New Model for Studying Species Distributions and the Mid-domain Effect Developed
Understanding why some parts of the world sustain more species than others is one of the most enigmatic problems in ecology. One particularly common pattern is a “hump-shaped” biodiversity gradient: for example, biodiversity peaks near the equator and declines going either north or south.
Historically, explanations for such gradients invoked coincident geographical variation in environmental factors hypothesized to reduce extinction rates or promote the evolution of new species.
Recently, however, random re-arrangements (“randomizations”) of species’ distributions in geographical space have been shown to reproduce these hump-shaped gradients (termed “mid-domain effects”).
Because randomizations do not explicitly include environmental factors, some have argued that such factors may be less important for biodiversity than previously thought. However, randomization analyses are controversial: critics argue that they are devoid of any ecological processes (not just environmental gradients), and thus have no explanatory utility.
Addressing this criticism requires models that make explicit biological assumptions about how species’ distributional limits are determined, consistent with a particular hypothesized cause of biodiversity gradients.
In an article in the July 2005 issue of The American Naturalist, Sean R. Connolly (James Cook University) develops a general framework for such models and analyzes specific models that omit roles for variation in the quality of environmental conditions.
Under a very general set of conditions, these models are shown to produce mid-domain effects. These are qualitatively similar in shape, but of substantially lower magnitude, compared to randomization analyses. These results reveal that the mid-domain effect is likely to be a real phenomenon, and thus cannot be ignored, but that comparing real biodiversity patterns to those produced by randomizations may be misleading. They also identify an alternative way forward: formulating process-oriented models of species distributions and testing them directly against empirical data.
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