I tend to disagree.
In the field of graphs/networks, many networks (social, sexual intercourse among humans, etc.) are found to have similar statistical properties to those of ecological networks (food webs, interactions among mutualists, etc.). However, just because these networks have similar statistical properties does not mean that the statistical properties of ecological networks have no biological meaning.
They make the argument that the common SAD fit may be an artifact of large data sets alone. However, I don't see any explanation of why they think large data sets is a valid explanation of SADs. Surely SAD's are fit to varying sizes of datasets. The problem with small datasets is lack of statistical power to detect a particular pattern, but surely you can get a fit for a particular SAD to a small dataset.
There are ecological mechanistic theories behind different SAD models. They argue that because very similar SADs are found in ecological and non-ecological datasets alike one option is that a universal mechanism structures ecological and non-ecological data (with the mechanism unknown in both). Why can't the same SAD pattern be generated by different mechanisms?
Are Warren et al, Nekola, and Nee right in questioning the utility of SADs? Questioning our theories and ideas only makes the theories better in the end by weeding out shortcomings, etc.
Warren, R., Skelly, D., Schmitz, O., & Bradford, M. (2011). Universal Ecological Patterns in College Basketball Communities PLoS ONE, 6 (3) DOI: 10.1371/journal.pone.0017342
The authors didn't say that SADs must be devoid of biological meaning, and I didn't either. There results are just reason to question the biological meaning. My point, and I think the authors' point, is that many large datasets (some of which broadly fit characteristics reminiscent of biology) fit SADS.
ReplyDeletePerhaps SADs do mean something, in different contexts. Isn't it also reasonable, as an alternative possibility, that SADs in biology are nonbiological in origin? Admittedly, I'm well beyond my traditional circle of knowledge here. This is just my humble two cents, that's hopefully not way off-base.
@Michael,
ReplyDeleteThanks for highlighting the paper originally. I did enjoy your post. I have to admit that I don't study SADs myself.
I would like to see better evidence than they provide of the large dataset argument. Surely you can get much larger datasets of non-ecological nature than ecological. I would argue that ecological datasets are quite small compared to most other datasets available.
It seems that manipulating the putative mechanisms at play and measuring resultant SADs would be a good way to get at this question, but would be hard to do at realistic scales, and would ignore other mechanisms that would play out on large spatial or temporal scales.
What do you mean by "nonbiological origin"?: statistical?
I'm again well outside my typical science area (analytical chemistry), but their basketball dataset, over several years, seems pretty big to me. In this case, I don't know how to increase the dataset size, besides increase the number of years. You'd probably want to increase the number of teams or players though.
ReplyDeleteI also don't see how they could practically manipulate the mechanisms in this case, e.g. change university funding. I think it would be interesting, but I'm at a loss for a dataset of known mechanisms that would allow you to rationally do this. Perhaps this can be a research proposal (for someone far more qualified than me).
Regarding nonbiological origin, I just mean "not of biology." Statistics is the first thing that comes to my mind, but I imagine there are other possibilities.
Right, they can't really manipulate basketball teams/leagues.
ReplyDeleteI meant manipulating ecological systems, not basketball, etc., which can be done, but like I said, I think would be very difficult and would have a lot of caveats. Perhaps a system of protists or baceria would be tractable in the timeframe of a thesis/dissertation/funding cycle???
I only gave the paper a quick skim this morning, but, I think the larger point here is that the SAD has been used as a justification of the Neutral Theory of biodiversity. However, using a dataset from a system (Basketball) clearly structured by competitive dynamics, one can arrive at the same distribution. Yes, the Neutral Theory may predict a commonly observed SAD. At the same time, so might others. And if a system - albeit a totally different one - can generate a SAD-like relationship when we know it is structured by competitive processes, then perhaps a given theory producing the commonly observed SAD isn't a sufficient condition for its being correct. Necessary, sure, but not sufficient.
ReplyDelete@byrnes: Right, I see your point. Does anyone have issues with the basketball dataset though? E.g., "abundance" in their paper is wins, not number of individuals per team (~species) as in ecology. The equivalent in ecology seems to me is single competitive interactions between two species (eg., competitive outcome measured from an exclusion experiment). Whereas, abundance in ecological communities is integrated over time, space, and many ecological processes, "abundance" in their paper is more like the single competition experiment. Am I wrong about this argument? Or is it just semantics?
ReplyDelete