DRAFT – do not quote or distribute
22 Oct 2001
Chapter 3:  GENETIC ANALYSIS OF FITNESS
Bill Sherwin and Craig Moritz
SUMMARY:
•	One important aspect of conservation genetics is to retain genetic variants that assist in continued evolution of species in their changing environment and ecological roles. This should direct choice of appropriate genes or traits for management and monitoring. The most appropriate traits are of course, those whose genetic variation will affect lifetime fitness in the short-or long-term future, in a changing environment.  Although it is difficult to specify exactly which loci are appropriate, some generalizations can be made.  First, fitness traits tend to be multi-factorial – affected by multiple loci and the environment.

•	Genetic architecture is the way in which multifactorial traits are controlled.  This includes the number of underlying loci, their physical arrangement (ploidy, linkages), their biochemical interactions (regulatory, protein-protein, and environment-gene), and the resulting pattern of additive and non-additive interactions such as dominance and epistasis.  The genetic architecture is far from static, since it can alter in response to mutation at any of the contributing loci, recombination, environmental changes, and random genetic changes.  Each of these can have substantial effects on multifactorial traits, especially under conditions that threatened species are likely to experience, such as periods of environmental stress or small population size.

•	Natural selection can appear to act at different levels from the individual locus through to complex traits such as disease resistance and reproductive output.  The apparent type and direction of selection may differ at these levels, even in the same system of interacting loci.  There can also be selection for traits to be more or less sensitive to variation at other loci or in the environment. Limited studies suggest that there is poor correlation between variation of different types, so the choice of appropriate suites of surrogates at different levels is an important direction for conservation genetics research.

•	Ideally, conservation managers would monitor and manage the genetic variance of lifetime fitness, but this strikes three problems: first, it is difficult to analyze multifactorial genetic variation in wild populations; second, it is difficult to analyze total fitness, and finally the analysis would be restricted to the current population.  The first problem may be alleviated somewhat by use of molecular techniques, although the current methods are restricted to populations with an extremely wide range of relatedness values; better methods may come from molecular reconstruction of wild pedigrees

•	In the absence of a generally applicable way of measuring fitness and its genetic variation, there is a need for judicious choice of a suite of traits and loci to monitor, as representatives of the types of selection-related variation to be managed.  There have been various suggestions about how to identify specific traits or loci for monitoring, but none have been validated. Maybe traits under strong selection could be identified by their insensitivity to environmental variation.  It would also be possible to monitor variation in suites of single loci chosen for reasons such as their known selective importance or significance in metabolic networks 

•	Heritability is important for short-term response to selection, but may be very low for fitness related traits.  Additive genetic variance or the related measure called evolvability is what conservation genetics strives to maintain, and is often high for fitness related traits, so monitoring and maintenance is a worthwhile exercise.  These measures do not generally show good correlation with molecular measures of variation.

3.1  Natural selection and conservation

Sticklebacks (Gasterosteus spp) are small fish that live in lakes in North America.  Two closely related, hybridizing taxa prefer benthic and open habitat respectively ({Schluter, 1996 #457}).  When individual fish were forced to live benthically or in open water, the growth rate reflected their origin (Figure 3.1a, {Schluter, 1996 #785}).  While this is not surprising from a short-term point of view, it raises the question:  “Since these taxa can hybridize, why don’t we see the emergence of a species with genes for high growth rate in BOTH habitats?”.  It is likely that such a species would out-compete both of the other taxa.  Or to put the question more generally: What are the limits to adaptation, and what role does genetic variation play in this?  A full answer to that question would tell us everything that we need to know as conservation geneticists.  This chapter will review some of the knowledge that we already have, and its implications for conservation.  

Figure 3.1 (a)  Mean growth rate (+ SE) of fish from open water (limnetics) and fish from the benthos, and their hybrids, in enclosures in open water or in the littoral zone. {Schluter, 1996 #785}

0

20

Littoral                      Open

Figure 3.1 (b)  This is the same data as Figure 3.1 (a) re-drawn to emphasise differences between benthic and limnetic (dashed line) genotypes in the same habitat

Surprisingly, a great deal of conservation genetics work focuses on genes that appear unrelated to adaptation.  Conservation genetics is sometimes accused of being schizophrenic in its treatment of genetic variation {Hedrick, 1996 #25}.  On the one hand, the field is built upon the assertion that higher levels of genetic variation allows adaptation to prevailing environment(s) and therefore reducing the chance of extinction of populations (Chapters 9&10).  Genetic variants that affect fitness in a particular environment are said to be subject to natural selection (Box 3.1).  Essentially, genetic diversity matters for conservation because of selection.

Box 3.1 Natural selection

Natural selection occurs when there are genetic variants that result in phenotypic differences between individuals, and these differences result in differences in survival or reproduction in the particular environment({Hartl, 1997 #762; Hedrick, 2000 #761}).  (Survival and reproduction are sometimes combined together and called “fitness”).  A natural consequence of the higher fitness of individuals carrying certain genetic variant is that these variants will increase in number relative to others.  This process is called natural selection, and it generally results in improved adaptation to the current local environment.

Natural selection can only occur if the variants have some genetic basis.  For example, the figure below shows a population in which short individuals have short offspring, because they carry certain alleles.  If the short individuals are less able to escape predators, then the frequency of short individuals will go down each generation, as short individuals are eaten before they can pass on the allele for shortness.  In the illustration below there are initially an equal number of tall and short individuals, but 2/3 of the short individuals die before they reproduce, compared to only 1/3 of the tall individuals.  As a result, after each survivor reproduces twice, there are more tall individuals than short.  The same result would occur if the “doomed” individuals stayed alive but did not reproduce.

As a result of the selection, the population is now better adapted to the predator-filled environment, being composed mostly of tall individuals who escape well.
 Note that the effectiveness of the selection depends entirely on the presence of genetic variation for the trait concerned:  if there were no alleles for shortness and tallness, then the selective predation would not affect the number of short and tall individuals next generation.

 Thus little response to selection would occur, and the population would not show adaptation to the selective predation by gradually becoming taller. This is because the height of the prey is  non-genetic and so the selected tall parents are no more likely to produce tall offspring than the short parents.

We say that individuals which have greater survival or reproduction have higher fitness.  Fitness is often expressed as total reproductive output over some time period, such as an individual’s lifetime.  Often we can only measure components of fitness, such as survival per year, or reproduction per year.  Where fitness is genetically determined, there can be natural selection.  Fitness can also be determined non-genetically, in which case natural selection cannot occur, despite selective death or reproductive failure.  In the case above, non-genetic variation in fitness could derive from  environmental variation, such as greater growth in individuals that find more food, or by chance during development.

There are many types of natural selection known and detected ({Endler, 1986 #77;), and an even greater a wealth of names for different types.  In some cases these names reflect different processes which the conservation manager might strive to maintain, but in other cases, they do not.  The typeshown in the first diagram above, which favours one allele at the expense of another, is called directional selection, so-called because of the gradual loss of one allele (eg, for shortness).  Some authors grace directional selection with other names such as positive (referring to the favoured allele) or negative or purifying (referring to the disadvantageous allele); these are simply names for different aspects of the same directional selection process, since selection can only occur relative to other genotypes.  Rather confusingly, ”positive selection”  is also sometimes used to refer to balancing selection.

In a single-locus case, selection can be expressed as the relative fitness (eg, proportions of individuals that survive) of the different genotypes.  The table below shows fitnesses for directional  selection on a diploid locus.  The selection coefficient “s” shows the additional deaths, relative to the fittest genotype (Z1 Z1); “h” expresses any dominance – if h is zero, then Z2 is only a disadvantage in the homozygote, not in the heterozygote.

	GENOTYPE	
	Z1 Z1	Z1 Z2	Z2 Z2	
Directional selection	1	1-hs	1-s	

 Balancing selection is qualitatively different from directional selection, in that it acts to maintain variation; there are several possible processes that can do this, and they each have a separate name.  Three well-known sub-types of balancing selection are overdominance in which heteroygotes are at a selective advantage to homozygotes,  frequency-dependent selection in which rare alleles are at an advantage, and patchy selection which varies spatially or temporally  (whether the latter is classified as balancing selection depends upon the scale of observation) .

The table below shows fitnesses for overdominant balancing selection on a diploid locus.  The selection coefficients s1  and s2 express the reduced fitness of the two extreme genotype (homozygotes) relative to the heterozygote

	GENOTYPE	
	Z1 Z1	Z1 Z2	Z2 Z2	
Overdominant balancing selection	1-s1	1	1-s2	

One of the earliest examples of overdominance in a natural populations was human Sickle-cell anaemia in malarial areas.  This disorder is caused by homozygosity for a variant beta-globin chain (HbBs/HbBs); in the absence of medical intervention, affected individuals have a shortened life-span because their haemoglobin has a tendency to form long crystals inside the red cells, which then assume a sickle shape and block capillaries.  On the other hand, homozygotes for the common allele worldwide (HbBa/HbBa) not infrequently die from the effects of malaria.  In malarial areas, heterozygotes (HbBs/HbBa) have the highest survival rate, only rarely suffering from malformed red cells, but having enhanced resistance to malarial infection, because any parasitised cells sickle and are broken down.  There are few examples of overdominance at single loci, possibly because of the difficulty of measuring selection in natural populations ({Curtsinger, 1994 #842}), but many more examples in loci of small effect underlying quantitative traits ({Falconer, 1996 #91}).

 The following figure shows single-locus selection coefficients from {Endler, 1986 #77}.

End Box 3.1

On the other hand, most models of loss of genetic variation in small or fragmented populations are based on what is called neutral variation {see Box 2.*} - genetic variants which do not affect reproduction or survival and are therefore not under selection ({Franklin, 1980 #68} {Falconer, 1996 #91}).  Additionally, most experimental investigations in conservation genetics use marker genes that are likely to be neutral, and thus may be poor representatives of the selective variants that we aim to conserve.  Unfortunately, during events such as population crashes, selective variation is likely to respond differently to neutral variants.  This will be discussed further in Chapters 9 and 10, but the difference in response is for two reasons - firstly because of the action of selection during and after the crash, and secondly because of other differences between the loci.  For example, relative to highly mutable markers such as microsatellites, other neutral loci with lower mutation rates are expected to have less alleles.   As well as this, loci under balancing or directional selection may have more or less variants than neutral expectations, respectively.  Finally, the markers are usually a set of independent loci, whereas traits such as tallness or reproductive success are often the outcome of complex interactions between many loci and environmental effects on development.

There are several ways out of this dilemma:

(1) We can recognise that some allelic variants may have very small differences in fitness, the selection coefficient, “s”.  Genetic variants with small selective coefficients may be reasonably modelled by neutral methods.  Ohta {Ohta, 1973 #739} suggested that selected variants may behave as if neutral when s < 1/(2Ne), where Ne is the effective population size (see Ch5).  However, variants with higher selective coefficients would not be reasonably approximated by neutral models except in very small populations, and yet these are likely to include many of the most important variants to be conserved in a management program.  

(2)  We can suppose that markers may be neutral for much of the time and only of selective importance in particular environments (Thoday) {Thoday, 1962 #740}, in which case neutral models could be a reasonable description of their behaviour during a population crash.  This explanation is probably true in some cases, but there may also be cases in which the sporadic selection is sufficient to prevent neutral behaviour of the variants, especially if harsh conditions which result in selection coincide with times of population crashes, as seemed to be the case in Keller’s (199*) work on sparrows, where selection against homozygous individuals was particularly intense after a fire.

