Hidden Drivers in Ecological Networks: Detecting Possible Latent Forcing from Time-Series Data

Ecological interaction networks are often influenced by unobserved upstream forcing, which can make an observed link between two variables appear causal even when it primarily reflects mediation within a deeper causal chain. This challenges the interpretation of trophic control, the identification of dominant ecological drivers, and the design of effective monitoring strategies. Here, we introduce detecting pairwise effect (DPE), a data-driven method based on empirical dynamic modeling and multivariable state-space reconstruction, to assess whether an observed directional link is more consistent with a direct ecological driver or with a mediated effect shaped by latent upstream forcing. Using benchmark dynamical system motifs, we show that DPE can identify conditions under which a strong upstream driver acting on the putative cause may distort pairwise causal interpretation. We further demonstrate its ecological relevance using real-world time series, where DPE reveals diagnostic signals consistent with hidden trophic forcing in a plankton food-chain dataset and possible latent external forcing in soil-temperature depth dynamics. Overall, DPE provides a diagnostic signal that is consistent with the presence of latent upstream influence, thereby improving the interpretability of inferred ecological causal relationships.