Journal Article

Permutation Tests to Identify Significant Constraint or Promotion Within Biological Scatterplots

Mazijk, Ruan, Mills, Anthony J.

2024

Overview

ABSTRACT Scatterplots of biological datasets often have no‐data zones, which suggest constraint or promotion of dependent variables. Although methods exist to estimate boundary lines—that is, to fit lines to the edges of scatters of data points—there are, to our knowledge, none available to assess the significance of the areal extents of no‐data zones. Accordingly, we propose a flexible boundary line definition paired with a permutation test of the magnitude of no‐data zones—rather than testing the shape or slope of the line as current methods do. Our proposed permutation test can be used with any method of defining a boundary line. We demonstrate our approach with empirical datasets, find no‐data zones that methods such as quantile regressions fail to detect, and discuss how our approach can quantify constraint and promotion relationships that are not always apparent with other statistics. Scatterplots of biological datasets often have no‐data zones, which suggest constraint or promotion of dependent variables by independent variables. We propose a flexible boundary line definition paired with a permutation test of the magnitude of areas without data—rather than testing the shape or slope of that line as previously developed methods do. Using empirical datasets, we demonstrate instances where—for example—quantile regressions fail to identify significant patterns.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: Biodiversity Ecology / Biogeochemistry / bivariate data / Botany / boundary line / Constraints / Data points / Datasets / Dependent variables / Ecosystem Ecology / Independent variables / Landscape Ecology / Macroecology / non‐parametric statistics / Permutations / Probability / quantile regression / Regression analysis / Soil Ecology / Statistical methods