Cultural Ecosystem Services Provided by Coralligenous Assemblages and Posidonia oceanica in the Italian Seas

Introduction

Coralligenous reefs and Posidonia oceanica meadows are two Mediterranean ecosystems that are important suppliers of highly valuable ecosystem services (ESs) and benefits and have a fundamental role in supporting human wellbeing (Salomidi et al., 2012; Campagne et al., 2015). Following ES theory (CICES V4.3, 2012), coralligenous reefs and P. oceanica meadows provide humans with several services belonging to provisional (i.e., food, raw materials, pharmaceutical molecules), regulating (i.e., carbon sequestration, nutrient recycling), and cultural ecosystem services (CESs), including numerous services (i.e., high biodiversity, fish abundance, complex habitats to explore, and water clarity) that enhance the quality and the enjoyment of underwater recreation activities (Campagne et al., 2015; Thierry de Ville d’Avray et al., 2019). However, coralligenous reefs and P. oceanica meadows are particularly vulnerable to several anthropogenic pressures that are increasingly threatening coastal waters worldwide, including those in the Mediterranean Sea. Indeed, the Mediterranean Sea is currently facing multiple anthropic pressures, which affect the ecological, economic, and social spheres (de Groot et al., 2012). Mediterranean marine ecosystems, including coralligenous reefs and P. oceanica meadows, are highly threatened by local and global stressors, which often interact with one another. These stressors include intensive coastal development, pollution, invasive alien species, unsustainable fishing practices, poorly planned tourism (Coll et al., 2012; Katsanevakis et al., 2014; Randone et al., 2017), and global drivers of climate change (Jordà et al., 2012; Marbà et al., 2014; Martin et al., 2014; Gaylord et al., 2015; Zunino et al., 2017). Indeed, in the business-as-usual scenario of anthropogenic emissions (IPCC, 2014), the observed and projected levels of ocean acidification (OA) and global warming may highly threaten P. oceanica and coralligenous ecosystems (Jordà et al., 2012; Gattuso et al., 2015; Chefaoui et al., 2018; Zunino et al., 2019).

Over the past two decades, the scientific community has developed and adopted the ES framework, aiming to highlight the complex relationship between human ecosystems and ecosystem functioning. The power of this framework lies in the integration of the ecological dimension into the economic and social dimensions by applying a common system of values. The goal is to improve environmental decision making by providing information regarding the benefits of nature conservation, the costs of degradation, and the consequences of ecosystem changes in terms of human wellbeing. Mapping and assessing ESs is considered to be a key action for environmental governance in support of biodiversity objectives and ecosystem-based planning (Maes et al., 2016). Moreover, monetary valuation tools can be used to raise awareness among users and provide information for managers and policy-makers (Wright et al., 2017). Although our understanding of the ways in which ESs support human wellbeing has increased over the last two decades, the available data on marine ecosystems and the methods used to assess them are much more limited when compared to those pertaining to terrestrial ecosystems (Liquete et al., 2013). In addition, assessing and valuing marine ESs is still a challenging task despite the recent methodological and operational advances (Hattam et al., 2015; Garcia Rodrigues et al., 2017; Newton et al., 2018). Indeed, Mediterranean marine ESs, particularly CESs, are often overlooked due to their limited visibility and accessibility (Liquete et al., 2013; Garcia Rodrigues et al., 2017) and due to the difficulties in valuing non-material benefits (Chan et al., 2012).

To a large extent, the economies of countries bordering the Mediterranean Sea rely on cultural activities including both coastal and marine tourism. Marine tourism in the Mediterranean Sea generates US$110 billion annually (∼€90 billion), representing, together with coastal tourism, 92% of the annual economic output of all sectors related to the sea (Randone et al., 2017). Coastal natural destinations are of particular interest to scuba divers, whose recreational activities have become one of the most important factors in the marine tourism sectors globally (PADI, 2017; Lucrezi et al., 2018a). Uncontrolled coastal tourism, including unregulated scuba diving, can negatively impact marine ecosystems (United Nations Environment Programme [UNEP], 2015; Habibullah et al., 2016), and as the number of scuba divers is currently increasing, concern related to environmental deterioration has also increased (Flores-de la Hoya et al., 2018). However, because high-value nature-based tourism largely relies on the maintenance of a good environmental status and on the preservation of all ecosystem functions (Drius et al., 2018), the sustainable management of this activity can represent a viable alternative to more destructive uses of the environment (De Brauwer and Burton, 2018). Hence, there is an urgent need to analyze, estimate, evaluate and communicate all these values, including the cultural values, provided by threatened marine ecosystems, such as coralligenous reefs and P. oceanica meadows; this information can be used to facilitate their inclusion in ecosystem-based management policies, which are also directed to improve and maintain sustainable blue jobs (EU Commission, 2017).

Here, we collected all the information on the existing valuation studies of cultural ecosystem services related to the Mediterranean marine ecosystem. Then, we performed an explorative evaluation study aimed at highlighting the societal implications of the degradation of coralligenous and P. oceanica meadow ecosystems, with a particular focus on the Italian diving sector. We apply non-market analysis techniques to assess underwater recreational services provided by coralligenous reefs and P. oceanica meadows using an online questionnaire (MEA, 2005; de Groot et al., 2010; Chan et al., 2012; Daniel et al., 2012).

Materials and Methods

Study Area

Italy is a country with 8309 km of coastline that essentially separates the Mediterranean Sea into Western and Eastern sub-basins. Coralligenous assemblages are widespread along the Italian coast, with the exception of the sandy-muddy seabed between the Po river delta and the Gargano peninsula (Ingrosso et al., 2018). P. oceanica is present along most of the West Mediterranean and in the western Adriatic Sea. Coralligenous assemblages and P. oceanica meadows are considered to be the most important hotspots of species diversity in the Mediterranean (Ballesteros, 2006) (Figure 1).

FIGURE 1

Figure 1. Location of the study site and distribution of P. oceanica meadows and coralligenous assemblages along the Italian peninsula Information contained here has been derived from data that is made available under the European Marine Observation Data Network (EMODnet) Seabed Habitats project (http://www.emodnet-seabedhabitats.eu/), funded by the European Commission’s Directorate-General for Maritime Affairs and Fisheries (DG MARE). Gray dots represent the diving centers that have received the questionnaire.

The Mediterranean seagrass P. oceanica creates the “climax community” of soft sublittoral habitats covering a known area of 1,224,707 ha in the Mediterranean Sea (Giakoumi et al., 2013; Telesca et al., 2015). In addition, coralligenous reefs have been described as mesophotic biogenic structures produced by the growth and accumulation of calcareous encrusting algae (Ballesteros, 2006). Bioconstructors, such as coralligenous species, can be very common from 18–20 to 100 m depth and more. Because a wide variety of human activities threaten P. oceanica and coralligenous ecosystems, they are both under protection. P. oceanica is included on both the Red List of marine threatened species of the Mediterranean (Boudouresque et al., 1990) and the list of priority natural habitats in Annex I of the EC Directive 92/43/EEC on the Conservation of Natural Habitats and of Wild Fauna and Flora (EEC, 1992), while coralligenous reefs were incorporated as habitats that require protection into the Protocol for Special Protected Areas (SPA/BD, 1995) of the Barcelona Convention for the Conservation of Mediterranean Biodiversity.

