The intrinsic dimensionality of plant traits and its relevance to community assembly

Daniel Laughlin. Journal of Ecology (early view). DOI: 10.1111/1365-2745.12187. The intrinsic dimensionality of plant traits and its relevance to community assembly

Plant traits, plant traits, everywhere, and plenty of root traits with which to absorb water. From Laughlin's paper.

Plant traits, plant traits, everywhere, and plenty of root traits with which to absorb water. From Laughlin’s paper.


Will Pearse

Will Pearse

I think I should read more essays, because I really enjoyed this one. I’ve been searching for good recovering-zoologist-friendly summaries of the plant trait literature and this is one. The methods were also nice, and it’s cool to see how machine learning is really finding its place as a useful tool and not just something to scare reviewers with. I’m going to talk a little bit about plant traits in general (which I’m not qualified to do), and then have a brief digression on dimensionality (which I’m also not qualified to do).

Gathering plant trait data is hard, and while Laughlin isn’t the first to point out that our view of the botanical world is biased by what we can easily gather data on, he does it very eloquently. I was at the TRY working group meeting a few months ago, and I was very struck by how hard everyone is scrambling to fill in the gaps in our trait knowledge. We know remarkably little about plant roots, and I can’t help but wonder if knowing more might get people thinking a little more about facilitation. I’ve heard all kinds of weird things about facilitation (whatever it is, I’m not certain it’s sufficient to just call it “negative competition”), and I feel the roots, and the mycorrhiza they harbour, are the key to understanding it. I’m a great fan of the leaf economic spectrum (even if the details are sparking some debate), and given the work on above-ground vs. below-ground allocation of biomass I think it’s only a short time until we start seeing root-above-ground economic spectra.

I agree entirely that we can and should reduce the dimensionality of the plant trait literature; TRY has 682 different traits when I last checked and I fail to believe they’re all completely independent. We know that niche convergence need not be convergence of traits, and finding the principle axes of variation should help us better understand the evolution of trait trade-offs. This many niche axes are also sufficient to allow co-existence of large numbers of species because there’s a lot of niche space to split among many species; this is the complex (and often chaotic) situation that allowed us to ‘solve’ the Paradox of the Plankton. Thinking in terms of a smaller number of niche axes does make things more tractable, and I do happen to think that Laughlin is right, but there is a sting in the tail of even a ‘simple’ six dimensional description of plants. If we (conservatively) assume there are only six niche axes and that species can either have low, medium, or high values along that trait axis, we have 729 (3^6) different kinds of species – or 2187 species with the 7 axes indicated on the figure at the top of this post! Discretising continuous variation like this also has its problems, but I still have a feeling that plant traits are going to remain complex for some time yet!


Lynsey McInnes

Lynsey McInnes

I wanted to read this paper this week simply because I was curious what the intrinsic dimensionality of plant traits was…I didn’t really have a feeling whether it would 3 traits or 10 or what those traits would be. So, I learnt quite a bit through reading this essay!

It looks like intrinsic dimensionality is about 5ish depending on what dataset (i.e., what traits) you look at. The general (n = 3 anyway) conclusion is you need a trait from each of the major bits of a plant (leaf, stem, root, etc.) and that within major organs, traits are correlated, bordering on redundant. This all sounds reasonable, but I’m pleased to see it documented here.

Of course, it would have been great if n could have been bigger to explore the wrapper relationship of the effect on dimensionality of the traits and species included in the tests. Presumably you might get an extra dimension if you ramp up either no. of traits or no. of species, but this could probably be anticipated and dealt with.

I know it wasn’t the subject of the essay, but how easy is it to measure these traits? The author mentions the lack of root measurements as these are (obviously) harder to measure than leaf traits for example. Is it realistic to suggest targetting them? Are there any non-root traits that could cover them (if I remember correctly, I think not really). What is the relationship of dimensionality with richness? Could species numbers cover for trait diversity? (Maybe both have asymptotic effects?). I’ve no real idea if that is a reasonable suggestion or not, I am a big fan of the idea that trait, or functional, diversity captures ecosystem processes better than species identity, but I’ve not actually thought before whether there is a simple relationship between the two (wouldn’t that be nice?).

The author alludes often to this exercise being useful for community assembly studies, especially in this time of rapid environmental change. Although (I think) we still don’t really know how stable communities are through time (transient mixes of species that happen to come together in one place vs. tightly co-evolved units), understanding how the mix of traits changes in a community through time could help reveal the answer (contingent on a ton of data being available). For instance, if just a small number of trait dimensions needs to be studied, perhaps one could extract how their trait values change through time (or through space). Does a community retain the same trait values over time for ‘optimal’ functioning? Or is a lot of change apparent? Is there turnover of species identity, but retention of trait diversity? Are there datasets available to use for these types of studies?

Exciting times.
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About will.pearse
Ecology / evolutionary biologist

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