Understanding 'it depends' in ecology: a guide to hypothesising, visualising and interpreting statistical...

Area of Science:

• Ecology
• Ecological statistics
• Ecological modeling

Background:

• Ecologists use statistical models to understand interactions between ecological drivers.
• Interactions are crucial for evaluating how effects change across different contexts.
• Key properties like measurement scale (additive vs. multiplicative) and symmetry are often overlooked.

Purpose of the Study:

• To identify and explain inferential errors arising from overlooked measurement scale and symmetry in ecological interaction analysis.
• To demonstrate how these overlooked properties can lead to misinterpretation of interaction detection, magnitude, sign, and underlying processes.
• To provide guidelines for improving the hypothesis generation, testing, visualization, and interpretation of ecological interactions.

Main Methods:

• Analysis of ecological interactions considering measurement scale (additive/multiplicative) and symmetry.
• Illustration of inferential errors (Type-D, Type-S, Type-A) using diverse ecological questions.
• Application to empirical and simulated datasets, including meta-analysis.

Main Results:

• Overlooking measurement scale and symmetry can lead to misinterpreting interaction detection, magnitude (Type-D error), and the sign of effect modification (Type-S error).
• Misidentification of underlying ecological processes (Type-A error) can occur.
• Meta-analysis, commonly used for context dependence, is particularly susceptible to these three errors.

Conclusions:

• Accurate interpretation of ecological interactions requires careful consideration of measurement scale and symmetry.
• Guidelines are proposed to mitigate inferential errors in ecological interaction studies.
• Improved methods for hypothesis generation, testing, visualization, and interpretation are essential for robust ecological science.