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date: 18 February 2020

The Economics of Marine Reserves

Summary and Keywords

Marine protected areas (MPAs) remain one of the principal strategies for marine conservation globally. MPAs are highly heterogeneous in terms of physical features such as size and shape, habitats included, management bodies undertaking management, goals, level of funding, and extent of enforcement. Economic research related to MPAs initially measured financial, gross, and net values generated by the habitats, most commonly fisheries, tourism, coastal protection, and non-use values. Bioeconomic modeling also generated important insights into the complexities of fisheries-related outcomes at MPAs.

MPAs require a significant investment in public funds for design, designation, and ongoing management, which have associated opportunity costs. Therefore cost-benefit analysis has been increasingly required to justify this investment and demonstrate their benefits over time. The true economic value of MPAs is the value of protection, not the resource being protected. There is substantial evidence that MPAs should increase recreational values due to improvements in biodiversity and habitat quality, but assumptions that MPAs will generate such improvements may not be justified. Indeed, there remains no equivocal demonstration of spillover in fisheries adjacent to MPAs, due in part to the variability inherent in ecological and socio-economic processes and limited evidence of tourism benefits that are biologically or socio-cultural sustainable.

There is a need for carefully designed valuation studies that compare values for areas within MPAs compared the same areas without management (the counterfactual scenario). The ecosystem service framework has become widely adopted as a way of characterizing goods and services that contribute directly or indirectly to human welfare. Quantitative analyses of the marginal changes to ecosystem services due to MPAs remains rare due to the requirements of large amounts of fine-grained data, relatively undeveloped bio-physical models for the majority of services, and the complexities of incorporating ecological non-linearities and threshold effects. In addition while some services are synergistic (so that double counting is difficult to avoid), others are traded off. Such marginal ecosystem service values are highly context specific, which limits the accuracy associated with benefits transfer. A number of studies published since 2000 have made advances in this area, and this is a rapidly developing field of research.

While MPAs have been promoted as a sustainable development tool, there is evidence of significant distributive impacts of MPAs over time, over different time scales and between different stakeholders, including unintended costs to local stakeholders. Research suggests that support and compliance is predicated on the costs and benefits generated locally, which is a major determinant of MPA performance. Better understanding of socio-economic impacts will help to align incentives with MPA objectives. Further research is needed to value supporting and regulating services and to elucidate how ecosystem service provision is affected by MPAs in different conditions and contexts, over time and compared to unmanaged areas, to guide adaptive management.

Keywords: marine protected area, valuation, cost-benefit analysis, ecosystem services, fisheries, costs, benefits

Introduction

Marine resources play an invaluable role in human welfare globally, as a source of food, energy and recreation, as well as being valued for existence and cultural reasons and playing a role in regulating the world’s climate (Barbier et al., 2011; de Groot et al., 2012). Marine resources are highly threatened by human activities including industrial fishing, raw material extraction oil, and gas exploration, shipping, and terrestrial source pollution (Barbier et al., 2011; Halpern et al., 2008). Marine protected areas (MPAs) are an ecosystem-based management tool that remain one of the principal strategies for marine conservation globally (Fox et al., 2012). To date, there are approximately 13,000 MPAs worldwide, which cumulatively covers more than 5% of the global ocean by area, with new MPAs continuing to be established at an increasing rate (O’Leary et al., 2018) (Figure 1).

The IUCN has defined an MPA as “a clearly defined geographical space, recognized, dedicated, and managed through legal or other effective means, to achieve the long term conservation of nature with associated ecosystem services and cultural values” (Laffoley, 2008, p. 7). MPA is an umbrella term for such areas that have been assigned many diverse titles (Boersma & Parrish, 1999; Kelleher, 1996). MPAs include a range of protection from areas where no use is permitted (e.g., marine reserves, conservation areas), areas where extractive use such as fishing, aquaculture, and industrial development is banned (e.g., no-take areas), sites that have zones with distinct regulations (e.g., multiple use MPAs), and areas with negligible restrictions (IUCN, 2008). The coverage of multiple-use MPAs is far greater than that of no-take MPAs (Brander et al., 2015; Wood, Fish, Laughren, & Pauly, 2008). MPAs are highly heterogeneous in terms of physical features such as size and shape, habitats included, management bodies undertaking management, goals, level of funding, and extent of enforcement (Hargreaves-Allen, Mourato, & Milner-Gulland, 2011).

The Economics of Marine Reserves

Figure 1. The cumulative percent coverage of all MPAs (light gray), all large-scale MPAs (dark gray), and strongly or fully protected areas in large-scale MPAs (black) designated and promised globally (O’Leary et al., 2018).

Historically, MPAs were established principally with goals related to fisheries and to a lesser extent habitat protection (Kelleher, 1999). Research sought to measure or model ecological improvements inside the MPAs compared to areas outside. Subsequently, MPAs were promoted as a “win-win” strategy, which could achieve multiple diverse goals simultaneously, including supporting sustainable economic development and livelihoods of local stakeholders, increasing tourism, increasing opportunities for education and research, and preserving cultural values and ways of life (O’Leary et al., 2018; Watson, Dudley, Segan, & Hockings, 2014).

Since MPAs are situated all over the world, they incorporate many types of tropical, temperate, and artic habitats, including coral reefs, sea grass beds, mangroves, wetlands, estuaries, and kelp forests. Many of the values of such natural habitats, including coastal protection, climate mitigation, biodiversity support and cultural values, are not traded in markets, despite having substantial benefits to people. For example, during the 2004 tsumani, damage to coastal infrastructure and human loss of life in South East Asia was significantly mitigated by the presence of healthy mangroves (Danielsen et al., 2005). Since these sorts of benefits are not represented directly in markets, they were historically effectively considered to be negligible. This lack of consideration in non-market values in policy creation and decision-making resulted in outcomes that have not maximized public social welfare or allowed for sustainable use of marine resources due to wide-scale habitat loss and destruction (Halpern et al., 2008; Lipton, Lew, Wallmo, Wiley, & Dvarskas, 2014; MEA, 2005; TEEB, 2010).

Economic analysis should help us make more informed decisions about how to use or manage such areas in an easily understood metric (Schmidt, Manceur, & Sepppelt, 2016; TEEB, 2010). Valuation of such areas has demonstrated that the environment makes a large contribution to well-being and generates a range of values generated for stakeholders locally nationally and internationally (Bockstael, Freeman, Kopp, Portney, & Smith, 2000). It has also helped to elucidate the causes of environmentally destructive behavior. For example, an economic analysis of blast fishing in Indonesia showed that while there are significant revenues generated for a large number of people, allowing it to continue is perverse as the economic costs to society are four times higher than the total net private benefits in areas with high potential value of tourism and coastal protection (Pet-Soede, Cesar, & Pet, 1999). Additionally, economic analysis may help to generate solutions to reverse environmental degradation. One of the key research questions remains under what conditions might the conservation gains attributed to MPAs provide the largest benefit for the smallest cost (Farrow, 1996; Hoagland, Yashiaki, & Broadus, 1995; Milon, 2000).