ES Framework

We performed a literature review to understand the state of the knowledge related to the identification and/or assessment of the marine CES in the Mediterranean Sea. The review was performed in June 2019 through a search of the SCOPUS database for the following relevant keywords: cultural OR “cultural ecosystem service” OR tourism OR recreat^∗ OR social AND “ecosystem service” AND “marine” OR “sea” AND Mediterranean. Subsequently, the abstracts that specifically targeted our research focus were selected.

Valuation of CES: The Questionnaire

An online questionnaire was distributed among Italian marine divers to assess their diving habits, environmental attitude, and preferences for different diving experiences following marginal changes to ecosystems due to degradation. This work expands the analysis conducted by Rodrigues et al. (2015), which assessed the extent of the negative preference for the degradation of coralligenous reefs in the Medes Islands Marine Protected Area (MPA) in Spain, to a different Mediterranean context of the Italian divers diving in the Italian Sea and with an analysis not limited only to MPAs. Moreover, this analysis addresses P. oceanica meadows, for which, despite their uncontroversial ecological relevance and contribution to the preservation of Mediterranean biodiversity, few cultural valuation studies exist (Dewsbury et al., 2016). Among the general public, the level of knowledge about seagrass ecosystems and their associated services is low (i.e., Unsworth et al., 2019), for this reason we addressed the valuation of seagrass cultural services among divers. Divers, as other stakeholders that directly interact with the sea, better understand the functioning of different marine ecosystems, including P. oceanica, and their associated services (Ruiz-Frau et al., 2018). The P. oceanica ecosystem is not a preferred diving location (Lucrezi et al., 2018b), and its inclusion in our study was made to provide insight into the general cultural benefits provided by seagrass to divers.

From August 2016 to August 2017, an online survey was e-mailed to 18 diving centeres and clubs distributed along the Italian peninsula. The link was also spread through the OGS (Italian Institute of Oceanography and Applied Geophysics) webpage and the FIAS (Italian Underwater Activities Federation) official webpage and to all of their clubs (Figure 1 and Supplementary Appendix Figure A1). The questionnaire was structured with two sections that addressed diver preferences for different levels of ecosystem quality. The first section collected personal and demographic data, such as gender, age, level of education, and diving certification, to identify factors that could be related to their responses. Additional questions explored the type of benefits that the scuba diving experience provided to them. The second section was a choice experiment (CE) which was used to model the preferences for different typologies of the diving experience using a choice modeling approach (CM) (Hanley et al., 2001). This methodology is a preference-based method; such methods are currently the most commonly used approaches to assess the economic value of ESs (Kumar, 2010). The power of the CE relies on not asking respondents directly for their willingness to pay (WTP) or to accept compensation (WTA) for a certain environmental change. In our CE, the divers were asked 6 times to choose the most preferred alternative between two choices. The alternatives differed in terms of the quality and type of attributes (technically called attribute levels) characterizing the diving experience and the state of the ecosystem.

Choice Experiment Design

We reviewed the literature to identify the attributes that maximized the divers’ utility and that would likely be impacted by climate change. In particular, following Wielgus et al. (2003), Gill et al. (2015), and Rodrigues et al. (2015), we tested the consistency of the selected attributes and of their respective levels administering the questionnaires to a pre-sample of divers selected from five Italian sites (Ischia, Ventotene, Ustica, Cyclops Island, and Siracusa). Thus, the selected attributes were the “Number of divers” (per dive trip), “Coral cover,” “Seagrass cover,” and “Price” (per dive) (Table 1), while the chosen levels represented a spectrum of environmental conditions from good conservation status to heavily damaged, as detailed below:

TABLE 1

Table 1. Example of choice set.

(1) Number of divers found on a diving trip: The crowding level is an important consideration when valuing the quality of a dive, as suggested by several studies (e.g., Wielgus et al., 2003; Gill et al., 2015; Rodrigues et al., 2015). We selected 25, 15, and 5 as the levels of this attribute.

(2) Coral cover (the expected status of corals): We used the term corals as a proxy for the coralligenous environments since the latter is mostly known in the academic context (Tonin and Lucaroni, 2017). Indeed, corals are considered to be attractive features of coralligenous ecosystems in Italian diving destinations that are highly threatened by multiple anthropogenic pressures exacerbated by the drivers of climate change (Ingrosso et al., 2018 and the literature therein). Scientific literature suggests that coralligenous reefs could disappear or shift to deeper sites (Ingrosso et al., 2018) which are not suitable for recreational divers. Three levels were defined for this attribute: (a) 100% of the corals are in good condition; (b) 50% of the corals are degraded; and (c) 0% of the coral cover.

(3) Seagrass cover (the expected status of P. oceanica): Although we are aware that this ecosystem is not among the favorites of divers, the presence and status of P. oceanica meadows were included in the questionnaires since this ecosystem is undergoing degradation as a result of anthropogenic activities (Short et al., 2011) declining by 34% in the last 50 years (Telesca et al., 2015) and possibly becoming functionally extinct by 2100 in the worst-case emission scenario (Chefaoui et al., 2018). Three levels were defined for this attribute: (a) 100% of the seagrass meadow is in good condition; (b) 50% of the seagrass meadow is degraded; and (c) 0% seagrass meadow cover.

(4) Price of the dive: This attribute indicates the price of a single 50-min dive and includes the cost of the boat trip, air and tank for diving, and dive insurance. The average price is €40 during the high tourism season. For the CE, the price levels were set at €20, €50, €70 and €90. An opt-out option was offered in all cases.

Starting from the attributes list -with their relative levels- we created a total choice set using ALgDesign package for R software (Wheeler, 2004). A full factorial design with three three-level factors (“Numbers of divers,” “Corals cover,” “Seagrass cover”) and one four-level factor (“Price” -per-dive attribute) was created. The full factorial design comprised 135 combinations of the levels of each attribute (3³ × 5). In order to make the questionnaire more manageable, we generated a fractional factorial design from the full factorial design with the function optFederov (Wheeler, 2004). Following the procedure described by Aizaki and Nishimura (2008) we obtained 24 alternatives that were considered to be cognitively manageable. The alternatives were then blocked into two sets of six paired choices, each with a “neither” alternative for consistency with market decisions and were presented to divers. The presence of this latter choice option mimicked real market situations in which the diver is not forced to make a choice but can opt-out (Rodrigues et al., 2015).

