Using regression trees to identify the habitat preference of the sea cucumber ( Holothuria leucospilota) on Rarotonga, Cook Islands

Abstract

In the Pacific Islands, invertebrates including sea cucumbers are among the most valuable and vulnerable inshore fisheries resources. As human activities continue to force substantial impacts on coral reef ecosystems, the management of inshore fisheries has become an increasingly important priority. Knowledge of the distribution, biology and habitat requirements of a species can significantly enhance conservation efforts. The sea cucumber ( Holothuria leucospilota) forms an important part of the traditional subsistence fishery on Rarotonga, Cook Islands, yet little is known of this species’ present spatial distribution and abundance around the island. We apply two machine learning approaches and a classical statistical approach to predict the number of sea cucumber individuals from site characteristics. The machine learning methods used are induction of regression trees and instance-based learning. These are compared to the classical statistical approach of linear regression. The most accurate predictions are obtained using instance-based learning, while the most understandable descriptions are obtained using regression tree induction.