Ecosystem Services 50 (2021) 101312

Contents lists available at ScienceDirect

Ecosystem Services
journal homepage: www.elsevier.com/locate/ecoser

An adaptive social-ecological system management matrix for guiding
ecosystem service improvements
Takuro Uehara a, *, Takeshi Hidaka b, Takahiro Tsuge c, Ryo Sakurai d, Mateo Cordier eaCollege of Policy Science, Ritsumeikan University, 2-150 Iwakura-Cho, Ibaraki City, Osaka 567-8570, Japan
School of Humanity-oriented Technology and Engineering, Kindai University 11-6 Kayanomori, Iizuka City, Fukuoka 820-8555, Japanc
Graduate School of Global Environmental Studies, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japand
College of Policy Science, Ritsumeikan University 2-150 Iwakura-Cho, Ibaraki City, Osaka 567-8570, Japane
Research Centre Cultures–Environnements–Arctique–Représentations–Climat (CEARC), Université de Versailles-Saint-Quentin-en-Yvelines, Université Paris-Saclay, 11
Boulevard d’Alembert, 78280 Guyancourt, Franceb

A R T I C L E I N F O

A B S T R A C T

Keyword:
Adaptive management
Marine ecosystem services
Narrative analysis
SatoumiSocial-ecological production landscapes andseascapes
Soft targets

Social-ecological system (SES) management requires targets to move in a desirable direction. However, setting
targets at the outset of a management program is challenging. People representing the demand side are notalways aware of the benefits of nature that are desirable. Simultaneously, managers–who represent the supply
side–have limited scientific information. Therefore, we propose an adaptive SES management matrix (ASESMM)
using soft targets that are temporary and hypothetical because such targets cannot be fixed at the outset. By
compiling both demand- and supply-side perspectives, the ASESMM helps managers choose feasible and desir­able management practices. Ecosystem services’ (ESs) classifications were adopted to capture the benefits andused as soft targets that can change over time. This ASESMM was developed by applying it to a Japanese coastalzone in consultation with the relevant stakeholders to maintain its practical value. A narrative analysis sub­stantiated the lack of peoples’ recognition of nature’s benefits and the influence of ES information on thatrecognition. It also substantiated the comprehensiveness of the ESs’ classifications. Moreover, the application
revealed its usefulness for realizing satoumi, a Japanese concept of social-ecological production seascapes, as it
might help managers enhance synergies as well as minimize the trade-offs associated with prioritized ESs.
Although it was applied to a seascape in this study, the ASESMM can be applied to any SES management site ingeneral, including landscapes.

1. Introduction
The social-ecological systems (SESs) perspective, of which human­–nature interactions are an integral part, has gained increasing attention
because it could lead to the sustainable use and conservation of nature(Berkes and Folke, 1998; de Groot et al., 2010; Folke et al., 2005). The
term “social-ecological production landscapes and seascapes” (SEPLS)
has been coined to refer to SESs wherein human–nature interactions
succeed in maintaining sustainable ecosystem use and biodiversity
conservation (Gu and Subramanian, 2014; UNU-IAS et al., 2014). SoundSES management is key to SEPLS and providing various ecosystem ser­vices (ESs); it provides multiple benefits while conserving nature (AriasArévalo et al., 2018; Bastian et al., 2013; Costanza et al., 2014; Reyers
et al., 2013).
Articulating management targets is a prerequisite for sound SES

management guidance (Abson et al., 2014; Uehara et al., 2016; Uehara
and Mineo, 2017). A “rule of thumb” such as “the more ES, the better”
cannot always be adopted because maximizing ESs is not always desir­able (Schröter et al., 2017) for both demand- and supply-side reasons.
First, ESs are not equally demanded or desired. Each individual may
have different preferences for ESs (Arias-Arévalo et al., 2017; Uehara
et al., 2019b), and the demand for some ESs is not always beyond thecurrent supply (Cord et al., 2017). Such preferences may also changeover time (Skourtos et al., 2010; Uehara et al., 2018). Second, it is notalways possible to supply ESs that meet peoples’ demands because of
trade-offs between ESs (Cord et al., 2017; Ellis et al., 2019; Wright et al.,2017) and the feasibility of supplying them owing to, for example,
budget, time, and human resource constraints. Thus, it is critical to set
management targets by understanding what is “desired,” articulating
what is “desirable,” and reflecting what is desired (demand side) andwhat is feasible (supply side).

* Corresponding author.
E-mail address: takuro@fc.ritsumei.ac.jp (T. Uehara).
https://doi.org/10.1016/j.ecoser.2021.101312
Received 13 January 2020; Received in revised form 12 May 2021; Accepted 25 May 2021
Available online 13 June 2021
2212-0416/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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The remainder of this paper is as follows. Section 2 introduces thestudy site and the ASESMM’s developmental steps. Section 3 reports theresults obtained from its application to the study site. Section 4 ad­
dresses its management implications and practicality. Finally, Section 5
concludes the paper.