Multinomial Logit Model

The CE technique is an application of the theory of value (Lancaster, 1966) combined with the random utility theory (Thurstone, 1927; Manski, 1977). According to Lancaster theory of value (Lancaster, 1966), individuals (i) obtain utility (U) not from goods themselves but from the attributes that describe these goods (Hanley et al., 1998). This theory assumes that individuals are perfectly able to discriminate between sites, basing the choice of the site j on a systematic, and observable, component, vij, which is based on the site attributes, and an additive random component ε_ij which is not observable. The utility function for the model used in this work is specified in Equation 1:

$$\begin{array}{llll}{\text{U}}_{ij}=\hfill & {\text{β}}_{optout}\ast Opt-ou{t}_i+{\text{β}}_{\mathrm{25}divers}\ast 25diver{s}_i+{\text{β}}_{\text{15 }divers}\hfill & \hfill & \hfill \\ \hfill & \ast 15{\text{divers}}_i+{\text{β}}_{Coral50\%}\ast Coral50{\%}_i+\text{β}Coral\mathrm{0\%}\hfill & \hfill & \hfill \\ \hfill & \ast Coral0{\%}_i+{\text{β}}_{Seagrass\text{50\%}}\ast Seagrass50{\%}_i+\text{β}Seagrass\text{0\%}\hfill & \hfill & \hfill \\ \hfill & \ast Seagrass0{\%}_i+{\text{β}}_{price}\ast PRIC{E}_i+{\text{ε}}_{ij}\hfill & \hfill & \hfill \end{array}(1)$$

Where Opt-out is a dummy variable that assumes a value of 1 for the no-choice option and 0 otherwise; 25 divers is a dummy variable that indicates a highly crowded diving experience, and 15 divers indicates a less crowded excursion; Coral 50% and Seagrass 50% represent dummy variables taking a value of 1 if the respondent chose the alternative including an environment that is 50% degraded and 0 otherwise; Coral 0% and Seagrass 0% are dummy variables taking a value of 1 if the respondent chose the alternative including a completely degraded environment and 0 otherwise, and, finally, PRICE is the price variable. The probabilistic odds that one alternative is selected over another can be estimated using a standard multinomial logit model (MNL), also called a conditional logit model (see Supplementary Appendix A1). The CE model analysis was conducted using the “survival” package (Therneau and Grambsch, 2000; Therneau, 2015) in the R statistical environment (R Core Team, 2015) using the clogit function (Aizaki, 2012).

Latent Class Model

As stated by Greene and Hensher (2003), the basic assumptions of the Latent Class Model (LCM) affirm that individual behavior is determined both by the attributes of the alternatives and by certain latent heterogeneity that is not observed by the researcher. Therefore, one of the main strengths of the model is its ability to capture the heterogeneity of preferences (Boxall and Adamowicz, 2002). This model assumes that the population consists of a number of latent classes that is exogenously determined and permits to overcome the limits of MNL model, which considers the utility coefficient as fixed among respondents. The unobserved heterogeneity is captured by these classes through the estimation of a parameter vector.

In this study, the LCM was used as an instrument to identify groups of divers interested in counteracting the degradation of coralligenous and P. oceanica meadow habitats, with a particular focus on the implications to the Italian dive sector. The LCM analysis was conducted using the program NLogit4^®. Both the MNL and the LCM models shared the same linear utility function (see Equation 1 above).

Willingness to Pay

The parameters estimated by the MNL and LCM models were used to identify the diver’s willingness to pay. The WTP for each attribute was derived from the models using the coefficients from each attribute level and the coefficients for price. The estimated coefficients represent the marginal utilities that are increments of utility. When the coefficients are compared with reference levels they reveal the relative importance of attributes and their levels and reflect respondents’ willingness to trade one attribute level for another.

The addition of the price attribute in the utility expression is essential in order to derive implicit price for marginal changes in attribute levels (Rodrigues et al., 2015), called the marginal WTP.

The marginal WTP for attributes/levels (non-monetary variable) is calculated as -β_nm/β_price (Equation 2); where β_nm is an estimated coefficient of the non-monetary variable and β_price is an estimated coefficient of the monetary variable:

$$WTP=-\frac{{\mathrm{\beta }}_{nm}}{{\mathrm{\beta }}_{price}}(2)$$

From the parameter estimates it is possible to derive welfare changes in monetary terms. These values are associated with changes in the level of an attribute compared with its reference level, provided that the remaining parameters are held constant. WTP measures reflect utility increases when the value is positive. This can be interpreted as WTP for a change in a certain attribute level. However, a negative value indicates a decrease in utility. This result suggests that individuals require compensation through lower prices (Train and Weeks, 2005) to have the same level of utility as that in the reference dive. We calculated the negative ratios of the parameters associated with each attribute level and price.

Results

ES Framework

The SCOPUS search listed 48 articles targeting CES in the Mediterranean Sea, which were further reduced to 14 after an analysis of the abstracts. The results were grouped into three categories, recreation and tourism, symbolic and aesthetic values, and cognitive effects, according to Liquete et al. (2013) (Table 2).

TABLE 2

Table 2. Review of the marine CES in the Mediterranean Sea.

Notably, only a few studies have focused on coralligenous and P. oceanica ecosystems. Among the others, Tonin and Lucaroni (2017) found that the general public is not generally aware of the existence and/or the value of coralligenous ecosystems; however, the authors highlight the important role of public education and communication to enhance awareness of endangered ecosystems.

Valuation of CES: The Questionnaire

The online survey was completed by 221 Italian scuba divers; of a total of 229 respondents, the surveys submitted by 8 were discarded due to incompleteness.

The respondents were Italian, with an average age of 43 years, and 71% were male (χ² = 37.88, df = 1, p < 0.001). The mean number of years of diving experience per diver was 13 years, ranging from 1 to 50 years, and the divers mainly held superior dive licenses from divemaster to technical licenses (χ² = 135.67, df = 2, p < 0.001). The mean number of dive trips per recreational diver (open, advanced) was 15 per year, ranging from 1 to 40 dives per year. Divers holding a superior dive license reported a mean of 53 dives per year, ranging from 5 to 250. Over half of the surveyed divers were employed full time (73%; χ² = 383.77, df = 4, p < 0.001), with an average annual gross income of between €30,001 and €40,000 (51%) (Supplementary Appendix Table A1).

The respondents were asked about the importance of some features determining the choice of a dive experience location (Figure 2). The most important factors that the respondents selected were the beauty of the underwater landscape (important for 91% of the divers; χ² = 77, df = 1, p < 0.001; Figure 2), the abundance of marine fauna (90%; χ² = 73, df = 1, p < 0.001; Figure 2), the wellbeing that they derived from the dive experience (85%; χ² = 58, df = 1, p < 0.001; Figure 2), and the acquisition of new knowledge (84%; χ² = 77, df = 1, p < 0.001; Figure 2), confirming the relevant role of the marine environment as a provider of important CESs. Significant preferences were also related to the presence of corals (66%; χ² = 11, df = 1, p = 0.001; Figure 2) and to the coastal landscape (67%; χ² = 14, df = 1, p < 0.001; Figure 2), while no significant preference for the P. oceanica meadows was found (44%; χ² = 1, df = 1, p = 0.25; Figure 2).

FIGURE 2

Figure 2. Percentages of diver preferences among different features determining the choice of a dive experience location. The green bars represent the responses classified as important and very important, while the gray bars represent the responses classified as unimportant and very unimportant.

