Speaker
Description
Species interactions—ranging from direct predator-prey relationships to indirect effects shaped by
environmental factors—are fundamental to maintaining ecosystem balance and biodiversity.
Although various empirical measures of these interactions have been proposed, their
interpretability, informativeness, and limitations in ecosystem analysis remain challenging. In this
study, we focus on the empirical interaction matrix, a widely used tool, and investigate its
temporal variability. Using analytical approximations, we demonstrate that fluctuations in
interaction measures—often interpreted as shifts between competition and facilitation—may
instead arise intrinsically from the temporal dynamics of populations with fixed ecological roles
(Fig. 1, left). We further show that, while interaction measures initially reflect direct species
couplings, they increasingly capture environmentally mediated effects and experimental biases
over time. Thus, assessing interaction measures at multiple time points offers a richer perspective
on ecosystem dynamics. This time-resolved approach enables a systematic separation of direct
and indirect species relationships (Fig. 1, right). Finally, we propose a model inference method
based on interaction measures, which leverages multiple short time series instead of the extended
longitudinal datasets typically required.
