble one another more closely than unrelated individuals.  The proportion of alleles which are shared by the pair of individuals is compared with the proportion of alleles which are shared by the average pair of individuals in the population.  There are a number of ways this calculation can be performed, the simplest of which have no genetic basis and simply use genotypes as any other trait to group individuals by UPGMA – this method worked surprisingly well in one dataset ((Blouin, Parsons et al. 1996)).  

There are four general classes of methods which rely on the inheritance of codominant markers, and then each of these has a number of ways to combine information across loci, producing a wealth of strategies ((Van de Casteele, Galbusera et al. 2001)).  By analysis of four different datasets, Van de Casteele et al showed that there is no generally optimal method, so that as with parentage, it is best to compare methods by analysis of individuals of known relationship, in the study species.  Relatedness is expected to show a distribution around certain values: zero if the pair of individuals are no more closely related than the average pair of individuals in the population, +0.25 for half–sib pairs, +0.5 for full sibling pairs and parent–offspring pairs, and negative values if the pair are less closely related than chance expectations.  Note that these figures are only expectations; because of the chances in transmissions from one individual to another, the actual values could differ, so that relatedness values are not ideal for diagnosis, only for establishing trends in relationships. However, Figure 4.4 shows that in the pedigreed wild bilbies, relatedness estimates for individuals from a known pedigree showed close agreement with the expected values, as has been found also in other studies of larger populations.  Two sampling and statistical notes are important.  Since this method uses allele proportions from the general population, there must be adequate sampling to give an accurate measure of these proportions (Box 4.1).  Also, significance testing for relatedness measures is complicated by the fact that there is repeated use of the same data – the genotype of each individual is compared with each other genotype in the population, which is unacceptable for many statistical tests.  Therefore, relatedness values are usually tested by permutation methods, which often must be written specially for each situation.
Fig 4.4  Relatedness values calculated from microsatellite data in Bilbies, for different classes of relatives, as in ferred from parentage exclusion (Figure 4.3).  From (Moritz, Heideman et al. 1997)

Dispersal and relatedness in hairy-nosed wombats.

The northern hairy-nosed wombat Lasiorhinus krefftii, is a large herbivorous burrowing marsupial in semi-arid Australia, sometimes referred to as the worlds most endangered mammal.  The one remaining population at Epping Forest numbers approximately 70, but possibly dipped to only about 25 individuals before cattle were removed from the area. The predominance of non-native grasses may be stressing the population, since the available habitat appears underutilised, and the recruitment rate is low.  Thus it is possible that there has been a recent change in the mating system and/or dispersal within the population, but these are difficult to study in a nocturnal underground animal.  However, a microsatellite study of relatedness has suggested that females tend to be found in the same burrow cluster as related females, more often than expected by chance alone; the same pattern holds for males, but not for different-sex pairs of individuals (Figure 4.5).  The importance of this for mating patterns is not understood, nor is it known whether this pattern occurred in the pre-decline population or in current populations of the southern species.  Continued genetic monitoring in the future would allow us to tell whether this pattern becomes more or less pronounced, and coupled with parentage analysis, would show us whether it affects mating patterns.  These data would also allow us to track reproductive output of individuals, and any lineage extinction, to gauge the effectiveness of habitat improvement..  Given the close management which is occurring in this population, and the possibility that some social groups will be moved to captivity as a backstop for this tiny population, a knowledge of how relatives behave will help choice of social groups should be moved to captivity – perhaps a pair of same-sex sibs would be a good start.  

Figure 4.5  The relatedness of male northern hairy–nosed wombats in a sample which contained virtually all living members of the species at the time (Taylor et al, 1994).  The data is presented as the frequency differential: the proportion of “same burrow cluster” wombats in a given relatedness class, minus that in wombats of different burrow clusters.  A value above the zero line indicates an excess of “same-burrow-cluster” pairs in that relatedness class.  The actual number of excess or deficit pairs is shown. From (Taylor, Horsup et al. 1997)

FREQUENCY
DIFFERENTIAL

                                                             RELATEDNESS VALUE
Relatedness and conservation
Do the examples above point to a more general utility of relatedness studies in conservation? There is some potential for this, but little data that directly shows levels of relatedness may cause low population viability.  If local groupings are genetic relatives, then threatening processes which remove whole local groups may have a greater effect on the genetic diversity, and therefore persistence of the population, than loss of a few individuals from each group.  A population which is vulnerable to this process is the European wild rabbits Oryctolagus cuniculus, in East Anglia, where genetic data suggest that breeding groups are genetically distinct from one another, and connected mainly by male gene flow ((Surridge, Ibrahim et al. 1999)).  If females are reluctant to disperse even into an area vacated  by extinction of a lineage, then there may be adverse effects not only on the level of genetic variation, but also directly on demography.  There is however, no evidence that this happens, and so any use of this idea in conservation would have to be preceded by considerable research.

