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

doi:10.1016/j.ocecoaman.2017.11.021

Ocean & Coastal Management

Volume 153, 1 March 2018, Pages 168-175

https://doi.org/10.1016/j.ocecoaman.2017.11.021 Get rights and content

Highlights

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Monitoring priorities for ecosystem services (ESS) mapping in data-poor coastal areas are explored.
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The mapping method is based on ESS providing, benefiting and connecting areas.
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Habitat mapping emerges as a key monitoring priority for marine and coastal ESS′ mapping.
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Mapping ESS benefiting and connecting areas can be kept simple in data-poor conditions.
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An aggregated ESS mapping method can be advisable for a first-tier assessment.

Abstract

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.

Introduction

Ecosystem-based management (EBM) shifts the perspective from a sectoral to an integrated approach to management, in which humans are considered as integral parts of the ecosystem (Agardy et al., 2011, McLeod et al., 2005, UNEP/GPA, 2006). A core focus of EBM is the maintenance of ecosystem structures, processes and functioning, so that the long term delivery of ecosystem services (ESS) to humans is secured (Agardy et al., 2011, McLeod et al., 2005). ESS is thus a central concept in EBM implementation, as it explicitly connects the ecosystems, and their functioning, with human beneficiaries. This connection is underlined by the concepts of ESS supply and demand (Burkhard et al., 2012), which emphasize that the potential to supply ESS, based on the functioning of the ecosystem, is converted into ESS only if there is a demand for these services from society (Burkhard et al., 2014).

ESS mapping, and particularly mapping of ESS supply and demand, are increasingly used in ESS assessments (Burkhard et al., 2012, Kroll et al., 2012, Nedkov and Burkhard, 2012, Sturck et al., 2014, Wolff et al., 2015), because of their usefulness for an effective planning (Andrew et al., 2015). On these regards, the concepts of “service providing area” (SPA), i.e. the spatial units where the service is sourced, and “service benefiting area” (SBA), i.e. the spatial units where the service is needed or readily used or consumed, allow to frame ESS supply and demand in a spatial dimension (Fisher et al., 2009, Serna-Chavez et al., 2014, Syrbe and Walz, 2012). In general, ESS are provided where the supply meets the demand, however, SPA and SBA do not necessarily need to overlap in order to have the actual ESS provision, as the ESS provision is not restricted to the area of the SPA and ESS can reach beneficiaries located also outside of it. In operational terms, this has led to the conceptualization of the “service connecting area” (SCA) (Serna-Chavez et al., 2014, Syrbe and Walz, 2012), which spatially represents a sort of connecting area through which the ESS can flow from the SPA to the SBA if the two do not overlap (see Costanza, 2008, Fisher et al., 2009 for a classification of the possible spatial relationships between SPA and SBA).

If marine and coastal areas are considered, however, data and methods for ESS assessment are much more limited compared to terrestrial systems, and consequently, a gap in literature exists concerning marine and coastal ESS (Liquete et al., 2013). Reasons include, among others, the lack of knowledge about marine habitats' coverage, the complexity of assessing connectivity between habitats, and the difficulty in identifying clear boundaries and assessment units in maritime areas (Liquete et al., 2013). In the case of the seagrass Posidonia oceanica, for example, although being the most widespread and a rather well-studied seagrass species in the Mediterranean sea, the knowledge about the meadows’ spatial distribution is patchy and characterized by mismatching assessment methods and spatial scales (Zucchetta et al., 2016). This makes the description of coverage and temporal trends at broad spatial scales quite challenging, and it is reflected in a knowledge about the ESS provided that mainly concerns specific case studies and a limited set of ESS (Campagne et al., 2014, Nordlund et al., 2016). On the other side, however, the recent directives (UNEP/MAP, 2012) engaged countries in the framework of the ecological status assessment and the consequent improvement of it, when needed. All these call for an increase of management capabilities and implies the decision on where to concentrate the effort and limited available resources. ESS mapping plays an important role in bringing ecosystems and their functioning to the attention of decision makers, highlighting their capacity to support human well-being and contributing to focus management efforts on the ecosystems whose ESS provision is critically decreasing. In data-poor coastal areas, where less research facilities and in situ data are available, the gap could become even larger, making the study of marine and coastal ESS even more challenging, in the perspective of an effective application of EBM and gaining of the Good Ecological Status.