A Historical Perspective on the Economics of Marine Protected Areas

The Economic Value of Resources Within MPAs

Research conducted from the 1990s demonstrated significant and varied values from marine habitats. Such valuation work built support for habitat protection measures including MPAs as it demonstrated value to humans, potential losses from threats, and benefits of resource protection (Dixon & Sherman, 1991; Turner et al., 2003). Often, the total economic value framework was used to understand the different types of values generated by natural resources (Figure 2). This distinguishes between direct use values (e.g., extractive uses and recreation), indirect values (e.g., biophysical or biological functions), option values (value of delaying irreversible decisions), and non-use values (Arrow et al., 1993; Barton, 1994). This framework was used as the basis of numerous valuation studies. Some focused on a specific use, others on a specific habitat or threat (e.g., see Cesar, 2001, for a compendium of coral reef economic research). The ease of valuation is highest for extractive uses, medium for most ecosystem services (ESs) and low for genetic resources, climate control, and science and knowledge, which is reflected in the number of studies for the different types of benefits (Schumann, 2012). Pelagic and continental shelf ecosystems were underrepresented in the literature compared to wetland and reef-related systems (Barbier, 2012; Schuhmann, 2012). See Vassilopoulos and Koundouri (2017) for a review of marine ecosystem valuation methodologies and research.

The Economics of Marine Reserves

Figure 2. The total economic value (TEV) framework to describe the value of environmental goods (Barton, 1994).

Fisheries and then tourism-related values were the most commonly measured initially (Alban, Apere, & Boncoeur, 2008). Fisheries values typically related to gross and net revenues from fisheries close to no-take MPAs. The implicit assumption was that these fisheries are indirectly supported through adult and juvenile spillover and larval transport from inside the MPA. Some of these showed very large values generated by artisanal or recreational fisheries adjacent to MPAs (e.g., Bell & Leeworthy, 1997; Hyder, Armstrong, Ferter, & Strehlow, 2014; Lipton et al., 2014; Prayaga, Rolfe, & Stoeckl, 2010; Ruitenbeek & Cartier, 1999). For recreational values, studies that reported gross revenues from MPA-related tourism (including tourist expenditures, park fees, and tourism taxes) may have contributed toward support, as they have shown that these may be substantial, especially if indirect and induced local and regional impacts were included (e.g., Dixon, 1993; Driml, 1994; Israel, 2004; Johns, Leeworthy, Bell, & Bonn, 2003; Ruitenbeek & Cartier, 1999). Such studies reported static fisheries or tourism valuations, which do not indicate if current levels are sustainable ecologically or culturally (Carter, 2003).

Financial flows, or economic impacts of fisheries and tourism, are important from a policy perspective since they will substantially influence stakeholder attitudes toward the MPA. Studies reporting substantial revenues from MPA-related tourism and fisheries helped to increase support for MPAs as they were seen to offset opportunity costs such as loss of access to traditional fishing and recreational areas. However, these transfers of funds and groups represent the financial impacts of MPAs, not the economic values.

Numerous valuation studies have been conducted to analyze visitor willingness to pay (WTP) for access to MPAs using mainly contingent valuation and travel cost analysis (e.g., Peters & Hawkins, 2009; Spash, 2000; Thur, 2010). These showed that recreational visitors have significant WTP for access to MPAs provided that threshold levels of quality and limits on use are maintained. WTP for MPAs was found to depend on numerous site-related factors including the activity being undertaken, site crowding, and the diversity of wildlife as well as respondent characteristics including age, education level, income, environmental awareness and attitudes, country of residence and non-use values (Brander, Florax, & Vermaat, 2006; Gelcich et al., 2013; McVittie & Moran, 2010; Paltriguera, Ferrini, Luisetti, & Turner, 2018; Spash, 2000). Value estimates were also found to sensitive to methodological aspects such as the methodology used, the information presented, the survey format, and the payment vehicle presented (Brander et al., 2006; Setiasih, 2000; Thur, 2010).

There have been relatively few studies to measure local (rather than visitor) WTP for MPAs, but both use and non-use have been demonstrated to be significant at certain MPAs in Belize, Madagascar, and Fiji (Hargreaves-Allen, 2010; O’Garra, 2006; Oleson et al., 2015). Traditional financial payments can be replaced with time or labor-based contributions where incomes are very constrained or discount rates very high (Gibson, Rigby, Polya, & Russell, 2016; Ison, Hills, Morris, & Stead, 2018; O’Garra, 2009). Local WTP to support MPA is influenced by proximity to markets, dependence on marine resources, food security, attendance in education activities, participation in decision-making, use of the MPA, and perception of MPA benefits (Hargreaves-Allen, 2010; Ison et al., 2018; O’Garra, 2009).

Significant progress was also made on measuring coastal protection values (Barbier et al., 2011; Russi et al., 2016) for seagrass, mangrove, saltmarsh, and reef habitats inside MPAs, which can be estimated using estimates of the value of avoided damages or replacement cost for the function (e.g., Burke, Greenhalgh, Prager, & Cooper, 2008; Pascal et al. (2016). Such values have been demonstrated to be large (e.g., Beaumont, Austen, Mangi, & Townsend, 2008; Burke, Selig, & Spalding, 2002; Fletcher et al., 2012; Ruitenbeek & Cartier, 1999). Comparatively little progress has been made on measuring research and education values or option and quasi-option values (Barbier, 2012).

Non-use values for protection of marine biodiversity can be worth billions of dollars when aggregated nationally and outweigh project costs by several orders of magnitude (e.g., Kenter et al., 2013; McVittie & Moran, 2010). Non-use values were found to constitute a large part of values at numerous MPAs, even at remote and unfamiliar MPAs (Brouwer et al., 2016). Interestingly, very little effort has been made to measure the relative contribution of existence and bequest values, which are typically measured together. Including these values allows the preferences of remote stakeholders and future generations to be considered (Carter, 2003). However, it is uncertain if MPAs could achieve the improvements in conservation that many valuations assume in practice, given MPA underfunding, current pervasive threats, and conflict associated with management (Christie, 2004; Hargreaves-Allen, Mourato, & Milner-Guilland, 2017; Zupan et al., 2018).

Values Net of Costs

The true measure of economic value should be net of costs, meaning “producer surplus” (PS) and “consumer surplus” (CS) values. Fisher producer surplus has, however, rarely been measured in practice at MPAs. This is likely due to the large data requirements needed to offset large amounts of variation in terms of ecological and human behavioral parameters. In Belize, a year of landings data, fisher surveys, and household surveys was needed to estimate producer surplus values for fisheries within a multi-use MPA (Hargreaves-Allen, 2010). The cost and effort for such analyses are typically prohibitive, especially in understaffed MPAs. Fisher PS is typically small compared to gross revenues, e.g., in the Great Barrier Reef Marine park, it was $31 million of the $128 million value of product landed per annum (Driml, 1994) due to the high costs of fishing. In contrast, recreational CS values are typically high as WTP is typically greater than the entrance fees charged (Arin & Kramer, 2002; Cesar, Van Beukering, & Goodridge, 2002; Depondt & Green, 2006; Green & Donnelly, 2003).

