Unified Statistical and ML Framework for Quantifying Linked Rare Events in Ecological Time Series

Thomas M. Menino Convention & Exhibition Center 

The increasing frequency of environmental shocks is challenging traditional mean-based statistical inference in ecological monitoring. We present a comprehensive framework for detecting, linking, and modeling extremes in multivariate time series. We contrast unsupervised anomaly detection methods (isolation forest and DBSCAN) to objectively identify rare events in environmental and biological data. Beyond detection, we quantify the synchronization of these extremes using the Matthews correlation coefficient with bootstrap-based inference, offering a robust alternative to standard correlation measures. Furthermore, to model the conditional distribution of biological responses to environmental stressors, we benchmark quantile regression forests (QRF) against linear quantile regression. Our analysis demonstrates that non-parametric machine learning (QRF) improves the quantification of tail dependencies often missed by parametric approaches. This work provides a flexible statistical toolkit for analyzing non-stationary driver-response relationships in complex systems.

Rare event detection

Quantile regression

Isolation forest

Extreme

Time series analysis

Ecological statistics 

Section on Statistics and the Environment