• A-Z
  • Directory
  • myUVM
  • Loading search...

Evolution in Structured Populations

Indirect effects, Individual Traits and Contextual Traits

Posted: April 11th, 2014 by Charles Goodnight

Another pure essay post.  I was surprised that last weeks post didn’t generate any controversy.  I guess that that proves that the only people who read my posts are people who agree with me.  Sigh.  As the song goes “I’d love to change the world, but I don’t know what to do, so I’ll leave it up to you”

ten_years_after_-_a_space_in_time_-_front1

This week I want talk about indirect genetic effects in comparison to contextual traits, something about which I have not been particularly clear.  In general it can be dismissed in two sentences.  Individual and contextual traits are part of the phenotypic compartment, and indirect genetic effects are part of the inheritance compartment.  As such they are independent concepts.

Whether a trait is an “individual trait” or a “contextual trait” depends entirely on what it is measured.  Thus, if it is a characteristic of an individual (height, weight, sprint speed) it is an individual trait.  If it is a characteristic of the group, neighborhood or other aspect of the context that an organism finds itself in, then it is a contextual trait.  One point here is that one of the whole points of contextual analysis is that we are treating “as if” they were traits of the individual, so perhaps from a rather odd perspective there really is no difference between individual and contextual traits.

Indirect effects on the other hand occur when genes in one individual affect the expression of a trait in another individual.  This is an idea that has been around for a long time, certainly it is an underlying theme in Griffing’s work (e.g., Griffing 1977 Selection for populations of interacting genotypes. In: Proceedings of the International Congress on Quantitative Genetics, August 16-21, 1976. E. Pollak, Kempthorne O, andBailey TB (eds.) Iowa State University Press,  Ames  Iowa., and references there-in), however the modern development of the idea, and the term “indirect genetic effects” can be traced to (Moore, Brodie, and Wolf 1997 Evolution 51, 1352-62.).  Indirect effects will almost certainly affect contextual traits, but in many circumstances they will also affect individual traits.  And that is the point of this essay:  individual traits can be influenced by both the genetics of the individual and the genetics of other individuals with whom they interact.  Similarly, contextual traits can be influenced by the genetics of the focal individual, and by the genetics of other individuals with whom they interact.

Thus just because a trait is clearly measured on the individual and correctly called an “individual” trait, does not mean that the genes reside in the individual expressing the trait.  A really good example is Griffing’s study of biomass in Arabidopsis.  If you recall, in this study Griffing grew pairs of plants together in sterile agar, and measured dry weight of the plants after they were harvested Clearly, biomass is a trait measured on an individual, and must be considered an individual trait.  Just as clearly in his study the trait biomass was determined both by the “direct effects”, that is the effects of in individuals genes on itself, and indirect genetic effects, the effects of its interacting partner on its phenotype.

At this point I am basically done with the issue I wanted to raise today, but it is worth discussing this point a bit more.  Just as with contextual traits, the realized heritability of individual traits will potentially depend both on the mating structure of the population and on the interaction structure.  Thus, even apparently pure individual traits can have there heritabilities change when the interaction structure changes.

Nobody has ever done a detailed manipulative study of the effects of interaction structure on the heritability of individual traits.  This is too bad, because it potentially has some profound implications.  I will give you one:  One of the truisms of evolutionary theory is that you can get a response selecting on just about anything.  However, In my thesis I worked with Arabidopsis selecting on leaf area (Goodnight 1985 Evol. 39, 545-58).  In this study I actually got a negative response to individual selection, a result that was predicted by Griffing (1977 In: Proceedings of the International Congress on Quantitative Genetics, August 16-21, 1976. E. Pollak, Kempthorne O, andBailey TB (eds.)).  Further, the one apparent exception to the idea that you can select on anything is competitive ability.  There have been a lot of experimental studies of the evolution of competitive ability that have failed to get a response (e.g., Futuyma 1970 American Naturalist 104, 239-52.).   Perhaps now we can put that old saw that you can select on anything into a new light.  Perhaps you can select on anything when you put the organisms in an environment where competitive interactions among individuals are minimized.  Apparently in both my study and Futuyma’s study the indirect genetic effects outweighed the direct genetic effects and prevented a response to selection from occurring.

A Reprise on Contextual Analysis.

Posted: April 4th, 2014 by Charles Goodnight

My life has gotten a bit hectic these days, so as usual a bit late, and perhaps a bit short.  At this point I have gone through most of the basics that I wanted to talk about before getting into more speculative stuff, but I think that a few weeks of review and revisiting past posts is probably warranted.  What I want to talk about for the next couple of weeks is something the difference between contextual traits and indirect genetic effects.  I think that my past discussions on the difference have perhaps not been very clear, and I hate to say it, part of the problem may have been a bit of confusion on my part.

Turning first to contextual traits.  It is important to remind ourselves that the classic breeder’s equation, R = GP-1S, divides evolution by selection into the ecological process of selection, and the heritable transmission represented by the G matrix.  Contextual analysis, in its standard form, deals only with S.  Thus if a trait is measured on the individual it is an individual trait, if it is measured on the context the individual finds itself in it is a contextual trait.  The heritable (genetic?) basis is entirely irrelevant.  The beauty of contextual analysis is that it is treats a trait that is measured on the context as if it were a trait of the individual.  Thus if an individual is in a group of 16 individuals then it has the trait of “group size = 16”, if it is in a group that is 30% altruists, then it has the trait of “altruism level = 0.30”, and so on.  Perhaps the correct way to think of it is that our individual is experiencing a group size of 16 or an altruism rate of 0.3.

In the early group selection days, people like Maynard-Smith insisted that group selection could only be invoked when groups were distinct entities that had clear borders.  That is, groups were things you could walk around.  Of course this becomes problematical when experimentalists examined the effects of migration (e.g., Wade 1982 Evolution 36, 945-61), or when you had group selection by differential migration (e.g., Wade and Goodnight 1991 Science 253, 1015).  Contextual analysis allows us to resolve this issue easily.  A contextual trait is a trait measured on the context.  Classic Maynard-Smithian group selection is but one extreme of a continuum that ranges from group selection at one end to frequency dependent selection at the other extreme.  Of course, this begs the questions: when is it group selection, and when is it frequency dependent selection.  If the two are part of a continuum then where you draw the line is at some level arbitrary, and a matter of aesthetics rather than science.  This also makes the interesting point that since the selective pressures on almost all traits are at some level dependent on the context the organism is found in, it suggests that pure individual selection, in which the fitness of an individual is solely dependent on its phenotype, and not at all influenced by the phenotype of its neighbors, is probably at least as rare as group selection acting by differential extinction and recolonization of whole groups.  My guess is that viewing the evolution by natural selection outside of a multilevel selection perspective is simplistic, and frankly, wrong.