Nomenclature
ASESMM Adaptive social-ecological system management matrix
BIBD
Balanced incomplete block design
BWS
Best–worst scaling
ES
Ecosystem service
SEPLS
Social-ecological production landscapes and seascapes
SES
Social-ecological system
SI
Supplementary information

2. Material and methods
We used STATA 16.0 (StataCorp LLC, http://www.stata.com) soft­
ware for all analyses except for the best–worst scaling (BWS), which was
calculated using Nlogit 6 (Econometric Software, Inc., http://www.li
mdep.com/) and Microsoft Excel.

Although such normative information is undoubtedly essential,
research that explicitly engages with normative issues remains scarce(Abson et al., 2014; Schröter et al., 2017). Abson et al. (2014) noted that
there were no accepted normative frameworks in the literature. Schröter
et al. (2017) formulated a concept of sufficiency to increase the potential
risk of an implicit assumption that maximizing ESs is deemed “good.”
Uehara et al. (2016) and Uehara and Mineo (2017) proposed the satoumi
concept as a desired state of SEPLS. Satoumi is a Japanese word that
captures the SEPLS concept in which active human–nature interactions
enhance the productivity and biodiversity of seascapes (Uehara et al.,2019a). However, none of these authors presented an operationalframework upon which management targets could be based.
However, in reality, it is challenging—if not impossible—to articu­
late fixed targets at the outset, which is complicated by ignorance anddynamics. It is difficult to say that, for example, a 20% increase in thearea of seagrass beds is optimal as it meets residents’ demand for ESs.
There are at least four reasons for this difficulty. First, peoples’ recog­
nition of the types of ESs that they receive and what ESs they desire may
be inaccurate (Avelino et al., 2019; Costanza, 2015). Second, ESs’knowledge is limited because ESs are products of complex SES (Blythe
et al., 2019; Boerema et al., 2016; Mehring et al., 2018). As demon­
strated herein, ES information does not meet the demand of practicality
despite being conceptually well developed (Wright et al., 2017). Third,
peoples’ preferences may change over time; the same is true for targets(Blythe et al., 2019; Folke et al., 2010; Uehara et al., 2018). Lastly, ESs’supply often changes over time owing to the dynamic nature of the SES
as a complex, adaptive system (Holland, 1992; Schlüter et al., 2014).
This fact might also influence demand (Mehring et al., 2018).
Given the importance of articulated management targets and theinitial difficulties in setting them, we adopt the soft targets inspired by
Norton (2015). Soft targets are temporal, hypothetical, and not pre­determined. In the complex dynamics of an SES, targets may changeover time as an SES is learned through iterative testing, monitoring, andreevaluation (Folke et al., 2005). This calls for an adaptive or incre­
mental learning strategy (“Adapt”) instead of an optimization strategy(or “Optim”) (Norton, 2015).
An approach to elicit soft targets should be instrumental toencourage its adoption by practitioners. It should provide information
on its instrumental use in concrete management decisions rather thanconceptual or strategic uses (Wright et al., 2017). However, few studies
exist on instrumental use (Wright et al., 2017) and the rapid advance­
ment of ES science and its potential, mainstreaming, and use in decisionmaking remain a challenge (Chaudhary et al., 2015; Potschin-Younget al., 2018). The approach should be accessible enough for use withall science-based management decisions and limited local technical ca­
pacities (Ruckelshaus et al., 2015).
This study aimed to develop an instrumental management tool withwhich soft management targets can be identified. Accordingly, wedeveloped an adaptive social-ecological management matrix (ASESMM)
that is accessible and can be updated periodically to reflect systemicchanges in social-ecological management decisions. We developed the
ASESMM by applying it to the Harima Sea, a coastal zone in westernJapan, to verify its development and usefulness in practice.

