Towards an understanding of the spatial relationships between natural capital and maritime activities: A Bayesian Belief Network approach

Highlights

• Natural capital is required by maritime activities to maintain their performance.

• We develop a conceptual framework, linking natural capital to maritime activities.

• Understanding of dependencies of maritime activities on natural capital is achieved.

• Significant contribution towards an ecosystem-based marine spatial management.

Abstract

Economic activities are dependent upon natural capital (NC), which are responsible for ‘Ecosystem Services’ (ES). Understanding dependencies on NC provides insight into the ecosystem’s capacity to maintain and develop activities into the future. To determine ‘NC dependencies’, we present a framework linking maritime activities (bottom trawling, artisanal fisheries, aquaculture and tourism) to their demand for ES and further, to the NC components responsible for their production. The framework was operationalised using a spatially-explicit Bayesian Belief Network (BBN), using the Basque coast (SE Bay of Biscay) to illustrate our approach, in identifying trends in the strength and spatial distribution of NC dependencies. For example, benthic trawling was dependent on sedimentary habitats, with ‘moderate’ to ‘high’ dependency of 52% of the study area. The model can also extrapolate NC dependencies to a larger area where the activity currently does not operate, where benthic trawling was estimated to have higher utilisation of ES in deeper waters. When NC dependencies are combined with economic and legislative factors, the current spatial distribution of the activity can be explained, and the potential socio-economic impacts of management decisions could be predicted. The integrative approach contributes towards ecosystem-based spatial planning.

Introduction

The economic concept of ‘capital’ as a stock that produces goods and services has long been applied to the natural environment (Ekins et al., 2003), where ‘Natural Capital’ (NC) are elements of nature that directly or indirectly produce value to people (NCC, 2017). Natural capital is responsible for the yield of certain goods and services (‘Ecosystem services’, ES) that in turn contribute to human well-being, not only in providing food and raw materials but maintaining a habitable environment and satisfying intangible needs (Bennett et al., 2015).

The production of benefits may involve economic activities (Palomo et al., 2016), which access NC through their supply of ES. Therefore, NC can be considered the foundation for activity development and performance. Relationships between economic activities and NC can be defined through ‘NC dependency’, which is the reliance of an activity on the NC components responsible for producing ES used by the activity. An understanding of NC dependency extends beyond ES supply in also examining the demand of an activity for ES. It further recognises that the presence of an ES does not equate to its access and use by an activity. As 60% of ES have been estimated to have degraded globally (MEA, 2005) and further losses predicted into the future (Costanza et al., 2014), management approaches based on NC allow can guide sustainable research use, to ensure continued ES supply into the future.

The concepts of NC and ES are increasingly incorporated into nature-resource policy at national and international levels (Bouwma et al., 2018), although the scientific literature is still exploring how to operationalize these concepts into the ‘real-world’ context (Jax et al., 2018). Given the complexity of an ecosystem and its interactions with human society, several conceptual models have been proposed, varying in the identification of key system components (i.e. NC, ES and human components) and the relationships between them (see Haase et al., 2014, Villamagna et al., 2013, Burkhard et al., 2012). One conceptual model is the ‘Ecosystem Service cascade’ (Haines-Young and Potschin, 2010), a ‘production chain’ approach linking ecological and biophysical structures and processes to human well-being. The model has been used to simplify and compartmentalise aspects of an ecosystem (Potschin-Young et al., 2018) and has previously been employed as an ‘analytical template’, modified to assess supply–demand relationships (Baró et al., 2016) or ES delivery across different environments (Guerry et al., 2012, Guisado-Pintado et al., 2016). However, previous studies focus solely on the supply of ES from NC or the demand for ES from human activities. Our approach simultaneously incorporates both supply and demand and is a crucial step forward in managing the spatial distribution and intensity of existing and novel activities. Decisions based on NC dependency acknowledge the NC and ES required by economic activities and places greater emphasis on guaranteeing their continued supply.

In identifying the NC dependencies of activities, we must first define relationships from NC to ES. Determining ES supply from NC empirically is a major challenge (see Smith et al., 2017, Culhane et al., 2018), considering that an understanding of ES supply is limited by data and expertise, and not all NC components can be accurately assessed within the spatial domain (Shucksmith and Kelly, 2014, Smith et al., 2018). To evaluate how a socio-ecological system may function with an incomplete understanding of the system, modelling approaches are used.

The concept of ES varies in definition, relative to the manner in which it is used to address problems (Nahlik et al., 2012), leading to a range of techniques to assess ES and their relationship with other system components (Grêt-Regamey et al., 2017b, Harrison et al., 2018). Many modelling studies are performed with a specific focus (i.e. biophysical, socio-cultural or economic) or non-spatial in their assessment of NC and ES, limiting their ability to relate flows from NC and ES to economic activities.

A complementary approach to existing analyses is the use of Bayesian Belief Networks (BBNs), which are flexible in relating causal relationships between system components. The strength of relationships between components are defined by conditional probabilities, which can build simple and transparent relationships that combined, represent complex systems (Aguilera et al., 2011). The cause-effect pathways between components can be learnt from correlations inherent within the data, defined by expert knowledge or equations based on prior models (Landuyt et al., 2013, Aguilera et al., 2011). Uncertainties in relationships between components can be explicitly addressed and updated as new data becomes available (Trifonova et al., 2015). A BBN is also adaptable in network structure, where components and the relationships between them can be altered to reflect changes within a conceptual model. This allows for the incorporation of different data sources and interactions between multiple NC and ES, informed by local and expert knowledge to better reflect the real-world context (Krueger et al., 2012).

The use of this approach is especially relevant within marine ecosystems, where complex relationships exist between NC, ES and marine economic activities (henceforth ‘maritime activities’). Traditional practices, such as fishing and navigation, exist alongside ‘Blue Growth’ sectors which, according to the European Union’s Blue Growth Strategy (COM/2012/0494 final), includes aquaculture, biotechnology, offshore renewable energy, seabed mining and marine tourism (see Lillebø et al., 2017, Eikeset et al., 2018). Improved access and novel applications of oceanic resources have led to these alternate avenues for economic growth, which has resulted in the widespread incorporation of ‘Blue Growth’ into strategic management plans (Hadjimichael, 2018). Whilst the concept of ‘Blue’ growth is promoted as sustainable, their concurrent development, reliance on differing ES and NC components and their cumulative impact on NC may lead to environmental degradation in the absence of adequate understanding and management (Eikeset et al., 2018).

Thus, we propose a conceptual framework linking NC to maritime activities, which is operationalised using a BBN modelling approach. For this purpose, the Basque coast and marine area (SE Bay of Biscay) has been used to exemplify our approach. The specific objectives were: (i) defining a conceptual framework to linking NC to four maritime activities (i.e. bottom trawling, artisanal fisheries, aquaculture and tourism) via ES; (ii) development of spatially-explicit BBN models to operationalise our conceptual model for each activity, incorporating relevant NC components and ES; (iii) defining the strengths and spatial distribution of NC dependencies for each activity; (iv) extending the approach to the entire study area (i.e. where the activities do not operate), identifying areas with similar NC required by each activity to function and lastly (v) exploration of the potential socio-economic impacts related to the proposed establishment of two Marine Protected Areas (MPAs). This approach provides an understanding of ES supply from NC, demand by economic activities and the relationship between them. Such generation of knowledge will contribute to integrated assessments and greater incorporating of NC into decision making.