When Can Species Abundance Data Reveal Non-neutrality?

Species abundance distributions (SAD) are probably ecology’s most well-known empirical pattern, and over the last decades many models have been proposed to explain their shape. There is no consensus over which model is correct, because the degree to which different processes can be discerned from SAD patterns has not yet been rigorously quantified. We present a power calculation to quantify our ability to detect deviations from neutrality using species abundance data. We study non-neutral stochastic community models, and show that the presence of non-neutral processes is detectable if sample size is large enough and/or the amplitude of the effect is strong enough. Our framework can be used for any candidate community model that can be simulated on a computer, and determines both the sampling effort required to distinguish between alternative processes, and a range for the strength of non-neutral processes in communities whose patterns are statistically consistent with neutral theory. We find that even data sets of the scale of the 50 Ha forest plot on Barro Colorado Island, Panama, are unlikely to be large enough to detect deviations from neutrality caused by competitive interactions alone, though the presence of multiple non-neutral processes with contrasting effects on abundance distributions may be detectable.Author Summary: In order to predict and mitigate the response of ecological communities to global change, we need to understand the processes that allow multiple species to coexist in close proximity. A classic idea in Ecology is that species coexist because they occupy different “niches”. However, random processes such as dispersal could also explain species coocurrence, without invoking niche differentiation. “Neutral” models embody this idea, omitting niche differentiation and assuming all species are identical. Such models are mostly statistically consistent with the relative abundances of tree species in tropical forests, but statistical procedures always contain an element of uncertainty and many other models could also be consistent with a particular data set. We compute how strong the non-neutral processes would need to be in order for their effect to be detectable in data sets of different sizes. We find that the largest ecological data sets currently available, such as the 50 hectare plot on Barro Colorado Island in Panama, are not large enough to distinguish between neutral and non-neutral models, unless multiple non-neutral processes are at work. This means that other types of pattern need to be studied, or larger data sets collected, in order to understand the mechanisms behind forest biodiversity.