2.1. Study site
The Harima Sea is part of the Seto Inland Sea, the largest enclosed sea
in Japan, with an area of 3,426 square kilometers (km2) and a mean
depth of 25.9 meters (m) (Ministry of the Environment Japan, 2019).
The Harima Sea is known for its fisheries (e.g., Japanese sand lance(Ammodytes personatus)), oysters (Crassostrea), and laver (Pyropia)
aquaculture. However, over the past two decades, the catch has been
declining (Ministry of Agriculture, Forestry and Fisheries, 2019). The
environment has degraded (e.g., red tides and undesirable levels ofchemical oxygen demand) during rapid economic development (Minis­try of the Environment Japan, 2019).
In response, the Japanese government enforced the Act on Special
Measures concerning the Conservation of the Environment of the Seto
Inland Sea in 1973. Its focus was on improving water quality, but the Act
was revised in 2015 to also provide diverse values from which people
could benefit. It also requires prefectures to design a plan suitable for thecharacteristics of each sea or bay. Hyogo prefecture enforced an action
plan for managing the Harima Sea (Hyogo Prefecture, 2016) that intends
to realize satoumi, a Japanese concept of SEPLS (Uehara et al., 2019a;
Uehara and Mineo, 2017; Yanagi, 2012). However, because satoumi
refers to a desirable coastal zone state, it is context specific (Uehara andMineo, 2017); therefore, its general description and management
indices are insufficient for managers seeking to implement it. In
response to residents’ unfamiliarity with ESs and the dynamic nature of
the SES, soft targets tailored to a specific context could assist in creating
an action plan for managing the Harima Sea (Hyogo Prefecture, 2016).
2.2. ASESMM
The ASESMM is a matrix that integrates demand- and supply-side
perspectives. It helps managers choose ES management practices thatmeet residents’ preferences while avoiding trade-offs that are critical ofthe demand-side viewpoint. It has four developmental steps that areiterated periodically to reflect changes in the SES (Fig. 1). Several
decision-making support tools share characteristics with the ASESMM.
For example, structured decision-making defines objectives and mea­sures and identifies and evaluates alternatives for making choices byintegrating diverse stakeholder values in the face of ecological uncer­
tainty (Failing et al., 2013; Guerrero et al., 2017; Martin et al., 2018).
However, such tools do not share other key characteristics of the
ASESMM, including the notion of soft targets, a comprehensive use ofESs’ classifications, and a combination of supply and demand analyses.
2.2.1. Step 1
Step 1 compiles the key states and benefits obtained from the SES.
These ideally concern matters that benefit residents. However, residents
are not always fully aware of what they want and actually receive(Costanza, 2015). Moreover, management targets in use may not always
comprehensively capture key states and benefits for residents. There­
fore, the ASESMM adopts the ES concept (Costanza et al., 2017) tocapture these aspects as comprehensively as possible. The ES concept
has gradually gained support in the policy arena (Bouwma et al., 2018).
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Ecosystem Services 50 (2021) 101312

Fig. 1. ASESMM procedure. The ASESMM is expected to be iterated periodically.

Table 1 exhibits a list of marine ESs. We adopted 18 generic marine
ESs’ classifications (Böhnke-Henrichs et al., 2013; Hattam et al., 2015)
to capture the benefits from the Harima Sea. Further, we added thebenefit of the “relationship between people and the sea,” given thegrowing recognition of the importance of relational values (Chan et al.,2018; Uehara et al., 2019a, 2018). This relationship, or human–natureconnection, is critical given that human disconnection from nature is afundamental cause of the current ecological crisis and lack of sustain­
ability (Ives et al., 2017; Restall and Conrad, 2015; Zylstra et al., 2014).
Relational values consider the relationship itself as a valuable end rather
than as merely a means to an end (Chan et al., 2018). While they can beperceived as a third class of values, adding to previously establishedconstructs of instrumental and intrinsic values (Chan et al., 2016),
relational values have been adopted for the valuation of ES (Himes andMuraca, 2018). Relational values may share cultural values with con­ventional cultural ESs but are not exactly the same (Chan et al., 2018);

relational values differ from cultural ESs listed in generic marine ESs
classifications because these treat the relationship as a means to an end.
Therefore, relational values offer an expansion of current constructs ofcultural ESs (Stålhammar and Thorén, 2019). In this study, relational
values were categorized—albeit, not typically—as cultural services (i.e.,the “relationship between people and the sea” in Table 1) for the sake ofthe analysis. We are aware of the debate regarding whether noninstru­
mental values, such as relational values, are part of ESs (Braat, 2018;
Díaz et al., 2018; Stålhammar and Thorén, 2019); however, this concern
is beyond the scope of this study, making space for future considerationsof relational values being placed in a category that is separate from
cultural services. Because we did not present the ES category (Table 1) tothe respondents, our choice of categorizing relational values into cul­tural services did not affect their answers. Another notable point that
requires articulation is that while respondents were provided thedescription of relational values rather than simply asking about “therelationship between people and the sea” (see the Supplementary In­formation (SI)), it may not sufficiently capture all aspects of the conceptof relational values. We intended to explain the distinction between
means and ends as it is a core notion of relational values (Chan et al.,2018). Relational values have typically been measured using multiple
items to develop a construct based on environmental psychology (Klain
et al., 2017; Kleespies and Dierkes, 2020; Schulz and Martin-ortega,
2018; Uehara et al., 2020. However, See et al. (2020) showed that
relational values using multiple items are nearly indistinguishable from
instrumental values. Therefore, while room to further investigate themeasurement of relational values remains, it is certain that thedescription of relational values requires careful attention to capture the
key aspects of the concept in each context.
Moreover, while ESs and benefits such as human wellbeing may not
be considered similar, the distinction is not always clear (Blythe et al.,2019). In this analysis, we focused on ESs as a proxy for benefits rather
than on the dimensions to which each ES contributes in terms of humanwellbeing.

Table 1
Nineteen marine ecosystem services (ESs).