I should clarify one aspect of the frequency dependent selection issue.  In mathematical modeling of selection there are frequency dependent models in which fitnesses change as gene frequencies change.  Call this mathematical frequency dependence.  In these models there is only one group, and as a result there can be no multilevel perspective.  Importantly, these models cannot be used in (short-term) studies of real populations for the simple reason that gene frequencies rarely change fast enough to see this mathematical frequency dependence.  To study frequency dependent selection in nature we need to find different populations that have different frequencies of the different phenotypes in different populations.  This is statistical frequency dependence.  I would argue that statistical, but not mathematical, frequency dependence should be studied as multilevel selection.

Another interesting point about contextual analysis:  it comes from another field. The earliest reference in my endnote is (Przeworski  1974 Contextual models of political behavior. Polit. Method. 1, 27-61), although the more definitive reference is (Boyd and Iversen 1979 Contextual analysis:  Concepts and statistical techniques. Wadsworth, Belmont, CA.).  Since that time there have been a number of developments, and independent derivations of the technique.  In 1987 Contextual analysis was introduced to the biological world (Heisler and Damuth 1987 Am. Nat. 130, 582).  In 1996 contextual analysis was reinvented and called direct fitness, later called neighborhood modulated fitness (Taylor and Frank 1996 J. Theor. Biol 180, 27-37, arguably, Queller 1992 Evolution 46, 376-80.).  In 2010 it was again rediscovered, although from a more genetic perspective, and labeled social selection (McGlothlin, Moorad , Wolf, and Brodie 2010 Evolution 64, 2558-74.).  Bottom line:  These are all the same thing. Contextual analysis has the precedence by nearly a decade over every other misbegotten term.  Can we please just call everything by one name, and can it please be the name that crosses back to other scientific disciplines, and can it please be the one that respects precedence?  All that those different things are contextual analysis.  It is the only term that fulfills all those criteria, can we please just use contextual analysis.  It is the correct term!

Finally, there is the interesting question of what is the correct trait.  In our original contextual analysis papers (e.g., Goodnight, Schwartz, and Stevens 1992 Am. Nat. 140:743-761) we used the group mean of the trait, whereas McGlothlin et al. chose to use the mean of the group excluding the focal individual.  Both of these make sense in the context that they were used.  In our theoretical studies using the raw group mean considerably simplified the math, and made our message much clearer, whereas in the McGlothlin study they were considering social interactions explicitly, and it made sense to leave out the focal individual and only include those they interact with.  Either and both of those are contextual traits, and as with any selection analysis the choice of which traits to include in the analysis depend on the situation.

OK, I will quit ranting.  Next week I will move on to the indirect genetic effects I meant to get to this week.

Measuring the heritability of contextual traits.

Posted: March 28th, 2014 by Charles Goodnight

First a plug for an upcoming conference.  If you are interested in artificial life there is a conference, Alife 14, being run by, among others, a friend of mine, Hiroki Sayama.  There is one week left submit abstract, and a good time should be had by all.  .  This meeting will take place in New York at the end of July, beginning of August.

I have been looking for a data set that could be used to illustrate calculating the heritability of contextual traits.  Happily one came along at the last minute, although I had to do some hard thinking to figure out how to interpret it as a contextual trait. . .

The paper I am talking about is a new one in Evolution,  Edward, Poissant, Wilson and Chapman 2014, Sexual conflict and interacting phenotypes:  A quantitative genetic analysis of fecundity and copula duration in Drosophila melanogaster. Evolution doi:10.1111/evo.12376  (http://onlinelibrary.wiley.com/doi/10.1111/evo.12376/abstract).  This is a well-done and analyzed article that is well worth reading.  However, as is my wont, I will misuse their data for my own purposes.  Thus, the caveat of the day is that I am in no way complaining about what they published, just trying to use their data to illustrate a point.

drosophila-mating

In the interests of having a picture of real organisms, I am including a pair of mating Drosophila.   The Edward et al (2014 Evolution) study is about the genetics of mating in Drosophila.  (Picture taken from http://www.wired.com/wiredscience/2011/06/flies-alter-their-ejaculate-to-get-the-best-bang-for-the-buck/)

What they did in this study was to use a half sib design, crossing each of 16 sires to 3 dams.  They then took the offspring from these crosses and put them in something that I once called an “intermixing ability” type design (sad story why I didn’t call it “ecological combining ability”) (Goodnight 1991 Am. Nat. 138, 342-54).  That is daughters and sons from each cross were crossed in all possible manners in a manner similar to a combining ability study, except that the progeny were not collected and raised, rather the mating behavior of the pair was studied.

This design is an important conceptual shift.  In effect they are treating the mating pair as a group, and the productivity of that group is a function of both the male and female phenotype, and the interaction between them.  My one complaint about their design, which I am sure is a not so much an oversight on their part, but a consequence of the already large size of the experiment, is that they only had one replicate for each full sib family pair, thus it isn’t possible to fully analyze the interaction between cross types.  Given that doing this would have at least doubling the size of the experiment, the decision not to have replicates within cells is hardly surprising.

They then measured three traits.  First, for each female they measured the egg laying rate of the females while they were still virgin, and before being placed with the male, the duration of mating, and the egg laying rate after mating.   Here is where I am going to do a little bit of perhaps inappropriate slight of hand.  First, I am going to call the virgin egg laying rate an “individual trait” of the female since it is done when there is no possible interaction, then second I am going to call the mating duration a “contextual trait” since it is a function of the interaction between the male and female, and third, I will call the post fertilization egg laying rate the “fitness trait”.

Doing this we can then do a regression of post-mating oviposition rate (fitness) on pre-mating oviposition rate (individual trait) and mating duration (contextual trait):

CA of Dros. mating

Click on the table for a clearer view.

It would have been great if the duration (Dur) and the interaction had been significant, but that’s what you get for using data designed for another purpose.  What this is basically telling us is that to the extent that selection is acting on egg laying rate, there is strong selection on female fertility (as measured by premating egg number) but no detectable selection on the copulation duration.

Even though there is no selection on the contextual trait of duration, we can nevertheless measure the heritability of this trait (remember selection and heritability are different things!).  What we need to do is simply do a nested ANOVA of duration, and since we are focusing on the females we will only include the sire and dam of the female.  We shouldn’t expect much since males are assigned to females in all possible combination there can be no population structure, and thus no shifting of the male genetics over to heritability measured using only females.  In any case the analysis looks like this:

dur heritability random assoc.

Click on the table for a clearer view.

The sire variance component is 0.56, so the additive genetic variance for mating duration is VA =  4*0.56  =  2.24.  Since the dam variance component is negative, we can say that VD = 0.  The total variance is 16.28, which implies that the heritability = h2 = 0.14.