The divers with more than 10 years of experience were asked to indicate their perception of the status of the underwater environments. 70% of the respondents agreed that the underwater habitat conditions had worsened since they began diving (χ² = 209.08, df = 4, p < 0.001). The main reasons were decreases in the numbers and size of the fish and corals (33%), increases in plastic litter, ghost nets and pollution (29%), and the increased abundance of stinging jellyfish, alien species and algae (9%).

However, almost 12% of the respondents with more than 10 years of diving experience acknowledged that the MPAs have significantly improved the environmental status through an increase in the presence of marine biodiversity within the MPAs. Most of the respondents (74%; χ² = 51.88, df = 1, p < 0.001) visited at least one MPA during their lifetime, and among them, more than 91% evaluated the experience as good or excellent (χ² = 152.67, df = 1, p < 0.001).

The respondents were also asked to choose among three species the one that they would like to see during a dive trip (Figure 3). The respondents were grouped by their level of expertise (i.e., beginners vs. experts). No difference was found between beginners and experts in the choice of emblematic species (χ² test, df = 1, p > 0.05; Figure 3), except for scorpionfishes and moray eels, which appear to be more appreciated by beginners (χ² test, df = 1, p < 0.05; Figure 3). Overall, more than half of the participants selected red corals (51%), groupers (42%), and seahorses (51%) (Figure 3).

FIGURE 3

Figure 3. Percentages of diver preferences regarding emblematic Mediterranean marine species. The blue bars represent the responses of beginner divers (open and advanced licenses, N = 79), and the orange bars represent the responses of expert divers (superior dive license, N = 142).

The results of the CE showed that the decision to take a dive was chosen by 81% of the respondents in the choice simulation. All coefficient estimates with the MNL were significant at the 99% level, except for the attribute level “Seagrass cover 50%,” which showed 90% significance (Table 3).

TABLE 3

Table 3. MNL and LCM results.

The coefficient of the variable “Price” was negative and significant, indicating the respondents’ preference for a cheaper option. The attribute levels “Coral cover 50%” and “Coral cover 0%” had significantly negative coefficients (p-value < 0.05), indicating that respondents highly prefer habitats with good coral coverage for their diving rather than those with partial or total degradation of the coral cover (Table 3).

The seagrass parameters indicate a slight preference of the divers (p = 0.02) for habitat with seagrass meadows in good condition (“Seagrass cover 100%”) and that they significantly rejected degraded habitat (“Seagrass cover 0%”) (Table 3). We found that the respondents significantly preferred less crowded dives instead of more crowded ones (p < 0.05; Table 3). Moreover, they also assigned a low preference to the intermediate level of crowdedness, the “15 divers” level (p < 0.05; Table 3), which differs from Rodrigues et al. (2015), who did not find a significant response in terms of the “15 divers” dive attribute level. The approximation of the mean WTP for the different levels of the attributes suggest that the divers were willing to pay approximately €56 less if the coral coverage decreased by 50% and €108 less if corals disappeared entirely. The same occurred for the P. oceanica coverage, for which the respondents were willing to pay approximately €18 and €34 less in the case of a partial or total loss, respectively. The results also suggest that the scuba divers were willing to pay less for highly crowded dive trips (25 divers).

The number of classes for the LCM analyses was identified before the evaluation of the parameters that was performed using the Bayesian information criterion (BIC) and Akaike information criterion (AIC) (Boxall and Adamowicz, 2002) (Table 4).

TABLE 4

Table 4. Latent class model statistics.

The three-class LCM (LCM-3) indicated that the sample showed heterogeneous preferences and that the respondents could be divided into three classes, representing 66, 13, and 21% of the divers, respectively.

It is interesting that the coefficients for class two were not significant (p > 0.05) except for “Price,” an intermediate number of divers and no seagrass cover. The members of this class when choosing the most preferred alternatives considered only the number of divers found during a diving trip (“15 divers,” p < 0.05) and seemed to be independent of the other attributes considered in our experiment. However, they showed also a negative WTP for the absence of seagrass cover. Each of the other two classes was characterized by a different structure of preferences. In detail, members of class one were more concerned about having a good quality of coral cover (“Coral cover 50%,” p < 0.05, and “Coral cover 0%,” p < 0.05), the level of seagrass cover (“Seagrass cover 0%,” p < 0.05) and a low crowding level of divers on a trip (“25 divers,” p < 0.05; 15 divers,” p < 0.05), while members of class three preferred a high coral cover (“Coral cover 50%,” p < 0.05, and “Coral cover 0%,” p < 0.05) and a high seagrass cover (Seagrass cover 0%,” p < 0.05; Seagrass cover 50%,” p < 0.05) and did not have a clear preference regarding the number of divers on a trip (“25 divers,” p > 0.05). We will refer to members of class two as “dive-alone divers” and members of classes one and three as “pro-habitat conservation divers,” although class one had a positive but insignificant low seagrass cover attribute value, meaning a low preference for a high abundance of these meadows.

Furthermore, members of class one had a lower (negative value) WTP for degraded coral cover on average (−€186) than the other classes. In addition, considering their WTP value for the highest level of the number of divers attribute, class one strongly preferred to avoid crowded trips (WTP −€187), while members of class three had a lower but negative WTP (€-15.7). The interactions between the attribute and socio-economic variables show that class one is composed of more experienced divers (in terms of years of experience) and those with a higher level of education (master’s degree), while there was no correlation with the license level. Regarding class two (also called the “dive alone divers”), the results show a correlation only with the more experienced divers. The Opt-out coefficients capture the degree to which respondents tend to choose no scuba diving experiences. The Opt-out coefficient was negative and significant (p < 0.05) for classes one and three, indicating that respondents were more likely to choose one of the alternatives.

Discussion

The results of the analysis of the data from the online questionnaire administered to Italian divers provide new insights into their attitudes toward coralligenous and P. oceanica meadow ecosystems and furthermore contribute to the valuation of the marine CESs they provide.

As can be inferred from the results, a diving experience is rated not only in terms of the quantity and quality of the charismatic environments or species (as indicated by the rates of preferences for the presence of corals, the abundance of animals or the underwater landscape features) but also for knowledge-related and environmental-related features, as expressed by the choices for new knowledge and the presence of P. oceanica meadows. The last choice, in particular, reveals a positive environmental attitude because the meadows themselves are not very attractive in terms of the diving experience. Therefore, the choice of a diving site, including those with P. oceanica, could indicate the respondent’s knowledge regarding the indirect functions that the P. oceanica meadows provide to the ecosystem (i.e., protection of juveniles from predators and allowing the aggregation of individuals and their reproductive success). The divers’ “pro-habitat” conservation attitudes were also confirmed by the LCM for the two most numerous classes (class one and class three), grouping 66 and 21% of the divers, respectively. This, in the end, supports the importance of “knowledge” as an important element which increases the “value” of the ecosystems and confirms that “understanding the ocean is essential to comprehending and protecting this planet on which we live,” as quoted from the Ocean Literacy Framework¹. A secondary benefit of ES valuation exercises lies in fostering the relationship with the environment, acting “as a tool of self-reflection that helps people rethink their relationships with the natural environment and increase their knowledge about the consequences of consumption, choices and behavior” (Brondìzio et al., 2010). Thus, the scientifically sound assessment of ESs can support local managers as well as marine governance bodies in the complex process of valuation aimed at supporting both ecosystem-based management and knowledge on ecosystem functions and values.

