Unraveling the temporal dependence of ecological interaction measures

TL;DR

This paper investigates how temporal variability and experimental protocols influence the measurement of ecological interactions, proposing a model inference approach using multiple short time series to better distinguish direct and indirect species interactions.

Contribution

It reveals that observed fluctuations in interaction measures can stem from population dynamics and experimental design, and introduces a method leveraging short time series for more accurate ecological interaction inference.

Findings

Short-term measurements capture direct species couplings.

Long-term observations reflect indirect community feedback.

Experimental setup influences interaction estimates.

Abstract

Species interactions (ranging from direct predator prey relationships to indirect effects mediated by the environment) are central to ecosystem balance and biodiversity. While empirical methods for measuring these interactions exist, their interpretability and limitations remain unclear. Here we examine the empirical matrix of pairwise interactions, a widely used tool, and analyze its temporal variability. We show that apparent fluctuations in interaction strength (and even shifts in interaction signs, often interpreted as transitions between competition and facilitation) can arise intrinsically from population dynamics with fixed ecological roles. Experimental protocols further shape these estimates: the duration of observation and the type of setup in microbial growth studies (e.g., chemostats, batch cultures, or resource conditions) systematically affect measured interactions. Considering interactions across timescales enhances interpretability: short-term measurements primarily capture direct species couplings, whereas long-term observations increasingly reflect indirect community feedback. Taken together, these results establish short duration inferences, obtained either directly or extrapolated, as a principled way to disentangle direct from indirect interactions. Building on this insight, we propose a model inference approach that leverages multiple short time series rather than extended longitudinal datasets.