At this point we can artificially impose population structure.  A convenient one would be to allow only brother sister mating.  The problem is that, with the data structured the way it is, it is not possible to include dams within sires in this analysis of a subset of the data, still we can get the effective additive genetic variance.  Note that this mating structure enforces a covariance between two partners in the mating group, and should affect the heritability.

variance estimates brother sister mating

Click on the table for a clearer view.

 

Indeed it does.  In this case the variance among sires goes up to 3.87, thus the effective additive genetic variance goes up to eVA = 15.48, and the heritability goes way up to eh2 =0.77.

This is the point I am trying to make about measuring the heritability of contextual traits.  Using the same data set, if we design our experiment using random mixing of interacting partners then the heritability will miss a lot of the variance that can contribute to a response to selection. In this case nearly all of it.  In contrast if we use a breeding design that preserves those interactions we can pull in the association that the interaction structure generates.  Designing such experiments will be like standard quantitative genetics, only hard, and the resulting experiments will be like standard breeding designs only big.  (For the uninitiated that is a joke.  Breeding designs are notoriously difficult to design, and result in notoriously huge experiments.)

Griffing, Associate effects, and heritability.

Posted: March 20th, 2014 by Charles Goodnight

Last week I talked about the effects of localized mating on heritability.  If you remember we discovered the effect was small, at least for weedy species like Plantago lanceolata.  This week I would instead like to talk about the effect of interaction structure on the heritability of traits.  Much of what I will be talking about is discussed more formally in Wolf, Brodie, Cheverud, Moore, and Wade (1998. TrEE 13, 64-9).  I figure there are two conceptual approaches to measuring interaction structure and heritability, one is to preserve the actual population structure in a breeding design – I don’t think anybody has ever actually done this, and the second is to estimate the indirect genetic effects and use them to estimate variance components.  The first is probably “better” in the sense that plugging in the indirect genetic effects into a formula to estimate heritabilities will never be as accurate as actually directly measuring these effects.  Nevertheless it is the second I want to talk about because it makes the point conceptually much clearer, and because in fact, somebody has done the experiment.

That somebody is Bruce Griffing.  If you have not read Bruce Griffing’s work and you are interested in thinks like interactions among plants you should.  He published a great deal between the mid 1950s and the late 80s.  If you want to understand his thinking probably the best paper to start with is Griffing B 1977 (Selection for populations of interacting genotypes. In Proc. Int. Cong. Quant. Gen.,  Pollak, Kempthorne, Bailey (eds.), pp. 413-34 – this is the “red book” that can sometimes be a bit hard to find), but I want to talk about what I believe is his last paper (Griffing. 1989. Genetics 122, 943-56), which is an experiment using Arabidopsis.

Arabidopsis
The mouse-ear cress, Arababidopsis thaliana in its natural habitat, yes, it does have a common name and a natural habitat!  (from http://www.weedimages.org/browse/detail.cfm?imgnum=5400154)
 

It is another great weed (I used them in my thesis) for experimentation, and the nice thing is that they can be grown in sterile medium on agar.

Arabidopsis in agar

A pair of Arabidopsis growing in sterile agar.  This is similar to the experimental unit that Griffing used in his experiments (image from http://research.iheartanthony.com/tag/d2o-effects-on-life-2/)

Griffing used two strains of Arabidopsis, CHI and DI.  Because Arabidopsis is normally self-fertilizing, these plants were homozygous, so he treated them as homozygous inbred strains.  He then made then used the three possible genotypes, CHI, DI, and the F1 hybrid.  These were grown in pairs in all possible combinations in sterile agar.  He also varied the growth temperature and nutrient levels, but we will ignore that for today.

cross types

The basic experimental unit:  Pairs of plants were grown in sterile agar medium.  Each vial contained one plant assigned as the “direct” genotype and one plant assigned as the associate genotype.

These plants were raised, then harvested, washed, dried and weighed, and gave the following results (at 28o, ½ nutrient level):
Means for Griffing

From this it is a small matter to plug these into a two-way ANOVA, and because of the nice balanced structure do a priori contrasts, yielding the following results:

ANOVA Griffing

Basically, what this tells us is that there are highly significant direct and associate effects, and most of those are due to the F1 hybrid that seems to be showing heterosis.

This analysis shows us that there is a genetic effect both on the individual and on its neighbors, however, it is not an estimate variance components the trait.   This is because the ANOVA was done as a balanced unweighted design.  Variance components are a property of both the genotype and the population it is found in.  Essentially, if we do it as a weighted regression of the direct effects this will give us the additive genetic variance for direct effects, and similarly, the weighted linear regression of the associate effects will give us the additive genetic variance for associate effects.  HOWEVER, what we are interested in is the effect of the associate effects on the genetics of the trait under consideration.  This will be given by the additive genetic variance for the associate effects multiplied by the correlation between the (direct) parent and offspring for the associate effects of the interacting individual.  In other words, if pairs are assigned randomly each generation the correlation is zero, and there is no heritability due to the associate effects.  On the other hand, if the interacting pair of the offspring is identical to the interacting pair of the parent, then the correlation is one, and the additive genetic variance of associate effects are fully translated into additive genetic variance for the trait.

direct and associate effects

The trait is measured only in the “direct plant”; however, it is influenced both by the direct effect of the plant on itself, and the associate effect of its partner.   If it helps, (it hurts my soul to suggest this) think of it as the genes in the associate plant affecting the trait in the direct plant.  These associate effects become heritable to the extent that there is a correlation between the associate effects in the parent pair and the offspring pair.

Thus, redoing the analysis using weighted regressions.  Because the effects are almost entirely seen in the heterozygote (i.e., this system mostly has heterosis) the best examples are seen when the gene frequencies are far away from 0.5.  In fact at a gene frequency of 0.5 there is no additive genetic variance for either direct or associate effects.  Therefore, I chose to use a gene frequency of 0.1 as an example.  In that case we get the following results:

Variance estmates Griffing

Thus, at a gene frequency of 0.1 there is additive variance for the trait due to the direct effects of an individual on itself, and there is potentially additive genetic variance due to the interacting partner.  Whether or not this associate effect additive genetic variance is heritable or not depends on the between generation correlation between the interacting partners.  Thus, if the interacting partner is randomly chosen from the population every generation then the correlation will be zero and the associate effect variance will be zero.  In contrast if the interacting individual always has the same genotype every generation then the associate effect variance will be the full value of 0.002.  It turns out that in this simple system unless we use clones of the parents this correlation will tend to be very low, so in this system the contribution of associate effects to the additive genetic variance for an individuals traits will be small. (sadly, it took me a lot of work to figure that out!).

Nevertheless, this raises an important point.  In this study no contextual traits were measured.  Nevertheless, it shows that that depending on the interaction structure the genetics of interacting individuals can contribute to the expression of traits in other individuals.  Under most circumstances the correlation in interaction between generations will be small enough that the associate effects can be considered to be environmental variance, but under certain circumstances that need not be the case.  This will be especially true for things like maternal traits, which because they are heritable means that, for example, a mammal that has a mother with rich milk might be likely to also have rich milk for her babies.  It also emphasizes the point that in traits associated with yield the genetic covariance between direct and associate effects tend to be negative (In this example the correlation is very nearly -1).  With individual selection this negative correlation can lead to an overall negative response to individual selection, something that I saw in my thesis (Goodnight 1985 Evol. 39:545).