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In some studies, residents have been asked directly as to what ESs
they received and how valuable they considered these ESs (Oteros-Rozaset al., 2014). However, we assumed that residents did not know muchabout the ESs that they received (Costanza, 2015); we compiled andverified the 19 ESs of the Harima Sea and their descriptions using
various sources—academic literature, government reports, statistics,
and consultations with managers and experts, including a consultation
with coastal zone managers of the Harima Sea in October 2018. The
quality of ES information was assessed through the following criteria:salience, credibility, and legitimacy (Cash et al., 2003; Wright et al.,2017). By adopting generic marine ESs’ classifications with relational
values, the ESs can be said to be comprehensive (a sub-criterion ofsalience) and legitimate because the list was created in an unbiased
manner. The ES information does not, however, satisfy performance—a
sub-criterion of salience measured by the outcome and irreversible
change (Wright et al., 2017). Because a clear and predetermined goalcannot be set in an adaptive view, ESs were not chosen based on or
targeted at a specific goal. Finally, we attempted to maintain credibility;
however, certain limitations remained owing to the limited information
available.
Given the paucity of ES information about the study site, wecollected information by proposing the following rules. First, some ESs
were provided with examples or a general description not specific to thestudy site. Second, while the flow of ESs should ideally be explained, thestate of the natural capital (e.g., a tidal flat) was used as a proxy owing to
the lack of information. Third, quantitative information was desirable
because it produces a more concrete result. Fourth, information onchanges in ESs might help with decision-making. Fifth, visual aids could
assist people in obtaining a clearer image of an ES (Boyle, 2017).
Addressing the uncertainty or inaccuracy of ES information is essential;
therefore, we explained the uncertainty clearly to the respondents.

Fine in current condition,” “4. Conditions could decline,” and “5. Service
could be potentially lost.” The items corresponded to the direction ofchange and its intensity to the amount of an ES compared to its current
amount (i.e., “3. Fine in current condition”), namely, how much man­agement effort should be put on the supply of each ES. We consulted the
managers of the study site regarding the validity of the options. These
options were also tested in a pretest; in practice, the options can berevised because the ASESMM is an iterative process. Improvement canbe qualitative, quantitative, or both, as clarified in the survey. Although
the differentiation between quality and quantity may be critical for
management (Uehara and Mineo, 2017), we did not ask the respondents
about this because it might have been difficult for them to answer.
Although we are aware of the ongoing controversy on this topic, we
treat the data as comprising interval, rather than ordinal, values byfollowing an argument based on the empirical findings regarding theirappropriateness (Allen and Seaman, 2007; Brown, 2011; Carifio andPerla, 2008; Willits et al., 2016). Specifically, we computed the meanvalue for each ES. An interval treatment was applied to the ESs (Bryce
et al., 2016; Langemeyer et al., 2015). However, we have also provided
the median values and bar charts to allow readers to further interpret theresults. Either treatment is suitable because the ASESMM requires ESs to
be ranked in terms of the direction of change to an ES compared to its
current state. If those who apply the ASESMM to their cases do not agreewith the interval treatment, an ordinal treatment (e.g., median values
rather than mean values) can be used instead.
After sorting the ESs according to the requests for desired manage­
ment directions as measured by the mean value for each ES, we grouped
the results into four categories. The first was given the highest priority,
the second the secondary priority, and so on. We grouped the ESs rather
than adopt a ranking because mean values do not always differ signifi­
cantly and are therefore not always directly comparable. Furthermore, it
can be more intuitive and practical for policymakers and residents to see
the priority according to the group. We presumed that, overall, the re­spondents would prefer improving all the ESs because ESs are beneficial;
the respondents were not asked to consider the costs of improvements
and trade-offs.

2.2.2. Step 2
Step 2 answers the question “how should ESs be improved?” (Fig. 1).
Accordingly, we proposed two measurements: the desired direction forchange in each ES compared to its current state (Measurement 1) and therelative importance of each ES compared directly to others (Measure­ment 2). The former asks residents for incremental or marginal changes
rather than desirable (or optimal) levels of an ES. This measure isnecessary but insufficient. Improvements in one ES may involve tradeoffs with another (Cord et al., 2017). If residents prefer, for example,
improving the migratory and nursery habitat and food provisioning by
the same degree, managers must ascertain which ES should be priori­
tized when a trade-off is inevitable. Therefore, managers also need tounderstand the relative importance of the ES in question. Following anarrative approach, we asked the residents about the benefits via openended questions (Arias-Arévalo et al., 2017). Because this is a suitable
method for capturing multiple values, including nonmaterial benefits(Arias-Arévalo et al., 2017; Ladle et al., 2016), it also verifies if the ESconcept captures key benefits comprehensively.
We conducted an online survey involving the residents of the Hyogo
and Kagawa Prefectures, which surround the Harima Sea, to collect data
for the demand-side analysis (see SI for the questionnaire). We recruitedparticipants through an Internet survey company; the company
announced the survey and collected responses until the number of re­
spondents reached the target volume. We asked that the sample follow
the target population (i.e., residents in the Hyogo and Kagawa Pre­
fectures) in terms of gender ratio, age composition ratios, and the size ofthe population in each prefecture. Prior to the main survey conductedduring March 14–19, 2019, a pretest was conducted during February
8–13, 2019. The sample sizes were 2047 and 189, respectively.

