Multivariate Functional Data Analysis Uncovers Behavioral Fingerprints in Invertebrate Locomotor Response to Micropollutants

TL;DR

This study introduces a novel multivariate functional data analysis framework applied to videotracking data of aquatic invertebrates, enabling the characterization of behavioral fingerprints for real-time micropollutant detection in wastewater.

Contribution

It is the first application of functional data analysis in ecotoxicology, formalizing behavioral fingerprints for improved biomonitoring of micropollutants.

Findings

Multispecies data enhances contamination fingerprint resolution.

Four recurring micropollution event types identified in field data.

Behavioral fingerprints can improve wastewater monitoring accuracy.

Abstract

The need for effective biomonitoring in wastewater has become clear due to the impracticality of continuously tracking all chemicals and emerging contaminants in the aquatic exposome. Effect-based biomonitoring provides a cost-effective solution. The ToxMate device, which uses videotracking of locomotor behavior in aquatic invertebrates, has proven efficient for real-time detection of micropollutant surges in effluents. To extend the approach, this proof-of-concept study evaluates the potential to formalize behavioral fingerprints from real-time videotracking data to characterize qualitative variations in effluent contamination. We present the first application of a functional data analysis (FDA) framework in ecotoxicology. Data were obtained by simultaneously tracking three sentinel organisms from distinct taxa (a crustacean, an annelid, and a gastropod) during pulse exposures to four chemicals in the laboratory (two metals, one pharmaceutical, and one insecticide). Individual and multispecies responses were analy-zed to determine whether combining species enhances the resolution of contamination fingerprints through multidimensional FDA. Applying the same data-driven approach to field data from a wastewater treatment plant (WWTP) revealed four recurring types of micropollution events. This proof of concept demonstrates the potential of behavioral fingerprints to improve wastewater monitoring and reduce pollutant transfer to the environment.