As a final note, I dedicate this post to Sunny, who was as fine a cat as I have ever known.  Recently she was diagnosed with lymphoma, and sadly today I had to take her for her last trip to the vet.  She will be missed.

Sunny sleeping

Local mating and heritability

Posted: March 12th, 2014 by Charles Goodnight

Focus focus focus.  There have been lots of articles this week about how kin selection explains everything.  It is very tempting to go off on a heated rant about how it is time to move past 1964, and maybe start doing some science around social evolution.  Instead, I will maintain focus and continue to talk about measuring heritability in natural populations.

One of the sad things about being a theoretically oriented population geneticist is that when you do work with organisms they are inevitably boring weedy plants.  This is a double whammy bad because first, nobody cares unless your study is somehow revolutionary, but also since weeds are, well, weeds, they may not always be the best choice of organisms.  That was the case in a paper I wrote with Steve Tonsor (Tonsor and Goodnight 1997. Evolution 51, 1773).  In this study we examined the effects of mating structure; however, since it was a weed there was no population structure, and thus mating structure had no effect.  Still we can use it as a lesson in how heritability studies might be profitably done in a more structured system.

This study was done using Plantago lanceolata, which we chose because it is so exotic and has such a lovely little flower – Ok, it’s an ugly weed that was chosen because it was easy to work with.

normal_004-plantago-lanceolata

Plantago lanceolata in its native habitat.  (from http://luirig.altervista.org/flora/taxa/index2.php?scientific-name=plantago+lanceolata)

 Actually, the real reason we chose it is that Steve Tonsor had done extensive work on gene flow in this plant, and we knew the pollen flow profile.

Pollen flow profile

(Tonsor and Goodnight 1997. Evolution 51, 1773)

 There are a number of gory details on these designs that you encounter when dealing with real data.  The main one was that we didn’t have enough plants or facilities to do a full half sib nested design.  Instead we ended up using a “pseudonested” breeding design.  I will ignore this detail but only bring it up to emphasize that reality often gets in the way of theory in the experiments.

In any case, we set up two parallel breeding designs.  The first was a standard half-sib design in which we randomly selected 100 pollen parents and mated each to 10 seed parents, with each producing three offspring.  Do the math, that is 100 X 10 X 3 = 3300 plants.  For this design the seed parents were randomly assigned to the pollen parents without regard to where they were physically located in the field.  The second design was identical, except that the seed parents were chosen based on their physical location in the field and, based on the pollen flow distance, the probability that they would have mated with the pollen parent in nature.  Now do the math:  that is now 6600 plants, or 2200 parents.  That is why we ran out of plants and needed to use a bit of statistical slight of hand.

The point is that these two breeding designs were identical, except in the choice of the seed parents.  In one they were chosen randomly using the standard methods such as you might find discussed in Falconer and MacKay  or Becker (If anybody wants to do a service to mankind they will find a way to get this on line because it is WAY out of print).  The second design mimics reality.  This is seen in the distribution of intermate distances.

seed parent distribtion

(Tonsor and Goodnight 1997. Evolution 51, 1773)

In this particular study we thought that population structure is what should be preserved, so we did that by choosing seed parents based on the pollen flow pattern.  The choice of seed parents was still random; however it was not chosen from a uniform random distribution, it was chosen from a distribution of the matings that might actually occur.

In any case these plants were planted out in a prepared field in random order and allowed to grow for one growing season.  At the end of the season they were measured for a number of traits, and the heritability measures for the two mating designs were compared.

Plantago Results

Results of the two breeding design.  “random” is the standard design, “localized” is a design where seed parents were chosen based on the pollen flow distribution.  (Tonsor and Goodnight 1997. Evolution 51, 1773)

The last column is the important one.  These are the estimated heritabilities for the two designs.  As I say, rather sadly, there are no significant differences between the two designs.  Grasping at straws, however, it is interesting that the heritabilities tend to be slightly higher for the localized mating design.

Although failing to get a discernible effect of breeding design was disappointing, but in retrospect not surprising.  I had postulated an increase in heritability for the localized mating design because the localized mating design would have preserved local gene associations.  In other words, the random design would have measured the additive genetic variance as defined by Fisher, whereas the localized mating design would have measured the effective additive genetic variance, that is the variance that was actually available to contribute to a response to selection in the population structure.

The fact that these two measures were not significant is not surprising because Plantago is a weed, and none of these fields are particularly old.  Thus, the plants we sampled were almost certainly relatively recent invaders.  As such it was probably too much to expect that they actually would have set up a significant population structure in that short time period.  We know that population structure is potentially important, thus I am tempted to conclude that it may not be important for outbreeding weedy species, but nevertheless may be important for species in more stable environments that have had time for population structure to develop.

My final thought is that this is actually something of a win-win situation.   On the one hand it does provide a nice experimental design for comparing the effects of localized mating on variance components.  On the other the lack of significance suggests that in many situations it may not be terribly necessary to use a more complicated localized mating design type of a study.

Mating structure, Interaction structure, and Selection structure

Posted: March 6th, 2014 by Charles Goodnight

Many years ago when I was a graduate student, Mike Wade  suggested that we need to consider two distinct types of population structure, mating structure and interaction structure.  At the time I was quite naïve and I constructed my own meaning around that idea.  I doubt he was thinking about it in the way I will talk about today.  Mike suggested is that for any population we really need to think about the mating structure and the interaction structure.  The mating structure being the range over which an individual mates, and the probability of mating with different individuals.  For interaction structure I believe Mike was thinking about indirect genetic effects and how non-random interactions would influence traits.

I want to suggest that the “interaction structure” is actually much more complicated than that.  First, there is the interaction structure, which is my vision of what Mike was talking about:  what does an individual interact with, and how does it affect their phenotype.  Second, when we are talking about heritability we are talking about intergeneration correlations.  This becomes particularly obvious with higher levels of selection, where the patterns of co-migration can qualitatively change the heritability of a trait.  Finally, when we are talking about evolution by natural selection we speak of selection among one object and within another object.  For example selection may be among individuals and within populations, or among cells within the organism, or among populations within a metapopulation.

There is no reason that any of these match up.  For example, in a plant the mating structure may be determined by pollinator flight patterns, the interaction structure might be at the level of the local neighborhood and much smaller than the pollen flow distance, seed flow distance might be the determinant of comigration and the heritability of contextual traits, and the selection structure is determined by the behavior of the herbivore that is deciding where and what to graze.