(3)  We can investigate the behaviour of selected traits and loci in large, declining, and small populations, and see in what ways this differs from neutral loci.  This option seems so sensible that one wonders why it is not done more often, until one realises that selection does not act directly on alleles or loci - it acts on the phenotypes that they produce, such as the ability to withstand different temperatures.  Each individual has its phenotype formed by the interaction of one or more loci and the environment; an individual whose phenotype is better matched to the current local environment will survive or reproduce better than those with alternative alleles.  To work on selected loci we must make a connection between allelic variants, their phenotypic effects, and the consequences for survival and reproduction; this is usually time-consuming and impractical.  

In this chapter, we will first review the evidence that some single loci may have a major effect on fitness, and survey methods for identifying these loci.  We will then take the opposite view, and see how traits such as survival, or fitness itself, can be analysed genetically, to establish whether there is genetic variation for the trait, or even identify the loci responsible, called “quantitative trait loci”, or “QTLs”.  With this background, we will then explore the ways that organisms can respond to environmental change.  The environment has two avenues of influence, since it can both affect the way that a trait is expressed (“plasticity”), as well as affecting the fitness of individuals that express the trait.  The major task for a conservation geneticist is to identify useful indicators of the ability to respond to environmental change, and the second half of the chapter will be devoted to this quest, first for single populations, and then for subdivided populations.

3.2 Selection on single loci
The explosion of molecular data has provided many examples of correlations between environmental factors and variation at one or more loci ({Endler, 1986 #77}{Mitton, 1998 #741}).   Some of these associations may be due to chance, but others appear to indicate the importance of selection on either the locus itself or tightly linked loci.  To predict evolutionary responses to constsant or changing selective environments requires not only detection of selection, but also the much more demanding task of measuring selection intensity.  However, a number of studies have demonstrated and measured selection, for both single-locus, and multilocus cases (See Box 1 and {Endler, 1986 #77}).  Suggestive data can come from patterns such as the distribution of allelic types within population, or between ppulations or species.  Establishing that genetic variants are under selection requires data on the physiology and genetics of different phenotypes, and their response to particular environments, in the short-term and as lifetime fitness.  Very strong confirmation, and accurate estimation of selection coefficients, can come from experimental manipulations of gene proportions, but only a few such studies have been performed in natural populations, because of the workload involved {Mckenzie, 1994. #109}.

An example of directional selection is seen in the fish Fundulus heteroclitus: water temperature appears to put strong directional selection on genotypes coding for variants of the enzyme Lactate Dehydrogenase, which is a vital part of energy metabolism ({Powers, 1978 #853} {Place, 1984 #852}).  Genotypes with a higher number of LDH-a alleles have higher enzymic activity at higher temperatures, while the reverse is true at lower temperatures (Figure 3.2a).  Since LDH is a vital part of metabolism, and poikilothermic fish immersed in water cannot escape the prevailing temperatures, it seems reasonable that this difference in activity explains the dramatic difference seen between LDH-a allele proportions at different latitudes (Figure 3.2b).  In hotter southern waters, LDH-a/LDH-a genotypes are more common, while the reverse is true in cooler northern waters, creating a cline of allele proportions.  In the centre of the range, LDH-a is at an intermediate proportion.  This could be due to dispersal from each end of the cline, or it could result from balancing selection at the centre of the cline due to local and temporal fluctuations in water temperature – patchy selection.  

Figure 3.2  Selection in killifish, Fundulus heteroclitus.  (a) Effect of temperature on Lactate dehydrogenase activity in fish with three different genotypes.  (b)  Latitudinal cline of LDH-a proportion  ({Powers, 1978 #853} {Place, 1984 #852}).
(a) 

(b)

 
An African finch provides an example of patchy selection: environmental variation in space or time favouring different genotypes.  These finches have a genetic polymorphism for bill morphology, which enables members of the different morphotypes to feed successfully on different sedges (hard and soft-seeded) {Smith, 1993 #59}, {Skulason, 1995 #58}.  

Overdominant balancing selection can be seen in wild populations.  The enzyme GPI (glucose-phosphate isomerase) is highly polymorphic in many species; in Colias butterflies ({Watt, 1983 #71}) there is evidence that this is due to overdominance.  GPI heterozygotes show higher enzymic activity than either homozygote; population studies suggest that the heterozygotes have higher survival than homozygotes, possibly as a result of this higher activity.

A more general example, which may involve three forms of balancing selection (overdominant, frequency-dependent, and patchy) is the major histocompatibility (MHC) loci, which produce proteins involved in binding antigens of foreign origin, such as portions of invading pathogens. High levels of polymorphism at MHC loci are very widespread {Hedrick, 1994 #158}, with a few exceptions (Mole rats Spalax, {Nevo, 1992 #856}; Moose Alces {Ellegren, 1996 #855} and elephant seals {Slade, 1992 #160}).  A high proportion of the differences between the MHC alleles are in the region that codes for the antigen-recognition site, and a high proportion of these substitions alter the amino acids (ie, they are non-synonymous substitutions) {Hughes, 1990 #70}.  This suggests that there are selective forces maintaining non-synonymous variation, while synonymous neutral substitutions are gradually lost by chance events.  The selective force is probably the diverse array of antigens seen on pathogens: an individual which is heterozygous at antigen-binding sites may be able to bind antigens from more types of pathogen, and would therefore be expected to be more resistant to disease, and thus have a higher life expectancy and reproductive output, resulting in overdominance.  Other benefits to heterozygous individuals include the possibility that heterozygous human fetuses may survive to term better than homozygotes ({Hedrick, 1988 #121}).     

Frequency-dependence may occur in MHC for two reasons, firstly because parasites which are good at infesting the most common host genotype will themselves be common, so that hosts with rare MHC genotypes may be at an advantage. Secondly, in laboratory mice; pairs of individuals who are mis-matched for MHC genotype are more likely to mate than those with matched genotypes, thus if a genotype became rare, it would also become a very sought-after mating type, and so its frequency would increase in the population ({Yamazaki, 1976 #120}); however, there are conflicting results concerning MHC and mate choice in other species {Edwards, 1998 #752}.

 It is also envisaged that MHC may experience fluctuating selection, due to an evolutionary arms race between pathogens and hosts.  The pattern of unusually frequent amino-acid replacements seen in MHC loci is paralleled in the pathogens by similar replacements between alleles at loci coding for antigens that MHC proteins recognize and attack.  The variation in the parasite genes is quite likely the result of fluctuating fitness of the pathogens as the frequency of different host genotypes fluctuates {Smith, 1995 #54}. However, the various chains of cause and effect postulated for MHC is not conclusively established, even for these intensively studied genes (O’Brien and Evermann {O'Brien, 1988 #88}{O'Brien, 1988 #52};  {Edwards, 1998 #752};{Penn, 1999 #746}). 

Selection is not confined to diploid genes or organisms. A huge variety of organisms are haploid, or have some haploid genes, such as those in mitochondria, chloroplasts, and Y or W chromosomes.  Haploid genes may respond differently to diploids, because a disadvantageous allele can never be sheltered from selection by a dominant allele in a heterozygote.  Thus directional selection against disadvantageous alleles will be more efficient than in diploids, so the allelic variants may not persist for such extended periods. A review of twelve studies of haploid bryophyte populations found no evidence for selection {Stenoien, 1999 #750}, but other studies of haploids show evidence of selection. Mitochondrial DNA shows evidence of past directional selection in its distribution of variation within and between species, as well as evidence of the existence of currently mildly deleterious mutations ({Ballard, 1995 #742}; {Rand, 1996 #56}; {Fry, 1999 #749}; {Nielsen, 1999 #260}).  Although overdominance is not possible without heterozygotes, other forms of balancing selection can occur in haploids; for example, there is evidence of balancing selection in the mating-system loci of the fungus Coprinus cinereus, presumably due to rare-mate advantage ({May, 1999 #221}).

In order to manage the process of selection, or the availablility of the genetic variation which is the raw material for this process, is important to know how often loci are under selection.  There has recently been an enormous expansion in the range of data and tests available to detect loci under selection (Table 3.1).  As the content of DNA databases increases, these methods could be used more commonly ({Wayne, 1998 #745};{Sherwin, 2000 #743}).  Some attempts at broadscale surveys for genes under selection have been successful {Endo, 1996 #27}.  Each of the tests has different sensitivity to the window of sequence analysed, timescale of selection, type of selection, recombination rate, and population expansion and subdivision ({Manly, 1985 #775}; {Endler, 1986 #77}; {Simonsen, 1995 #195}; {Endo, 1996 #27}; {McDonald, 1996 #59}; {Fu, 1993 #60}; {Kelly, 1997 #194}; {Munte, 1997 #193}; {Wayne, 1998 #745}).  Also, the same locus can be under different selective pressures in different populations of the same species ({Hamblin, 1997 #196}).  Given all these provisos, and the low power of many of the tests, considerable strength and/or long-term consistency of selection is necessary to produce consistently detectable signals in two or more of the tests from Table 3.1.  Therefore, this approach favours the detection of individual loci whose variation is likely to be most important for fitness and adaptation.

The bias towards detection of loci under very strong consistent selection has both drawbacks and advantages for conservation purposes.  For attempts to assess the general importance of selection in maintenance of genetic variation, it is a serious shortcoming.  On the other hand, this bias directs us to precisely those loci and alleles of greatest relevance to population viability: genes that are under strong and consistent selection.  Additionally, many of the detection methods bias us towards variants that have been significant over long periods of time, and thus are part of the historical component of diversity, unlikely to be readily replaced, and therefore of high conservation significance.  Later in this chapter, we will discuss possibilities for monitoring of such loci.

Table 3.1  Examples of tests for loci under selection.  None of these methods has high power, although the current estimates can be made powerful if extensive studies are done.
CRITERION
	Sensitivity to selection
at different times	REFERENCE	
Heterozygote excess/deficit,
	Current	{Hedrick, 1985 #69}; {Huttley, 2000 #747}	
Allele frequency distributions	Recent	{Lewontin, 1973 #774}; {Ewens, 1979 #189}; {Tajima, 1989 #57}; {Rand, 1996 #56}; {Hartl, 1989 #72}
	
Number of common alleles p>>0.05
	Recent	{Ewens, 1979 #189}; {Hartl, 1989 #72}	
Multilocus disequilibrium	Recent	{Huttley, 2000 #747}	
Recovery of frequencies from perturbation
	Current	{Mckenzie, 1994. #109}	
Family data
	Current	{Hedrick, 1985 #69}	
Enzyme activity studies
	Current, but selective effect in wild must be confirmed by other studies	{Watt, 1985 #36}	
Ratio of neutral to synonymous nucleotide substitution within one population
	Historical	{Muse, 1996 #49}
	
Ratio of neutral to synonymous nucleotide substitution between 
different taxa
	Historical	{McDonald, 1991 #65}; {Muse, 1996 #49}
	
Comparison of levels of polymorphism within taxa to levels of variation between taxa or lineages (eg duplicated genes or major allelic variants)
	Historical	 {McDonald, 1996 #59}; {Fu, 1993 #60}; {May, 1999 #221}	

Most of the examples we have given infer selection from patterns seen in natural populations.  More conclusive results come from manipulations of the loci or the environment.  While these manipulations are usually done in the laboratory, there are a number of examples in the wild.  Sometimes these manipulations are done unintentionally: Nevo ({Nevo, 1992 #856}) studied barnacles in the cooling water of an Israeli power plant, and found a significant difference in allozyme frequencies between the intake and the warmer output, indicating that the heat caused directional selection in favour of certain allozymes.  After introduction of Drosophila subobscura into North America, latitudinal clines of allele proportions were established that paralleled those in the species’ native range, suggesting that theclines are maintained by selection ({Ayala, 1989 #857}; {Mestres, 1995 #858}).  In the sheep blowfly, Lucilia cuprina, use of insecticides led to selection for resitance alleles, and in this case it was possible to both track the build-up of resistance phenotypes in response to the selective agent (insectidice) and to identify a single amino acid replacement responsible for the resistance {Newcomb, 1997 #859}.