2.2.2.2. Measurement 2. While the direction of improvement explains
how much each ES should be improved, it does not explain their relative
importance. We adopted the BWS method (Finn and Louviere, 1992)
because it enables a direct comparison of all ESs’ improvements insteadof between individual improvements, as in previous studies. For
example, choice experiments, a type of nonmarket stated-preference
valuation method, consider several scenarios but struggle to manage
large numbers of attributes at once (Holmes et al., 2017). Alternatively,
some studies have asked respondents to pick several important ESs(Lopes and Videira, 2016; Oteros-Rozas et al., 2014). The BWS method is
becoming more widely used in measuring the importance of ESs (Sotoet al., 2018; Tyner and Boyer, 2020; Uehara et al., 2019b).
Balanced incomplete block design (BIBD) was used to construct 57
choice sets (SI). In the BIBD, each alternative appears the same numberof times and is paired equally with each of the other alternatives acrossall choice sets (Louviere et al., 2015). To reduce the number of questions
per respondent, we divided 57 choice sets into seven groups. Answering
57 choice sets can be psychologically demanding (Aizaki et al., 2015);
thus, each respondent was presented with eight or nine choice sets.
Following the common practice in choice experiments that divides
choice sets into blocks (Aizaki et al., 2015; Holmes et al., 2017), the dataobtained from seven groups were pooled for analysis. Table 2 presents
an example of a choice set. Each set contains different combinations of
three ESs’ improvements. Each respondent was asked to choose the mostand least important ES from a series of such sets.
We analyzed the data obtained from the BWS questions using
counting and econometric analyses. In the former, we noted the numberof times each ES was chosen as being the most or least important for the

2.2.2.1. Measurement 1. We asked the respondents how much theywanted the amount of each ES to improve with options ranging from “1.
Needs significant improvement,” “2. Needs some improvement,” “3.
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The lead author coded the answers for the 19 ESs (by following thedescriptions of 18 ESs (Böhnke-Henrichs et al., 2013; Hattam et al.,2015) and relational values (Chan et al., 2018)) as follows: “don’tknow,” “don’t want,” and “benefits do not fit into these categories.” An
answer could include and therefore be classified into multiple ESs. The
coding was validated by a coauthor and corrected accordingly.

Table 2
Example of a choice set.

2.2.3. Step 3
Step 3 included the supply-side analysis. Stakeholders (e.g., coastalzone managers and experts) were asked to complete a matrix (Table 3)that listed management practices and the corresponding trade-offs andsynergies between ESs. During this analysis, coastal zone managers andexperts answered the question “How can ESs be improved?” (Fig. 1).
Assuming that residents did not have an accurate knowledge of ESs,
including how they are coproduced (i.e., their trade-offs and synergies)(Avelino et al., 2019; Costanza, 2015), we asked only managers andsuppliers this question. Although the answer depends on how residents
receive and perceive them, it is the suppliers who control the amount of
ES.
In a meeting held in August 2019, we invited three experts and three
coastal zone managers of the Harima Sea. Prior to the meeting, we
consulted the managers to verify the practicality of the trade-off andsynergy matrix (Table 3) through meetings and email communications.
In the meeting, we explained the findings from the demand-side anal­
ysis. The attendees were asked to complete the matrix together in themeeting. For example, the improvement of ES2 through management
practice M2 (e.g., the restoration of seagrass) had a positive impact on
ES1 (“↗” in the cell ES2XES1). The improvement of ES19 by imple­
menting M19 (e.g., prohibiting fishing to secure places for the arts) had anegative impact on ES2 (“↘” in the cell, ES19XES2). “→” indicates noimpact. To finalize the matrix, we discussed it with the attendees via
email.

total-best (ΣBi) and total-worst (ΣWi) numbers, respectively. The
best–worst (B–W) score of ESi was calculated by subtracting the ΣWi of
ESi from the ΣBi of ESi. The standardized B–W score of an ESi wascalculated by dividing the B–W score of ESi by the number of times ESi
appears in all questions (Ti) (Eq. (1)). The standardized B–W adjusts theinfluence owing to the differences in the number of respondents between
groups. A higher standardized B–W score implies that an ES is evaluatedas being more important.
∑
∑
Bi −
Wi
std B − Wscorei =(1)
Ti
In the econometric analysis, we employed the maxdiff model—avariant of the conventional multinomial logit model (Louviere et al.,2015). In this model, respondents are assumed to examine all thepossible pairs of ESs in a choice set (in this case, 3 × 2 = 6 pairs) andchoose the pair that maximizes the difference in importance between thetwo most and least important ESs, respectively. Differenceij in Eq. (2)represents the difference in the importance of ESi and ESj; εij is an error
term (Lusk and Briggeman, 2009).
Differenceij = βi − βj + εij(2)