A number of studies have addressed MPA costs, which include one-off establishment costs as well as ongoing management costs and opportunity costs such as forgone fishing or tourism revenue and unintended costs such as cultural impacts (Adams, Mills, Jupiter, & Pressey, 2011; Cook & Heinen, 2005; Sanchirico & Wilen, 2001; White, Vogt, & Arin, 2000). MPA establishment and operational costs were found to be highly heterogeneous and depend on scale and location (Balmford, Gravestock, Hockley, McClean, & Roberts, 2004; Ban, Adams, Pressey, & Hicks, 2011; McCrea-Strub et al., 2011). Opportunity costs of MPAs are challenging to measure but are starting to be considered in MPA design (e.g., Adams et al., 2011; Leathwick et al., 2008), although these tend to focus on fisheries, not so much other sectors that may be affected such as mineral extraction, shipping, or renewable energy sectors (Kenter et al., 2013). MPA costs can outweigh benefits if large compensation payments to fishers are necessary, e.g., (Hunt, 2013). However, simply choosing MPAs that minimize cost will not be effective (Devillers et al., 2014).

MPAs are an investment of public money, which entail an opportunity cost of those funds (Carter, 2003; Cook, Hockings, & Carater, 2010; Sanchirico, 2000). Therefore, as is the case for other public investments, cost-benefit analysis (CBA) of MPAs was increasingly understood to be justified and necessary. MPAs are underfunded, which limits their effectiveness, and direct start-up and management costs can limit implementation (Balmford et al., 2004; Gravestock, Roberts, & Bailey, 2008; Mora et al., 2006). Hence it is important to establish that the benefits generated outweigh the costs to make a case for better funding (Brander et al., 2015; Sanchirico, Cochran, & Emersen, 2002).

The Value of Protection

Studies reporting the value of habitats inside MPAs imply that the value of such areas would disappear totally and immediately should management cease. However, this assumption is unlikely to be true, as environmental degradation would be expected to occur gradually. Furthermore, some values may not be greatly affected by degazetting. For example, in the Bonaire National Marine Park, tourist visitation continued to increase despite the park being dissolved for several years. The true value of MPAs is the savings in loss of value that would occur if the area was not an MPA minus the costs of protection—that is, the protection itself, not the resource being protected (Pendleton, 1995). Using this approach, the economic value of paper parks (where no management actually takes place) would be correctly reported to be zero. To address this issue, Pendleton (1995) recommended using a dynamic approach and estimating shifts in demand curves with travel cost analysis and discrete choice random utility analysis.

Despite recognition of the need to value the marginal benefits of protection, few analyses were done to undertake this due to the necessary complexities entailed and the unavailability of necessary socio-economic data (Barbier, 2012; Carter, 2003). One approach to measuring the marginal value of protection indirectly is to measure CS for changes, namely expected improvements or avoided losses in quality that would be likely result from effective MPA management (Carter, 2003). Such changes might include larger fish, greater sightings of charismatic species, higher levels of coral cover, or visitor information or infrastructure (Alban et al., 2008). Numerous studies have demonstrated WTP for habitat quality improvements (e.g., Bhat, 2003; Spash, 2000; Uyarra et al., 2005; Wright, 1994; Wielgus, Chadwick-Furman, Duckinsky, Schechter, & Zeitouni, 2002). Conversely Parsons and Thur (2007) show that declines in visibility, species diversity, and coral cover are worth $45–192 per person in the Bonaire National Marine Park, depending on the severity of the decline. Since these reported values are likely to be very dependent on the location and the scenario and payment vehicle presented, it is difficult to generalize these results. There is also little evidence to tease apart the contribution of distinct attributes including changes to tourist facilities (Williams & Polunin, 2000), and the assumptions that MPAs will achieve these changes may not be realistic, as MPA performance is highly heterogeneous (Edgar et al., 2014; Gill et al., 2017; Hargreaves-Allen et al., 2011).

Another approach is to develop scenarios and to value resources with and without protection (Figure 3). The difference, then, between the counterfactual (what would have occurred otherwise) and the observed or predicted state can (with careful study design) indicate the impact on the system attributable to the MPA (Fulton et al., 2015). The counterfactual needs to be carefully chosen so that the scope of the model matches the scope of the question. An early example of this approach was conducted for the Leuser National Park in Indonesia (van Beukering, Cesar, & Janssen, 2003). The park total economic value was calculated for 11 benefit categories under three scenarios using a dynamic simulation model and showed the large increase in net benefits of not allowing deforestation to occur.

The Economics of Marine Reserves

Figure 3. Example of benefit streams with and without an MPA (Fulton et al., 2015). In this example fisheries are the output, and two alternative forms of management are compared in Australia, but this pattern might be expected for many different benefit streams.

Bioeconomic Modeling

Models are a simplified description of certain features and processes of interest which are abstracted from reality (Fulton et al., 2015). Bioeconomic modeling has been used to better understand the contribution of MPAs to fish biomass, catch levels, and the present value of the fishery under different scenarios including MPA features, biological aspects of the habitats and fisheries, and different regulations (Alban et al., 2008). One of the first papers on the economics of marine reserves for fishery management demonstrated that marine reserves can sustain or increase yields for moderate to heavily fished fisheries using data on red snapper (Holland & Brazee, 1996).

Bioeconomic models have two main types. Spatially non-explicit models such as equilibrium models (e.g., Goñi, Hilborn, Díaz, Mallol, & Adlerstein, 2010; Walters, Hillborn, & Parrish, 2007) typically model the effects of one species and one gear and assume spatial homogeneity to show the effects of closing an area to fishing and then calculating spillover using a transfer function. Spatially explicit models are multispecies models (e.g., Sanchirico, 2005; Sanchirico & Wilen, 2001), which usually focus on the location of MPAs rather than the size. These include oceanographic, ecological, and dispersal-related parameters as well as socio-economic factors. They require simulations of fisher behavior or mobility, which are incorporated using several techniques, notably gravity models, random utility, game theory and multi-agent modeling (Alban et al., 2008). The appropriate model to examine MPA outcomes depends on the research objective. Fulton et al. (2015) describe models used in analyzing MPAs, and Sumaila and Charles (2002) and Grafton, Kompas, and Schneider (2005) review the literature of bioeconomic modeling.

Such models are important since they can be used to explain or predict how systems work and might respond to internal and external changes, to identify unanticipated outcomes, and to test if indicators and management strategies are robust to uncertainties. They can be used in data-poor situations and can allow a comprehensiveness that would be too costly to conduct using surveys and include conditions that are hard to observed or have not yet occurred (Fulton et al., 2015).