Deliberate manipulations of the genetics of wild populations are as rare as well-documented “accidental experiments”.  McKenzie and Batterham {McKenzie, 1994 #109} manipulated ADH allele proportions in a wild population of Drosophila melanogaster, and demonstrated the effects of balancing selection, which has been inferred from worldwide clines, and enzyme activities.  Arnason {Arnason, 1991 #753} performed similar experiments in the laboratory, and cautioned that the results may be due to linked loci rather than the loci themselves.  Even when detected, selection may have limited explanatory power:  frequency-dependent balancing selection has been confirmed in wheat, by creating plant populatins with different genotype proportions, and challenging them with stripe rust fungus{Brunet, 2000 #756}.  However, in some cases, apparent frequency dependence was observed without the fungus present, so it may not be the sole agent of selection, and the selection coefficients did not seem sufficiently strong to maintain the polymorphism observed in the plant populations.

Not only allele frequencies, but also the allele sequences and loci themselves, can be manipulated to investigate selection.  For adaptation, one important class of loci is those encoding heat-shock proteins, which can both alter their expression to allow phenotypically plastic responses to extreme temperatures, and be responsible for genetic differences in the ability to mount such a response  {Coleman, 1995 #652} {McKechnie, 1998 #122}).  Recent attempts to manipulate these genes has indicated their importance in adaptation, although there are concerns that the artificially modified loci may not be exactly analogous to natural loci ({Feder, 1999 #755}; {Tatar, 1999 #754}).

Having detected selection, it is important to ask whether the selection intensity is strong enough to affect population viability.  This is a crucial question for conservation biology.  In the short-term, will depressed fitness of some genotypes during the selection process be sufficiently large to affect the population viability?  The answer depends, amongst other things, on the intensity of the selection, and the effect on the population of such a loss or survival and reproduction.  If, for example, selection takes place through death of some juveniles, but this is followed by a period when only a fixed number of juveniles find permanent nest sites, etc, then there may be little demographic result, and the selection can proceed without short-term adverse effects, and possibly make a long-term improvement in adaptation.  This is particularly important in considering recovery from perturbations such as inbreeding or release of maladapted genotypes, as we will see in later chapters.  The other part of this question concerns evolvability: in the long-term, will selection be fast enough to allow adaptation to track environmental change and reduce extinction risk?  

It is not easy to estimate selection intensity in natural populations (Lewontin 1974), but some attempts are being made {Slatkin, 2000 #744}.  Box 1 shows a summary of some of those efforts; in cases where selection differentials have been measured in wild populations, they are often no more than 10% difference in fitness between genotypes (s=0.1).  Even much smaller selection coefficients are large enough to have a major effect over many generations, and can be very important for adaptation.  However, coefficients of this size will not have a large effect on short-term demography, unless the selection intensity varies, having periods when it is very intense, such as in the sparrows after fire ({Keller, 1994 #140}).  In this case, although the long-term average selection is low, at certain times, the short-term survival of the population could be strongly dependent upon the availability of certain genetic variants.  Attempts to estimate the rate of directional selection that can be tolerated by a population have been plagued by necessary generalizations, and it will be a long time (if ever) before reliable general answers can be given, although in specific cases, this can be investigated by perturbation experiments like those discussed above.

3.3   What does selection act on? – Quantitative or multifactorial traits, and Genetic Architecture
Multifactorial traits 
A focus on single loci is inadequate for conservation purposes.  Clearly variation at loci such as the MHC, which affect survival and reproduction, could be important for management, but to assess their importance, we need to know how many such loci are there, how they interact with other loci and the environment, and how many different characteristics do they affect.  Generally selection acts on the phenotype, such as a protein, a physiological tolerance, or a morphological characteristic.  We must recognise that many aspects of the phenotype have complex control.   For many phenotypic traits, development and maintenance are influenced by several different genetic loci and environmental factors.  For example, tolerance of Silene vulgaris plants to heavy metal pollution is controlled by at least two loci which affect one another’s expression ({Schat, 1996 #426}).  Such “multifactorial” control produces a smooth distribution as seen in Figure 3.3, and for this type of trait, we characterize variability by a mean and variance, rather than by assigning discrete states like “resistant” or “non-resistant” ({Falconer, 1996 #91}; {Hartl, 1997 #762} ;{Lynch, 1998 #92}; {Hedrick, 2000 #761}).Most characteristics that a wildlife manager cares about (eg, growth rate in fish {“Pop gen and fish Mgt book”}) show quantitative variation, and often a substantial part of this variation is genetic, so genetic mismanagement has the potential to adversely affect a species.

Even apparently binary traits may derive from the interaction of many loci.  The cricket Gryllus firmus shows two wing morphs, long and short.  This polymorphism appears to be associated with two sets of loci, one controlling production of the enzyme juvenile-hormone-esterase, and another set controlling secretion of the enzyme into the blood ({Zera, 1999 #767}).  In a review of insecticide resistance, McKenzie and Batterham ({McKenzie, 1994 #469}) have suggested that response to environmental changes within the normal range experienced by a population will often be polygenic, whereas single-locus responses may be more typical of populations experiencing large environmental changes; there have been criticisms of this view ({Groeters, 1995 #759}; {Tabashnik, 1995 #760}).  It is nevertheless vital to understand polygenic variation, since many important aspects of fitness and adaptation are polygenically controlled, and their reponse to selection may be different to traits controlled by single genes. To go further into this question, we need to know the genetic basis for characteristics that we care about in management, especially major fitness traits - survival and reproduction.  

Figure 3.3  A continuously distributed “quantitative” or “multifactorial” trait, such as the frequency of individuals with a certain growth rate

                                      Frequency

                                                                                             Mean
                                                                                                Standard deviation

Increased knowledge of the genome and its expression is revealing the complexity of the interactions between loci and their products, producing interaction maps like the yeast one shown in Figure 3.4.   Even in organisms without completely mapped genomes, we are getting much better at identifying how a particular trait is affected by particular chromosomal regions, called “QTL”s or “quantitative trait loci”.  Some phenotypic traits appear to be determined by such a large number of loci, with small effects ({Mitchell-Olds, 1995 #33}; in other cases, many of the QTLs have little effect, but there are one or a few loci which have a much greater effect on the variation of the trait (Shrimpton and Robertson 1988, Tanksley 1993) {Shrimpton, 1988 #42} {Tanksley, 1993 #43}; these loci are called major genes, and if they are discovered, it obviously simplifies the task of working on the quantitative traits.  It may well be that any QTL could be a major gene, given the right mutation ({Frankham, 1997 #517}; {Mackay, 1995 #782}).   In the long run, the “genes-up” approaches may lead to a complete understanding of the way total fitness is derived, but given the complexity of the interactions relative to the crudity of the analyses, this is unlikely to occur in the near future.  Note that for all its intricacy, the study summarised in Fig 3.4 did not consider allelic variation, or environmental effects.  To be able to add these to our thinking, we must use an even grosser simplification of biology than Figure 3.4.

Figure 3.4  Map of protein-protein interactions in the yeast proteome; shown here are 1,879 proteins (circles) and 2,240 identified physical interactions.  The harmful effect of deleting a particular protein is positively correlated with the number of interactions in which it takes part (r = 0.75, significance unreported).  After Jeong fig 1a ({Jeong, 2001 #330})

A complementary approach to the study of a quantitative trait such as growth rate or disease resistance is called the “traits-down” method, in which traits are studied under conditions of controlled genetic and environmental similarity or difference.  Like other genetic work, it is based on families - the stronger the genetic component of a characteristic, the more closely the relatives will resemble one another; for example, in many species, height is partly genetically determined, and tall individuals therefore tend to have tall relatives. One of the simplest investigations of quantitative traits is to relocate families or lineages between localities.  If trait differences are maintained when the groups are raised in the same environment, then there must be genetic factors acting;  this is sometimes called the  common garden experiment.  Figure 3.1(b) shows two such experiments: in a common environment  (eg, open) the benthic and limnetic fish showed different growth rates, indicating a genetic basis to the growth rate.  The experiment can also be done in a laboratory environment not closely related to the natural one, but when a pair of reciprocal transplants is done as in Figure 3.1(b), it makes particularly clear the genetic basis of the adaptation to the natural environments.  These experiments tell us that there is genetic control, as well as environmental effects, but more complex anlayses are necessary to discover the details of these.

 From appropriate data, we can work out how much of the trait’s variation in is caused by genetic variants, and what portion is not under genetic control, as well as investigating more complex interactions  (Table 3.1).  This information is adequate for many purposes, and will become more powerful as it merges with the genes-up approach.  The traits-down analysis obviously requires a great many simplifying assumptions, most of which are violated to some extent in every study.  Frequent assumptions include: random mating, no genetic linkage, absence of three-way or higher order interactions between genes or between genes and the environment, as well as the idea that each trait’s variation is influenced by alleles at a large number of loci which each have a very small effect.  It is now known that the latter assumption is not true, with the discovery that for each trait, the effects of individual QTLs range from major to minor ({Payne, 1918 #766}; {Lai, 1994 #764}).   Nevertheless, the method has proved very useful in the genetics of domestic and wild species. Also, each assumption can be deleted or relaxed as appropriate; for example, in conservation applications, non-random mating and QTLs of major effect may both be relevant.

Table 3.1  Some of the possible sources of variation in particular traits, shown as the effects that results in each component of the total phenotypic variance for the trait, with the symbol for the variance component (Vp, Vg, etc).  One trait can be affected by many different influences for example, although height is under strong genetic control in many species, it is also affected by nutrition, which  of course is an environmental factor  The contribution of each factor can be determined from an appropriately designed analysis of variance or regression analysis ({Falconer, 1996 #91}).  Depending upon the design of the investigation, subdivisions between the different influences can be made in different ways.  For example, sometimes Ve is used to denote all variance that is not due to additive genetic variation (Va); as long as the division is made clear, this can be a very useful analysis.  Measurement error is usually not estimated separately, but included in Ves.

f          Vp – Total phenotypic variation in the trait         g
	
Vg – Genetic effects
	Ve – Environmental effects	Vgxe

Interaction of genes and environment in expression of trait, e.g. the phenotypes produced by particular genotypes are altered, or even reversed, in different environments (see text)	Cov ge

Covariance of genes and environment, e.g. certain genotypes are more common in certain environments	
Va  -  Additive interactions
between alleles and loci	Vna  -  
Non-additive interactions between alleles and loci	Veg –
General environmental effects	Ves –
Micro-environmental effects particular to each individual, developmental noise, measurement error			
	Vd  -  
Non-additive interactions between alleles
(dominance)  	Vi -
Non-additive interactions between loci (epistasis)	Major environmental variation, e.g. different soil types in different experimental plots or natural regions	Parental effects not due to transmission of the parents’ genomes, e.g. effect of mother’s phenotype on nutrition, transmission of intracellular parasites	Between- individual variation	Within- individual variation between  measures of the trait at different times, or in different parts of the body, such as symmetrical or repeated structures			

 Additive genetic effects
Table 3.1 shows some of the ways that we can partition variance in a multifactorial trait.  At first, it may seem rather trivial to investigate the different sources of genetic variation shown in this table, but they actually have considerable significance for conservation genetics.  Additive genetic variance (Va), is the component of genetic variation that results in resemblance between relatives. In Box 3.1, we saw how this resemblance is vital for natural selection to occur.  The heritability, h2, is the proportion of the total variance which is due to additive genetic effects; h2 equals the ratio Va/Vp, varying from 0 to 1, where a high h2 means that offspring strongly resemble their parents (Figure 3.5).  Because of the importance of additive genetic variation in determining resemblance of relatives, h2 it is commonly used to predict the result of selection in agricultural species: if offspring tend to resemble their parents in some trait, then natural or artificial selection in favour of parents with particular values of that trait will result in offspring who are taller than average (Figure 3.6).  To put it another way, with a higher heritability, there will be a larger response to a given strength of selection.  Thus conservation of existing Va, and the rate at which mutation generates new Va, was one of the earliest considerations in conservation genetics ({Franklin, 1980 #68}). 

