It’s the demography, stupid!

Of Sharks, Giraffes and Malthus. Mostly, Malthus.

First, let me make it clear that, as far as I know, the sentence “It’s the economy, stupid!” was never used, orally or in writing, by the (Bill) Clinton campaigns. It may be a nice summary, but it was never used as such. But let us go back to our topic.

One day, I was teaching teachers who teach biology teachers how to teach biology (yes, it is a true sentence). And I asked the teachers’ teachers to spell out the mechanism of evolution by natural selection. Everybody told me: there is variation; variation is heritable; then the best individuals survive/produce more offspring and evolution happens.

That’s right. Selection is perceived as a sort of mechanism testing ‘adequacy’ (we call it ‘adaptation’) of individuals to their environment. Somehow, we’re still pretty much innately Lamarckian*, after all: we think in terms of how an individual copes with its everyday problems. While this is certainly an important component of ‘adaptation’ in general terms, and while for sure it is individuals (and not genes, or populations, or – heavens forbid – species) who survive or die, reproduce or not, the description falls short of describing the actual mechanism of evolution, because it misses an essential component.

Thinking in terms of individual properties and problems is not the exactly right way of looking at selection and adaptation. As the Australian saying goes, “when there is a shark in the water, you do not need to swim faster than the shark: you need to swim faster than the slowest swimmer” (I’ll let you generalise to the case where there are n sharks in the water, I know you can manage; and I’m sure you know a regional version of the saying, with other threats than sharks). The point is that selection is not about individual relationships to the environment, but about whether you do better or worse than somebody else.

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And this brings to the fore the essential cog of selection’s machinery that many people (including biologists – except for evolutionary biologists themselves) miss: demography. It is because there are always many more siblings than the environment can carry that eventually some of them die / do not reproduce.

King_Penguins_(Youngs)

The “fitness” part of the game is, of course, that those who exploit the available resources better / better cope with stress perform better from the survival / reproduction point of view, and leave more offspring (the way the parents’ traits are inherited does not change a thing). If there were infinite space and resources, nobody would suffer from selection, and everything would behave according to neutral evolution. Darwin borrowed the idea from Malthus, as everybody knows, and this is the piece that makes the difference between any evolutionary hypothesis and the Modern Synthesis’ successful one** (in terms of explanatory power). It is because some individuals die / do not reproduce that there is adaptation. Somehow, we should be happy to observe (moderate amounts of) mortality (in forests there’s a lot of it) and unequal fecundity in populations, because this is how adaptation occurs.

The Malthusian piece of Darwin’s genius idea is understandably hard to swallow. As one of the teachers’ teachers exclaimed, after I had pointed out the strict necessity of the cruel Malthusian piece in the Theory: “oh, that’s so SAD”. Yes, life is unfair, but adaptive biological evolution happens only if there are winners and losers. Now, if I were in the losers’ camp, I’d rather prefer no evolution-by-selection to happen at all, but this is the way it is – no social or anthropocentric judgement to be attached.

 

 

*Lamarck, in spite of his post-Darwin very bad press covfefe, was a true evolutionary biologist, and a clever one at that. He lacked some important pieces of understanding of how selection works, but then again, Darwin too had silly views about heritability of traits.

** I refrain from attributing the idea entirely to dear uncle Charles for two reasons. First, he lacked the mathematical formalism to model the mechanism; second, I disagree with the identification of Evolutionary theory with one person, no matter how grateful we should all be to the genius that was Charles Darwin. After all, nobody talks in terms of “Einsteinism” or “Röntgenism”, so why should we talk about “Darwinism”?

Where have all the forest geneticists gone?

Missing mass of forest population geneticists at conferences leaves me wondering why they stay home

I’m back from a couple of conferences: the ESEB meeting in Groningen and the SIBE meeting in Rome.

Both were terrific, and both allowed me to come back home with the usual mix of excitement (for the impressive amount of good science that people do, and for the truckload of good ideas I could grab) and frustration (for not having done myself all that good science!).