This is something that is rarely thought about, but it actually can have profound influences on evolution.  Consider the interaction of two of these structures, mating structure and selection structure.  In nearly every selection experiment I have ever seen these two are set to be the same.  Thus, in a typical Drosophila selection experiment all of the flied in a particular bottle will be subjected to the same selection regime.  Thus, the mating structure is the bottle (although it may not be random mating within that bottle), and the selection structure the “within” is also the bottle.  How might you change that?  Well, you could have a set of bottles, some with, say, an insecticide, and some without.  Selection is now taking place at the level of the bottle.  If the bottles were mixed and redistributed every generation then the mating structure would be taking place over a set of bottles (a metabottle?).  Thus, the selection structure would be smaller than the mating structure.  Conversely, you could have a set of bottles, and choose the most resistant flies from the entire set of bottles, and then use a migration scheme between bottles in which flies preferentially went back to their own bottle, but when their home bottle was full they would migrate to less successful bottles.  In this case the mating structure would be smaller than the selection structure.

Considering the first scenario in which the selection structure is smaller than the mating structure, this is pretty close to something we already do.  It is a relatively common practice in integrated pest management to leave refugia for pest species.  Unsprayed crops are maintained so that the insecticide sensitive pest individuals can breed and hopefully slow down the evolution of resistance.  Indeed the FDA requires that no more than 80% of a corn field be planted with Bt-Corn (corn with the Bacillus thuringiensis gene)

refuge

Different patterns of planting corn to minimize the evolution of resistance to the Bacillus thuringiensis gene (from http://www.bt.ucsd.edu/crop_refuge.html)

 resistance

If only life were that simple. . . (from http://www.bt.ucsd.edu/crop_refuge.html)

I really suspect that the scenario of mating structure being larger than selection structure may be that simple.  In effect the larger mating structure simply lowers the intensity of selection and slows down the response to selection.  Of course with lower intensity of selection, particularly with insecticides, comes qualitatively different responses to selection.  The intense selection seen in the early stages of insecticide spraying appears to select for single gene resistance, where as lower rates of mortality appear to select for a more quantitative response to selection.

More interesting is what happens when the selection structure is larger than the mating structure.  Here we are imagining localized mating, and selection acting over a much larger area.  My first thought was that Hamiltonian sex ratios would be an example of this, but of course that is not true.  Female biased sex ratios are a function of multilevel selection acting at the level of the mating unit.  Rather I am thinking about a situation in which there is gene interaction.  In this case the localized mating could result in alleles at interacting loci becoming associated.  At a single locus this would result in the Wahlund effect, that is an excess of homozygotes, and a dearth of heterozygotes.  At multiple loci it could result in the development of what might be called gene associations:  Sets of interacting alleles at different loci that by random chance become associated with each other.  If we imagine a field with a plant with localized mating structure, it could potentially become a mosaic of these gene associations (as well as being dominated by homozygotes within loci).  Because mating is localized these associations would become heritable, and the effective additive genetic variance would be greater than the Fisherian additive genetic variance.  Now our herbivore, say a cow, is wandering over this field and selecting those plants that are most palatable.  It is likely that some of these gene associations would be better tasting, and the focus our cows attention (to their detriment), and others would be less palatable, and the cow would avoid them.  These less palatable patches could then spread bringing their gene association with theme (dare we now call it an adaptive gene complex?).

Note that in this scenario selection is strictly at the individual level.   The cow chooses the best grass to eat, but because mating is localized selection is able to build adaptive gene complexes in a way that would not be possible if the selection structure and the mating structure were matched.

 

Some Initial Thoughts on Breeding Designs

Posted: February 28th, 2014 by Charles Goodnight

This week I don’t particularly have any papers to review, just some thoughts.  In the past few weeks I have been showing the power of contextual analysis as a means of measuring the strength of multilevel selection in natural populations.  The problem, of course, is that a selection analysis is, correctly, strictly a phenotypic analysis of selection.  Mathematically this can be illustrated with the breeder’s equation:

R = G P-1S

Selection analyses, including contextual analysis only apply to the blue P-1S part of the equation. This is why the Molofsky data can be used both as an analysis of community ecology, or (mis) interpreted as community selection acting on species richness (when fitness is defined as below ground biomass of the reed canary grass).  The big determinant of whether it is community ecology or evolution by community selection is in the G matrix, which is not measured in selection analysis.

Actually, at this point, it is probably worth reiterating the famous list made by Lewontin in his article on the Units of Selection (R. C. Lewontin 1970, Ann. Rev. Ecol. Syst 1:1 – yes that citation is real).   In that paper Lewontin lists the properties of a population that are necessary and sufficient for evolution by natural selection.  To quote him exactly:

1. Different individuals in a population have different morphologies, physiologies, and behaviors (phenotypic variation).

2. Different phenotypes have different rates of survival and reproduction in different environments (differential fitness).

3. There is a correlation between parents and offspring in the contribution of each to future generations (fitness is heritable).

(Lewontin 1970 Ann. Rev. Ecol. Syst 1:1)

The rather mundane point is that if we are really going to think about the evolution of contextual traits, we need to understand what we mean by the heritability of these types of traits.  There are actually two problems, a conceptual problem:  What do we mean by the heritability of contextual traits, and a practical one:  how do we measure the heritability of contextual traits.

The conceptual problem of what do we mean by the heritability of a contextual trait is actually pretty straightforward.  Consider a contextual trait such as population density.  Then the heritability variance for population density would simply be covariance between the population density the parent experienced and the population density the offspring experienced.  Aside:  Note that I am calling it heritable variance, rather than the additive genetic variance.  As discussed earlier this is additive genetic variance has a very specific definition, which under most circumstances does not cover contextual traits.  Indeed, it is not at all clear to me that the term “genetic” necessarily applies to all causes of heritability of contextual traits, thus, I go with the generic term “heritable variance”.

In any case, this line of reasoning can be applied to any contextual trait:  The heritable variance is the covariance between the parental (or weighted? average parental) value of the trait and the offspring value of the trait.  That is fine for a conceptual definition of the heritable variation for a contextual trait, not so good from a practical perspective.

The problem is that our standard methods of estimating heritability and additive genetic variance specifically remove the effects of interactions among individuals and environmental effects.  Consider a classic method of estimating additive genetic variance, the half sib breeding design.  In this design a set of males (sires) are each mated to a set of females (dams):

Breeding design

Standard half sib breeding design.  A set of sires (blue squares) are each mated to a set of Dams (pink circles).  Each dam produces a set of full sib offspring (green dots).  A nested analysis of variance is used to divide the total variance among the offspring into variance among sires (covariance of half sibs), and variance among dams with sires (covariance of full sibs).  The additive genetic variance is 4 times the variance among sire half sib families.

