Ratios in Aquatic Sciences: Statistical Shortcomings with Mean Depth and the Morphoedaphic Index

Abstract

Researchers in aquatic sciences frequently employ empirically derived models to predict productivity, yield, and abundance of fish. We demonstrate that predictive models employing ratios of standardized biomass and lake morphometric variables are biased by spurious correlations due to mathematical transformations and the use of inappropriate null models. Our findings emphasise that studies incorporating ratios like mean depth or the morphoedaphic index require cautious interpretation. Future research should focus on more appropriate analytical approaches such as regression-based models like the analysis of covariance. Alternatively, where ratios are employed and spurious correlations are likely, statistical evaluations must incorporate randomization tests to assess the significance of such results.