Among other things, I must stress the feeling of being (at 47) among the eldest at both conferences – and this is a very positive remark: of course, one gets older and thus climbs the pyramid of ages, but I reckon that evolutionary biology conference-goers are, on average, pretty young and impressively competent. This spells good for the future of evolutionary biology!

primaryschool

Yet, I’ve been wondering throughout both conferences where all my fellow forest evolutionary biologists were hiding. Certainly, those two conferences do not focus on forests, but they do not focus on fruit flies and mice either, and I’ve been hearing plenty of talks on those critters. For sure, forest trees are not “model” species, but the share taken by model species at both conferences was, globally, very small, so there must not have been a “filter” against papers on trees. The fact is, there were very few forests across the conference landscape. Somehow, I felt slightly lonely with my forest population genetics talks and posters.

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Yet – although I’ll provide no list, for fear of omitting somebody – I know plenty of forest scientists having provided major contributions to asking and answering overarching (*) evolutionary questions and to developing evolutionary theory: evolutionary biology is a relevant playground for forest geneticists. So why was I so lonely? Why the attendance of forest geneticists, young and old, to general conferences is decreasing? Are they all busy tending to their science, with nothing worth sharing in their hand? Or is their budget, both in terms of time and money, decreasing so abruptly that they cannot afford those meetings any more? Or maybe they are folding back on their community?

To check, I had a look at the program of the IUFRO general meeting, that will be held in Freiburg next week – IUFRO is the United Nations of forestry research, every forest scientist goes to a IUFRO meeting every so often. And even there, although I have carefully scrolled all symposia and checked speaker lists, I could barely find the names of acclaimed and less known forest geneticists. Essentially, our research field will not be represented there, either (well, I confess: I am not attending, but I could not go to three conferences in less than a month).

Forest geneticists are deserting both general evolution / evolutionary genetics events and forest-focused meetings. Why? And – apart from forest genetics conferences – where do they go? I’d very much like to know the answers to those questions. Plus, I would like to say that it is very important, for junior and senior scientists alike, to get out of our “comfort zone”, and mix with people doing (relatively speaking) entirely different things. As I said above, one comes home with his suitcase full of great ideas.

(*) It is good to fit the word overarching into a text, from time to time. It makes you feel important.

A whole biome ablaze?

And it burns, burns, burns,
The ring of fire, the ring of fire.

Mediterranean forests are burning.
All of a sudden, Portugal, France, Italy, Greece… fires – sometimes large, out-of-control, deadly wildfires – are burning all over Mediterranean forest ecosystems.

Hot temperatures, little rainfall, strong winds, and a dense human population: all factors are there for the perfect firestorm. If this is what climate change has in store for us, well, the outlook for Mediterranean forests is bleak.

Incêndios_Pedrógão_Grande_2017-06-18_(02)

Besides stopping climate change (ha ha ha!) and fencing humans out of forests (unlikely to work, either) what can be done?

There is only one word: MANAGEMENT (the alternative is: ashes).

My lab‘s director, Eric Rigolot, has provided some clues  in an interview (in French) with the French Huffington Post website. What does he say? That we have to use managed fires to prevent big, uncontrolled wildfires. This technique is current in other continents, but not in Europe.

I would add: vegetation itself (the fuel) must be managed in ways that minimise fire expansion, if not ignition. This is particularly true where human beings are likely to wander, because they are most of the time, albeit often unconsciously, the source of fires.

Forests must be tended to, must be gardened. In Europe, they’ve stopped being wilderness a long time ago, so the potential argument that, by managing forests, we alter some fancy natural equilibrium, is nonsense. It is maybe valid for some truly pristine biomes (if there is any), but not in Europe, not around the Mediterranean basin.

This means we are responsible for the health of our forests, including by limiting the effects of fires that we are the primary cause of.

Firs are dying, beeches are almost fine, but for how long?

Going through and iconic mountain forest in Southern Europe leaves little hope for what is coming next.

Yesterday I was on Mont Ventoux (Southern France) to sample beech leaves for the BEECHGENOMES project.