However, they have a limited ability to incorporate fine-scale details, which is important given the median area of MPAs globally is less than 5 km2 (Fulton et al., 2015). There are also important limitations related to oversimplified assumptions, such as spatial heterogeneity or constant prices, which can lead to inappropriate conclusions (Alban et al., 2008; Rudd, Tupper, Folmer, & Van Kooten, 2003). Since there is a gap between the empirical data available and the data required to estimate models for economic analyses, many studies are theoretical and limited to one specific change with regards to a specific activity. Such models fail to take into account critical ecological impacts, such as habitat protection, which may enhance fishery sustainability in numerous interacting ways (Rodwell & Roberts, 2000). However, more complex models include more uncertainty (Jiang et al., 2008). If all the parameters that affect fisheries outcomes from MPAs were included, the resulting model would be too complex to provide meaningful results (Rodwell & Roberts, 2000). Model projections can, however, be improved by calibrating them with field data, using sensitivity analyses and running multiple realisations to evaluate the robustness of the model result and conditioning the model with retrospective analyses (Fulton et al., 2015).

Bioeconomic models have yielded valuable insights into the relationship between MPAs and fisheries. They have demonstrated that MPAs can increase biomass inside and outside of MPAs and reduce the variability in stock levels and hence variations in harvest levels over time (Conrad, 1999; Hannesson, 1998; Savina, Condie, & Fulton, 2013; Sumaila, 1998). They have also elucidated the variability of fishery responses to protection, depending on the specific circumstances (Holland, 2000; Rodwell & Roberts, 2000; Sciberras, Jenkins, Kaiser, Hawkins, & Pullin, 2013) and the extent of fisheries management outside the MPA (Carter, 2003). For example, spillover has been shown to be dependent on numerous factors, notably the target species, reserve size, habitat connectivity, fish mobility, reserve demarcation, level of enforcement, and availability of alternative sites (Boersma & Parrish, 1999; Gerber, Kareiva, & Bascompte, 2002; Goñi, Badalamenti, & Tupper, 2011; Sumalia, 2002; Pelletier & Mahévas, 2005). Reserve size has been shown to have a non-linear relationship with fishery yield (Gaines, White, Carr, & Palumbi, 2010; Holland, 2000; Pelletier & Mahévas, 2005; Sumaila, 1998), which in turn will be affected by spatial heterogeneity inside and outside the reserve (Schnier, 2005).

Such models have underscored the importance of human behavioral responses to MPAs, including the degree and location of displaced fishing effort across fishing grounds and into other fisheries, as well as non-compliance, which can dissipate fishery benefits (Boersma & Parrish, 1999; Edwards & Plagányi, 2011; Fulton & Gorton, 2014; Pelletier & Mahévas, 2005; Sanchirico & Wilen, 2001; Smith & Wilen, 2003). Indeed a marine reserve alone will do nothing to eliminate or even reduce rent dissipation by itself even if it does increase stock sizes and even catch (Hannesson, 1998), and under certain conditions MPAs may increase costs, overcapacity, and conflict. Unfortunately, despite advances in agent-based models used to investigate human behavior in different situations (e.g., McDonald et al., 2008), such human responses to MPAs remain poorly understood (Alban et al., 2008).

More recent research combines several models with datasets to generate highly sophisticated information related to fisheries outcomes at MPAs. For example in Fiji, Adams, Pressey, and Naidoo (2010) combined habitats/species abundance models with catch, fishing effort, market value, fishing, and cost-profit models used as inputs into Marxan planning software to identify socially acceptable configurations for community-managed MPAs. In the gulf of Carpentria, fisheries models, biophysical models, assessment models, and management models were coupled and considered in the context of five performance measures, one of which was economic performance, to generate an output decision table for alternative management options (Bustamante et al., 2011).

Emerging Research on the Economics of MPAs

Marginal Values of Ecosystem Services

The Millennium assessment in 2005 first characterized consistent and usable terms for ESs (defined as outputs from ecosystems from which people and society derive benefits) and underscored how they were linked to human well-being (Figure 4). This framework is very useful to conceptualize many different benefits and is being broadly adopted by government agencies in developed economies (Fisher & Turner, 2008; Lipton et al., 2014). It divides services into provisioning, supporting, regulating, and cultural (see Liquete et al., 2013, for a systematic review of marine ESs).

There have been advances in detailing qualitative links between marine habitats and suites of ESs. For example, Potts et al. (2014) developed matrices of services for each marine habitat in the United Kingdom using export opinion and a literature review. They also estimated the relative importance of each habitat type in providing the ES and the level of confidence in the evidence. Nevertheless, the Millenium Ecosystem Assesment (MEA) categorisation, which is the most commonly used, is not straightforward because of the intangibility of the categories, their interactions, and bias in terms of which services are included due to complexity of valuation (Liquete et al., 2013).

The Economics of Marine Reserves

Figure 4. Classification of coastal and marine ecosystem services (Potts et al., 2014).

No single tool can achieve all goals for ocean management, and MPAs do not mitigate global stressors such as climate change, ocean acidification, and pollution (Allison, Lubchenco, & Carr, 1998; Hilborn, 2018; Zupan et al., 2018). However, MPAs should help to maintain habitats in a healthy, productive, and resilient condition by maintaining biodiversity and acting as a hedge against fisheries and environmental management failures and a replenishment area in the event of a catastrophic event due to a reduction in local threats (Alban et al., 2008; Boersma & Parrish, 1999; Mellin, MacNeil, Cheal, Emslie, & Caley, 2016; Olds et al., 2014; Roberts et al., 2017; Sanchirico, 2000; Worm et al., 2006). This means that MPAs should maintain or even enhance the provision of ESs situated inside MPAs (Palumbi et al., 2009; Potts et al., 2014; Russi et al., 2016; TEEB, 2010). The keys ESs supported are likely to be food provision, tourism and recreation, education and research, coastal protection, carbon sequestration, and biodiversity, as well as supporting services (Brander et al., 2015).

Management activities will change the type, magnitude, and mix of ESs provided by ecosystems (Rodriguez et al., 2006). Valuing these changes involves a number of stages, including (1) parametising the direct and indirect link between the utility, functionality, and extent of ecosystems, (2) estimating how ES supply will change if there is a change to the ecosystem, (3) knowing how this change will affect the provision of direct and indirect benefits once behavioral responses to the changes in ES have been accounted for, and (4) finding suitable methods to measure the monetary value of this change to benefits (Bateman, Mace, Fezzi, Atkinson, & Turner, 2011). The conceptual framework for this analysis is illustrated in Figure 5.

The Economics of Marine Reserves

Figure 5. Conceptual framework for quantifying the link between MPAs and ESs (Hanley et al., 2015).

Many studies that value ESs implicitly assume that habitats are totally destroyed or degraded, but the extent to which this might occur without protection within an MPA is very context specific and poorly understood. MPAs may only slow degradation rather than totally stop it (Glenn et al., 2010). Some authors have addressed this by valuing MPA benefits in terms of reduction or halting current trends in habitat loss and then calculating difference in service generated using mean values per area (see, e.g., Beaumont, Jones, Garbutt, Hansom, & Toberman, 2014; Brander et al., 2015). The size of the protection benefit then depends significantly on the imminence of environmental degradation. This approach does not take into account marginal value changes associated with scale or increases in the values of services generated through management so will likely significantly underestimate MPA values.

