A methodological approach to understand functional relationships between ecological indices and human-induced pressures: the case of the Posidonia oceanica meadows

J Environ Manage. 2013 Nov 15:129:540-7. doi: 10.1016/j.jenvman.2013.08.008. Epub 2013 Sep 7.

Abstract

The European Water Framework Directive (WFD) 2000/60/EC requests the achievement of the Good Status for all surface waters, including the coastal waters, by 2015. In order to check compliance with the needs of Directive, Italian national monitoring data on Posidonia oceanica meadows have been explored and the relationships among the Posidonia Rapid and Easy Index (PREI), and human-induced pressures have been analyzed along the Italian coasts. The aim of this work is to establish functional relationships between a response variable (i.e. the PREI) and a set of potential pressure (i.e. land use, potential organic and nutrient loading, pesticides) and status (i.e. transparency, trophic level and stability of the water column) indicators in a quantitative way. The ecological responses of coastal marine environment have been evaluated using appropriate statistical tools, such as the multiple linear regression analyses and "linear programming" techniques. Results show that more than 70% of the variability of the P. oceanica meadows status, expressed as PREI value, is significantly explained only by a few pressure/status indicators (namely: potential organic load, specific nitrogen load, natural areas extent, water column transparency), among all those initially considered in the model. The application of the proposed model could allow decision makers to better address remedial actions and to achieve the environmental targets proposed by the EU Directives.

Keywords: Ecological indexes; Ecological status; Human pressures; Posidonia oceanica; WFD.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alismatales / physiology*
  • Conservation of Natural Resources / methods*
  • Ecosystem*
  • Human Activities
  • Humans
  • Italy
  • Models, Biological
  • Regression Analysis
  • Water Pollutants, Chemical / analysis*
  • Water Quality*

Substances

  • Water Pollutants, Chemical