Figure 3.5  Resemblance of offspring and parents.  A trait was measured in offspring and plotted against the average value of the same trait in their of their parents.  The heritability can be estimated from the regression or correlation of relatives.  In the case of offspring plotted against mid-parent mean, h2 = b, the slope of the regression line; h2 varies from 0, when the offspring show no similarity to their parents (dotted line), to 1, when the average offspring has the same trait value as the mid-parent mean  (solid line) ({Falconer, 1996 #91}

Figure 3.6  The effect of additive genetic variation on selection on a multifactorial trait, temperature tolerance.  The bold curve shows the frequency of different phenotypes in the parental generation.  The asterisked area shows the individuals with high temperature tolerance selected as parents for the next generation (eg, by a hot spell in the wild, or artificial selection in the lab).  The heavy dashed line shows the selection differential, S, which is the difference between the mean tolerance of the parents and the mean of the population from which they were selected.  The dotted line shows the response to selection: the difference between the means of the original and offspring populations (called “R”).  The more strongly offspring resemble their parents, the larger the response will be;  in algebra, R = h2S, so that there is a larger response when Va, and therefore h2, is higher.    R and S can be measured in absolute units (oC in this case) or in standard deviations from the mean, as long as the standard deviation is the same in the parents and the offspring.

A major aim of conservation genetics is to ensure the continuation of both Va, and the mechanisms which naturally produce and maintain this variation.  Since high h2 is a consequence of high Va relative to other sources of variation, adaptation to current or changing conditions is heavily dependent upon genetic variants that are able to contribute additively to variation of fitness.  However, there are other ways of adapting to the environment: instead of having genes for heat-tolerance , an organism can simply avoid the midday sun.  Also, the amount of additive genetic variation (as a proportion of total variation) can be rather labile, especially under the unusual conditions that may prevail in threatened populations.  As a result, we will need to look carefully at the ways in which this and other types of genetic variation are produced and maintained, and their contribution to adaptation under different conditions, before proceeding to suggestions of appropriate monitoring.  Box 3.2 discusses some

Box 3.2 Physical nature of the variation 

The ability of a trait to evolve depends ultimately on the physical make-up of the genetic variation that underlies the trait.  The origin and behaviour of the genetic variation depend upon the number of QTLs, their physical arrangement in the genome, their interaction at any level from regulation of transcription to protein-protein interactions, and the types of random mutations that alter those parts of the genome.   As we have said before, many of these details can be ignored for conservation purposes, but some warrant attention.  

Genetic linkage occurs when two loci are so close on one chromosome that their alleles are often inherited together.  In this case, two important evolutionary consequences arise, both concerning a doubly heterozygous individual, with two chromosomes, such as: A1----B4  and  A4----B7. (these two different chromosomal regions are sometimes called haplotypes).  First, if loci A and B are both QTLs for the same trait, and A1 and B7 are alleles which increase the value of the trait, then an environment which exerts directional selection in favour of larger values of the trait will favour both haplotypes, thus giving the appearance of balancing selection, unless recombination frequently disrupts the haplotypes.  This and similar effects are sometimes called “associative overdominance” ({Arnason, 1991 #753}).  Thus the loci experience directional selection while the haplotype is under balancing selection.  This effect creates difficulties when tying to identify a particular set of genetic variants, under particular types of selection, for conservation monitoring (section 3.5).  A second way in which linkage can affect the response to selection is seen when the loci A and B are QTLs for two different traits.  In this case, selection for an increase in one trait (eg for A1) will select for chromosomes containing the A1----B4  haplotype, thus decreasing the frequency of B7 alleles, and thus reducing the average value of the second trait.  In other words, because of the genetic linkage, directional selection for an increase in one trait will result in selection for a decrease in another, but this decrease may be opposed by selection on the second trait.  Such correlated responses due to linkage of QTLs have been seen in Drosophila ({Nuzhdin, 1999 #484}).  This is one possible reason for limitations on a species’ ability to adapt to environmental variation, as seen in the sticklebacks, and will be discussed further below.

Because of the number of loci involved, and the importance of physical arrangement, a wide variety of mutations may affect multi-locus traits, ranging from single base substitutions or deletions, through recombination, transposition and rearrangement of DNA, to grosser chromosomal alterations.    Since the QTLs may include not only multiple protein coding regions, but also the (often much larger) DNA sequences which regulate expression of these regions, the target for mutation is very large indeed (Lynch and Walsh, 1998).   As a result, it appears that the generation of new variants proceeds at a higher rate in traits determined by multiple loci than in single-locus traits ({ Lande, 1975 #47; Franklin, 1980 #68; Lynch, 1988 #46}), although the rate varies between species, possibly as a result of generation length, genome size, or DNA repair efficiency ({Lynch, 1999 #491}).  Thus losses of genetic variation may be recovered faster in multi-locus systems than in other single-locus systems, resulting in different genetic management guidelines (chapter 10, Frankham, 1980).   This large target-size for mutation may be especially true for fitness-traits, which often sum the action of many loci – survival to a particular age summarises ability to withstand disease and temperature fluctuations, to find food, etc, most of which are probably polygenic, and therefore susceptible to mutation at many different loci ({Houle, 1992 #573} {Johnston, 1995 #674}).  However, it is important to remember that not all new mutants increase fitness – far from it.  In an experimental population of Drosophila, mutational increase in bristle number variance (Vp) occurred as a result of mutations at QTLs scattered across many chromosomes.  As a result of these mutations, Vp increased each generation at a rate of about 1000x Ve, but the response to selection was lower than predicted from the mutational variability available; this is probably because a high proportion if the variants lowered fitness, and were lost quickly ({Mackay, 1994 #810; Fry, 1995 #489}).  In a variety of organisms, the average new mutation seems to have a mildly deleterious effect on fitness (about 2% reduction as a heterozygote; {Lynch, 1999 #491}).  There were reports that the bacterium Escherichia coli was able to create new mutants that were adaptive for a stressful environment, but this is now known to be simply that the cells increase the general rate of mutation when stressed ({Sniegowski, 1995 #871}).  This elevated mutation could be simply an unavoidable consequence of the stress, or could be selected as a last-ditch adaptive trait, since a (small) proportion of the mutants are better able to cope with the stressor.  It is unclear whether such mechanisms are present outside microbes, although the opportunity certainly exists, especially in organisms with vegetative reproduction such as plants.  It is well known that branches of one tree, or suckers from the same root-stock, are not genetically identical, so it is possible that the level of variation could be under the same sort of control (or lack thereof) as in the bacteria.
End Box 3.2

Non-additive genetic effects
Studies of the metabolic interactions of gene products show that non-additive interactions within and between loci are very frequent (Kacser, 1989, Dean et al 1988){Kacser,  1989 #44}{Dean, 1988 #45}.  Dominance is an example of non-additivity of alleles at a single locus, and leads to surprises in the next generation of offspring, such as a double-recessive mutant phenotype being produced by two normal heterozygote parents.  These surprise offspring tend to blur the resemblance between relatives, in contrast to the straightforward inheritance of additive genetic variation, and this contribution to overall phenotypic variation is called Vd.  There may also be non-additive interactions between alleles at different loci, leading to interaction or epistatic variance (Vi).  For example, in humans, genetic variation in the sex chromosomes interferes with the expression of the baldness allele(s) on an autosome.  In heterozygotes for the baldness locus, the expression of the single baldness allele depends upon the hormonal status of the individual – XY males go bald, but XX females do not. This is an extreme example of epistasis, which usually is taken to include any non-additive interaction between loci

 The relative size of Va, Vd and Vi are not fixed, and can be altered both artificially, by changing the way the trait is perceived, and by various natural phenomena which are important  in conservation.  The way that a trait is examined by the scientist is called the “scale of measurement”, and the associated theory is called allometry ({Soule, 1982 #765} ;{Lynch, 1998 #92}).  For example, Figure 3.7 (a) shows a rather odd-looking lizard that has flattened disc-shaped legs, used for heat collection, and for lifting the lizard off the ground to reach food, etc.  The remainder of the body is invariant, but size of the legs varies.  This variation is genetically determined, but the dominance or additivity of the alleles depends upon the use we are considering for the legs – as part of the  heat collection system (for which area is most important) or standing up (for which linear dimensions are most important).  Thus our exact choice of trait makes a difference to the way the genetic variance appears to be apportioned.  What then is the “real” scale – linear or squared (area)?  This depends upon which trait is most relevant to our aim (conservation).  In general we do not know this, although total fitness is obviously very important!  

In later chapters, we will see that the occurrence of non-additive effects in multifactorial traits is fundamental to understanding the adverse effects which inbreeding often produces.  Can we extend the example of the lizard legs to a trait affected by many QTLs?  In this case, the phenotype depends upon the frequency of alleles at each QTL, and their interactions.  For a start, even a locus encoding a protein that is vital for the trait will be irrelevant to phenotypic variation if the population has no allelic variation; for example, hair is largely made of a keratin, but if there is no genetic variation at the keratin locus, then variation in the hair would be due to variation at other loci which modify the protein, so these loci would be identified as QTLs whereas the keratin locus would not be identified by conventional QTL analyses (Box 3.4).  In one Drosophila laboratory population, QTLs for the trait “bristle number” fell into two groups, which were identified in different experiments: some QTLs harbored alleles that conferred higher bristle number, which rose to higher frequency during selection for higher bristle number, while other QTLs were unresponsive to selection for higher bristle number, but had some alleles hat reduce lower bristle number, and were thus important for response to selection in that direction ({Nuzhdin, 1995 #487}).   Secondly, some loci may have a much greater effect on the trait than others (larger “a”, Fig 3.7b).  There is now known to be a wide range of effects: Figure 3.8 shows that some loci are of major effect, while there are many other loci with small effect.  Also, some loci may show dominance, while others do not (Figure 3.8) ({Mitchell-Olds, 1995 #33}).  If some loci show dominance for the larger value of the trait (positive “d” in Figure 3.7b), while others show dominance for smaller values (negative “d”), then the effects could cancel out, leaving no apparent dominance at the phenotypic level.  If dominance is in the same direction at most QTLs for a trait, then there would be an appreciable Vd for that trait.  Finally, there are a number of cases where the heterozygote is not intermediate to the two homozygotes (d>a, so d/a>1, Figures 3.7b, 3.8b), called “overdominance”, which again could lead to Vd for the trait ({Mitchell-Olds, 1995 #33}).  Finally, interactions between the proteins coded by the different loci may be non-additive, contributing to Vi;  some of the QTLs for bristle number in Drosophila are known to act epistatically ({Mackay, 1995 #782}).

