Guest post by Carsten Dormann (Uni Freiburg) / @CarstenDormann
The relationship between species richness and ecosystem function is a field of ecology that has always puzzled me. I learned the scientific rope in a department of vegetation ecologists: vegetation was the result of environmental conditions, and indeed a substantial part of their research was to quantify what a plant species indicates about its environment (think Ellenberg indicator values). While of course species may be absent from a community due to competition, those species that are there reflect climate, soil, management.
Thus, when I see a paper showing a strong effect of species richness, I feel that there must be something amiss. (This paranoid and blanket scepticism goes far beyond “biodiversity” effects.) Can it really be true that in a give-or-take “natural” system we can boost productivity by 100-200% by having more species? Looking out of my office window, I can make out the Black Forest, and a nice large monoculture of spruce. Will adding a random local tree species increase the productivity? And does a mixture of, say, beech and spruce with a higher productivity demonstrate a TSR effect on P?
Actually, this blog post is an appetizer for our re-analysis of Liang et al. (2016, Science). But bear with me for another brief excursion. Let me first repeat an argument I read in Donald Maier’s scathing critique of “biodiversity research” (Maier 2012: “What’s So Good About Biodiversity?”, Springer): When we plot species richness on the x-axis, we assume that the species we count are equivalent. If they weren’t, their number is not helpful, and we should quantify something else, e.g. a trait or their abundance or their composition; but not their number. And, when investigating the effect of TSR, the x-axis implies random species composition. If it wasn’t random, then richness would be confounded with something else. (Admittedly Maier put it better, but also more verbose.)
Liang et al. (2017, Science: “Positive biodiversity-productivity relationship predominant in global forest”) present such a figure, with an increase in productivity on from around 3.5 to well over 10 m3ha-1yr-1, as “relative species richness” increases from little to 100% on the x-axis. Such a figure rings my alarm bells. So, together with two BSc students, we re-analysed the data presented in that paper.
There are various points that we consider problematic (be it extremely unrealistic values for P; Euclidean distances between plots on a spherical world; non-stratified sampling of biomes; computation of “bootstrapped” error bars), and we investigated them one by one, but the pivotal point is the x-axis: What does “relative species richness” mean? Quite simply, it is the number of tree species in a plot divided by 270, the highest species richness in the data set considered. (Now that is a tiny bit unfair, but it is essentially what it is. In the rundown of the re-analysis we of course use Liang et al.’s definition.) So, a 10-species plot in Finnland receives a value of 3%, while a plot in Panama gets a value of 100%. Can you spot the problem? Yes: the TSR gradient is in fact a latitudinal gradient. That, in turn, means that the plot does not depict the effect of TSR on P, but of latitude on P!
We were still charmed by the idea of constructing an x-axis that is relative. Instead of “relative to the highest richness in the tropics”, however, we constructed a tree-species richness relative to the highest number of tree species observed in that region. So 100% means “as many as you can get around here”, and varies between 5 tree species in Siberia and 500 in Panama.
Using this definition (and stratifying by biome, and correcting for spatial distances on a sphere, and using subsampling correction for error bars) we find — nothing. (A tincy effect to the eye indistinguishable from a horizontal line.)
Of course, when looking at each biome separately, we find more or less positive effects, but never as strong as in the original global analysis.
Interested? Read more in our preprint on bioRxiv here!
What to take home? Well, perhaps that observational data are tricky for estimating richness effects. It’s so easy to miss effects and then wrongly attribute changes in productivity to species richness (And yes, I include Duffy et al.’s meta-analysis 2017 in this criticism; it’s part of my paranoid scepticism).