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One can see the silver firs dying there (at around 900 m a.s.l.). The understory shows the occasional fir (and more commonly, beech) sapling and seedling, but what mostly grows there is a shrub, boxwood (Buxus sempervirens), and even boxwood, when it grows in a gap, does not fare so well. What will be left of the beautiful Ventoux forest in 30 years?

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Out of 166 adult beech trees belonging to the long-term survey cohort, we’ve found “only” nine dead (“only nine”!? that’s 5.4%… and the last check was only few years ago). Most of the others looked fine with no visible sign of stress, but this year, with so little rainfall and many strong heatwaves, they are likely to shed their leaves early August. Growing season over.

Not very happy, my goodness.

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Of budgets and schedules

You know perfectly your grant proposal’s cost per nucleotide and per fieldwork day. But did you budget data analyses?

Once again, I had to write a project’s final report. Once again, I found myself writing that ‘data have been produced, and we are carrying out data analyses’. This seems to be accepted as consolidated report. Nobody expects that, when the project is over, the data have been analysed. Yet, we all obviously claim that science is not about accumulating data, but producing and interpreting results*.

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Why do we take it as granted that a research program is complete without data analyses? The answser is ridicously simple: it is because we seldom schedule or budget data analyses. In our unconscious mechanistic-positivist-reductionist-platonian mindset (yes, you too you have such a mindset. You were raised like this, as a scientist), data analyses automatically derive from first principles, so they cost no time and no effort; they are an instantaneous act of revelation of patterns and laws from the data.

This reminds me of the joke, common among physicists, about the mathematician who dies of starvation because he never actually cooks his meals: once he has verified that all ingredients are in the cupboards, he considers that the meal is done. So he never eats. [I do not think mathematicians are like this. Physicists, especially experimental physicists, do].

But when we think again, we perfectly know that there is no such thing as instantaneous, self-organising data analysis. Data analyses cost “blood, toil, tears and sweat and enormous amounts of time and money (think not only computers and licence fees, but also salaries).

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Since I have stopped spending my days doing silly things like pouring acrylamide gels and scoring bands on an X-ray film or even peak profiles on a screen (that’s a long time ago, luckily), data analyses take about 80% of my working time (not counting for grant proposal writing, paper writing, emptying the coffee room compost tank, and writing blog posts about time spent writing blog posts).

It should be obvious to all, but a project is only over when (at least) data analyses are over. To achieve this feat, we first need to honestly schedule data analyses.

So atone, you sinner, and go back correctly scheduling the next six months of your activities.

 

*The publication of ‘data papers’ is becoming current, but this is a different matter: the need to publish stand-alone data sets highlights even more the need to make them available to a larger community, so that they can be more easily analysed.

Genes, genes all over the place

Genome-wide association studies show that characters are genome-wide associated. So what’s the point?

Years ago, while I was screening Table of Content alerts for interesting stuff (I’ve been doing this once a week for years: Friday, I’m in love with newly published science), my eye was caught by one of those high-impact, very technical human genetics papers where they find a new gene underlying some very serious disease. It turned out that the newly identified causal variant accounted for 0.6% of genetic variance. Wow. Then I said to myself: hey, what do you know about how the field of disease genetics works? Even a 0.6% effect can be important, if it can save a life.

And then came the Boyle et al. paper, few days ago, on the ‘omnigenic’ genetic structure of complex traits.The paper shows that the genome is not even scattered with, it is smeared with loci controlling complex traits. Many of those loci have very small effects, and could only be detected in studies with very large sample sizes. There is nothing strange to this: the more intensely you chart the territory, the more detailed the map, and the smaller the features that appear in the map.

zooMap

The vast lists of genes associated to a given trait are not particularly enriched in some functional categories; probably, many genes have an impact on many characters (they are pleiotropic) and are involved in many partially overlapping regulation networks, as suggested by the fact that many causal variants happen to be in regulatory regions.

Indirectly, this also suggests that candidate-gene strategies may have a problem (you’ll certainly find a particular category of genes to be associated with the trait: all categories are…), as well as looking for relevant variants only in coding regions (well, we all knew this, did we not?).

But let us go back under the canopy.