The importance of these non-additive interactions to conservation depends upon their frequency, and how they effect fitness in different environments.  From the available data so far, the frequency of loci which show heterozygote advantage or disadvantage is quite high (Figure 3.8b).  The importance of heterozygote advantage for conservation biology rests on its tendency to maintain variation (Chapters 5 and 9) as well as its possible involvement with fitness losses in inbred individuals, and possible remedies for this loss (Chapter 10).  Heterozygote disadvantage is of critical importance to one of the most vexing questions in conservation biology: the likelihood of fertility or viability problems when crossing distantly related populations (Chapter 1).  Coyne ({Coyne, 1998 #805}) has pointed out that we are now able to identify and study the QTLs which result in these phenomena, at least in experimental situations.

Figure 3.7 (a).  Inheritance of leg size in a lizard.  Note that leg length is linearly proportional to the number of A2 alleles, giving exactly intermediate heterozygotes, while leg area is non-additive – heterozygotes do not have an exactly intermediate area.
Genotype	A1/A1	A1/A2	A2/A2		
Leg Length (cm)	1 	2	3	Additive	
Leg area (cm2)	1	4	9	Non-additive	

Fig 3.7 (b)  Lizard leg-length and leg-area expressed as additive and dominance effects, “a” and “d”.

(Length –2) in cm:
A1A1 _________A1A2___________A2A2
-1 ______________0______________+1
-a _____________0______________+a
where the additive effect, a =1

(Area –3) in cm2:
A1A1 ______A1A2______________A2A2
-4___________-1_________________+4
-a____    _____d_______    ________+a
Where the additive effect, a =4, and the dominance effect, d = -1, showing that the heterozygote is one cm2 smaller than the mean of the two homozygotes.

Figure 3.8 Addtitive and non-additive effects of QTLs  ({Falconer, 1996 #91})  (a)  Distribution of effects of QTLs affecting sternopleural bristle number on chromosome 3 in Drosophila melanogaster  The effect is given a multiples of the phenotypic standard deviation.  (b)  Distribution of degrees of dominance (d/a) among QTLs affecting vegetative traits in tomatoes.  Note that there are a number of cases of overdominance (d/a > 1 or d/a < -1).

(a)

(b)

Interaction between genes and the environment
The two components of variation which involve both genes and environment are often ignored, but are extremely important for adaptation and evolution, as well as understanding the response of populations to environmental change.  Vgxe is the variation due to interaction of genes and environment, such that the phenotypes associated with the different genotypes are not constant across all environments.  In extreme cases, genetic differences may only be apparent in certain environments, for example the expression of cadmium tolerance only when the pollutant is present ({Shirley, 1999 #862}). Another possibility is that the relative fitnesses of the phenotypes are reversed in certain environments, such as the reduced fitness of cadmium-tolerant lines in cadmium-free environments ({Shirley, 1999 #862}).  A single-locus example of this is seen in the fish Fundulus in Figure 3.2.  Vgxe represents the differential adaptation of particular genotypes to particular environments, and the ability of organisms to attain this is fundamental to adaptive radiation.  The other association between genes and environment, Cov ge, is seen when certain genotypes are more common in certain environments.  Aside from being an important contributor to overall phenotypic variation, some of the different genotypes in environmentally different regions or microhabitats may be involved in adaptation.  As a consequence, maintaining a species’ ability to generate this type of geographically structured variation is a major theme of conservation genetics.  Covariance between genes and environment can occur simply by chance in migration or mutation ({Fernandez, 1997 #819}), or they can occur through directed processes, such as the selective death of certain genotypes in a particular environment, or selective dispersal patterns.  Again, the fish LDH example (Figure 3.2) shows how Cov ge results in adaptation to the local environment (although we can only guess at the details of how this covariance came about).

Special Environmental Effects 
Table 3.2 lists a further twist to the control of multifactorial traits: Ves, the special environmental effects, which differ between or even within individuals.  In fact this is a miscellaneous category, into which biologists often sweep all irritating oddities, including measurement error.  But some of these effects are not trivial for evolution, and some have been proposed to be extremely informative for ecological and genetic monitoring of threatened species.  Between-individual variation includes any aspect of the environment which may affect each individual’s trait values separately; one well-studied effect is the contributions (other than genes) from other members of the population.  These influences can be environmental or genetic, and from family members or from other members of the population ({Meffert, 1995 #818; Wolf, 1998 #817}).  Examples of substantial parental effects are the effects on juvenile growth of egg-yolk in Chinook Salmon (Onchorhynchus tschawytscha)({Heath, 1999 #815}) and seed size in plants ({Roach, 1987 #816}).  These are often called maternal effects, but paternal contributions can be relevant as well.  They can be genetic or environmental: for example, a maternal plant that provides large amounts of endosperm to a seed may do so because of good nutrition, or because of its genes ({Roach, 1987 #816}).  Wolf ({Wolf, 1998 #817}) has suggested that evolution can be greatly accelerated when indirect genetic effects are present; of course, the evolution could also be a great deal more complicated, especially when the transmitted effects are between non-relatives, such as mating partners ({Meffert, 1995 #818}).
Ves also includes epigenetic effects – loosely defined as inheritance outside the DNA, such as learned behaviour, and modification of DNA, that can affect the expression of loci.  An example of DNA modification is seen in sex-chromosome dosage compensation.  The pattern of modification is sometimes random, as in human X-chromosomes, but in marsupials, it is influenced by the origin of the chromosome – paternal or maternal ({Cooper, 1993 #872}).  These modifications can occur on any chromosome, and their significance in phenotypic variation is only just beginning to be appreciated.  Epigenetic effects may be regarded as subclasses of the effects in the previous paragraph, but their quite different mode of transmission makes their evolutionary properties very different.  Here we will only note that the importance of these effects in many species adds considerable problems to the (already extremely difficult) task of bringing extinct species back to life from their DNA, which some propose to do.
Within- individual variability includes several types also.  The developing organism often produces several copies of the same trait: left and right leg, or seventh, eighth, ninth, etc hair on the leg.  Also, some traits are expressed repeatedly through time, such as platelet count in the blood, which varies from day to day.  The variation between these repeated trait measures derives from three sources:  genes, environment, and “noise”, where noise might include both random processes in the organism’s development and maintenance, and measurement error. The extent to which the organism changes its phenotype, in the face of environmental fluctuations such as temperature change or pollution, is called plasticity.  Plasticity can occur at the individual scale, but also on a larger geographic scale, if the appropriate environmental cues are present, as seen in dandelions (Taraxacum spp) which grow flat to the ground in heavily trampled areas, but in a more bushy form where regular trampling is not present.  Since adaptation could be achieved just as well by a plastic response to the environment as by an innate response, it is likely that the level of plasticity is usually determined by selection.  The ability to produce the same trait value irrespective of environmental pressures, is also clearly important at times.  These will be discussed further below, where we consider the effects of environmental change, but it is worth highlighting here that variation within an individual, especially between symmetrically-placed structures (eg, left and right leg) has been proposed as a sensitive indicator of the degree to which the genetic and environmental influences are within their normal ranges, or outside those.

Multiple traits: genetic correlation between traits
The example of sticklebacks with which we started this chapter (Figure 3.1) suggests that there may be correlations between traits – the behavioural preference for open water was associated with a higher growth rate in open water.  It is possible that this correlation has little to do with genes: the preference may be learnt and the ability to grow in different conditions may be acquired during the individual’s lifetime.  However, in Figure 3.1, artificial hybrids show an approximately intermediate position, suggesting some genetic involvement (which is likely to have both additive and non-additive components, since the hybrid phenotype is not exactly intermediate).  There are a number of genetic reasons why two or more traits might be correlated.  First, there may be departure from the assumption of  random mating.  Field cricket (Gryllus integer) females prefer males with a certain number of pulses per trill (PTT); both the female preference and the male PTT are heritable.  Because females with certain preference alleles mate with males with certain PTT alleles, the possession of these alleles, and therefore the two traits, are correlated (genetic correlation r = +0.51), but after one generation of enforced random mating, recombination eliminates the correlation ({Gray, 1999 #814}).  Secondly, some of the QTLs for the traits might be very tightly linked, as described above; this can have important short-term effects, but in longer evolutionary times, the linkage will be broken up by recombination.  Thirdly, the genes for two traits can interact in some way; perhaps one gene regulates the transcription of two others (one for each trait), or the products of two genes interact (Figure 3.4).  In general, as well as one trait being affected by multiple loci, it is also known that one locus often affects more than one trait (“pleiotropy”).  We have already seen several examples of pleiotropy: in the hypothetical lizards, we saw a single locus that affects two traits: heat absorbance and height (Figure 3.7), and we also saw the real examples of multiple traits affected by MHC locus variation – disease resistance, mating success, fetal survival, etc.  Given the complexity of cellular and physioloigcial interactions, it would be possible for a single locus to affect virtually any pair of traits, but there is some evidence that pleiotropy is restricted to related traits ({Leamy, 1999 #812}).  Finally, and very importantly, the two traits may simply be detrimental to one another: the ability to fit into small cracks in the ground is not compatible with the ability to swallow entire pigs.  This may sound hardly genetic, but selection for alleles favouring one trait would often be disadvantageous for the other trait.  

The end result of these interactions is that there can be both genetic and environmental causes of correlation between traits.  Each of these may be negative or positive.  In the case of milk volume and percent butterfat in the milk, a study of cattle revealed a negative correlation both environmentally and genetically ({Falconer, 1996 #91}).  The full details of the mechanism are unknown, but perhaps the negative environmental correlation derives in part from the effects of increased water drinking (more water in the milk leading to higher volume but lower percent butterfat), while the negative genetic correlation derives from pleiotropic effects of the same alleles – those that increase butterfat production decrease total milk production, and vice versa.  Whether the same correlations hold for other mammals, or even other populations of cattle, is unknown.  Like heritability, the correlations are population-specific.  The environmental and genetic correlations do not have to have the same sign, as seen in a study of various aspects of rat shape and size ({Atchley, 1981 #737}).  In summary, just like single traits, the correlation between two traits can respond to the environmental changes in two independent ways: by genetic change, or by phenotypic plasticity, which are limited by the genetic correlation and the environmental correlation respectively.  We will therefore look carefully at ways in which this affects adaptation, and the ways in which these capacities can be monitored in conservation management (section 3.5).

3.4 Response to the environment
Stability or plasticity?
When an individual or species is faced with environmental change, three basic responses are possible:  to change, to resist alteration, or to go extinct locally.  Which of these may be an adaptive response depends on the particular situation.  Experimental releases of  five to ten  Anolis lizards on different islands in the Caribbean have shown that adaptation happens predictably, despite random genetic differences in the released populations ({Losos, 1998 #535; Losos, 1997 #534}).  What methods can a conservation biologist use to predict particular changes, and most importantly, general ability to adapt?   How a trait will respond to changes of the environment depends upon the extent to which the genetic architecture allows change or prevents it (phenotypic plasticity and stability), the potential for genetic change (genetic adaptability), and the ability of the population to withstand the poor survival and reproduction that selection may involve.    Plasticity and genetic adaptability can each be limited in a number of ways. 

There can of course be no genetic response without genetic variation at one or more loci affecting the trait.  Fisher ({Fisher, 1930 #822}; {Price, 1972 #465}; {Frank, 1992 #462}) showed that under certain conditions, the rate of change in fitness due to natural selection depends upon the amount of additive genetic variation.  The exact conditions under which this is true are still controversial, but it is important to note that the result is limited to short time periods only, and that it ignores changes in fitness due to non-additive genetic effects and to environmental fluctuation, such as the greater lifetime breeding success of Soay rams (Ovis aries) born in years of low population density ({Coltman, 1999 #825}).   Fisher’s work has sometimes been interpreted to mean that, especially if the trait being studied is lifetime fitness itself, then the additive genetic variance will gradually be used up, and response to selection will cease.  A single locus example of this would be if allele H1 coded for higher fitness than allele H2, then H1’s frequency would rise to 100%, resulting in zero Vg, after which further selection would not be able to change fitness.  As a consequence of this, fitness-related QTLs are expected to have high proportions of high-fitness alleles; the differential response to selection for high and low levels of fitness-characters supports this idea ({Frankham, 1991 #37}).  Experimental evidence seems to support the contention that heritability is low for fitness characters (Kearsey and Kojima 1987 {Kearsey, 1967 #40}, Mousseau and Roff, 87; Roff and Mousseau 87; Falconer and McKay 96).  