The probability Pij that the respondent chooses ESi and ESj as the mostand least important ESs out of J (in this case, J = 3) ESs in the set is equal
to the probability that the difference in importance between the two ESs
is largest among all of the differences in the importance of every possible
pair in the choice set. Assuming that εij is distributed independently andidentically with a type I extreme value distribution, the multinomial
logit model can then be derived (McFadden, 1973). Pij is described using
Eq. (3) (Lusk and Briggeman, 2009). The parameters can be estimatedusing the maximum likelihood method.
)(
exp βi − βjPij = ∑J ∑J(3)
k=1
l=1 exp(βk − βl ) − J

2.2.4. Step 4
Step 4 combines the demand-side analysis (Step 2) with the supplyside analysis (Step 3) to create the finalized ASESMM. Thus, the ESs in
the trade-off and synergy matrix (Table 3) are reordered according to the
management preferences elicited from the residents in the demand-side
analysis. The ASESMM illustrates the trade-offs caused by each man­agement practice to improve targeted ES needs that must be considered.
Using a matrix for ES information is not new (Wright et al., 2017).
However, the matrices used in previous studies are less comprehensive
and less focused on their practical use. For example, Baral et al. (2014)
developed a matrix showing the relationships between various future
land uses and their impact on ESs; Oteros-Rozas et al. (2014) developed
a matrix demonstrating the relationships between ESs and their socialand personal importance. The ASESMM, by contrast, shows the re­lationships between ESs ascribed to specific management practices or­dered by residents’ ES demands.

We randomized the order of the 19 ESs’ questions, which measure
the desired directions for the changes in an ES compared to its current
states and the order of the ES presented in each subset in BWS. In thepretest, the order of the 19 ESs and the ranks of their mean values werecorrelated (rank correlation coefficients (tau-b) = 0.4971, p < 0.01),
implying a possibility that the order of the ESs influenced their ranks.
Therefore, we randomized the order of the ESs in the main survey.

3. Results
3.1. Key benefits of the Harima Sea

2.2.2.3. Narratives. In addition to Measurements 1 and 2, we adopted
the narrative approach and asked people about the benefits of the SES
using an open-ended questionnaire that included nonmaterial values(Arias-Arévalo et al., 2017; Ladle et al., 2016).
The respondents were asked to answer the same open-ended ques­tion twice–before and after the introduction of marine ESs in the Harima
Sea—“The Harima Sea offers a variety of benefits. From those of which
you are aware, which would you like to enjoy? Please be as specific as
possible.” By comparing the “before” and “after” responses, we assessed
how the ES information (i.e., the description of 19 ESs listed in Table 1;
see SI for the case of the Harima Sea) changed the respondents’ under­
standing of the benefits.

We compiled site-specific ES information by following the five rules
previously specified (see SI). Because the availability of information
varied, the degree of information about each ES also varied, as shown in
Table 4. In some cases, we had to provide a general description that was
not specific to the Harima Sea. In others, we provided a source of the ES,such as tidal flats or seagrass beds (i.e., biophysical structures or func­
tions (Haines-Young and Potschin, 2010)). Quantitative information andchanges were provided when available. In addition, we provided pic­tures and figures, if available, that helped respondents imagine an ES.

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Table 3
An example of the trade-off and synergy matrix showing the trade-offs and synergies between ES improvements.

↗: synergy, →: no relation, ↘: trade-off.
Table 4
Types of information collected for the ESs.
ES Type No.

ES Type

Overall Evaluation

ES or ES Source

Quantitative

ChangeVisual Aid

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

Food production
Biotic raw materials (non-food)
Air purification
Climate regulation
Disturbance prevention or moderation
Regulation of water flows
Waste treatment and assimilation
Coastal erosion prevention
Biological control
Migratory and nursery habitat
Gene pool protection
Leisure, recreation, and tourism
Aesthetic experience
Inspiration for culture, art, and design
Cultural heritage
Cultural diversity
Spiritual experience
Information for cognitive development
Relationship between people and the sea

Comprehensive
General description
General description
Example(s)Comprehensive
Comprehensive
Comprehensive
Comprehensive
Example(s)Example(s)General description
Example(s)Example(s)Example(s)Example(s)Example(s)Example(s)Example(s)Example(s)ES––
ES source
ES source
ES source
ES source
ES source
ES source
ES
–
ES
ES
ES
ES
ES
ES source
ES
ES

Yes
–
–
Yes
Yes
Yes
Yes
Yes
Yes
Yes
–
Yes
Yes
Yes
Yes
–
–
Yes

Mixed
–
–
Increase
Mixed
Mixed
Decrease
Mixed
Decrease
Decrease––
Decrease
–
–
–
–
–
–

