Selection and evolution of causally covarying traits

Michael Morrissey. Evolution 68(6): 1748-1761 DOI:10.1111/evo.12385. Selection and evolution of evolution of causally covarying traits

I'm baaaaaaaaaaad at thinking of image captions. Soay sheep, picture courtesy of BBC / Arpat Ozgul.

I’m baaaaaaaaaaad at thinking of image captions. Soay sheep, picture courtesy of BBC / Arpat Ozgul.


Will Pearse

Will Pearse

This was not the paper I was expecting, but it was a paper I enjoyed. Morrissey argues that we can use path analysis to tease apart the evolution of different aspects of species’ traits, and I think he makes a very good case for it. I quite enjoyed reading about Soay sheep again – it was a bit like bumping into an old friend in the street.

I know a lot of people who use aster models to examine different components of fitness, and estimate how traits affect fitness. I’m not really capable of assessing which of aster and path analysis is better, but I do feel that they’re complementary and so I doubt that it’s valid to ask which is best anyway. A thing that always confuses me in these analyses is how we define fitness: fitness isn’t just whether you reproduce that year or survive until the next – it’s inherently multi-faceted. Fitness also changes with temporal scale – the total number of offspring is a great measure, but it matters whether those children reproduce. Indeed, you could keep going on down that road with grandchildren, and great-grandchildren… until no one really knows what’s going on. One of the strengths of a structural equation model (…call it a path analysis if you prefer…) in other settings is that one can have multiple ‘predictor’ variables. I wonder if the same sort of approach could be used in an evolutionary setting, at the very least to explore the ‘decision’ behind whether to reproduce one year or wait in hope of a better season.

I finished the paper wondering about generalities. Figures 2 and 3, which show how what contributes to fitness, look very different to me: for sheep, there are lots of indirect effects and everything maps onto fitness, whereas for the plants not everything maps onto fitness and everything seems less messy. Do we tend to find clustered groupings of traits and environmental conditions that all interact with one-another and nothing else (germination –> flowering time –> other things –> fitness), or does everything interact with everything else with no neat modules (birthdate –> fitness and (birth weight –> fitness and (weight in August –> fitness)))? I wonder if the structure of these relationships can alter the stability of the system too – is a tightly-connected system more stable, or do isolated units have some capacity to compensate for one-another? I feel like path analysis might be the way to find out!


Lynsey McInnes

Lynsey McInnes

My heart sank a little when I opened Will’s paper choice this week. a hard core extension of existing quantitative genetic theory. I can barely get to grips with everyday quantitative genetics, let alone pull apart a new extension. So, I closed the paper and my eyes, then opened them again to give it a go.

The jist of the paper appears to be an extension to the quantification that includes more than just direct effects of traits on fitness. Rather, it tries to include all indirect effects of multiple traits, an idea that can be visualised in a path analysis. To be honest, I have no idea if this is a major advance for the field or not, but it does sound like a good, though perhaps dangerous, idea.

I am only familiar with path analyses from various papers that implement structural equation modelling to infer direct and indirect effects of various biotic and abiotic factors on macroecological patterns. Although I have used them myself, I remain wary of how comprehensive they can be. As Morrissey also indicates here, they rely on the user specifying what can and can’t be a direct or an indirect effect and and are susceptible to missing variables or poorly specified paths. OK, you could argue all models might miss a variable or two and that that is not the point, my worry though is that path analyses in particular create a kind of false confidence that all bases are covered and that the model is bang on. Dangerous.

On the flip side, that these models provide scope for accounting for both direct and indirect effects is good. Both quantitative geneticists and macroecologists then have the capacity to incorporate a broader number of variables and are more or less forced to think about how variables might interact with each other. And the modelling framework itself can to some extent remove erroneously included variables. Heck, you could even throw in a latent variable if you know something else matters, but you can’t measure it or don’t know what it is.

I am a bit dubious despite the intrinsic appeal of drawing parallels between two pretty different fields (and in particular data types), but remain sucked in by frameworks that try to do just that, e.g. Vellend‘s between genetic and ecological diversity. I wonder then how much useful crosstalk could occur between quantitative geneticists on the one hand and macroecologists on the other in terms of how best to set up their paths? Perhaps it is more useful to think more generally about how geneticists and ecologists might talk to each other. What benefits would come from thinking about the ecological background/drivers of trait variation or of selection coefficients or of the genetic variation (available or sought after) for ecological adaptation. OK OK, I don’t want to pretend to reinvent the wheel, lots of such analyses are already occurring, but it is far from the norm. Perhaps another path to add to the analysis?

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About will.pearse
Ecology / evolutionary biologist

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