Unraveling the temporal dependence of ecological interaction measures

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.