 However, there are problems with the argument that selection reduces or eliminates genetic potential to respond to selection for fitness traits, in comparison to non-fitness or “peripheral” traits.  Certainly selection can produce change in fitness traits, which would not be expected if genetic variation had already been exhausted.   Wade {Wade, 1991 #428} showed high heritable variation for population growth rate in natural and wild populations of flour beetles, Tribolium castaneum, and selection did not deplete the variation ({Wade, 1991 #823}).   Results such as this may be accounted for by realizing that there are number of over-simplifications in the argument for exhaustion of genetic variation in fitness, of which we will present three.  Firstly, the argument ignores non-additive interactions such as dominance, which could allow some deleterious recessive alleles to persist by hiding from selection in heterozygotes, etc.   Secondly, the argument ignores the possibility that altered environmental conditions may reverse the selection, so that a particular set of alleles is never completely eliminated {Kondrashov, 1996 #565}).  Fluctuating selection is seen in Soay sheep {Coltman, 1999 #825}, where hindlimb length is subject to different types of selection in years of different population density.   Thirdly, the difference between heritability and additive genetic variance is crucial.  Some authors have pointed out that since heritability is h2=Va/Vp, lowered heritability of fitness characters may not be due to low additive genetic variance, but could be a result of high Vp, as a result of high environmental variances or non-additive genetic variances {Houle, 1992 #38}, {Frankham, 1997 #517}).  In a study of human women’s reproductive traits (age at menarche, age at first reproduction, and age at menopause) environmental variation was very high, but when this was removed from the analysis, the traits showed heritabilities from 0.23 to 0.5 {Kirk, 2001 #564}.  Studies of the genetics of fitness in wild populations are exceedingly difficult and rare, but recently there have been two: in red deer (Cervus elaphus) and collared flycatchers (Ficedula albicollis), despite there being low heritability of fitness-associated traits, additive genetic variation for fitness is not significantly lower than for peripheral traits {Kruuk, 2000 #315}, or even higher {Merila, 2000 #320}.  Thus Va is available to allow response to selection, in times when other components of variation such as Ve are low.   In fact, non-additive genetic contributions to variance may well be higher in fitness-related characters ({Frankham, 1997 #517}).  Thus although there is the capacity to respond to selection for fitness, the difference between heritability and additive variance in fitness traits creates a complex problem in monitoring the evolvability of populations for conservation, as we shall see later.  As for any other trait, the likelihood that fitness traits will respond to selection also depends upon other aspects of the genetic architecture, such as dominance relationships and their evoution (below).
 
Although the range of variation in a trait is very important to adaptation, it is not always adaptive for a trait to respond to particular selective forces.   If the direction of selection reverses from generation to generation, then response to selection in generation 1 will result in less adapted phenotypes in generation 2 ({Kawecki, 2000 #827}).  Nevertheless, the conditions each generation may require some kind of response, and this is where phenotypic plasticity comes into play.   Plasticity is when a particular genotype can produce more than one phenotype.  In Anolis lizards, longer legs allow faster sprinting on broad surfaces, while short legs permit careful movements on narrow surfaces; the leg-length of different species reflects substrate availability, suggesting selection for appropriate leg-length during evolutionary diversification of these species.  But what if the environment changes too fast for such a selective response?  Genetically similar  Anolis sagrei juveniles raised in different environments (broad or narrow surfaces) showed different lengths of legs (Figure 3.9) ({Losos, 2000 #830}).  Because of the genetic similarity of the juveniles, the different leg-lengths must reflect phenotypic plasticity, rather than a selective response as seen in the longer-term evolution.  However, plasticity itself is likely to under genetic control, and can be considered as an adaptation (Bull 1985). Because the development of legs is under environmental and genetic control, there is the possibility that there could be selection, not for leg-length, but for sensitivity of the development process to the substrate. The “reaction norm” is the name given to the way that a range of genotypes maps to a particular range of phenotypes, and there can be selection for changes to the norm, although in general, we do not know exactly what “plasticity loci” are selected ({Via, 1995 #473}).  

It also may be adaptive for the organism to regulate the amount of phenotypic variation that results from particular genetic changes, rather than environmental changes.  One example of genetic changes affecting trait variation is the frequent suggestion that high or low average heterozygosity could result in changes to the level of  developmental stability in traits, but the results are equivocal ({Lynch, 1998 #92}).  Whatever the exact pattern of phenotypic changes resulting from genetic change, it is hypothesized that, as with plasticity (variation due to environmental change), there could be genetic control of the extent of variation due to genetic changes.  A simple example is in full epistasis, when certain loci may have alleles which completely prevent expression of variation at other loci.  Of course, in any population, exactly which loci are interacting will depend upon the range of variants present at each locus.  Resistance to genetic alteration of the phenotype has sometimes been called canalization (and this is sometimes extended to include resistance to environmentally induced variation - {Waddington, 1949 #828}; {Lynch, 1998 #92}).  As with plasticity, the genetically-controlled capacity for canalization can potentially be altered by selection; in sheep blowflies (Lucilia cuprina), selection for the insecticide resistance allele at the Rdl locus cause increased asymmetry, a sign that development is not stable, since the repeated structures on each side of the body are not developing in the same way ({Clarke, 1987 #829}).  The resistance biochemistry and the morphological structures are not closely related, but Rdl is presumably pleiotropic, affecting not only insecticide resistance but also the morphology.  Pleiotropic interactions may be generally important in canalization ({Wagner, 1997 #394}).  

Either a genetic response or plasticity can enable a population to retain viability in a changed environment, so the potential for plasticity or stability, and their costs and benefits {DeWitt, 1998 #832}, must be part of the overall evaluation of fitness (section 3.5).

Figure 3.9   Phenotypic plasticity in Anolis lizards: Relative hindlimb growth (calculated as [final hindlimb length initial hindlimb length]/[final snout-vent length initial snout-vent length]) for lizards raised in two different environments (broad or narrow surfaces).  All the juveniles were taken from a single population, and were presumably genetically similar, with any genetic differences randomized between the treatments. (from {Losos, 2000 #830} ).

 

Trade-offs and adaptation
An important limit to adaptation is thought to be trade-offs between different components of fitness, with improved ability at one aspect of reproduction and survival coming at the expense of reduced ability at another part, so that overall fitness is unaltered.  There are numerous examples of tradeoffs.   There are trade-offs between early flowering time and large size at first reproduction in Arabidopsis (Mitchell-Olds).  In captive populations of Drosophila, Mueller et al  ({Mueller, 1991 #844}) showed that selection for high fitness in crowded populations did produce lines which showed higher fitness than unselected lines tested in the same conditions, however, the selected lines were at a disadvantage under low density conditions, so adaptation to high density was negatively correlated with adatation to low density.   For a conservation biologist, the relevance of these trade-offs is that we cannot expect that adaptation will occur just because there is genetic variation for a trait – a tradeoff against some other trait may limit this.  Trying to predict the occurrence and nature of tradeoffs has become an important part of evolutionary biology, but it is not always successful: a predicted tradeoff between vegetative and sexual regeneration of the plant Erica australis did not occur ({Cruz, 2001 #836}).  

As described above, correlations between traits can occur for genetic or environmental reasons.  Here we will concentrate on genetic reasons.  Genetic tradeoffs will rely upon genetic correlations between traits; one possible reason for this is pleiotropic effects of the QTLs for each trait, upon one another, and upon other traits important for fitness ({Baer, 2000 #843}).  Curtsinger et al ({Curtsinger, 1994 #842}) suggested that a trade-off mechanisms involving antagonistic pleiotropic effects of QTLs (in which variation at one or more loci affect the two traits in opposite directions) is very poorly supported by theory, which required not only frequent dominance variation but also that Vd needed to have a different magnitude and basis for the two traits.  More recent work has shown frequent dominance variation in QTLs ({Falconer, 1996 #91}), and some evidence that tradeoffs are based on differences of dominance relationships between traits in the criket Gryllus firmus ({Roff, 2001 #841}).   Other work has shown no strong evidence for pleiotropic tradeoffs, for example the study of temperature tolerance in the fish Heterandria formosa {Baer, 2000 #843}.  Additionally, some authors ({Fry, 1996 #792}; {Whitlock, 1996 #703}) have suggested that addition of genetic tradeoffs to evolutionary models does not necessarily improve their predictive power.  Other authors, however, use genetic tradeoffs in modeling (Case, Schluter).

For conservation monitoring, two points emerge from trade-offs.  Firstly, we need a better understanding of the importance of relationships between traits in predicting evolutionary change.  Secondly, the possible importance of trade-offs suggests that we need to pay more attention to non-additive genetic variance, despite the simplistic idea that all selective response is due to Va.  

Changed genetic architecture under selection
The type of genetic variation can actually change when the environment changes.  For example, in the lizard example in Figure 3.7, if the environment became heavily vegetated with vertical grasses, then heat would reach the lizards from directly above, striking only the lizard’s back, and the importance of the legs for heat collection might be to simply lift the lizard above any fallen grass stems.  Here, the linear dimensions, with additive genetic variance, would determine variation for heat collecting ability.  The legs would not be able to function as heat collectors, so the (non-additive) genetic variation for leg area would not influence heat collection ability.  Thus a simple change in the environment could alter the apparent genetic basis of the trait “heat collection ability”.  This change would be transient, lasting only as long as the new environment persists, but permanent changes are also possible – the level of non-additive variation can evolve, perhaps through alteration of regulatory loci.  There are few studies on dominance modification at individual loci, and the results are equivocal (Curtsinger et al {Curtsinger, 1994 #842}).  For multilocus traits, Hoffmann and Merila ({Hoffmann, 1999 #318}) summarized a number of studies in invertebrates and birds, and showed that in many cases, unfavourable conditions resulted in altered heritabilities - sometimes increased, sometimes decreased.  Some studies show no evidence for altered architecture as a result of selection ({Roff, 1999 #323}).  Changes to genetic architecture could be because of a change in any of the components of variation shown in Table 3.2, as a result of altered allele frequencies at regulatory loci, altered recombination frequencies, etc.  Some species have elaborate systems of chromosomal inversions which may inhibit recombination, while others may be relatively free to change in this and other aspects of the architecture.  Whatever the mechanism, a change to heritability will obviously affect selection response, and other chances to the genetic architecture may do so also, if non-additive genetic variation has significance for adaptation.  Since conservation biology aims to foster the ability to respond to altered selective regimes, and the genetic architecture underlies this ability, if further research confirms that architecture will often alter in times of stress, it will have considerable potential significance for conservation.  Our expectations of the ability of populations to respond to stress will have to be revised upwards or downwards, and our choice of measures to monitor will be affected also.