Coralligenous and P. oceanica meadow ecosystems provide a variety of ESs whose valuation requires a combination of different approaches to assess all the different types of benefits. Our study focused on a subset of these services, CESs, which are classified as non-consumptive benefits that are related to wellbeing, aesthetic inspiration, cultural identity, and spiritual experience.

The WTP estimates obtained from the MNL and LCM confirm the importance of structured and complex ecosystems as providers of benefits for scuba divers. In the present study, as expected and in agreement with Rodrigues et al. (2015), we found that scuba divers have a strong predilection for coralligenous habitats but are also sensitive to the loss of P. oceanica meadows. Declines in the coverage of both corals and P. oceanica would result in significant economic losses to the recreational dive industry in the Italian peninsula. Conversely, proper management that promotes habitat conservation will likely have a positive economic impact on diving tourism and significantly influence the choice of a dive site destination.

However, when considering the present valuation study, we must note that our results are based on the responses of people who voluntarily participated. This could mean that our sample was potentially biased toward scuba divers who were likely more interested and committed to environmental issues, as confirmed by the high percentage (74%) of respondents who had visited at least one MPA during their lifetime.

Another important point to be taken into account is that the number and origin of the sample of 221 Italian divers, despite providing statistically significant results, should be treated and interpreted in the proper context. As for any other local valuation study, the extrapolation of these results requires the application of a benefit transfer analysis that allows scaling up and transposing the values obtained in an original study to a different policy context (Smith et al., 2002). WTP is strongly related to the socio-economic context in which the valuation takes place, and the generalization of the results should only be conducted by adjusting the results to the different contexts. This could allow us to extend these results to members of the entire international community who dive in Italy and to the coralligenous and P. oceanica ecosystems across the Mediterranean Sea. This valuation, however, as for any other valuation of ES, should not serve as a substitute for other scientific or ethical reflections and systems of values related to biodiversity conservation. Instead, valuation should be used to complement them and to provide information that can guide policymaking (Turner and Daily, 2008).

Conclusion

This study contributes to the valuation of Italian marine benthic ecosystems and estimates how their disappearance could lead to economic losses. The coastal underwater landscape attracts millions of scuba divers yearly and depends on the presence of healthy environments. The current threats imposed by human pressures on the marine environment, which are exacerbated by climate change, are degrading these ecosystems and the flow of their ESs. The potential loss of economic value may, in turn, negatively impact other economic activities directly or indirectly related to scuba diving tourism. By highlighting the losses that could be caused by habitat destruction, our estimates can be used to support sustainable and non-disruptive diving tourism activities and the implementation of local conservation policies. However, the consideration of an interest in coralligenous and seagrass should be treated with caution, as excessive scuba diving activities in these ecosystems may ultimately cause ecological damage. Balanced uses of diving sites must be considered by mitigating the negative effect of human presence pressures with the positive effects derived from increasing people’s awareness of these ecosystems, especially for the P. oceanica meadows (Lucrezi et al., 2018b), see Figure 4.

FIGURE 4

Figure 4. Ocean and Ecological literacy can promote the sustainable use of marine ecosystems and contribute to both ecological conservation and blue growth. In fact, even if divers value (and are willing to pay for) pristine and well-preserved ecosystems mainly for aesthetic reasons, they appreciate, respect, and value them even more, when they are aware of the ecological importance of those systems. Positive feedback arises when extra-value is used to sustain education, conservation and mitigation.

Different activities should be spread and routinely implemented, such as ecological guided tours and citizen-science projects that actively involve citizens in the data collection, using pictures, videos and dive computer data. Improving divers environmental awareness might be seen as a tool to support viable local-scale management solutions to dampen the degradation of coastal ecosystems. By highlighting a change in the WTP, our results indicate that the Italian diving sector might be willing to invest and implement sustainable actions for the protection of the marine ecosystems, thus fostering the development of sustainable jobs, as suggested by the EU Blue Growth initiative (EU Commission, 2017).

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SZ and DM developed the initial idea, designed and analyzed the questionnaire. SZ performed the statistical, MNL and WTP analysis. ST and FM performed the LCL and WTP analysis. SZ and DM wrote the manuscript with contributions from all coauthors, discussed the results, and revised the manuscript.

Funding

This work was funded by the Acid.it Project “Science for mitigation and adaptation policies to ecological and socio-economic impacts of the acidification of Italian seas”.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

Many thanks to Bruno Iacono, the FIAS president Bruno Galli, Flavio Gaspari, Gianluca Coidessa, and all the diving clubs who helped distribute the questionnaires.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2019.00823/full#supplementary-material

Footnotes

1. ^ http://oceanliteracy.wp2.coexploration.org/ocean-literacy-framework/

References

Aizaki, H. (2012). Basic functions for supporting an implementation of choice experiments in R. J. Stat. Softw. Code Snip. 50, 1–24. doi: 10.18637/jss.v050.c02

CrossRef Full Text | Google Scholar

Aizaki, H., and Nishimura, K. (2008). Design and analysis of choice experiments using r: a brief introduction. Agric. Inf. Res. 17, 86–94. doi: 10.3173/air.17.86

CrossRef Full Text | Google Scholar

Ballesteros, E. (2006). Mediterranean coralligenous assemblages: a synthesis of present knowledge. Oceanogr. Mar. Biol. Annu. Rev. 44, 123–195. doi: 10.1201/9781420006391.ch4

CrossRef Full Text | Google Scholar

Boudouresque, C., Ballesteros, E., Ben Maiz, N., Boisset, F., Bouladier, E., Cinelli, F., et al. (1990). Livre Rouge Gérard Vuigner des Végétaux, Peuplements et Paysages Marins Menacés de Méditerranée. UNEP/IUCN/GIS Posidonie. MAP Technical Report Series No. 43, Athens: Aires Spécialement Protégées.

Google Scholar

Boxall, P. C., and Adamowicz, W. L. (2002). Understanding heterogeneous preferences in random utility models: a latent class approach. Environ. Resour. Econ. 23, 421–446. doi: 10.1023/a:1021351721619

CrossRef Full Text | Google Scholar

Brondìzio, E. S., Gatzweiler, F. W., Kumar, M., and Zograftos, C. (2010). “Socio-cultural context of ecosystem and biodiversity valuation,” in The Economics of Ecosystems and Biodiversity Ecological and Economic Foundations, ed. P. Kumar (London: Earthscan).