While marginal changes are known to be the correct metric to value MPAs, they are rarely measured in practice. This is due to the complexity of underlying ecological non-linearities, limited understanding of bio-physical linkages, and requirements for simplifying assumptions that reduce the accuracy of these estimates (Barbier, 2012; Bateman et al., 2011; Pascal et al., 2018). Non-linearities in service provision occur over time and space, and there are also likely scale and threshold effects depending on numerous variables, which are often poorly understood. Such non-linearities are difficult to predict or incorporate into ES valuations (Ban et al., 2011; Barbier et al., 2008). Assuming flows are linear or static is likely to result in inaccurate valuations and lead to inappropriate policy recommendations. Koch et al. (2009) provide an overview of this issue and recommendations to reduce this problem. The fine-scale local data required to value ESs are also rarely available and costly to collect. Hence, there is limited scope to value ESs, even when biophysical models exist to use as a framework for analysis, which is often not the case. The greater the complexity entailed in the valuation, the less precise the valuation estimates (Sanchirico et al., 2002). Economists often therefore need to use proxies or make simplifying assumptions to undertake these studies. Sensitivity analyses should be conducted to clarify the effect of assumptions on results, although this is rarely done in practice (Pascal et al., 2018).

There remain significant uncertainties regarding MPA impacts on fishery yields, tourism revenues, coastal protection, and other ESs (Boersma & Parrish, 1999; Pascal et al., 2018; Russi et al., 2016). Generally there is also insufficient understanding of MPA impacts on the provision of supporting and regulating services, which makes them the least amenable to valuation, especially for climate mitigation, coastal protection, and water quality (Barbier, 2012; Hanley, Hynes, Patterson, & Jobstvogt, 2015; Jobstvogt, Watson, & Kenter, 2014; Keller et al., 2009). Some MPA benefits are unlikely monetised because theoretical foundations to do so do not yet exist. These include the reduced chance of ecosystem collapse or species extinction, increased research opportunities to compared with unmanaged areas, increased resilience to environmental shocks, hedge against poor fisheries, or environmental management (Lipton et al., 2014; Sale et al., 2005). See Brander et al. (2015) for a comprehensive review of all literature relating to potential changes in ES provision in MPAs.

Furthermore, many services are synergistic, e.g., erosion control and recreational values and nursery values (Russi et al., 2016), but many are also traded off locally at different scales or over time (e.g., tourism and effluent storage in wetlands; Barbier et al., 2011; Brown et al., 2001; Hargreaves-Allen et al., 2017; Rodriguez et al., 2006). Care should be taken to avoid double counting, especially where benefits are not complementary or when provision of one service such as biodiversity underlies the provision of many others (Mace, Norris, & Fitter, 2012; Worm et al., 2006). Double counting can be reduced by only valuing final and not intermediary services (Fu et al., 2011; Potts et al., 2014).

Most ES provision and value is highly context specific, depending on, for example, environmental quality, scale, resource scarcity, and availability of substitute sites (Barbier, 2012; Turner et al., 2003). Localized and non-localized threats and coastal management outside of MPAs will influence ES provision (Jameson, Tupper, & Ridley, 2002), and these may be enacted directly or indirectly and in an additive way, may cancel one another out, or may be synergistic (Halpern et al., 2008). The cumulative impacts of threats on the provision of ESs are largely unknown or highly uncertain (Hanley et al., 2015; Keller et al., 2009; Noone, Sumaila, & Díaz, 2014). Furthermore, network properties of MPAs may emerge, which are poorly understood (Claudet, Garcia-Charton, & Lenfant, 2011; Lester et al., 2009, Roberts & Hawkins, 2000). Therefore, valuation of marginal changes to resource quality or service provision from MPAs is difficult to undertake and cannot be aggregated to value the resource as a whole or from local to regional scales (Barbier, 2012; Schuhmann, 2012). These complexities in MPA valuation have yet to be widely incorporated into political decision-making (Laurans, Rankovic, Billé, Pirard, & Mermet, 2013).

Valuing the full range of benefits is generally not feasible (Lipton et al., 2014). Which benefits to include in valuation can be informed by difficulty of estimation, whether the benefit is excludable, and whether a loss in benefits would be reversible (Dixon & Sherman, 1991), as well as MPA goals and which activities have been restricted or undertaken (MPA regulations and management activities, such as education or restoration). A one-size-fits-all method may grossly underestimate true economic value and may not be sufficient for informing policy (Schuhmann, 2012). It is also usually not advisable to add values generated with different methodologies together, since they often have different theoretical foundations and measure different things (Russi et al., 2016).

While environmental valuation methods have become considerably more sophisticated, no new methods have been developed since the 1980s (Hanley et al., 2015). Use of secondary market data virtually ensures that the significant components of value associated with non-market uses and passive uses will be omitted and potentially ignored (Schuhmann, 2012). Contingent valuation is a useful and flexible tool to measure cultural values associated with MPAs. However, it has key limitations, including being vulnerable to strategic behavior, having significant hypothetical and instrumental biases, and being highly sensitive to the information, the valuation scenario, and payment vehicle presented (Alban et al., 2008; Carson, 2000). Choice modelling can also be useful to examine the relative values of different attributes and to value ex ante the potential benefits of different management scenarios to inform decision-making (Beharry-Borg & Scarpa, 2010; Glenn et al., 2010; Wattage et al., 2011). For example, McVittie and Moran (2010) used CM to look at values associated with biodiversity conservation, ESs, alternative levels of fishing restrictions, and resource extraction for a system of MPAs in the United Kingdom. Authors are also increasingly combining multiple methodologies in single surveys to measure recreational values associated with MPAs ( e.g., Jobstvogt et al., 2014; Kenter et al., 2013). Stated preferences may include both use and non-use values, which require being cautious about adding these to other types of valuation studies (McVittie & Moran, 2010).

Benefits transfer (the extrapolation of values from a source site to another site with similar characteristics) can be used to provide an indication of ES values, although its application is limited due to the inherently case-specific nature of natural resource value and the marginal aspects of resource change. Benefits transfer has a number of important limitations including uncertainty in the primary valuations (due to weak methodologies, unreliable or unavailable data, analyst error and biases, and inaccuracies associated with valuation methods), the dearth of studies to measure some ESs and biases in terms of disseminations of results, errors in the transfer process, and temporal generalization errors (Brander, 2013; Hussain et al., 2010; Rosenberger & Stanley, 2006). Therefore, while it is highly cost effective, it should not be used in situations where very accurate estimates are required. Guidance for best practice benefits transfer has been developed (Bergstrom & Taylor, 2006; Rosenberger & Loomis, 2003; Wilson & Hoehn, 2006).

Cost-Benefit Analysis Measuring Marginal Impacts of MPAs on Ecosystem Services

Cost-benefit analysis is a rapidly emerging field of research but remains rare compared to static analyses of total economic values (Wielgus, Balmford, Lewis, Mora, & Gerber, 2010), despite an increasing number of policies requiring or encouraging environmental valuation and CBA (Börger, Hattam, Burdon, Atkins, & Austen, 2014; Hanley et al., 2015). Recent studies have made significant methodological advancements in terms of estimating marginal impacts of MPAs on ecosystem services, although not all significant impacts on ESs can yet be valued.

