We recently developed a new metric of association in co-occurrence data (Mainali et al., 2022 in Science Advances) because the traditional indices suffer from fundamental flaws, as elaborately discussed and conclusively shown in our published paper. Standardization of the indices or the co-occurrence counts under the null distribution can fix much of the sensitivity of the raw quantities to the properties of a dataset that do not reflect association between two entities (Keil, 2019). However, based on statistical reasoning, simulations and analysis of empirical data, we show in this repo that standardization suffers from multiple inadequacies.
Our paper was adversely commented on by (Ulrich et al., 2022), hereafter USSG, who argue that our metric suffers from three problems. In this repo, we respond to each of the comments with proper analysis of their real and simulated data representing null and non-null scenarios. USSG concluded that our metric was not novel and did not provide insight beyond null-hypothesis standardization techniques. But as we show here, their conclusions arose because they did not examine a variety of non-null scenarios.
Keil, P. (2019). Z-scores unite pairwise indices of ecological similarity and association for binary data. Ecosphere, 10(11), e02933.
Mainali, K. P., Slud, E., Singer, M. C., & Fagan, W. F. (2022). A better index for analysis of co-occurrence and similarity. Science Advances, 8(4), 1–9.
Ulrich, W., Sfenthourakis, S., Strona, G., & Gotelli, N. J. (2022). Simple null model analysis subsumes a new species co-occurrence index: A comment on Mainali et al. (2022). Journal of Biogeography, 00, 1–4.