Google Scholar

Campagne, C., Salles, J., Boissery, P., and Deter, J. (2015). The seagrass posidonia oceanica: ecosystem services identification and economic evaluation of goods and benefits. Mar. Pollut. Bull. 97, 391–400. doi: 10.1016/j.marpolbul.2015.05.061

PubMed Abstract | CrossRef Full Text | Google Scholar

Carlucci, R., Maglietta, R., Buscaino, G., Cipriano, G., Milella, A., and Pollazzon, V. (2017). “Review on research studies and monitoring system applied to cetaceans in the gulf of taranto (northern ionian sea, central-eastern mediterranean sea),” in 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017. Institute of Electrical and Electronics Engineers Inc, (Italy: IEEE). doi: 10.1109/AVSS.2017.8078473

CrossRef Full Text | Google Scholar

Chan, K. M. A., Guerry, A. D., Balvanera, P., Klain, S., Satterfield, T., Basurto, X., et al. (2012). Where are cultural and social in ecosystem services? a framework for constructive engagement. Bioscience 62, 744–756. doi: 10.1525/bio.2012.62.8.7

CrossRef Full Text | Google Scholar

Chefaoui, R. M., Duarte, C. M., and Serrão, E. A. (2018). Dramatic loss of seagrass habitat under projected climate change in the Mediterranean Sea. Glob. Chang. Biol. 24, 4919–4928. doi: 10.1111/gcb.14401

PubMed Abstract | CrossRef Full Text | Google Scholar

Chimienti, G., Stithou, M., Mura, I. D., Mastrototaro, F., D’Onghia, G., Tursi, A., et al. (2017). An explorative assessment of the importance of mediterranean coralligenous habitat to local economy: the case of recreational diving. J. Environ. Account. Manag. 5, 315–325. doi: 10.5890/jeam.2017.12.004

CrossRef Full Text | Google Scholar

CICES V4.3 (2012). Common International Classification of Ecosystem Services (CICES, Version 4. 3). Nottingham: CICES.

Google Scholar

Coll, M., Piroddi, C., Albouy, C., Ben Rais Lasram, F., Cheung, W. W. L., Christensen, V., et al. (2012). The Mediterranean Sea under siege: spatial overlap between marine biodiversity, cumulative threats and marine reserves. Glob. Ecol. Biogeogr. 21, 465–480. doi: 10.1111/j.1466-8238.2011.00697.x

CrossRef Full Text | Google Scholar

Daniel, T. C., Muhar, A., Arnberger, A., Aznar, O., Boyd, J. W., Chan, K. M. A., et al. (2012). Contributions of cultural services to the ecosystem services agenda. Proc. Natl. Acad. Sci. U.S.A. 109, 8812–8819. doi: 10.1073/pnas.1114773109

PubMed Abstract | CrossRef Full Text | Google Scholar

De Brauwer, M., and Burton, M. (2018). Known unknowns: conservation and research priorities for soft sediment fauna that supports a valuable SCUBA diving industry. Ocean Coast. Manag. 160, 30–37. doi: 10.1016/J.OCECOAMAN.2018.03.045

CrossRef Full Text | Google Scholar

de Groot, R., Alkemade, R., Braat, L., Hein, L., and Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272. doi: 10.1016/j.ecocom.2009.10.006

CrossRef Full Text | Google Scholar

de Groot, R., Brander, L., van der Ploeg, S., Costanza, R., Bernard, F., Braat, L., et al. (2012). Global estimates of the value of ecosystems and their services in monetary units. Ecosyst. Serv. 1, 50–61. doi: 10.1016/j.ecoser.2012.07.005

CrossRef Full Text | Google Scholar

Dewsbury, B. M., Bhat, M., and Fourqurean, J. W. (2016). A review of seagrass economic valuations: gaps and progress in valuation approaches. Ecosyst. Serv. 18, 68–77. doi: 10.1016/j.ecoser.2016.02.010

CrossRef Full Text | Google Scholar

Drius, M., Bongiorni, L., Depellegrin, D., Menegon, S., Pugnetti, A., and Stifter, S. (2018). Tackling challenges for Mediterranean sustainable coastal tourism: an ecosystem service perspective. Sci. Total Environ. 652, 1302–1317. doi: 10.1016/J.SCITOTENV.2018.10.121

PubMed Abstract | CrossRef Full Text | Google Scholar

EEC (1992). Council Directive 92/43/EEC on the conservation of natural habitats and of wild fauna and flora. Off. J. Eur. Commun. 206, 7–50.

Google Scholar

EU Commission (2017). Report on the Blue Growth Strategy. Towards More Sustainable Growth and Jobs in the Blue Economy. Commission Staff Working Document on Blue Growth 2013–2016. Available at: https://ec.europa.eu/maritimeaffairs/sites/maritimeaffairs/files/swd-2017-128_en.pdf (accessed November 22, 2018).

Google Scholar

Fleischer, A. (2012). A room with a view—A valuation of the Mediterranean Sea view. Tour. Manag. 33, 598–602. doi: 10.1016/J.TOURMAN.2011.06.016

CrossRef Full Text | Google Scholar

Flores-de la Hoya, A., Godínez-Domínguez, E., and González-Sansón, G. (2018). Rapid assessment of coastal underwater spots for their use as recreational scuba diving sites. Ocean Coast. Manag. 152, 1–13. doi: 10.1016/j.ocecoaman.2017.11.005

CrossRef Full Text | Google Scholar

Garcia Rodrigues, J., Villasante, S., Drakou, E. G., Kermagoret, C., and Beaumont, N. (2017). Operationalising marine and coastal ecosystem services. Inter. J. Biodiver. Sci. Ecosyst. Serv. Manag. 13, 1–15. doi: 10.1080/21513732.2018.1433765

CrossRef Full Text | Google Scholar

Gattuso, J.-P. J., Magnan, A., Bille, R., Cheung, W. W. L., Howes, E. L., Joos, F., et al. (2015). Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349:aac4722. doi: 10.1126/science.aac4722

PubMed Abstract | CrossRef Full Text | Google Scholar

Gaylord, B., Kroeker, K., Sunday, J. M., Anderson, K. M., Barry, J. P., Brown, N. E., et al. (2015). Ocean acidification through the lens of ecological theory. Ecology 96, 3–15. doi: 10.1890/14-0802.1

PubMed Abstract | CrossRef Full Text | Google Scholar

Giakoumi, S., Sini, M., Gerovasileiou, V., Mazor, T., Beher, J., Possingham, H. P., et al. (2013). Ecoregion-based conservation planning in the Mediterranean: dealing with large- scale heterogeneity. PLoS One 8:e76449. doi: 10.1371/journal.pone.0076449

PubMed Abstract | CrossRef Full Text | Google Scholar

Gill, D., Schuhmann, P. W., and Oxenford, H. A. (2015). Recreational diver preferences for reef fish attributes: economic implications of future change. Ecol. Econ. 111, 48–57. doi: 10.1016/j.ecolecon.2015.01.004

CrossRef Full Text | Google Scholar

Greene, W. H., and Hensher, D. A. (2003). A latent class model for discrete choice analysis: contrasts with mixed logit. Transport. Res. Part B 37, 681–698. doi: 10.1016/s0191-2615(02)00046-2