3.5  Monitoring genetic variation for fitness traits.
In conservation monitoring, it is tempting to suggest that we should simply study variation of the single most important quantitative trait: lifetime reproductive output, which is essentially fitness itself.  However, some complications arise in this approach, and the major challenge in conservation genetics monitoring is to choose a suite of informative and accessible measures which cover not only what is known to be important for evolvability, but also what may become known to be important, as research proceeds.  Since baseline data are paramount in monitoring, it is worth being over-zealous in including factors that we suspect may later be known to be important, since it will often be too alter to measure them at a later date, unless they can be analysed from gross phenotype of preserved specimens, or molecular analyses of tissue samples.  What we seek is some measure that will tell us how well an organism will repsond to whatever changes are required by environmental changes?  This will necessarily be an imperfect, generic measure, because even if we knew what pressures are likely to occur in the future, it would be nearly impossible to predict the range of possible responses.  In general we do not know what a key innovation is – would a biologist operating some millennia ago have realized that a burst of adaptive radiation would be ushered in by the acquisition of nectar spurs in Columbine flowers (Aquilegia -  {Hodges, 1995 #867}), or the development of a mitochondrial enzyme resistant to a certain toxin ()?  In hindsight, it is easy to see the importance of these, but at the time, there would be so many other possibilities!  For example, addition of a new aerial predator which eats a proportion of the eggs of a species could be countered by increased egg production, increased guarding behaviour, switching from partly subterranean to fully subterranean habitat, or a host of other options.  These options may not be fully compatible: guarding behaviour may use up energy resources which could otherwise be used for egg production.  Loosely following the terminology of Wright ({Wright, 1968, 1969, 1977, 1978 #864}), we would say that there are multiple adaptive peaks, each with a particular combination of these responses.  Which combination eventuates would depend upon two chances:  first, the presence of plasticity or genetic variants which allow such change, and secondly, random variation in the frequency of these variants, which may make one or other a more likely response at the instant when the predator is introduced.  Thus, even if it survives the introduction of the predator, the prey population will not necessarily evolve to reach the highest possible adaptive peak.  A simplified example involving only two traits is seen in Figure 3.10.  In part (a) of this figure, we see that two traits can show positive or negative correlations.  If these correlations are transmissible to offspring (ra, the genetic correlation) this correlation will affect the ability of the two traits to evolve. There are plenty of examples of positive and negative genetic correlations ({Falconer, 1996 #91} and section 3.3 above).  If trait 1 is lifespan and trait 2 is egg-production per year, then a positive correlation (solid ellipse) indicates that selection for an increase in one of these traits would result in an increase in the other; but if the correlation is negative (dashed ellipse), then selection for one would decrease the other.  In the first case, such a selective pressure is expected to result in an increase overall fitness.  However, if there is a negative genetic correltation, then selection on either trait may not result in an increase in overall fitness, since improvement of one trait would result in a reduction in the other.  Schluter ({Schluter, 1996 #457; Schluter, 2000 #865}) has suggested that we can summarise the results of a graph such as Figure 3.10 (a) by calling the long axis of the ellipse gmax, the direction of “evolutionary least resistance”.   To put it another way, in the case of the solid ellipse, the path of least resistance is for both traits to increase as a result of selection on only one of them, but for the dashed ellipse, an increase in one favours a decrease in the other, and so selection on either is unlikely to lead to changes unless there is some alteration in the genetic architecture – the pleiotropic and linkage relationships that cause the negative correlation.    The preceding discussion can also be expressed as G, the genetic variance-covariance matrix; Figure3.10 b; the direction of least resistance gmax can be calculated from G (it is called the “eigenvalue”).  We can also calculate tables of total phenotypic variation (P, the phenotypic variance-covariance matrix), and environmental variation (E, the environmental and non-additive genetic variance-covariance matrix); from E and P or their associated graphs, we can calculate corresponding values pmax and emax, the capacity for total and plastic change, respectively.  

Figure 3.10  Covariance between two traits  (a)  two examples of possible covariance: in one case, the two traits are positively correlated (solid ellipse), in the other case, they are strongly negatively correlated (dashed ellipse).  The axes could be the measurement of the traits in individuals themselves, in which case this plot would show total phenotypic correlations (rp), or the axes could be the average trait –values in the individual’s offspring (“breeding values”), in which case the correlation is the additive genetic correlation between the two traits (ra).  The genetic correlations can be converted to covariances in the standard statistical way, by multiplying them by the square-root of the respective variances for each trait.  (It is also possible to work out the environmental correlation re, or extent to which plastic responses and non-additive genetic variation in one trait, are correlated with responses in another.  The calculation uses the equations rp = h1h2ra + e1e2re, where h1 and h2 are the square roots of the two traits’ heritabilties, and e12 = 1- h12.)
(a)

Figure 3.10 (b)  Table of Variances and covariance for the pair of traits in 3.10 (a).   The covariance would be positive for the case of the solid ellipse in (a), and negative for the case of the dashed ellipse in (a).    In this table we have used Va and the additive genetic covariance (derived from the genetic correlation ra in 3.10a) to construct the table G, which is also called the “additive genetic variance-covariance matrix”.  If we had used Vp and the phenotypic correlation, then this table would be P, the “phenotypic variance-covariance matrix.  Similarly, we can construct E, the environmental matrix.  

	Trait 1	Trait 2	
Trait 1	Va1	Cov12	
Trait 2	Cov12	Va2	

In general, there will be more than two traits that are significant for evolution, and this results in more rows and columns in tables (P, E, and G), but also raises a new question in predicting the likely result of selection on any one trait: if the trait is positively correlated with advantageous change in one trait, and negatively correlated with advantageous change in another, what will be the result of selection on the first trait?  Schluter ({Schluter, 1996 #457; Schluter, 2000 #865}) has suggested that we can best investigate this by finding one or more groups of traits which are strongly correlated with one another but weakly correlated with others, and estimating gmax for each such group.  In pictorial terms, the first gmax in Figure 3.10 would be the long axis of the ellipse, and the second would be at right angles to it and summarise a much smaller proportion of the variation in the data.  In the case of three or more traits, matrix algebra has to be used to estimate gmax.  Schluter has shown that in several vertebrate and invertebrate species, where five characteristics were analysed in each species, the 5-dimensional plot equivalent to Figure 3.10a can be reduced to a plot with one major axis (like the dashed ellipse in Figure 3.10a) or occasionally two such axes of similar importance, like the solid ellipse in Figure 3.10a.  If this finding is confirmed by studies of more species and traits, then it suggests that each population will have a single major gmax value which would be expected to dominate the direction of evolution, thus greatly simplifying the task of making predictions.  Where the gmax axis points in the same direction as the environmental changes between areas or generations, then response to selection is expected to be fast, but if they are in different directions, then response would be slower.  In the second case, Schluter would say that the genetic architecture biases evolutionary change in a direction different to that required by the environmental change.  

Does this theory work?  There is some evidence that gmax has predictive power, from Schluter’s re-analysis of studies of various vertebrates (stickleback fishes Gasterosteus sp; Galapagos medium ground finches, Geospiza sp, collared flycatchers Ficedula albicollis, and old-field mice Peromyscus spp).  In pairs of populations within these groups, Schluter estimated the direction of selection for differentiation between pairs of populations from current morphological differences (eg, do the poopulations differ more in their body length or their body depth?).  He also estimated gmax for the current populations.  For each pair of populations, he then used these data to calculate the “bias” - the difference in direction between gmax and the direction of selection pressures (Figure 3.11).   For each population pair, he also estimated the rate of evolution of the morphological traits, by using allozyme differentation as an estimate of time of separation.  He showed that a greater bias led to slower evolutionary change, and also found a non-significant tendency for the bias to reduce with greater evolutionary time, which suggests that the genetic architecture was gradually changing in response to the selective pressures.

Figure 3.11
(a)  Direction of gmax:  Genetic correlations between two traits gmax is the long axis of the ellipse – a large change in body length would be expected to be associated with only a small change in body depth

Figure 3.11
(b)  LOW bias:  Optimal values for two traits are shown for two different geographical areas to which a species might adapt.  Fro a population expanding from one area to the other, selection would favour a small change in body length but a large change in body depth.  This is consistent with the direction of gmax in (a), so there is low “bias” in the direction of least resistance in evolution.  The same could be said if Area 1 and Area 2 were two different time periods in the same population.

Figure 3.11
(c)  HIGH bias:  Optimal values for two traits are shown at two different areas – selection would favour a large change in body length but a small change in body depth.  This is NOT consistent with the direction of gmax in (a), so the direction of least resistance in evolution is biased away from the direction of the change in the selection regime between areas.

Of course, as conservation biologists, we can probably never measure every element of tables G, P, and E; even in cases where the technical barriers are surmountable, we are unlikely to be able to afford the money, or delay conservation actions while waiting for the result.  Thus we are restricted to monitoring some elements of these tables – indications of the genetic variation at the molecular or quantitative level for a few traits, or information on total phenotypic variation or environmental plasticity. As pointed out in section 3.3, the corresponding elements of the matrices E, G, and P are not necessarily the same in either direction or magnitude, so our choices will have a major impact on informativeness - the ability of our indicator variables to forecast short-term viability or long-term adaptation.  For each particular conservation situation, we must choose a set of indicators which give the most relevant information at the least cost.  The usefulness of individual methods will be the discussed in later chapters.  In general we need methods that are easily applied to natural populations with the minimum of sampling effort and disturbance.  The sample size will be loosely dependent on the exact question being asked: the sample necessary to test to test whether Va > 0, will not necessarily be the same as the sampling needed to testing whether Va has changed by a specified amount (eg. 10%).  Because baseline data is very important when monitoring change or its management, it is preferable that our chosen indicators will be able to be applied retrospectively to samples taken before the present time, or in the future be applied to samples which can be collected now but cannot be analysed yet because of cost.  The potential for future analysis is also desirable because presumably many technical methods are not fully developed yet, and the theory which guides the choice of methods is not complete: although Schluter’s framework has been used to illustrate this argument, there is very little agreement on the best way to measure “evolvability”.  Table 3.3 covers some of the indicators that have been used or suggested, and the remainder of this section is devoted to assessing the general utility of these indicators, which will also be revisited in various chapters of the book.
Table 3.3  Possible indices for monitoring genetic variation
Index	Measurement method	Possible Information content	Sampling requirements and other drawbacks	Potential to be analysed retrospectively	References	
(a) Single locus:						
Suite of neutral loci	Microsatellites, SSCP, etc. (Ch2)
Ideally use unlinked markers for maximum information content

