Abstract
Posidonia oceanica meadows rank among the most important and most productive ecosystems in the Mediterranean basin, due to their ecological role and to the goods and services they provide. Estimations of crucial ecological process such as meadows productivity could play a major role in an environmental management perspective and in the assessment of P. oceanica ecosystem services. In this study, a Machine Learning approach, i.e. Random Forest, was aimed at modeling P. oceanica shoot density and rhizome primary production using as predictive variables only environmental factors retrieved from indirect measurements, such as maps. Our predictive models showed a good level of accuracy in modeling both shoot density and rhizome productivity (R2 = 0.761 and R2 = 0.736, respectively). Furthermore, as shoot density is an essential parameter in the estimation of P. oceanica productivity, we proposed a cascaded approach aimed at estimating the latter using predicted values of shoot density rather than observed measurements. In spite of the complexity of the problem, the cascaded Random Forest performed quite well (R2 = 0.637). While direct measurements will always play a fundamental role, our estimates could support large scale assessment of the expected condition of P. oceanica meadows, providing valuable information about the way this crucial ecosystem works.
Inter-annual variations over a decade of primary production of the seagrass Posidonia oceanica
