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Consideration of species community composition in statistical analyses of coral disease risk
Citation:
YEE, S. H., D. L. SANTAVY, J. W. KERN, AND S. HESSION. Consideration of species community composition in statistical analyses of coral disease risk. MARINE ECOLOGY PROGRESS SERIES. Inter-Research, Luhe, Germany, 431:83-96, (2011).
Impact/Purpose:
Changes in coral condition have been attributed to a number of environmental factors including climate change, pollution, and overfishing. However, many studies pool data across taxonomic groups, disregarding host species composition when comparing sites or assessing environmental impacts. We used simulated data under a known environmental effect to assess the ability of standard statistical methods to detect a significant environmental effect on pooled disease prevalence with varying species abundance distributions and relative susceptibilities to disease. Investigators should be cautious of underlying assumptions of species similarity in susceptibility and species composition when interpreting pooled data on coral condition.
Description:
Diseases are increasing in marine ecosystems, and these increases have been attributed to a number of environmental factors including climate change, pollution, and overfishing. However, many studies pool disease prevalence into taxonomic groups, disregarding host species composition when comparing sites or assessing environmental impacts on patterns of disease presence. We used simulated data under a known environmental effect to assess the ability of standard statistical methods (binomial and linear regression, ANOVA) to detect a significant environmental effect on pooled disease prevalence with varying species abundance distributions and relative susceptibilities to disease. When one species was more susceptible to a disease and both species only partially overlapped in their distributions, models tended to produce a greater number of false positives (Type I error). Differences in disease risk between regions or along an environmental gradient tended to be underestimated, or even in the wrong direction, when highly susceptible taxa had reduced abundances in impacted sites, a situation likely to be common in nature. Including relative abundance as an additional variable in regressions improved model accuracy, but tended to be conservative, producing more false negatives (Type II error) when species abundance was strongly correlated with the environmental effect. Investigators should be cautious of underlying assumptions of species similarity in susceptibility and species composition when interpreting pooled data on disease risk.