Ecosystem services' mapping in data-poor coastal areas: Which are the monitoring priorities?

Last modified: 
December 13, 2019 - 7:39pm
Type: Journal Article
Year of publication: 2018
Date published: 03/2018
Authors: Silvia Rova, Roberto Pastres, Matteo Zucchetta, Fabio Pranovi
Journal title: Ocean & Coastal Management
Volume: 153
Pages: 168 - 175
ISSN: 09645691

A crucial goal of ecosystem-based management is to maintain the delivery of ecosystem services (ESS) over time. This requires ESS to be assessed repeatedly over time, a task that becomes extremely challenging in data-poor coastal areas, where the lack of data and resources sums up with the intrinsic difficulties of assessing marine and coastal ESS. This implies the need to develop simple ESS assessment methods and to optimize the monitoring effort required to implement them. The aim of this work is to identify which are the key monitoring priorities for ESS mapping in data-poor coastal areas, in the perspective of ecosystem-based management implementation. In order to do so, the ESS provided by Posidonia oceanica meadows in the northern African Mediterranean coastal area have been chosen as a case study, and assessed by mapping the service providing, benefiting and connecting areas. Different input data and methods have been tested to explore how the mapping approach can be kept as simple as possible to ensure a broad applicability, and which are the crucial data required, in order to optimize the monitoring effort. The spatial distribution of the habitat providing the ESS resulted to be the data to which the mapping outcomes are more sensitive, and should be thus considered a key monitoring priority. The other input data can be kept as simple as (1) an expert-driven estimate of the service connecting area, to be understood as an ecologically meaningful range of influence of the focal habitat, and (2) globally available datasets for mapping the service benefiting areas. Overall, this results in an aggregated mapping of the multiple ESS provided by a marine habitat, which, according to our results, seems to be an advisable strategy for a first ESS assessment suitable for application in a data-poor context.

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