Nonparametric Statistical Inference for Multivariate Niche Overlap

TL;DR

This paper introduces a robust nonparametric framework for measuring and testing multivariate ecological niche overlap, overcoming limitations of parametric methods in complex settings.

Contribution

It develops a new nonparametric overlap index, estimators, and bootstrap inference procedures, with demonstrated robustness and practical ecological applications.

Findings

Estimators maintain correct size across scenarios.

Bootstrap methods enable reliable hypothesis testing.

Application reveals ecological niche differentiation.

Abstract

In ecological studies niche overlap is often used to quantify species interaction and dynamics. This paper develops a robust, nonparametric statistical framework for quantifying and analyzing multivariate niche overlap. Parametric methods are often constrained by restrictive assumptions and tend to underperform in complex multivariate settings. We introduce a nonparametric overlap index and propose estimators for it. Further, we investigate asymptotic properties of the estimators. We also propose bootstrap-based inference procedures that enable statistical testing and simultaneous confidence intervals in small sample settings. Extensive numerical examples demonstrate that our proposed methods maintain correct size and exhibit robust power across various scenarios. We illustrate the practical utility of our methodology using stable isotope measurements from multiple fish species and provide distinct ecological insights regarding species niche differentiation.