There are a couple of things that should be obviously wrong with this.  The traditional half sib breeding design was designed for the agricultural industry.  In agriculture the breeder has control over the mating system, and as a result starting with the assumption of random mating makes sense.  Nature is not like that there is localized mating.  Thus, individuals that live physically close to each other are more likely to mate than ones that are physically widely separated.  Thus, in the traditional half sib breeding design dams are randomly assigned to sires.  In nature it won’t be like this.  So, perhaps the first thing we should reconsider is whether dams should be assigned to sires randomly given each dam an equal probability of being chosen, or perhaps it would be better to choose mates based on their probability of actually mating in nature.

The second thing is that technically the design could be reversed (each dam mated to multiple sires).  Besides a number of technical problems, the real reason we do this is that there are “maternal effects”.  Thus, at least in animals, the mother contributes a lot of “stuff” to the offspring that the father does not contribute.  These include cytoplasm – mitochondria are mostly maternally inherited, and potentially vertically transmitted pathogens, and this influence of the mother continues past birth.  For example, in mammals the female, but not the male, nurses the young, so there is much more potential for non-genetic resemblance between mothers and offspring than between fathers and offspring.

This is all fine if we are interested in only traits of the individual, but if we consider things like quality of mothers milk to be a contextual trait, then we are designing this trait out of our experimental design.  This becomes more extreme if we are using contextual traits such as neighborhood characteristics as the contextual trait.  In standard quantitative genetics we would be careful to randomize the environments in which the offspring are raised – we might, for example plant offspring in random order in a field or randomize the position of pots.  This is in fact destroying the very covariance we are interested in.  The covariance among contextual traits in parents and offspring is as often as not driven by the ecology of the setting, and it is this ecology, and thus the covariance that we destroy when we randomize the environments in which we raise the offspring.

Of course, randomization is the hallmark of good experimental design, so it would seem that measuring the heritability of contextual traits is at some level at odds with good experimental design.  Is all lost?  I don’t think so.  I think it just means that we need to carefully think through what it is we want to measure, and to preserve the ecological associations that we think are important, but randomizing what we can.  For example, we could collect seeds from not only our experimental offspring, but also from the neighborhood of the seed parent.  Then we could plant neighborhoods randomly in the field, and have each experimental offspring surrounded by the offspring of its parent’s neighbors.

 

Group selection in Monkeyflowers?

Posted: February 19th, 2014 by Charles Goodnight

I found a good very recent article that can be used to illustrate my point that we have a lot of data sets that show multilevel selection; however, because of the data sets were collected for a different purpose, they have never been used to look for multilevel selection.  The paper is on the effects of community composition on the pollination biology of Mimulus (Arceo-Gómez and Ashman 2014.  Am. Nat 183:E50 – E63).  First off, it is a good paper, and I have no concerns about their analysis at all.  Dr. Arceo-Gómez was kind enough to give me access to his data, for which I am quite grateful.  They collected data in several experiments, and only some of those data can be used for contextual analysis.  There is also the hidden joy of looking for group selection in one of Doug Schemske’s favorite plants – Doug was a starting assistant professor at the University of Chicago when I was a graduate student.  We never did see eye to eye on the matter of group selection . . .

In this study Arceo-Gómez and Ashman studied monkey flowers growing in isolated “seeps”.  Seeps are springs where the soil is moist and thus can support a variety of flowering plants.  They are surrounded by much drier soils that are primarily grassland.  In each seep they measured a number of traits, including the flower morphology of the Mimulus, and the number of co-flowering species in the seep.  The goal of this study was to discern whether the number of coflowering species affected the reproductive biology of the monkeyflowers, and as you might expect this is exactly what they found.

Mimulus guttatus

Mimulus Guttatus, the monkeyflower.  (from http://fizzynotions.wordpress.com/author/axldebaxar/page/6/)

For example, they observed a significantly lower pollinator visitation rate and a higher deposition of heterospecific pollen in sites with a high diversity of coflowering species.

pollinator visits by site diversity HP pollen by site diversity

 
Mean (+/- SE) for insect visitation rate (A), and proportion of heterospecific pollen (HP) on the stigmas (C) of Mimulus guttatus flowering at sites with high coflowering community diversity and low coflowering community diversity. Asterisks denote significance of preplanned contrasts between high and low diversity; one asterisk indicates P <.05 and two asterisks indicates P < .001.  (not shown:  (B) conspecific pollen (CP) receipt on stigma after 1 day of open pollination) (Arceo-Gómez and Ashman 2014.  Am. Nat 183:E50 – E63)

 

They also found that flower longevity was associated with the number of coflowering species.

flower longevity by sp richness

Correlation between Mimulus guttatus flower longevity assessed under common greenhouse conditions and the in situ number of coflowering species at the site (r = 0.52, P = .01, n = 23). (Arceo-Gómez and Ashman 2014.  Am. Nat 183:E50 – E63)

Some of their data cannot be used in a contextual analysis.  In particular, the pollinator visitation rate data is based on visitation to Mimulus in general, not to the specific individuals. Thus, we cannot do a regression of individual fitness on the trait.  If we look the effect of coflowering on flower longevity it appears that there is is “community selection”  occurring.  In this case it turns out that there is a strong relationship between the mean flower longevity, and species richness, I get the same values they get, but that there is enough variation at the individual level flowering times that the effect of species richness is not quite significant (p = 0.079).   Nevertheless, it argues that to the extent that longer flower life is a fitness component that there is “community selection” for increased coflowering.

Another rather iffy measure of “fitness” is amount of heterospecific pollen.  As shown above it is significantly affected by coflowering species richness.  Again we can do the regression and find that we come up with the same result.  It is actually interesting to throw in a community, a population and an individual trait.  For the community trait I chose species richness, for the population trait, mean flower size, and for the individual trait, flower size.  If we run that analysis with amount of heterospecific pollen as our fitness trait we get:

heterospecific pollen

Contextual analysis of heterospecific pollen load as a function of community, population and individual level traits.

You can decide whether the individual trait is significant, but the community and population traits clearly are significant.

My problem is that I don’t see why heterospecific pollen is a good “fitness trait”.  Thus, I think it makes more sense to use conspecific pollen load as our fitness trait.  If we do the same analysis using conspecific pollen load as the dependent variable we get:

conspecific pollen analysis

Contextual analysis of conspecific pollen load as a function of community, population and individual level traits.

In this case, it turns out that there is only one trait that is important, that is the mean size of flowers in the population.  The smaller the flowers, the more pollen gets deposited apparently.  This analysis seems to confirm the pattern we have seen before:  Group selection is very important in plants, and, in many cases, probably of considerably greater importance than individual selection.

I doubt that this is the best data set I could have used for this demonstration, and there are a number of things that would make me less than enthusiastic about publishing my “re-analysis”.  One is that I would want to understand the biology of monkeyflowers a lot better before I started calling flower longevity and heterospecific pollen loads as fitness traits.  I also picked flower size more or less randomly.  Again, understanding the biology would help a lot.

