Ecosystem services values and changes across the Atlantic coastal zone: Considerations and implications
Introduction
Coastal zones are among the most important regions for humanity. More than 30 % of the world population live in coastal communities – which are twice as densely populated as inland areas [1], [2], [3] – and nearly 2.4 billion people (about 40 % of the world population) live within 100 km (60 miles) of the coast [4]. Out of the 33 world megacities, 21 are found on the coast [5] and their resident population directly benefits from as well as impacts on the environment and coastal ecosystems. There are numerous interactions between coastal communities and natural ecosystems, and it is increasingly recognized that natural ecosystems play a crucial role in determining human wellbeing [6].
Coastal areas are diverse, highly productive, ecologically important on the global scale and highly valuable for the wide range of ecosystem services (ES) they supply to human beings (e.g. [7], [8], [9]). The ecosystem service types include (1) Provisioning services, such as supply of food, fuel wood, energy resources and natural products; (2) Regulating & maintenance services, such as shoreline stabilization, nutrient regulation, carbon sequestration, detoxification of polluted waters and waste disposal; and (3) Cultural services, such as tourism, recreation, aesthetics, spiritual experience, and religious and traditional knowledge [6], [10]. These ES and associated values are of inestimable importance to life and human wellbeing, both to communities living in coastal zones as well as to national economies and global trade. However, they are highly vulnerable to anthropogenic pressures such as climate change impacts, sea level rise, erosion and storm events as well as being subject to population growth and economic development pressures [11].
Historically, in the late 1990 s and early 2000 s, the concept of ES slowly found its way into the policy arena, namely through the “Ecosystem Approach” and the Global Biodiversity Assessment [12]. In 2005, the concept of ES got wider interest after the publication of the Millennium Ecosystem Assessment (MEA) by the United Nations [13]. Numerous projects and groups are currently working towards better understanding, modeling, valuing and managing ES and natural capital [13].
In the scientific arena, an increasing number of scientific publications seeks to assign monetary values to ES at different spatial scales, from local to global [7], [13], [14], [15]. These types of contributions look at the value of a wide range of ES with a variety of methods and aim at reducing the shortcomings associated with the recurrent unavailability and, hence, exclusion of nature's values from policy and decision-making.
An important way to investigate the human dependence on coastal ES is to examine their estimated values, paying attention to their evolution over time. Generating estimates of their value can help informing policymakers by providing them insights about the costs and benefits of their actions when managing and developing coastal areas. Also, this type of analysis can be used to support policy decisions, especially under data poor situations [3].
Monetary valuation advocates for a more efficient use of limited resources and helps in deciding where protection and restoration are economically more efficient and can be delivered at least cost [16], [17]. The outcomes of ES valuation studies can support coastal management decisions and conservation policies through, e.g., the establishment of compensation schemes [18], estimation of payments for environmental services [19] and assessment of rates for the use of an ecosystem based on costs of ecosystem degradation [20]. Even considering the limitations of the monetary valuation of ES [21], it is far better to work with rough and ready figures than to ignore large amounts of natural capital goods by pretending they do not exist [22].
The first step in estimating ES values is to develop a biophysical assessment of their availability that, more than determining their overall provision or accessibility, focusses on their actual use and benefit by humans [3]. However, this exercise proves to be extremely difficult for many reasons. While some ES are inherently spatial, easier to evaluate and more directly measurable than others, assessments need to rely on mapping or modeling of their flow in space and time [6]. The fact that biophysical assessments depend on the status of scientific knowledge and data availability, pushes several authors to rely on proxies to identify service provision, as opposed to benefits, especially in cases where there is lack of consensus on the best or ideal measurement units for these ES. Thus, finding a common metric is crucial -and also challenging- to inform robust policy decisions. That is why monetary valuation, even in the absence of biophysical assessments, becomes a common language and framework in which the available information can be analyzed, and trade-offs can be evaluated [3].
