A Machine Learning approach to the assessment of the vulnerability of Posidonia oceanica meadows

Abstract

Abstract Posidonia oceanica is an endemic Mediterranean seagrass that ranks among the most important and valuable species, with regard to both its ecological role and the services it provides. Despite this species is one of the main targets of conservation actions, the current regression trend of P. oceanica is alarming, underlying the urgent need for reliable methods capable of assessing meadows vulnerability. To address this need, we developed a Habitat Suitability Model (HSM) aimed at assessing the vulnerability of P. oceanica meadows in the Italian marine coastal waters using the Random Forest (RF) Machine Learning technique. Building on the current knowledge on both spatial distribution and condition of meadows in the Italian seas, the RF was used as a classifier aimed at modeling the habitat suitability for P. oceanica, rather than for predictive purposes. The assessment of the potentially most vulnerable P. oceanica meadows at increasing risk of regression was performed through the analysis of the RF output. The HSM showed a good level of accuracy, i.e. Cohen’s K = 0.685. The proposed approach provided valuable information regarding the vulnerability of P. oceanica meadows over the Italian marine coastal waters. In addition, an evaluation of the relative importance of the predictors was carried out using the permutation measure. The developed HSM can support conservation and monitoring programs regarding this species playing a crucial role in the marine ecosystems of the Mediterranean Sea.