Robust methods for detecting a small island effect

Abstract

Aim: The small island effect (SIE), i.e. the hypothesis that species richness below a certain threshold area varies independently of island size, has become a widely accepted part of the theory of island biogeography. However, there are doubts whether the findings of SIEs were based on appropriate methods. The aim of this study was thus to provide a statistically sound methodology for the detection of SIEs and to show this by re-analysing data in which an SIE has recently been claimed (Sfenthourakis & Triantis, 2009, Diversity and Distributions, 15, 131-140). Location: Ninety islands of the Aegean Sea (Greece). Methods: First, I reviewed publications on SIEs and evaluated their methodology. Then, I fitted different species-area models to the published data of area (A) and species richness (S) of terrestrial isopods (Oniscidea), with log A as predictor and both S (logarithm function) and log S (power function) as response variables: (i) linear; (ii) quadratic; (iii) cubic; (iv) breakpoint with zero slope to the left (SIE model); (v) breakpoint with zero slope to the right; (vi) two-slope model. I used non-linear regression with R2adj., AICc and BIC as goodness-of-fit measures. Results: Many different methods have been applied for detecting SIEs, all of them with serious shortcomings. Contrary to the claim of the original study, no SIE occurs in this particular dataset as the two-slope variants performed better than the SIE variants for both the logarithm and power functions. Main conclusions: For the unambiguous detection of SIEs, one needs to (i) include islands with no species; (ii) compare all relevant models; and (iii) account for different model complexities. As none of the reviewed SIE studies met all these criteria, their findings are dubious and SIEs may be less common than reported. Thus, conservation-related predictions based on the assumption of SIEs may be unreliable. © 2010 Blackwell Publishing Ltd.