CrossRef Full Text | Google Scholar

Habibullah, M. S., Din, B. H., Chong, C. W., and Radam, A. (2016). Tourism and biodiversity loss: implications for business sustainability. Proc. Econ. Finance 35, 166–172. doi: 10.1016/S2212-5671(16)00021-6

CrossRef Full Text | Google Scholar

Hanley, N., Mourato, S., and Wright, R. E. (2001). Choice modelling approaches: a superior alternative for environmental valuation? J. Econ. Surveys 15, 435–462. doi: 10.1111/1467-6419.00145

CrossRef Full Text | Google Scholar

Hanley, N., Wright, R. E., and Adamowicz, V. (1998). Using choice experiments to value the environment: design issues, current experience, and future prospects. Environ. Resour. Econ. 11, 413–428. doi: 10.1023/A:1008287310583

CrossRef Full Text | Google Scholar

Hattam, C., Atkins, J. P., Beaumont, N., Börger, T., Böhnke-Henrichs, A., Burdon, D., et al. (2015). Marine ecosystem services: linking indicators to their classification. Ecol. Indic. 49, 61–75. doi: 10.1016/j.ecolind.2014.09.026

CrossRef Full Text | Google Scholar

Ingrosso, G., Abbiati, M., Badalamenti, F., Bavestrello, G., Belmonte, G., Cannas, R., et al. (2018). Mediterranean bioconstructions along the italian coast. Adv. Mar. Biol. 79, 61–136. doi: 10.1016/BS.AMB.2018.05.001

PubMed Abstract | CrossRef Full Text | Google Scholar

IPCC (2014). “Climate change 2014 synthesis report,” in Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, eds Core Writing Team, Pachauri, L. A. Meyer (Geneva: IPCC).

Google Scholar

Jackson, E. L., Rees, S. E., Wilding, C., and Attrill, M. J. (2015). Use of a seagrass residency index to apportion commercial fishery landing values and recreation fisheries expenditure to seagrass habitat service. Conserv. Biol. 29, 899–909. doi: 10.1111/cobi.12436

PubMed Abstract | CrossRef Full Text | Google Scholar

Jordà, G., Marbà, N., and Duarte, C. M. (2012). Mediterranean seagrass vulnerable to regional climate warming. Nat. Clim. Chang. 2, 821–824. doi: 10.1038/nclimate1533

CrossRef Full Text | Google Scholar

Katsanevakis, S., Wallentinus, I., Zenetos, A., Leppäkoski, E., Çinar, M. E., Oztürk, B., et al. (2014). Impacts of invasive alien marine species on ecosystem services and biodiversity: a pan-European review. Aquat. Invas. 9, 391–423. doi: 10.3391/ai.2014.9.4.01

CrossRef Full Text | Google Scholar

Kontogianni, A., Tourkolias, C., Machleras, A., and Skourtos, M. (2012). Service providing units, existence values and the valuation of endangered species: a methodological test. Ecol. Econ. 79, 97–104. doi: 10.1016/j.ecolecon.2012.04.023

CrossRef Full Text | Google Scholar

Kontogianni, A. D., and Emmanouilides, C. J. (2014). The cost of a gelatinous future and loss of critical habitats in the Mediterranean. ICES J. Mar. Sci. 71, 853–866. doi: 10.1093/icesjms/fst194

CrossRef Full Text | Google Scholar

Kumar, P. (2010). The Economics of Ecosystems and Biodiversity: Ecological and Economic Foundations. Nairobi: UNEP.

Google Scholar

Lancaster, K. J. (1966). A new approach to consumer theory. Source J. Polit. Econ. 74, 132–157. doi: 10.1086/259131

CrossRef Full Text | Google Scholar

Liquete, C., Piroddi, C., Drakou, E. G. E. G., Gurney, L., Katsanevakis, S., Charef, A., et al. (2013). Current status and future prospects for the assessment of marine and coastal ecosystem services: a systematic review. PLoS One 8:e67737. doi: 10.1371/journal.pone.0067737

PubMed Abstract | CrossRef Full Text | Google Scholar

Lucrezi, S., Egi, S. M., Pieri, M., Burman, F., Ozyigit, T., Cialoni, D., et al. (2018a). Safety priorities and underestimations in recreational scuba diving operations: a european study supporting the implementation of new risk management programmes. Front. Psychol. 9:383. doi: 10.3389/fpsyg.2018.00383

PubMed Abstract | CrossRef Full Text | Google Scholar

Lucrezi, S., Milanese, M., Sarà, A., Palma, M., Saayman, M., and Cerrano, C. (2018b). Profiling scuba divers to assess their potential for the management of temperate marine protected areas: a conceptual model. Tour. Mar. Environ. 13, 85–108. doi: 10.3727/154427318X15225542424207

CrossRef Full Text | Google Scholar

Maes, J., Liquete, C., Teller, A., Erhard, M., Paracchini, M. L., Barredo, J. I., et al. (2016). An indicator framework for assessing ecosystem services in support of the EU biodiversity strategy to 2020. Ecosyst. Serv. 17, 14–23. doi: 10.1016/j.ecoser.2015.10.023

CrossRef Full Text | Google Scholar

Manski, C. F. (1977). The structure of random utility models. Theory Decis. 8, 229–254. doi: 10.1007/BF00133443

CrossRef Full Text | Google Scholar

Marbà, N., Díaz-Almela, E., and Duarte, C. M. (2014). Mediterranean seagrass (Posidonia oceanica) loss between 1842 and 2009. Biol. Conserv. 176, 183–190. doi: 10.1016/j.biocon.2014.05.024

CrossRef Full Text | Google Scholar

Martin, C. S., Giannoulaki, M., De Leo, F., Scardi, M., Salomidi, M., Knitweiss, L., et al. (2014). Coralligenous and maërl habitats: predictive modelling to identify their spatial distributions across the Mediterranean Sea. Sci. Rep. 4, 1–8. doi: 10.1038/srep05073

CrossRef Full Text | Google Scholar

MEA (2005). Millennium Ecosystem Assessment Ecosystems: Ecosystems and Human Well-being Synthesis. Washington DC: Island Press.

Google Scholar

Menegon, S., Depellegrin, D., Farella, G., Sarretta, A., Venier, C., and Barbanti, A. (2018). Addressing cumulative effects, maritime conflicts and ecosystem services threats through MSP-oriented geospatial webtools. Ocean Coast. Manag. 163, 417–436. doi: 10.1016/j.ocecoaman.2018.07.009

CrossRef Full Text | Google Scholar

Newton, A., Brito Ana, C., Icely John, D., Derolez, V., Clara, I., Angus, S., et al. (2018). Assessing, quantifying and valuing the ecosystem services of coastal lagoons. J. Nat. Conserv. 44, 50–65.

Google Scholar

PADI (2017). Worldwide Corporate Statistics 2017. Data for 2011-2016. Rancho Santa Margarita, CA: PADI.

Google Scholar

Randone, M., Di Carlo, G., and Costantini, M. (2017). Reviving the Economy of the Mediterranean Sea: Actions for a Sustainable Future. Rome: WWF Mediterranean Marine Initiative.