Yes
–
–
Yes
Yes
Yes
Yes
Yes
Yes
Yes
–
Yes
Yes
–
Yes
Yes
Yes
–
–

3.2. Demand for ES improvement

Although differences existed according to the respondents’ location ofresidence, improvement was desired for all ESs (i.e., less than 3.00 of themean). Overall, regulating and provisioning services were preferredover habitat and cultural services. Although the ESs were ordered bytheir mean values, the difference between two services in a sequence
was not necessarily significant (see SI for the paired t-test results); thus,
they were grouped into four ranks as shown below.
The ES number corresponds to that in Table 1. “Pooled” includes all
respondents (i.e., “Living close to the Harima Sea” and “Living far awayfrom the Harima Sea”).
Fig. 3 shows the relative preference for each ES over other ESs
measured according to their standardized B–W scores (Eq. (1)) based ona counting analysis by region. No significant differences in their relative
importance by region were found. Similar to their desired direction forchanges to ESs (Table 5), the respondents tended to prioritize regulating
services over cultural services. Kendall’s rank correlation coefficients(tau-b) between these two measures was high (0.731, p < 0.01) for all
respondents, but the measures were not perfectly correlated.
We confirmed the validity of the results by comparing them withmore sophisticated econometric estimates using the maxdiff model (Finnand Louviere, 1992) (see SI). The Pearson’s correlation coefficient forthese two estimates was r = 0.9997 (p < 0.01).
Using these two measures for ES management preferences, wegrouped the desired directions for changes to ESs into four rank groups
and then reordered them within each according to the relative impor­tance of each ES in its category (Table 6).

We asked the respondents the following question: “Do you live in thevicinity of the Harima Sea?” We did not clarify if “vicinity” meant
physical or psychological distance; we assumed that this depended on
their individual perceptions. Less than half (40.4%) responded affir­
matively. Approximately half of them (52.72%) had no contact with theHarima Sea; most lived elsewhere (82.87%). As for voluntary partici­
pation in activities related to the preservation of the Harima Sea (e.g.,
bird-watching and shore clean-ups), only 2% of the respondents living in
the vicinity periodically participated in such activities; the participationof respondents living elsewhere was much lower (0.08%). Only 5.08% of
the respondents were previously acquainted with the ES concept.
Fig. 2 shows the respondents’ preferences for the direction of change
to the ES compared to its current condition. Overall, the respondents
advocated more improvement. There were two types of frequency dis­tributions; “2. Needs some improvement” (ES 1–10, 12, 13, and 19) and“3. Fine in current condition” (ES 11, 14–18) were dominant. There
were two medians: “2. Needs some improvement” for ES 1–10, 12, 13,
16, and 19 and “3. Fine in current condition” for ES 11, 14, 15, 17, and18.
Table 5 shows the mean value of the respondents’ requirement for
improving an ES according to their residency. The smaller the meanvalue was, the more the improvement was desired. The order ofpreferred change did not differ considerably across the origins of theparticipants. Kendall’s rank correlation coefficients (tau-b), a nonpara­
metric test, were high (0.9649, 0.9766, 0.9415 for Pooled vs. Close,
Pooled vs. Distant, and Close vs. Distant, respectively, with a signifi­
cance level of p < 0.01). Overall, the respondents living close to the sea
demanded more improvements than those living far away from the sea.

3.3. Supply-side analysis and compiling the ASESMM
The supply-side analysis elicits trade-offs and synergies between ESs
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Fig. 2. Preferred directions of changes to the amount of an ES compared to its current condition: 1. Needs significant improvement; 2. Needs some improvement; 3.
Fine in current condition; 4. Conditions could decline, and 5. Service could be potentially lost.

owing to ES improvements. Prior to this analysis, we held a meeting inJuly 2019 and had email communications with governmental officials
involved in the management of the Harima Sea to test the validity of the
ASESMM and correct it accordingly. In August 2019, another meetingwith coastal zone managers of the Harima Sea and experts involved incoastal zone management took place. In the meeting in August, we asked
the stakeholders about management practices to improve ESs.

Fig. 4 shows the matrix, which combines the results from the de­mand- and supply-side analyses. “↗,” “→,” and “↘” indicate synergy, no
relation, and trade-offs, respectively.
Because disturbance prevention or moderation is the most highly
prioritized, its improvement is preferred even when this involves tradeoffs with other ESs. Biological control through the conservation of
ecosystems has a trade-off with waste treatment and assimilation, which
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Table 5
Desired directions for change to an ES. N = 2,047, 827, and 1,220.

