Biodiv Sci ›› 2023, Vol. 31 ›› Issue (12): 23299.  DOI: 10.17520/biods.2023299

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A review of the small-island effect detection methods and method advancement

Gao De1,2(), Wang Yanping1,*()()   

  1. 1 College of Life Sciences, Nanjing Normal University, Nanjing 210023
    2 College of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024
  • Received:2023-08-24 Accepted:2023-12-11 Online:2023-12-20 Published:2023-12-30
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Background & Aim: The small-island effect (SIE) describes a phenomenon when below a certain threshold area, species richness varies independently of island size or increases at a lesser rate than for larger islands. The SIE has become a fundamental framework in biodiversity science and island biogeography because the area threshold of an SIE may be the turning point for many biogeographic patterns and ecological processes across spatial scales, and such phenomenon is significant in the research field of biodiversity conservation. In this paper, we first briefly introduce the relationship between the SIE and species-area relationships (SARs). Secondly, we summarize the five detection methods of SIEs found in the literature. Finally, we discuss the advantages and shortcomings of each detection method. We provide feasible suggestions for the accurate detection of an SIE to improve the theoretical framework in this field of research.

Progress: Currently, five SIE detection methods have been published, including SAR shape comparison, breakpoint/piecewise regression, null model, path analysis, and a tree-based model. The following shortcomings for each method are presented. In the SAR shape comparison method, large islands may be outliers, causing the shape of the SAR curve to be poorly represented by a sigmoidal curve. In the piecewise regression method, the logarithmic data transformations may make the SIE just a statistical artifact. In the null model method, the randomization process ignores the differences in ecological characteristics between species, causing the island species richness expected values to have a reduced credibility. In the path analysis method, the habitat diversity is difficult to quantify and the SAR slope within the limits of the SIE may lower this method’s applicability. In the method of tree-based model, it is questionable whether the model split first with the independent variable of area. Moreover, an SAR may actually have two area thresholds which brings into question which threshold is best for model splitting.

Perspective: To avoid the inadequacies of a specific method, we propose using multiple methods for SIE detection. An SIE in the system can only be considered when at least two methods simultaneously detect the SIE. In addition, improvements can be made to address the defects in the existing methods. For example, for the existing null model method, some restrictive conditions can be added to its randomization processes to make the expected results more reasonable.

Key words: area threshold, piecewise regression, species-area relationship, island biogeography, random extinction, habitat diversity