However, the big issue here is that (miss?) using contextual analysis it is possible make the claim of  “community selection”  any time a trait is correlated with a community level characteristic.  This is the problem of all phenotypic based selection analyses.  That is, there is no statement about heritability.  I am very doubtful that there is any sort of heritability for species richness in Mimulus, although there may be contextual heritability for flower size.  Thus, while we may indeed be able to show that plants in some types of communities have higher fitnesses than plants in other types of communities, as long as we are working strictly at the phenotypic level, it almost seems a matter of taste whether we call that community evolution or community ecology.  This raises a big question about the heritability of contextual traits.  Its not one that I have a nice clean answer for, but it is something I can at least discuss some of the issues.

Contextual Analysis and Community Ecology

Posted: February 14th, 2014 by Charles Goodnight

OK, last, or maybe next to last essay on the use of contextual analysis.  The reason I say maybe next to last is that I strongly suspect that a lot of people have data sets that could be used for contextual analysis.  Thus, I would suggest that without collecting any more data we could greatly increase our examples of group selection in nature.  If I can find the right data set I will show you what I mean.   This week, however, I want to look at an entirely different use for contextual analysis, and in the process perhaps point out the surprisingly tight linkage between population genetics and community ecology.

In a study published in 1999 Jane Molofsky and her graduate student, Shannon Morrison and I published a paper on the invasability of Reed Canary Grass (Phalaris arundinaceae) (Molofsky, Morrison, and Goodnight1999 Biological Invasions 1:181).  Reed Canary Grass (RC grass) is an invasive grass species that was originally introduced as a forage crop, and to stabilize areas prone to erosion (we never will learn, will we).  Since then it has become a problem, and is now classified as a pest species.  The interesting thing is that this pest does not always invade an area, so the question becomes why  does it invade some times and not others?  We figured there were two possibilities to consider.  First it might well be that there are some strains that are more invasive than others, and second it may be that some aspects of the environment or the community may affect the ability of the plant to invade.  It would be nice to separate these apart, and because RC grass can reproduce clonally it is something that is experimentally feasible.  That is, we can plant out clonal replicates into the field.  Of course the problem with this is that a natural field cannot be standardized, and these differences in community and environment will be confounded with any genetic effects we see.

Reed Canary Grass

Reed Canary Grass, Phalaris arundinaceae (photo from https://www.uwgb.edu/Biodiversity/herbarium/invasive_species/phaaru01.htm)

 To test this we (OK, I will be honest, Shannon) chose three clones of RC grass that Shannon knew were distinct, and did two things.  First she took 50 ramets from each of the three clones and planted them out in 150 randomly chosen locations in a flat section of pasture in Jericho Vermont.  She allowed one week for them to establish, then randomly selected 30 individuals from each surviving clone.  Around each clone she measured the community, both for the area covered by each species, and measures for diversity and species richness around each plant.  This gave us the community measures.   At the end of the season the plants were all harvested, whether or not they were alive, tiller number, and above ground and below ground dry weight were measured.

For the genetic measures she took 10 ramets of each clone and grew the in a greenhouse individually in large pots under uniform conditions.  The large pots were chosen because they were large enough to allow the plants unrestricted growth.  After one month the plants were measured for the number of tillers they had produced and the above and below ground biomass (dry weight).

Note the important point here.  We have two very disparate types of data.  From the field we have “fitness” measures, growth, and survivorship in a natural competitive environment, and measures of the species and diversity in the community with which they are interacting.  From the greenhouse we have the growth the plants in the absence of competition.  To combine these we did a contextual analysis suing the “fitness” traits (really measures of their ability to invade), and used the community and greenhouse data as independent variables.  We fully recognized that growth in the greenhouse was not a measure their performance in nature, however, our thought was that growth rate in the greenhouse would be correlated with characteristics that were important in a competitive environment.  If that was the case then the greenhouse measurements should be predictive of growth in nature.

There were a lot of parameters measured, percent cover  of 11 different species, species richness and community diversity, soil moisture, and the green house measures (tiller number, above and below ground biomass), so we chose to use a stepwise regression to select a subset for the analysis.  This is always a problem:  We did not have an apriori idea of what would be important and therefore measured as much as possible.  This will inevitably be a bit “post hoc”, but then that is often the nature of field work.

In any case, on to the results.  First we found that the survival of the different clones was entirely do to clonal differences, and apparently unaffected by the community or environmental factors we measured.

Molofsky figure 1

(Molofsky, Morrison, and Goodnight1999 Biological Invasions 1:181)

This difference in field survivorship was significant in a logistic regression, with tillering rate being the best predictor of survivorship.

Molofsky CA table 2

 

(Molofsky, Morrison, and Goodnight1999 Biological Invasions 1:181)

 

The other analyses become more complex.  For your interest I will put in the analysis:

Molofsky CA table 3

(Molofsky, Morrison, and Goodnight1999 Biological Invasions 1:181)

but I think a verbal description is more useful.  Basically we found that above ground biomass is primarily a function of the community, and particularly the percent cover of Anthozanthum odoratum.  The greenhouse measured traits having almost no effect on above ground biomass.  In contrast the below ground biomass was a function of both clonal characteristics (tillering rate) and community characteristics (cover of A odoratum and species richness).  Finally the above ground to below ground biomass was a function primarily of the community charactistics and the soil moisture, with a possible, but non-significant effect of tiller number.

In short, the ability to survive appeared to be primarily a function of the genetic characteristics of the plant, and particularly its tillering rate, whereas the competitive ability, and a particular plants ability to establish and spread in the community appeared to be a function of both the plants intrinsic characteristics and the specifics of the community and environment in which it was placed.

This study is rather far afield from the emphasis of this blog on evolutionary theory, and yet it really isn’t.  It shows that the basic concepts of contextual analysis can be applied in a wide range of situations.  In this study the greenhouse and field data are really incompatible and cannot be combined using traditional methods, and yet with contextual analysis they can be combined.  The important caveat, of course, is that while the significance levels are useful, the actual values the regression coefficients take on are perhaps less useful because of the very different nature of the greenhouse data.

The second point that this shows is that community ecology and multilevel selection are really not that different.  Because of the experimental design, in this situation we know, that the  “heritability” of the community structure is zero, so it makes sense to interpret this using an community ecology framework.  However, it does not seem that far fetched that a similar study could be interpreted in a community selection framework instead.

As a final note, I will also mention that this similarity is not unique to contextual analysis.  For example, the community diversity measure used in this study is Hurlburt’s PIE.  It turns out that this is mathematically identical to the formula used to calculate F, the inbreeding coefficient.