Hussain et al. (2010) used benefits transfer to estimate ES values of GBP10-23 billion for a potential network of MPAs in the United Kingdom, compared to a counterfactual of no designation. This was equivalent to a cost-benefit ratio of 5.5–12.7. They considered three configurations of MPAs under two management regimes that differed in their level of restrictions. They first identified 35 landscape types and 11 ES categories. They then scored the impact of designation in each category on a per hectare basis, which accounted for expectations as to threats and threat mitigation, so that highly threatened services would receive a score showing they would respond significantly to MPA designation. They then valued this impact and aggregated it based on landscape area data. The study was innovative since they incorporated a temporal dimension for the benefit provision by using three trajectories—linear, exponential, and logarithmic—and modelling the number of years it would take to achieve the threshold level of change. Generally, provisioning services were shown to have a low value since they would be banned or minimised, whereas services such as nutrient cycling and culture heritage and identity would be greatly enhanced. There was uncertainty with regard to the impact of MPAs on climate regulation, which was significant, since this was the ES with the largest value.

Pascal et al. (2018) undertake CBAs for two very different MPAs considering six ESs: fish biomass, scenic beauty and emblematic species for tourism, damages avoided from coastal protection, bequest value, ability to gain external assistance through grants or technical support, and carbon sequestration by mangroves. Results were highly sensitive to the expected different levels of impact of ESs, which remains poorly understood. Fishery productivity enhancement was used as a proxy for the effect of the MPA on fisheries, whereas tourism enhancement was based on attitudinal responses of visitors and the social capital effect from the ratio of grants linked to the MPA and those not. The authors estimated a contributing factor of the MPA where there was limited data or theoretical models of 5% for coastal protection, 10% for carbon sequestration, and 35% for bequest values. The authors also estimated direct operational costs, initial investment, and opportunity cost of time lost in fisheries or spent employed at the MPA. In both MPAs, tourism effects represented 60–70% of benefits followed by fisheries and coastal protection. Both return on investment and CB ratio were positive, although the choice of MPA factor influenced results highly. Pascal et al. (2018) were unusual in calculating both CBA and return on investment to capture both economic values and cash flows to the local economy.

In an ambitious and comprehensive analysis, Brander et al. (2015) consider the economic costs and benefits for six scenarios of a global expansion of MPAs, where 10 to 30% of marine habitats become no-take areas. The different scenarios included different configurations of MPA locations and sizes, with data on human impacts (Halpern et al., 2008) used to create priority maps. Costs modeled included set-up and operational costs, as well as opportunity costs to commercial fisheries, and depended on MPA scale and location. The ESs included were coastal protection, fisheries, tourism, recreation, and carbon storage. Where possible, they used benefits transfer based on functions obtained from meta-analyses of valuation studies, so that value estimates would reflect site, socio-economic, and context characteristics. Habitat loss was assumed to fall to zero for habitats situated inside MPAs and to continue at the current rate of loss otherwise. The estimated changes in the rates of habitat loss were based on publish studies but were subject to sensitivity analyses, since they are highly uncertain. For reefs, the economic value of services was highest due to the fast rate of loss of reefs of around 2% per year that would occur with the status quo (Bruno & Selig, 2007). They also incorporated data on the abundance of marine ecosystems locally to account for changes in terms of substitute or complementary ecosystems, using the method reported in Brander et al. (2012). The counterfactual scenario was creating using spatially explicit threat levels from Burke, Reytar, Spaldig, and Perry (2011). Carbon sequestration was the only service that was not scale dependent and was calculated based on the social cost of carbon. Fisheries benefits were also a reduction in the rate of decline of fisheries, and sensitivity analyses were used to test the sensitivity of results to the rates used, since this effect is highly uncertain and showed that the assumed baseline rate of decline (the status quo) used affected fisheries values to a large degree. The cost-benefit ratio ranged from 3.17 to 19.77 and confirmed that the net benefits of MPAs are highly dependent on design and location and that benefits show diminishing returns to scale. The difference in provision from reefs was highly significant. Missing benefits that could not be valued included fishery spillover, bio-prospecting, opportunity costs of extractive industries, non-use values, and network effects. It should be noted, however, that this analysis has yet to be peer reviewed and that the authors note significant limitations in terms of necessary generalizations and extrapolations due to the global scale, the lack of inclusion of performance and future threats on values, missing determinants of cost, effects of displacement of human activities to outside areas, the time horizon only extending to 2050, and the existence of other marine management tools outside MPAs.

Distributional Impacts of MPAs

MPAs will have complex distributional impacts, which will depend on the MPA features, location, regulations enforced, and the local and national socio-economic and institutional context (Mascia, 2004; Pollnac et al., 2010; Pomeroy, Mascia, & Pollnac, 2007; Sanchirico et al., 2002). Such distributive impacts are not reflected in valuation studies as they produce aggregate measures of benefits (Steiner, McCormick, & Johnson, 2004). Different types of access rights can be lost, secured, or gained, which will also largely determine the extent and equity of MPAs impacts, both positive and negative, which will in turn affect governance, economic well-being, health, education, social capital, and culture (Mascia & Claus, 2009). The impacts of re-allocating rights to MPA resources vary within and among social groups, inducing changes in society, in patterns of resource use, and in the environment (Adams et al., 2011; Holland, 2000; Mascia, Claus, & Naidoo, 2010).

Each individual will incur costs and benefits associated with the MPA, which will vary for different stakeholders (Alban et al., 2008; Ferraro, 2002). The main costs and benefits are outlined in Table 1. The relative magnitude of these costs and benefits will determine stakeholder support and behavior (Heinen, 1996; Kremen et al., 2000; Sanchirico, 2000). Indeed, even if total benefits of an MPA are much greater than costs, it does not mean that certain stakeholders will not face substantial costs (Adams et al., 2010; Bennett & Dearden, 2014). Incentives are poorly aligned where there is a mismatch of those receiving benefits and those costs (Balmford & Whitten, 2003; Norton-Griffiths & Southey, 1995; Wells, 1992), resulting in perceived inequity. There can also be an intertemporal trade-off, where costs incur immediately and benefits largely arise in the future (Vandeperre et al., 2011; Weigel et al., 2014). Where local stakeholder incentives do not support conservation, compliance is likely to be low, enforcement costly, and conflict high, which will undermine MPA effectiveness (Agardy et al., 2003; Bennett & Dearden, 2014; Carter, 2003; Christie, 2004; Christie et al., 2017; Gutierrez, Hilborn, & Defeo, 2011; Hutton & Leader-Williams, 2003).

