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.

Highlights

  • 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

  • Due to the fact that data on shoot density are obtained from laborious field activities, usually expensive and time-consuming, we proposed a cascaded approach aimed at modeling the rhizome primary production of P. oceanica using predicted density values, rather than observed ones

  • The Random Forest (RF) proved effective in modeling shoot density of P. oceanica using environmental factors acquired only from maps as predictive variables

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Summary

Introduction

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. The primary production of P. oceanica meadows ranks among the largest on Earth in terms of quantity per unit surface a­ rea[2] In this context, estimation of P. oceanica productivity could result essential in an environmental management perspective and for the assessment of the ecosystem services this species provides. Direct measurements of a complex ecological process such as P. oceanica primary production are difficult to carry out, and expensive and time-consuming, underlying the necessity for indirect methods. When P. oceanica leaves die, the blade is lost, while the sheath remains attached to the rhizome showing cyclic variation in its thickness that has a period corresponding to a lepidochronological y­ ear[6]. Despite the rhizome primary production contributes for approximatively 6–10% of the productivity

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