If helpers who assist with raising offspring are relatives, their loss through threatening processes, or through removal for captive breeding, maybe more difficult to redress than if helpers are unrelated.  Whether helpers are indeed relatives needs to checked, since they are not always so ((Haydock, Koenig et al. 2001)).

Perhaps the greatest potential for conservation implications of relatedness is the use of relatedness as a determinant of dispersal.  In wild dogs, dispersal events frequently occur when pack dominance hierarchy changes, and dispersers favour areas with a high proportion of close relatives ((Girman, Mills et al. 1997)).  In the ant Myrmica tahoensis, workers control whether larvae develop as sexual breeder or not; they do not allow this when the colony has relatedness levels below about 0.6 ((Evans 1995).  In the dogs and the ants, loss of genetic variation for other reasons may result in changed dispersal and reproductive behaviour, respectively, though there is no evidence that such a process is occurring, nor leading to reduced population viability.

4.5 Structure:  mixtures, neighbourhood size, assignment.

All of the above discussion pre-supposes that we are dealing with a single population.  Allele proportions are used in estimation of discrimination errors, and calculation of relatednesses, and lod-scores for possible parent-offspring combinations.  Each time it is assumed that there is a random-mating population.   However, there are several reasons why this might not be so, and it can have affect the calculations by orders of magnitude ((Nichols and Balding 1991)).  Firstly, there may be a mixture of two or more populations which interbreed poorly or not at all (as in the witch flounder example used to introduce this chapter).  Secondly, there may have been recent addition of individuals from another population – something which is quite likely in the disturbed areas studied by conservation biologists.  Finally, even if the population’s distribution is homogeneous and undisturbed, the area studied may be so large relative to the lifetime mobility of the species that there is some opportunity for differentiation between different areas; thus the population would be best considered as a series of overlapping “neighbourhoods” which are essentially random mating ((Rousset 2000)).  A number of methods discussed in this chapter are relevant here.  

Quite simple genetic methods can suggest some population structure.  A deficit of heterozygotes relative to random mating expectations (Ho<He, or Fis positive, chapter 2) can be the result of sampling a mix of more than one population.  There are other explanations for such a departure, such as inbreeding (chapter 10), so  a heterozygote deficit should be followed up by studies targeted to hypotheses of admixture.  

If a neighbourhood structure is suspected, physical tagging studies can be used to estimate dispersal, or the same can be done using individual identification by genetic methods ((Palsboll 1999)).  The dispersal estimates can then be used to calculate the neighbourhood size.  An alternative method is based directly on the probability of identity of alleles at different separations  ((Rousset 2000)).

Use of markers which are inherited differently in the two sexes (Y- or W-linked DNA, mitochondrial or chloroplast DNA) can aid in understanding whether two two sexes disperse differently, both at small scales within local populations ((Piertney, MacColl et al. 2000)), and over larger scales, which is discussed in Chapter 6.

4.6  Are genetic studies of  individuals, their interactions, and reproduction vital for conservation?

These studies are in their infancy, and their full potential has not been explored in general field biology.  There has been little work examining their connection to population viability and adaptability, so further research is needed, targeting possible conservation applications.

Even the simplest conservation actions such as a population census can be seriously hampered by incorrect diagnosis of individual sex and identity, since the number of individuals, and their potential to evolove, could be seriously underestimated without genetic studies.   

Many organisms have mixed reproductive strategies, and these are routinely incorporated in demographic forecasting models.  Genetic studies can sometimes help identify the relative contributions of different types of reproduction.

More intensive actions such as translocations to wild or captive populations may require detailed attention to family relationships or mating system; if these are run as management experiments, it would be possible to evaluate the effects of differing levels of relatedness.

Molecular methods can be used for non-invasive analysis of wild populations, in concert with other low-invasion or remote methods, to allow  population censuses, monitoring of sex ratio and tracking individual movements. However physical recaptures remain necessary to test error rates and provide essential information such as condition, growth rates etc.

Molecular methods have some power to give species identity, and other information, and this area will develop further.

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