Table 1. Economic Benefits and Costs Generated by MPAs for Different Stakeholders

BENEFITS

Fishers

Increased fishery stock abundance or age/size composition

Increased spawning stock biomass

Increased catch levels or catch per unit effort or market value of fishery

Reduced variability in annual catch levels

Reduced likelihood of fishery collapse

Support for traditional livelihoods, ways of life, and sense of place

Reduced vulnerability to environmental shocks

Formalize or strengthen traditional fishing access rights

Increased food security and poverty reduction

Visitors

Increased habitat quality

Increased species density and diversity

Increased access and facilities

Public

Improved ecosystem health

Protection of marine biodiversity

Reduction in probability of irreversible changes

Increased resilience to environmental shocks

Option and quasi-option values

Enhanced ecosystem services, e.g., waste assimilation, coastal protection

Support of existence and bequest values

Managers

Revenues from fees and permits

Donor investment

Increased scientific knowledge

Opportunities for education

Savings in enforcement costs over other management models

Hedge against uncertain stock assessments

Local stakeholders

Increased demand or prices for local goods and services

New, increased, or more sustainable income and employment

Increased food security or nutrition

Indirect and induced economic impact of increased local visitation

Increased environmental awareness

Improved tourism-related infrastructure

Increased access to services from MPA-related development grants

Improved institutional capacity and governance

Increased participation and representation of marginalized groups

Reduced conflict

COSTS

Fishers

Loss of access to fishing grounds

Decreased control over natural resources and alienation

Reduced catches

Increased travel and search costs and decreased profitability

Increased poverty and food insecurity

Fisher congestion in open areas

MPA-related fines and penalties

Increase in safety risks

Increased conflict

Visitors

Entrance fees

Overcrowding and environmental damage

Public

Opportunity costs of other uses of public money and effort

Managers

Costs associated with MPA designation, e.g., design, consultation, boundary demarcation, visitor infrastructure

Management costs, e.g., monitoring, enforcement, staffing and equipment, compensation payment

Local stakeholders

Loss of recreational access and tenure

Environmental damage from visitation and tourism-related development

Loss of cultural identity

Increased crime

Forgone income from extractive activities, e.g., oil or mineral extraction

Greater social inequality and marginalization of vulnerable groups

Weakening of traditional institutions

Decreased adaptive capacity and resilience

For conservation initiatives to work effectively, the benefits may need to outweigh the costs at numerous scales (Kremen et al., 2000). However, it is locally where MPAs will have their most marked effect, as it is local users whose behavior, compliance, and support will most influence MPA ecological and socio-economic outcomes (Bennett & Dearden, 2012; Pomeroy et al., 2007). In particular, fisher responses are a key determinant of success as they can dissipate fishery improvements through poaching or re-allocation of effort or unsustainable practices (Lundquist & Granek, 2005; Sanchirico et al., 2002). Fisher support will be influenced by incentives to poach and perceived equity in terms of beneficiaries, rules, and enforcement as well as involvement in decision-making about MPA design and management (Adams et al., 2011; Agardy et al., 2003; Charles & Wilson, 2009; Gutierrez et al., 2011; McClanahan, Marnana, Cinner, & Kiene, 2006; Pollnac, Crawford, & Gorospe, 2001; Pollnac et al., 2010). It is also local extractive and non-extractive threats that need to be mitigated by MPAs, as their ability to directly counter large-scale or remote threats is very limited (Agardy, Di Sciara, & Christie, 2011; Boersma & Parrish, 1999; Hargreaves-Allen et al., 2011; Jameson et al., 2002; Zupan et al., 2018).

Evidence of Economic Benefits of MPAs

The increases in species biomass, size of target species, and species richness inside MPAs compared to areas outside, which bioeconomic models predict, have been observed in practice (Edgar et al., 2014; Fenberg et al., 2012; Guidetti et al., 2014; Lester et al., 2009). There is also evidence of spillover from MPAs (Goñi et al., 2010; Halpern, Lester, & Kellner, 2010; Roberts, Bohnsack, Gell, Hawkins, & Goodrich, 2001; Russ, Alcala, Maypa, Calumpong, & White, 2004; Stobart et al., 2009). However, fisheries benefits are difficult to detect, and increased catches may not always occur or may be marginal (Willis, Millar, Babcock, & Tolimieri, 2003). Indeed, there are remarkably few well-designed studies of MPA that can rigorously demonstrate recruitment effects of MPAs, or sustained or enhanced fishery yields and much of the evidence of spillover is equivocal (Buxton, Hartmann, Kearney, & Gardner, 2014; Goñi et al., 2011; Sale et al., 2005; Willis et al., 2003). This is due to the ecological and temporal variability of species, fisheries, and habitats as well as site-specific anthropogenic stressors, MPA attributes, and human behavior. There remains considerable uncertainty with regard to larval dispersal, species mobility, ecosystem impacts of fishing, hydrodynamic patterns, and ecosystem interconnectedness (Pascal et al., 2018; Sale et al., 2005). Even in well-managed MPAs, species and site-specific factors impact the effects of protection, including MPA age, species interaction and exploitation levels prior to protection (Claudet, Osenberg, & Benedetti-Cecchi, 2008; Edgar et al., 2014; Selig & Bruno, 2010).

In terms of impacts on fishers, increased fishery yields have been demonstrated at MPAs (Garcia-Charton, Perez-Ruzafa, & Marcos, 2008; McClanahan, Glaesel, Rubens, & Kiambo, 1997; Moland et al., 2013; Roberts et al., 2001; Russ et al., 2004), and several studies have shown minimal impacts on fisher profits or incomes despite displacement of fishing activity (e.g., Mangi, Rodwell, & Hattam, 2011; Stevenson, Tissot, & Walsh, 2013). More research is needed to quantify the effects on fishing communities and the effects of changes to spatial distribution of fishing effort on fishery sustainability and to test against projected outcomes predicted by models (Agardy et al., 2011; Fulton et al., 2015; Mascia et al., 2010).

A number of studies have demonstrated benefits to local communities adjacent to MPAs over time or compared to those without MPAs. These have included benefits in terms of nutrition and health, economic development, income diversification or poverty reduction, resilience, as well as education, stronger governance, and security of tenure (Aswani & Furusawa, 2007; Cohen, Valemei, & Govan, 2008; Hargreaves-Allen et al., 2011; Leisher, Van Beukering, & Scherl, 2007; Mascia et al., 2010). However, rigorous evidence of causality is rare due to reliance on perceptual surveys and poor experimental design (Agrawal & Redford, 2006; Ferraro & Pressey, 2015).

Many MPAs are set up with the expectation of increasing local tourism revenues (Sorensen & Thomsen, 2009). The “designation effect” of MPAs alone can generate tourism in a previously unvisited area and so support sustainable use of marine resources as well as create jobs directly in management activities (Alban et al., 2008; Edwards, Sutton-Grier, & Coyle, 2013; Fletcher et al., 2012; Hargreaves-Allen et al., 2011; Lemelin & Dawson, 2014). MPAs have been shown to have increased tourism-related revenues (Garcia-Charton et al., 2008; Jobstvogt et al., 2014; Rees, Rodwell, Attrill, Austen, & Mangi, 2010; Roncin et al., 2008). However, tourism increases may not occur or can be lost through leakage from the local economy (Bennett & Dearden, 2014). Recreational impacts of MPA designation may vary for different types of activity, affecting different types of operators differently (Rees et al., 2015).