Although the results of ES valuation studies are increasingly applied, non-market valuations typically have a limited geographical scope and are also dependent on socio-economic and cultural contexts. By using results from earlier empirical studies and applying their conclusions to new policy sites, different from that of the original study, benefit transfer arises as an attractive possibility that helps dealing with time and budget constraints whenever reliable primary valuations are unavailable [15], [23], [24]. However, local characteristics, such as ES location, accessibility, quality, territorial extension and socio-cultural dimensions, are crucial factors when estimating the ES value [20], [25]. Thus, the value transfer technique is particularly troublesome when the study site (i.e. where the primary valuation study took place) and the policy site (i.e. where the values from the primary valuation study are transferred to) are in different geographic and socio-economic contexts [26]. However, the value function transfer technique, in particular when based on meta-analysis (MA),1 arises as an alternative to the value transfer technique as it considers phenomenon-intrinsic and context-specific factors, such as the methods and variables used in the primary valuation study [23].
Previous MA have derived value functions for specific coastal biomes and ES types, including coral reefs [27], aquatic systems [28], recreational services of coastal ecosystems [15] or shoreline protection values of mangroves, coral reefs and wetlands [3]. Albeit including a wide range of observations, these studies are limited to analyzing only one or few biomes and/or ecosystem service types [3], [15], [28], [29], [30]. Hence, there is a need to assess the full range of coastal biomes and ecosystem service types to allow policy makers to consider coastal ES and values in their coastal management decisions, development strategies and conservation policies. Also, there is a lack of comprehensive studies covering the whole Atlantic coast.
Therefore, the objective of this study is to map and assess the values for 12 biomes and three different types of ecosystem service types (Provisioning; Regulating & maintenance; Cultural) for a coastal zone of 100 km of all the 63 countries that, together, form the Atlantic coastal zone, over the period 2005–2015. To this end, global value functions for a wide range of coastal biomes and ES value types are used and applied to countries on the Atlantic coastal zone, using land use and socio-economic data for 2005, 2010 and 2015. Hence, evolutions and transformations of ecosystem services values on the Atlantic coastal zone are mapped, assessed and considered. Finally, results are discussed and reflected upon, and the relevance for coastal management and policy makers is emphasized.
Section snippets
Materials and methods
The approach adopted in this study integrates global ecosystem service value functions (derived by [31]; see Section 2.1) and historical land use and socio-economic data for countries on the Atlantic coastal zone (see Section 2.2), to understand changes in land use, income and population (Section 3.1) that underpin changes in unit ecosystem service values (Section 3.2) and that are used to estimate changes in total ecosystem service values (Section 3.3) for countries on the Atlantic coastal
Land-use and socio-economic changes on Atlantic coastal zone
When conducting ES value analysis, it is crucial to understand the land-use and socio-economic dynamics, not only to provide a diagnosis of the study area but, in particular, to understand the land use, income and population evolution over time that underpin changes in unit (Section 3.2) and total (Section 3.3) ES values. We divided our analysis by continents (Africa, Europe, Latin America & Caribbean and North America) and calculate their total area by biome, for each year. Table 4 presents
Comparison with other coastal ecosystem service valuation studies
As to validate the ES values found in our analysis, we compare our results with similar previous studies in coastal areas. Studies performed until now offer rather disparate results (see Table 11). Average unit ecosystem service values in these studies, across all types of ES and all types of biomes, vary between 87 and 8379 €/ha/year. Lowest unit ecosystem service values are observed in Martinez et al. [5], which is explained by their coastal zone delimitation (100 km buffer from coastline,
Conclusions
This study presents a global ecosystem service value function application to map and assess the ecosystem service values (ESV) associated with Provisioning (ESVProv), Regulating & maintenance (ESVReg&Main) and Cultural (ESVCult) ecosystem services (ES) for the Atlantic coastal zone. Results show that, although there was a decrease in natural areas along the Atlantic coastal zone over the period 2005–2015, the ESV increased over time, mainly due to the increase in population density (PDen) and
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Thanks are due for the financial support to FCT/MCTES for the financial support to CESAM (UIDB/50017/2020 and UIDP/50017/2020) through national funds and the co-funding by European funds when applicable. This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and acknowledges the support from the ECOMAR project funded by the CYTED program. This work was financially supported by the project “Integrated Coastal
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