Zero tolerance ecology: improving ecological inference by modelling the source of zero observations

TLDR

Ecological data sets often contain many zero values, which can lead to inefficient or incorrect inference if not properly modeled. This paper proposes a framework to understand the origin of zero‑inflated data and guide appropriate modeling choices. The authors classify zeros as true or false, review recent modeling developments, and illustrate with examples how neglecting zero sources impairs relationship detection. Explicitly modeling zero sources sharpens insights and improves robustness of ecological analyses.

Abstract

A common feature of ecological data sets is their tendency to contain many zero values. Statistical inference based on such data are likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for understanding how zero-inflated data sets originate and deciding how best to model them. We define and classify the different kinds of zeros that occur in ecological data and describe how they arise: either from 'true zero' or 'false zero' observations. After reviewing recent developments in modelling zero-inflated data sets, we use practical examples to demonstrate how failing to account for the source of zero inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The adoption of methods that explicitly model the sources of zero observations will sharpen insights and improve the robustness of ecological analyses.