In this context, the present study aims to identify the monitoring priorities for ESS mapping in the perspective of application of EBM in data-poor coastal areas. The ESS, indeed, represent good indicators being easily comprehensible also by policy makers and managers called to decide how to invest limited resources in environmental programs. In order to explore these aspects, the ESS provided by the P. oceanica meadows in the northern African Mediterranean coastal area have been chosen as a case study, being the seagrass meadows an example of submerged habitat for which ESS are still understudied, and the Southern Mediterranean coasts a data-poor area for which, to our knowledge, no scientific literature on ESS is available as yet. The mapping procedure has been repeated using different data and methods, in order to (1) check how the area of ESS provision vary in response to different inputs, and (2) compare the case in which ESS are resolved in an aggregated way (presence/absence of one or more ESS) with the case in which ESS are resolved one by one, including, in the latter case, the effect of applying different weights to different ESS within a simple benefit transfer exercise. The results have been used to explore which is the simplest mapping methodology capable to provide a reliable first ESS assessment suitable for application in a data-poor context, and which are the crucial information needed to perform the assessment.

Section snippets

Study area and data availability

The study area covers the North African coastal area located within the Western Mediterranean Sea UNEP-MAP sub region (Garmendia et al., 2015, UNEP/MAP, 2012) (Fig. 1). The area has been subdivided into 6 subzones, using a zonation adapted from Garmendia et al. (2015). Along the coast-to-open-sea direction, coastal areas (CAs) and offshore water bodies (OWBs) are distinguished, the first ones including the land-sea interface from 1 km landward to 1 nm seaward respect to the coastline, and the

Baseline results and sensitivity analysis

The baseline mapping approach (SPA from P. oceanica distribution model, SBA from “screening method”, SCA 1 km wide) results in a ESS area (surface with provision of one or more ESS) of 6307 km² (Fig. 2).

Concerning the sensitivity analysis, the variation respect to the baseline due to the alternative definitions of SPA and SCA (Table 1) are shown in Fig. 3. A +50% and −50% variation of the SPA's surface area produces a +26% and −36% variation in the ESS area, respectively. Concerning the SCA, a

Discussion

One of the crucial goals of EBM is to maintain the provision of ESS over time. This requires that an ESS assessment is performed and, possibly, repeated over time, in order to understand the temporal trend, to implement management actions aimed at maintaining the ESS provision and to evaluate their effectiveness. In data poor areas, lack of data and resources are often limiting the capability to perform such assessments. In Africa, for example, 52 studies concerning ESS have been found (Wangai

Conclusions

This work explores how the mapping of ESS provided by marine and coastal habitats can be simplified in order to be widely applicable in data-poor areas, and which are the resulting monitoring priorities. The recommendations for the managers that emerge can be summarized as follows:

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A first assessment of the multiple ESS provided by a marine/coastal habitat can be done in an aggregated way, gaining a much easier to apply method respect to a service-by-service assessment, while still obtaining

Acknowledgements

This work was partially funded by the EU FP7 Collaborative Project MEDINA (Marine Ecosystem Dynamics and Indicators for North Africa, Grant agreement no: 282977; www.medinaproject.eu, http://www.medinageoportal.eu/). We thank the three anonymous reviewers for the constructing comments that improved the manuscript.

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

Highlights

Abstract

Introduction

Section snippets

Study area and data availability

Baseline results and sensitivity analysis

Discussion

Conclusions

Acknowledgements

Adv. Parasitol.

J. Environ. Manag.

Ecol. Indic

Biol. Conserv.

Ecosyst. Serv

Ecol. Econ.

Ecol. Indic.

Global Environ. Change

Land Use Pol.

Ecol. Indic.

Ecol. Indic.

Ecol. Indic.

Ecol. Indic

Int. J. Sustain. Built Environ.

Ecol. Indic.

Ecol. Indic.

Taking Steps toward Marine and Coastal Management

Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review

GIScience Remote Sens.

European seagrasses: an introduction to monitoring and management, the M&MS project

Ecosystem service potentials, flows and demands – concepts for spatial localisation, indication and quantification

Landsc

The seagrass Posidonia oceanica: ecosystem services identification and economic evaluation of goods and benefits

Mar. Pollut. Bull.