Cultural benefits are often omitted from CBAs as they are difficult or costly to quantify. However, MPAs can support cultural services related to spiritual enrichment and well-being as well as a sense of maintenance of the culture, identity, and lifestyle of local communities (Jobstvogt et al., 2014; O’Garra, 2009). In the United Kingdom, such values were dependent on spirituality (an emotional connection providing sense of place, peace, and tranquility), the extent of community involvement, research and education being undertaken, and marketing efforts (Pike, Johnson, Fletcher, Wright, & Lee, 2010). It is difficult to unequivocally link particular changes in socio-ecological systems to particular changes in cultural benefits, and cultural benefits are associated with many services, not just cultural ES, which is likely to leads to underestimates of MPA value (Chan et al., 2012).

Evidence of Economic Costs of MPAs to Local Stakeholders

A number of studies have shown unintended negative impacts from MPAs for local communities, notably increased inequality and conflict and reduced access rights and local participation in management (Bennett & Dearden, 2014; Brondo & Woods, 2007; Christie, 2004; Fabinyi, 2008). Such costs are of greatest concern when the cost-bearing group is relatively poor (Steiner et al., 2004).

The effectiveness of MPAs depends to a large extent on support from coastal fishing communities, yet these can be subject to the greatest costs (Boncoeur, Alban, Ifremer, & Ifremer, 2002; Kiringe, Okello, & Ekajul, 2007; Madden, 2004; Mascia et al., 2010; Smith, Lynham, Sanchirico, & Wilson, 2010). Small-scale fisheries in developing countries are most vulnerable to negative impacts since they typically operate in a small range and have limited spatial and occupational mobility (Carter, 2003; Cinner et al., 2009; Mascia, 2004). Fishers can be impacted differently depending on gears and locations used (e.g., Adams et al., 2011). Hence it is difficult to predict costs for fishers since they are so context specific (Boncoeur et al., 2002).

Environmental degradation due to increased visitation has been recorded at many MPAs, for example, though trampling on corals, mooring impacts, or changing fish or marine mammal behavior (Garcia-Charton et al., 2008; Hawkins & Roberts, 1993; Juhasz, Ho, Bender, & Fong, 2010; Loper et al., 2008; Milazzo, Chemello, Badalamenti, Camarda, & Riggio, 2002). Overcrowding can also reduce recreational values (Carter, 2003; Davis & Tisdell, 1995), hence increased visitation may eventually depress tourism (Lindberg, Enriquez, & Sproule, 1996). An understanding of visitor CS values can help to identify the correct level of fees to keep visitation within the MPA’s carrying capacity in terms of ecological and socio-cultural impacts, although it is difficult to restrict access at many MPAs (Arin & Kramer, 2002; Carter, 2003; Green & Donnelly, 2003).

New approaches using rigorous experimental design are needed to analyze the distributive impacts of MPAs on local community well-being, stakeholder sensitivity to these impacts, and, in turn, how perceived incentives influence MPA outcomes (Christie et al., 2017; Mascia & Claus, 2009). Since there have been so many available methodologies, there is a need to identify and standardize economic indicators to quantitively measure changes in outcomes for adaptive management (Fox et al., 2014; Hargreaves-Allen et al., 2017; Johns, Lee, Leeworthy, Boyer, & Nuttle, 2014). The sustainable livelihoods framework, which distinguishes between natural, social, human, physical financial, cultural, and political assets, is one promising area of development (Bennett & Dearden, 2014; Igoe, 2006; Scoones, 1998). Research has shown that social and ecological systems are highly linked, so economic factors cannot be investigated in isolation from ecological and socio-cultural factors (Ban et al., 2017; Fox et al., 2012; Pollnac et al., 2010).

Mitigating Distributive Impacts

MPAs can create trade-offs between different uses or objectives that are not compatible (Gaines et al., 2010; Jiang et al., 2008; O’Leary et al., 2018). Goals need to be explicit so that design and management can prioritize benefits and evaluate performance (Christie & White, 2007; Hargreaves-Allen et al., 2017). Whether MPAs will be able to provide ecological, economic, and socio-political benefits concurrently, and the conditions that can make this more likely, remains an important research question (McShane et al., 2011).

Although the ecological benefits from MPAs (e.g., species or habitat recovery) are greatest for strongly or fully protected areas (Edgar et al., 2014), multiple-use MPAs can also be effective (Di Franco et al., 2016). Zoning of MPAs, where different uses are permitted and restricted, may help to limit visitor-related environmental impacts, reduce costs, and balance social, ecological, and economic objectives (Day & Dobbs, 2013). Zoned MPAs could also be fertile ground for comparative studies looking at impacts of different anthropogenic threats (Cook & Heinen, 2005). Indeed, spatial models suggest that zoned MPAs can maximize benefits to extractive and non-extractive users concurrently (Davis, Kragt, Gelcich, Schilizzi, & Pannell, 2015; Merino, Maynou, & Boncoeur, 2009) and benefit both fisheries and conservation outcomes, although this is not guaranteed (Gaines et al., 2010).

MPAs need to carefully consider distributional impacts of MPAs and, where necessary, use compensation programs such as alternative livelihood initiatives, access to micro-finance, equipment buy-backs or additional fisheries management measures to ensure incentives are aligned with management goals (Gutierrez et al., 2011; Mascia et al., 2010; McClanahan et al., 2006; Rettig, 1994). Direct compensation payments should be transitional as they have been problematic in developing countries. Indirect benefits such as improvements to infrastructure, access to health, education, and alternative livelihood training can also increase local support (Niesten & Gjertsen, 2010).

Conclusion

In order to protect marine resources, it is important to understand the magnitude of values, to whom they accrue, and the relative value of activates that degrade them. Lack of recognition by policymakers of the economic value of natural assets most certainly leads to inefficient resource allocations, leaving society worse off. Valuation studies have made explicit the direct, indirect, and non-use values generated by ecosystems inside MPAs, which makes a powerful case for conservation. Research has also demonstrated that MPA benefits are typically much greater than their costs, so they constitute a worthwhile investment in public funds.

However, MPA outcomes are highly complex, as each MPA operates in a unique context, so that it is difficult to generalize or predict effects on ecosystem services. Not all expected benefits may occur, and multiple objectives may not be compatible. Economic analysis is most challenging when understanding of biophysical processes is limited, quantitative information is scarce, and uncertainties are great, as is the case with ESs in MPAs. In addition, there are problems related to lack of a scientific baseline, temporal and non-linear aspects of service provision, and the risk of double counting, which means that MPA valuation remains to be widely incorporated into political decision-making. Questions remain about how human-induced threats change the provision of ESs, how interaction between ecosystems affects this, and how changes in the provision of services, mediated by human responses, ultimately affect the welfare of different stakeholders.

Socio-economic factors may shape MPAs more than biological or physical factors, but the human aspects of MPAs are not yet well understood. MPAs will produce both positive and negative impacts for different groups of stakeholders over time and for individuals. Social benefits of MPAs are principally linked to income and employment benefits from improved natural resource management and tourism, the support of ecosystem services delivered locally, and non-use values. Many costs associated with MPAs are unintended and accrue to local stakeholders. Better understanding of costs and social impacts will help to inform appropriate compensatation initiatives or alter MPA design or regulations so as to increase the support and compliance and reduce conflict, which will enhance MPA performance.

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