Authors
Kishi, S.
Abstract
1. Detecting causal relationships from ecological time series remains challenging because community dynamics are often nonlinear, high-dimensional, noisy, and strongly synchronized. These characteristics can reduce the reliability of existing causal inference methods and complicate the identification of ecological interactions in complex natural systems. 2. I introduce Forest-weighted Causal Correlation (FORCC), a machine-learning-based framework that infers directional causality from adaptive neighborhood structures learned by random forests. Rather than evaluating causal direction through cross-map reconstruction, FORCC quantifies directional dependence from the preservation of neighborhood structure using rank-based associations among forest-weight matrices. 3. Simulations of coupled nonlinear systems showed that FORCC accurately recovered causal direction while substantially reducing synchronization-induced false-positive detections relative to conventional convergent cross mapping. Chatterjee's {xi} exhibited the strongest resistance to synchronization-induced errors among the evaluated rank-based statistics. 4. Application to a 12-year marine community time series from Maizuru Bay, Japan, identified asymmetric relationships between individual taxa and community dynamics, revealing candidate driver-like and passenger-like roles that were not readily explained by seasonal synchrony alone. 5. By combining adaptive neighborhood learning with rank-based causal inference, FORCC provides a scalable framework for causal discovery in ecological time series. The method offers a practical tool for screening causal relationships, generating ecological hypotheses, and investigating complex interactions in multivariate natural systems.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 09 Oct 2026.
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