Modeling Polyp Activity of Paragorgia arborea Using Supervised Learning

TL;DR

This study uses machine learning to analyze high-resolution data and reveals that Paragorgia arborea's polyp activity is primarily driven by tidal current patterns, with current direction being the most predictive factor.

Contribution

The paper introduces a novel application of supervised learning to understand in situ polyp activity patterns of cold-water corals using high-resolution time series data.

Findings

Polyp extension is best predicted by current direction with a three-hour lag.

Temperature and salinity are less informative for polyp activity prediction.

Laminar flow sampling predicts polyp activity more reliably than turbulent flow sampling.

Abstract

While the distribution patterns of cold-water corals, such as Paragorgia arborea, have received increasing attention in recent studies, little is known about their in situ activity patterns. In this paper, we examine polyp activity in P. arborea using machine learning techniques to analyze high-resolution time series data and photographs obtained from an autonomous lander cluster deployed in the Stjernsund, Norway. An interactive illustration of the models derived in this paper is provided online as supplementary material. We find that the best predictor of the degree of extension of the coral polyps is current direction with a lag of three hours. Other variables that are not directly associated with water currents, such as temperature and salinity, offer much less information concerning polyp activity. Interestingly, the degree of polyp extension can be predicted more reliably by sampling the laminar flows in the water column above the measurement site than by sampling the more turbulent flows in the direct vicinity of the corals. Our results show that the activity patterns of the P. arborea polyps are governed by the strong tidal current regime of the Stjernsund. It appears that P. arborea does not react to shorter changes in the ambient current regime but instead adjusts its behavior in accordance with the large-scale pattern of the tidal cycle itself in order to optimize nutrient uptake.