	Measure generic effects of general population processes such as dispersal and population size.  Often only a low correlation to quantitative variation or overall fitness?	20-50 individuals /population
Low-impact sampling (eg  hairs) possible	Good if tissue samples stored (dried is OK)	{Luikart, 1999 #868} {Sunnucks, 2000 #845; Sunnucks, 2000 #846}
Frankham 2001; 
{Lynch, 1996 #178}
{Briscoe, 1992 #29}	
Single candidate genes eg MHC, Self-incompatibility genes	Molecular (Ch2)	Part of genetic component of fitness variation (Vg) Low correlation to overall fitness?	20-50 individuals /population
Low-impact sampling possible (eg  hairs)
Need analysis of single-base differences. 	High, if tissue samples stored (dried is OK)	{Hughes, 1991 #28}
{Hedrick, 1994 #158}
{Dudash, 2000 #147}
{Sunnucks, 2000 #846}	
Suite of selected genes	Molecular as above.  See Table 3.1 for method for identifying and choosing loci	Represent behaviour of loci under different types of selection	Sampling as above.
Difficult to establish type of selection – may improve with high-throughput sequencing etc	High, if tissue samples stored	See table 3.1
and above	
QTL candidates for fitness-traits	Box 3.3 – Ideally develop QTLs for each population, since the same ones may not show variation in different populations	Relationship to particular traits under strong selection 	Large study with 100s of crosses, several generations. Selection intensity and direction on QTLs may differ from selection on traits they affect (Box 3.3)
	High, if tissue samples stored, and QTLs inferred from data on other populations or even other species.	See Box 3.3 and Section 3.3
{Nuzhdin, 1999 #484; Lynch, 1984 #516; Lynch, 1998 #92; Falconer, 1996 #91}	
QTL suite	See above	Represent traits under different types of selection	See above	See above	See above	
(b) single trait (not lifetime fitness):		Represent variation in general?  Especially if fitness-related	Poor correlation to lifetime fitness?		Section 3.3. 3.4, 3.5	
h2  from family studies (parent-offspring, etc)
in lab	Controlled crosses, raise offspring to comparable stage and measure	As above	Many crosses (~100s).  
Not directly applicable to wild 	No, unless crosses done and then specimens or measurements kept for later analysis	{Falconer, 1996 #91}
{Lynch, 1998 #92}

	
h2  from relative pairs (parent-offspring, etc) in wild	Uncontrolled crosses, raise offspring or relatives to comparable stage and measure	As above, but directly applicable to study population	Many crosses (~100s).  
Difficult to control in natural environment Power usually low unless use maximum likelihood methods to combine results of different types of relative pairs 	No, unless crosses recorded, specimens or measurements kept for later analysis	{Falconer, 1996 #91}
{Lynch, 1998 #92}
{Cheverud, 1994 #85}
{Meyer, 1991 #870}
{Grant, 1995 #629}	
h2 or H2 by molecular-phenotypic analysis in wild populations	Microsatellite or other estimates of relatedness, plus measures of quantitative traits	As above	100s of individuals.
Limited to populations with wide range of relatedness values.  Very large sampling errors often lead to unrealistic results. Not yet validated by comparison with other methods	Yes, if DNA samples and measurements or specimens available	{Ritland, 1996 #185}
{Lynch, 1998 #92}	
h2 “realized”
from R=h2S	Selection experiments, or following the course of natural selection in the wild	Actual evidence for ability to evolve	100s of individuals.
Only assays response to a particular selective pressure.
Lab studies not applicable to wild?	Possible, if samples retained and family relationships or h2 known	{Falconer, 1996 #91}
{Lynch, 1998 #92}
{Grant, 1995 #629}
Montgomery and Frankham pers comm	
H2  (or  “degree of genetic determination” H2 = Vg/Vp) “common garden”	Move organisms from two areas into a common environment.  Persistence of differentiation for 1 generation indicates heritable differences (Vg), or possibly parental effects (Ves) Section 3.3	Genetic variation  Vg  between poulations (not within) Cov ge	20-50 individuals.  Must be moved out of area, so highly invasive.	Cannot measure Va, only Vg.  May be confounded by Ves – parental effects.  Applies only to common (usually lab) environment.	{Falconer, 1996 #91}
{Lynch, 1998 #92}	
H2 and Vge reciprocal transplant	As above but transfers are between two wild populations which appear differently adapted	As above.
Also Vge – different relative responses of genotypes to different environments	20-50 individuals.  Must be moved out of area, and placed in another natural environment, so highly invasive and potentially difficult to reverse.	As above but applies directly to studied wild environments and all adaptive differentiation between them.
Has potential to reduce this differentiation through hybridisation	{Falconer, 1996 #91}
{Lynch, 1998 #92}
{Berven, 1982 #646}	
Va	Measurement as for h2 above (any method)
Va = h2 * Vp
	Additive genetic variation, not obscured by variation in Vp due to Ve and Vna	As for h2	As for h2	{Lynch, 1998 #92; Falconer, 1996 #91}	
CVa 	As above;
CVa = h2 /mean of trait.
Lynch suggests alternatives which may be more easily used in algebraic treatments	As above, scaled for comparison with other traits of populations	As for h2	As for h2	{Houle, 1992 #501}
{Houle, 1998 #492; Lynch, 1998 #92; Falconer, 1996 #91}	
Ves- based methods
(1) Repeatability 	Estimate variation between same trait measured in more than one part or stage of the same individuals Repeatablity = (Vp-Ves)/Vp	Set upper limit to h2 and H2.	100s of individuals, preferably estimate measurement errors separately.
Family data not needed
	If measureable on preseverd samples.  Family data not needed	{Falconer, 1996 #91}
{Lynch, 1998 #92}	
Ves – based methods (2) Fluctuating asymmetry

		Reveals sensitivity of development to genetic (inbreeding) or environmental (pollution) perturbations? May give early warning before fitness decline?	As above
Ability to act as a surrogate for monitoring genetic variation questioned	As above	{Palmer, 1986 #31}
{Leamy, 1999 #813}  
{Leary, 1989 #32} (Gilligan, ****)
{Clarke, 1995 #30}
Clarke 1996
	
Vp 	Measure trait	May be related to Vg, if Ve and Vg are correlated.  Fluctuations in Vp would determine h2 if Va remains constant.	50-100 individuals.
Family data not needed	As above	{Cheverud, 1982 #866}	
Suite of possibly important traits	See all single-trait methods above; identify appropriate traits by known relationship to fitness, or by canalization?	Changes represent the behaviour of all such traits?	Sampling depends upon traits and methods.   See discussion earlier this section on relationship between traits and overall evolvability.	Depends upon traits and methods	{Wagner, 1997 #394}	
(c) Multiple trait:						
G, Additive genetic covariance-Variance Matrix, and
gmax – direction of least resistance	See above section 3.5	Total evolvability – if the correct traits measured	Multiple studies as for single trait Va. G May not reflect long-term evolvability since altered architecture could change G	Multiple studies as for single trait Va	{Lynch, 1999 #550} 
{Schluter, 2000 #865; Schluter, 1996 #787}	
P, Phenotypic covariance-variance Matrix	See above section 3.5	Total evolvability – if the correct traits measured, and P and G similar 	As for single trait Vp. Altering achritecture, or dissimilarity of P and G would reduce useful information content of P	As for single trait Vp.	{Lynch, 1999 #550} {Lofsvold, 1988 #448}
{Cheverud, 1982 #866}	
E, Covariance-Variance Matrix	From  G and P or from transplants	Capacity for plasticity	50-100 individuals.
Invasive.	No, unless samples saved from prior experiment		
Ves multivariate	See single-trait Ves above	See single-trait Ves above.  May be better representation of general variability	See single-trait Ves above	See single-trait Ves above	{Leung, 2000 #490}	
(d) Total fitness in wild populations	Full pedigrees and detailed data on survival and reproduction, over at least one full generation	The ideal, but only applies under current conditions, not to future changes	Prohibitive in most conservation situations.  Even very intensive long-term studies of large number of individuals in defined populations produce debatable results.  Low Va versus high Vp gives poor power for estimating Va	Unlikely!	{Merila, 2000 #320; Kruuk, 2000 #315}	
(f) All levels						
Environmental amplitude 
	Survey range of environments in which the species is found	Surrogate for monitoring genetic variation thought to be adaptive to those environments	Information content depends heavily on choice of environmental variables to measure, and measurement effort.	Can be anlaysed from prior distribution maps and environmental data.	{Sherwin, 2000 #743}	
Prior evolutionary rate	Measure rate of evolution from phylogenies	Faster evolving clades more likely to change in future?	Invasibility depends upon type of data: molecular or trait.  Association between past and future rate only known for one species (influenza).	Good	{Fitch, 1997 #192} {Sherwin, 2000 #743}	

Box 3.4
 Finding the  multiple loci which affect each quantitative trait (quantitative trait loci, or QTLs).

Finding QTLs, There are two requirements, and a number of other things that can help us to find QTLs.  We need multiple hypervariable marker loci, distributed throughout the genome, and we need variation of the trait.  We can then search for associations between trait variation and marker allele variation, and from this infer that a QTL is linked to the marker (or even, in rare cases, the marker locus itself is the QTL).  We can search for these associations in natural populations, in inbred lines, or (best of all) in crosses between inbred lines.  If we suspect certain candidate loci of being involved in the variation, then we can include them as markers.  Unless we hit the locus itself, the result is the identification of one or more large regions of the genome (2-20 centimorgans each) which appear to be associated with the trait.  Sometimes this huge chunk of DNA is called a QTL, but it is better if the DNA of each region is cloned and sequenced, and then a fine-scale association study identifies the QTL(s) themselves.  QTLs will be identified more easily if they are closely linked to the markers, and if that QTL is segregating for alleles that have a major effect on the trait (which depends upon the allele proportions in the population under analysis).  Thus the identification of QTLs is biased by the mapping density, and  it is best to have a high-density of marker loci on the genomic map, to avoid bias in favour of  QTLs which have major effects and/or happen to be closely linked to one of the markers. Because they are only detected if they are variable, QTLs are expected to often be specific to particular populations, although sometimes the same QTL loci may be identified in different populations ({Schat, 1996 #426}).  

Although we are principally interested in overall fitness, we are often restricted to studying components of fitness, or even individual loci which may be QTLs for some fitness trait.  Thus it is important to understand how the components of fitness will predict fitness itself.  The apparent type and direction of selection may differ at these levels, even in the same system of interacting loci (above, and section 3.5).  Both additive and non-additive effects are present in the loci underlying fitness {Houle, 1997 #493}{Frankham, 1997 #517}, but the extent to which they are reflected in the trait itself depends upon the interaction of all the loci.  For example, if the allele that increases the value of the trait is dominant at one QTL, but at another QTL a recessive allele increases the value of the trait, the trait may not show considerable Vd.  Also, when a trait is under strong selection, the QTLs will quite likely appear neutral, if no one of them is the major locus which causes most of the variation in the trait {Lynch, 1984 #516}.   Also, different QTLs may appear important, depending upon the nature of selection – in Drosopihla, selection for increased bristle number acted on different QTLs to selection for a decrease {Nuzhdin, 1999 #484}; presumably in that population, some QTLs harboured rare alleles for lower bristle number, while others had some alleles for high bristle number.
End of Box 3.3

The choice of measures of evolvability is an area of intense current research, with the answers being needed as much for conservation genetics as for evolutionary biology in general.  Heritability can be measured from any pairs of relatives, but the need for knowledge of relatedness in a wild population, or the removal to a laboratory to estimate it, constitute a major barrier to anslysis of quantitative traits in wild populations {Falconer, 1996 #91}.  Molecular analyses can allow non-invasive and even retrospective analyses of genetic variation, but their relationship to the traits is questionable, even it QTLs are identified.  The ability to analyse total fitness is limited, and it is difficult to predict what may be useful adaptations in changed environments.  Thus we will always be looking for a suite of variants which allow us to monitor what is happening to particular classes of genetic variation (Figure 3.11.  One clear message that emerges is that because of the complexity of the relationships between genetic and non-genetic variation, the absence of any erosion of total phenotypic variation in a threatened population is no reason for complacency about conservation genetics.  This suggestion is contrary to statements by many managers.

Figure 2  Classification of genetically determined variation, for use in conservation planning.  This classification can be applied to variation within or between populations, but the relative sizes of the categories (1 to 8) may differ.  

HISTORICAL:  Variation which accumulated over thousands of generations or longer (categories 1,3,5,7)

RECENT: Variation of relatively recent origin (categories 2,4,6,8)

AD-NOW: Variation which is of adaptive significance at present (categories 3,4,5,6)

AD-FUT: Variation which will be of adaptive significance in the future (categories 5,6,7,8).

Any variants, including those not encompassed by AD-NOW or AD-FUT, may have been of adaptive significance at some time in the past.  From ({Sherwin, 2000 #743})

 
8

7

6

5

2

4

3

1

AD-FUT

AD-NOW

RECENT

HISTORICAL

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