Google Scholar

R Core Team (2015). R: A Language and Environment for Statistical Computing, (Vienna, Austria: R Foundation for Statistical Computing) Available at: http://www.R-project.org/.

Google Scholar

Rodrigues, L. C., van den Bergh, J. C. J. M., and Ghermandi, A. (2013). Socio-economic impacts of ocean acidification in the Mediterranean Sea. Mar. Policy 38, 447–456. doi: 10.1016/j.marpol.2012.07.005

CrossRef Full Text | Google Scholar

Rodrigues, L. C., van den Bergh, J. C. J. M., Loureiro, M. L., Nunes, P. A. L. D., and Rossi, S. (2015). The cost of mediterranean sea warming and acidification: a choice experiment among scuba divers at medes Islands, Spain. Environ. Resour. Econ. 63, 289–311. doi: 10.1007/s10640-015-9935-8

CrossRef Full Text | Google Scholar

Ruiz-Frau, A., Krause, T., and Marbá, N. (2018). The use of sociocultural valuation in sustainable environmental management. Ecosyst. Serv. 29, 158–167. doi: 10.1016/j.ecoser.2017.12.013

CrossRef Full Text | Google Scholar

Salomidi, M., Katsanevakis, S., Borja, A., Braeckman, U., Damalas, D., Galparsoro, I., et al. (2012). Assessment of goods and services, vulnerability, and conservation status of European seabed biotopes: a stepping stone towards ecosystem-based marine spatial management. Mediterr. Mar. Sci. 13, 49–88. doi: 10.12681/mms.23

CrossRef Full Text | Google Scholar

Short, F. T., Polidoro, B., Livingstone, S. R., Carpenter, K. E., Bandeira, S., Bujang, J. S., et al. (2011). Extinction risk assessment ofthe world’s seagrass species. Biol. Conserv. 144, 1961–1971. doi: 10.1016/j.biocon.2011.04.010

CrossRef Full Text | Google Scholar

Smith, V. K., Van Houtven, G., and Pattanayak, S. K. (2002). Benefit transfer via preference calibration: “Prudential Algebra” for policy. Land Econ. 78, 132–152. doi: 10.2307/3146928

CrossRef Full Text | Google Scholar

SPA/BD (1995). Protocol concerning Specially Protected Areas and Biological Diversity in the Mediterranean. Barcelona: Barcelona Convention.

Google Scholar

Telesca, L., Belluscio, A., Criscoli, A., Ardizzone, G., Apostolaki, E. T., Fraschetti, S., et al. (2015). Seagrass meadows (Posidonia oceanica) distribution and trajectories of change. Sci. Rep. 5:12505. doi: 10.1038/srep12505

PubMed Abstract | CrossRef Full Text | Google Scholar

Therneau, T. (2015). Package Survival: A Package for Survival Analysis in R. R Packag. Version 2.38.

Google Scholar

Therneau, T. M., and Grambsch, P. M. (2000). Expected Survival. New York, NY: Springer.

Google Scholar

Thierry de Ville d’Avray, L., Ami, D., Chenuil, A., David, R., and Féral, J. P. (2019). Application of the ecosystem service concept at a small-scale: the cases of coralligenous habitats in the North-western Mediterranean Sea. Mar. Pollut. Bull. 138, 160–170. doi: 10.1016/j.marpolbul.2018.10.057

PubMed Abstract | CrossRef Full Text | Google Scholar

Thurstone, L. (1927). A law of comparative judgement. Psychol. Rev. 4, 273–286.

Google Scholar

Tonin, S., and Lucaroni, G. (2017). Understanding social knowledge, attitudes and perceptions towards marine biodiversity: the case of tegnùe in Italy. Ocean Coast. Manag. 140, 68–78. doi: 10.1016/j.ocecoaman.2017.02.019

CrossRef Full Text | Google Scholar

Train, K., and Weeks, M. (2005). “Discrete choice models in preference space and willingness-to-pay space,” in Applications of Simulation Methods in Environmental and Resource Economics The Economics of Non-Market Goods and Resources, eds R. Scarpa, and A. Alberini (Dordrecht: Springer).

Google Scholar

Tribot, A.-S., Mouquet, N., Villéger, S., Raymond, M., Hoff, F., Boissery, P., et al. (2016). Taxonomic and functional diversity increase the aesthetic value of coralligenous reefs. Sci. Rep. 6:34229. doi: 10.1038/srep34229

PubMed Abstract | CrossRef Full Text | Google Scholar

Turner, R. K., and Daily, G. C. (2008). The ecosystem services framework and natural capital conservation. Environ. Resour. Econ. 39, 25–35. doi: 10.1007/s10640-007-9176-6

CrossRef Full Text | Google Scholar

United Nations Environment Programme [UNEP], (2015). The 10YFP Programme. (on) Sustainable Tourism. Nairobi: United Nations Environment Programme [UNEP].

Google Scholar

Unsworth, R. K. F., McKenzie, L. J., Collier, C. J., Cullen-Unsworth, L. C., Duarte, C. M., Eklöf, J. S., et al. (2019). Global challenges for seagrass conservation. Ambio 48, 801–815. doi: 10.1007/s13280-018-1115-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Wheeler, R. E. (2004). Optfederov. AlgDesign. The R Project for Statistical Computing. Available at: http://www.r-project.org/ (accessed August 25, 2015).

Google Scholar

Wielgus, J., Chadwick-Furman, N. E., Zeitouni, N., and Shechter, M. (2003). Effects of coral reef attribute damage on recreational welfare. Mar. Resour. Econ. 18, 225–237. doi: 10.1086/mre.18.3.42629397

CrossRef Full Text | Google Scholar

Wright, W. C., Eppink, F. V., and Greenhalgh, S. (2017). Are ecosystem service studies presenting the right information? Ecosyst. Serv. 25, 128–139. doi: 10.1016/j.ecoser.2017.03.002

CrossRef Full Text | Google Scholar

Zanoli, R., Carlesi, L., Danovaro, R., Mandolesi, S., and Naspetti, S. (2015). Valuing unfamiliar Mediterranean deep-sea ecosystems using visual Q-methodology. Mar. Policy 61, 227–236. doi: 10.1016/j.marpol.2015.08.009

CrossRef Full Text | Google Scholar

Zunino, S., Canu, D. M., Bandelj, V., and Solidoro, C. (2017). Effects of ocean acidification on benthic organisms in the Mediterranean Sea under realistic climatic scenarios: a meta-analysis. Reg. Stud. Mar. Sci. 10, 86–96. doi: 10.1016/j.rsma.2016.12.011

CrossRef Full Text | Google Scholar

Zunino, S., Canu, D. M., Zupo, V., and Solidoro, C. (2019). Direct and indirect impacts ofmarine acidification on the ecosystem services provided by coralligenous reefs and seagrass systems. Glob. Ecol. Conserv. 18:e00625. doi: 10.1016/j.gecco.2019.e00625

CrossRef Full Text | Google Scholar