Contextual Analysis and Sexual Selection in a Human Population

Posted: February 6th, 2014 by Charles Goodnight

Evidence from Google analytics seems to suggest that people like examples of studies involving contextual analysis (or maybe its sex, hard to tell).  With that in mind I will spend the next two weeks on two different studies.  Next week I will discuss an example involving community ecology, however, this week I want to talk about Jake Moorad’s study of sexual selection in humans (Moorad 2013 Evolution 67: 1635).

Humans differ from other organisms in at least three respects.  First, by in large manipulative studies are not possible, second, humans keep records about themselves, and third sometimes they get offended when their privacy is compromised.  The first of these is a problem if for no other reason than it is difficult to remove confounding factors.  The second can be a huge advantage.  The Moorad study uses the genealogical records kept by the Mormons.  The data set is very complete, and the sample size is 741,851 reproductive adults born between 1840 and 1970.  The third is a problem in that the Mormon Church is very careful with these data, and is very cautious about whom they allow to have access to their records.  We are very lucky that Jake was able to get approval to work with these data.

These data are interesting because 1840 is the year that the Mormons founded a major city (Nauvoo) in Missouri.  At this time the Mormons were in the middle of a long migration that would eventually land them in Utah.  Survivorship of children was relatively low and the Mormons actively practiced polygyny.  By 1850 the Mormons had moved out to Utah, and finally started the process of settling down.  By 1890 the Mormons had renounced polygyny, and had established themselves in what would end up being their permanent home.  Thus these data span an important demographic transition from a period of high infant mortality and reproduction to a “demographic transition” to much lower reproductive rates and much higher survival.

Moorad Mormon Lambda

“Intrinsic population growth rates decreased over time. Dark circles indicate intrinsic population growth rate for each birth year cohort using the two sex model. Open circles indicate intrinsic population growth rates for the female-only population, as estimated by Moorad (2013 Evolution 64:1622).” (Moorad 2013 Evolution 67: 1635). There is a demographic transition that occurs around 1850 – 1860 when the family size suddenly starts to decline.  This corresponds to when the Mormons started to settle in Utah.

We also see that rates of polygyny peak around 1860, the time of the demographic transition.  It turns out that rates of polyandry also decline.  I am told that Mormon women never had more than one husband at a time, thus, this decline probably reflects a decline in mortality of reproductive age men, that is women only remarry if their husbands die at a young age.

Moorad polygyny rates

Rates of polygyny and polyandry in individuals (G and A) and in their parents (Gsire and Adam) changed with time. (Moorad 2013 Evolution 67: 1635).  Note the peak around the time of the demographic transition.

One other interesting sort of a graph that makes intuitive sense, but took me a bit by surprise.  It turns out that there is much more variance in whether persons father was polygynous than whether an individual male is polygynous.  I am pretty sure that this is because a fairly large fraction of males come from polygynous families, whereas only a relatively small fraction of males become polygynous.

variance in polygyny

“Expression variances for parental multiple mating exceeded variances for individual multiple mating in both sexes.” (Moorad 2013 Evolution 67: 1635).  G = polygynous male, A  = polyandrous female, Gsire = offspring of polygynous fathers, Adam = offspring of polyandrous mothers.

One trait that Moorad examined was whether an individual ever mates.  An individual can fail to mate either because they do not survive to reproductive age, or they can fail to mate because they never marry or are sterile (the records account for births, thus a barren marriage would count as non-mating).

Moorad regression coefficients by birth year

The strength of selection for male mating traits was greater than that for females. Family- and individual-level selection for polygyny was equally strong, and both declined over time. Family-level selection for polyandry exceeded individual-level selection.  (Moorad 2013 Evolution 67: 1635)

That there is individual selection is hardly surprising: polygamous males and females had a higher chance of producing offspring than those with a single spouse or no spouse.  Selection on the family of origin is more interesting.  Here it can be seen that there is selection favoring individuals that come from polygynous families.  That is individuals born into multiple female families were more likely to survive and reproduce.

So is this “group selection”?  Well this is the problem with the old terminology.  It is certainly safe to say it is not individual selection.  The family of origin is a contextual trait, it is not a trait of the individual.  Thus, we need to call it something.  Traditionally that “something” would be group selection, however, it is probably better to go by the more recent term “multilevel selection” and to give that level a more descriptive term, such as family level selection or family of origin level selection.

The problem with this data set is that there is so much data, and it covers a time period in which different birth years cannot be considered comparable.  For example, those with birth years prior to 1850 were born in a time when a significant proportion of families were headed by a single male with multiple wives, whereas very few if any of the 1890 birth year was born into such families.  Making summary statements over this time period is difficult.  Moorad’s solution was to define fitness as the reproductive value at birth, which is roughly the age and population size weighted lifetime reproductive success.  He then used this measure of fitness to calculate the opportunity for selection, I, defined as the variance in relative fitness.  Finally, he used contextual analysis in a regression of relative fitness on the individual traits (whether or not an individual reproduced, whether or not they were polygynous or polyandrous) and the group traits (whether or not their parents were polygynous or polyandrous).  Rather than reporting the individual regression coefficients, he plots the proportion of the variance in relative fitness that explained by each of these factors.  The problem with this approach is that there are an unimaginable number of statistical tests that can be done, and the multiple comparison problem gets out of control.  Thus, it is best to just look at the plot and interpret it.

Selection on humans

“Mating-related traits contributed differently to the opportunity for sexual selection, and these relationships changed with time. Most notably, the effect of female ever-mating variation increased and the effect of polygyny decreased.” (Moorad 2013 Evolution 67: 1635)  In the early years there is a notable variance in reproductive success that is attributable to whether or not an individual is polygynous.  I(M), I(F) = opportunity for selection due to whether or not an individual male or female ever produced offspring, I(G), I(A) = opportunity for selection due to whether or not an individual is polygynous or polyandrous, I(Gsire) + I(Adam) = selection due to polygyny or polyandry in an individuals family of origin.

What this plot shows is that when looking at overall fitness, family level selection has relatively little impact on the overall variance in relative fitness.  Interestingly, even whether or not a male was polygynous had much less effect on the variance in relative fitness than does whether or not an individual ever mated.  This is actually a fairly typical result of sexual selection.   The number of males that have a large number of offspring is small enough that it doesn’t contribute much to the variance in reproductive success.  Far more important is the number of males that don’t produce any offspring.  The importance of the polygyny on the proportion mating can actually be seen in the figure above.  Note that up until 1860 the intensity of selection on males as to whether or not they ever mate  is stronger than it is in females, whereas the two become equal in 1860 which is when polygyny was declining.

In sum this is a great example of the use of contextual analysis in an unmanipulated human population.  Studies such as this show that the multilevel selection approach applies in situations that are very different than the traditional “group” of group selection.  Even though these are not groups in the sense that Maynard Smith defined groups, they are part of the same mathematical continuum, which indicates that where you draw the line between what is and is not group selection is at best arbitrary.

Contact Us ©2010 The University of Vermont – Burlington, VT 05405 – (802) 656-3131