In trees, it is quite common that traits diverge across populations (forest scientists call them “provenances”: the word is the heritage of the strategy of planting multiple populations together to assay their performances), but gene frequencies do not. This phenomenon is well described by Kremer and Le Corre (2003, 2012 (1), 2012 (2)) and suggests that adaptation (and therefore the control of underlying adaptive traits) is highly polygenic. There you are. One can expect that, given enough power, association studies in trees will end up detecting very large numbers of small signals, too.

There are very few GWAS’s in trees (Fahrenkrog et al. (2016), for example, has  about 18,000 genes mapped from a population of about 400 trees. I can hear the average human (or Arabidopsis) geneticist sneering).
One good reason is that to perform a GWAS you first need a G[enome], and there are not so many tree genome sequences so far. Other reasons may be less clear, except for the argument that GWAS is still relatively expensive and forestry is not the research field that attracts the largest funds. Forest GWAS studies in which millions of trees are screened are even rarer (read: non-existent). Plus, if you are looking for weak effects, you must be very careful about how you pick your sample: even slight, undetected differences in the ontogeny (developmental path: how the trees have grown) or environmental conditions can have a larger effect than weak genetic differences, which will be drowned in the background noise.

Yet, there is hope.

From the evolutionary point of view, small effects may be more relevant than when trying to predict susceptibility to a disease. First because, as stated above, they can have a cumulative effect on fitness (even without accounting for interactions, which could amplify their effects); and secondly, because selection is a powerful force (we have commented on this before, haven’t we?) and can lead to major allele and phenotype frequency changes over relatively short time scales. As an excercise, you can compute the time to fixation for an advantageous allele starting at an arbitrary frequency, using the equations in Kimura and Ohta (1969) (NB: compare to time to fixation for a neutral allele in the same configuration to check what the effect of selection really is). You can play around with the formula using this simple code that I have written in R:

#Calculations from Equations (17) and (14) in:
#Kimura M, Ohta T. 1969.
#The Average Number of Generations until Fixation of a Mutant Gene in a Finite Population.
#Genetics 61: 763–71.
#
#
#sel coefficient
s<-6e-3
#effective size
Ne<-10000
#Ne * s = S
S<-Ne * s
#starting frequency of positively selected allele
p<-0.005
#
# Equation (17): fixation of selected allele
#function to integrate for term J(1)
J1der<-function(csi)
{
(exp(2*S*csi)-1)*(exp(-2*S*csi)-exp(-2*S)) / (csi*(1-csi))
}
#function to integrate for term J(2)
J2der<-function(csi)
{
((exp(2*S*csi)-1)*(1-exp(-2*S*csi))) / (csi*(1-csi))
}
#coefficient for the integrals J(1), J(2)
Jcoef<- 2 / (s*(1-exp(-2*S)))
#u(P) function
uP<-(1 – exp(-2*S*p)) / (1 – exp(-2*S))
#average time to fixation under selection
t1p<- Jcoef * integrate(J1der, lower = p, upper = 1)$value + ((1-uP)/uP) * Jcoef * integrate(J2der, lower = 0 , upper = p)$value
#
# Equation (14): fixation of a neutral allele
#average time to fixation (neutral)
t1pNeutr<- (-1/p)*(4*Ne*(1-p)*log(1-p))

Moreover, part of the ‘omnigenic’ effect is caused by linkage disequilibrium, which extends over much larger spans in humans than in most tree species. In trees, it is less likely that a variant correlated to a causal SNP will also appear as a causal SNP.

Another consideration: for reasons of power and efficiency, many rare variants are eliminated from GWAS studies through the cruel MAF (minimum alelle frequency): anything with a frequency under a certain threshold is usually thrown out. This makes sense, because some of them may be artefacts, and anyway statistical power at those loci will be low. But what if they have a large effect? In natural tree populations, rare variants with large effects may be just waiting for selection to pick them up. I, for sure, do not throw them away!

And finally: first things first. Let us find major effects, if they are there (some have already started: see Sam Yeaman’s et al. Science paper as a brilliant example), and then we’ll scratch our heads with minor ones.