Fig. 3. The relative importance of an ES to other ESs, measured in B–W scores.

is more highly prioritized. Therefore, the improvement of biologicalcontrol should consider the trade-offs involved. It is worth reiteratingthat the managers and experts asserted that whether an improvement
involves trade-offs and synergies depends on what management applies
in practice. The list of management practices is merely an example todemonstrate the potential trade-offs and synergies so that otherpractices can also be implemented.
Notably, there is no management applicable to improving air puri­
fication and climate regulation in the Harima Sea. Further, coastal
erosion prevention, migratory and nursery habitats, the relationshipbetween people and the sea, and gene pool protection involve various
trade-offs with other ESs. Overall, cultural ESs are synergetic within
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Table 6
Demand for ES improvement.

Fig. 4. Adaptive Social-Ecological System Management Matrix (ASESMM) for the Harima Sea. The ASESMM was developed through consultations with Harima Sea
coastal zone managers and coastal zone management experts. ↗: synergy, →: no relation, ↘: trade-offs.

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themselves.

ESs’ classification system increased from 67 to 276. These included, forexample, “I receive natural gifts from the Harima Sea” and did not
specify the kind of ES they stood for.

3.4. Narratives

4. Discussion

Table 7 shows the number of ESs and other keywords mentioned in
the open-ended questionnaire before and after explaining the ESs ob­
tained from the Harima Sea. Before the ES information was provided,
the respondents almost exclusively discussed food production (ES1,1107 respondents); Leisure, recreation, and tourism (ES12, 197 re­spondents); and Aesthetic experience (ES13, 122 respondents). Several
people said, “I do not want to benefit from the Harima Sea” (291 re­spondents) and “I do not know what benefits I obtain from the Harima
Sea” (114 respondents). Some respondents mentioned “healing (iyashi inJapanese)” (24 before and 62 after the ES information was provided).
According to a Japanese dictionary, this means “[t]o heal illnesses andinjuries. Also, to eliminate mental worries and anxieties” (Kitahara,
2010, p.133). Healing does not fit into the ESs’ classification as it may beconsidered a type of human wellbeing (Millennium Ecosystem Assess­ment, 2005), but there seems to be a strong link between ESs and humanhealth, including healing (Karjalainen et al., 2010; Sandifer et al., 2015).
Comparing the answers before and after the provision of the ES in­formation, the number of ESs mentioned in total increased by 16.7%.
Fifteen of the ESs were mentioned more often; all ESs were appreciatedafter the information was provided. Four ESs had not been mentioned at
all before. Interestingly, the number of respondents mentioning intrinsic
values (i.e., not for the sake of humans but for that of nature) increased
from 1 to 62. Both “Don’t want” and “Don’t know” decreased by 10.0 %and 43.9%, respectively. The mention of benefits that did not fit into the

4.1. Management implications
4.1.1. Current state and key benefits of the Harima Sea
The quality of ES information required for the instrumental use of the
ASESMM, as in this study, is high (Wright et al., 2017). The respondents’demand may be sensitive to the information provided. In addition, therespondents may not be well acquainted with the benefits that theyreceive from nature (Costanza, 2015). The narrative analysis substan­tiated these points in that the residents were unaware of the Harima
Sea’s benefits; their recognition changed significantly after the ES in­formation was provided (Table 7).
As shown in Table 7, the degree of information quality varies fromgeneric descriptions (e.g., “Gene pool protection”) to detailed timeseries data on the ES (e.g., “Food production”). However, we must
provide ES information regardless of its limitations. Owing to thecomplex and dynamic nature of the SES, we cannot expect to glean
complete information. In addition, information about the limited data
availability (Table 7) along with people’s preferences over ESs obtained
from demand-side analyses help managers identify what ES information
should be emphasized. Because the ASESMM is an iterative process, this
could help managers incorporate updated information in management
decisions over time.

Table 7
The number of occurrences of benefits from the Harima Sea mentioned before and after ES information was provided.

N = 2047. The subtotal is the summation of ES Type Nos. 1–19.
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4.1.2. Demand-side analysis
First, because all ESs are “beneficial” and we did not explicitly ask
the respondents to consider the costs of improvement and the corre­
sponding trade-offs among ES improvements, the respondents on
average preferred to improve all ESs, as expected (i.e., the mean values
in Table 5). Although we treated the data as interval, it can also betreated as ordinal (e.g., using median values).
Second, distance matters for people’s requests for improvement but
not for their order. In the survey, we asked “Do you live in the vicinity of
the Harima Sea?” without providing a clear definition of what “vicinity”
means. Therefore, we presume that the respondents answered thisquestion based on their physical and psychological distance, i.e., “vi­cinity” could be a physical and psychological construct. For future
research, differentiating between these constructs would be informative;
for example, if the psychological aspect matters, appealing to people’s
psychology might change their answers.
Third, we applied two distinct measures to the demand for ES
improvement (i.e., ES improvements and their relative importance)
because they differ conceptually and can capture the various aspects of
ES demand. The measures were correlated albeit imperfectly, thereby
implying the meaningfulness of using both.
Fourth, the simple counting analysis using spreadsheets provided
almost identical results as those from the sophisticated econometricanalysis (e.g., the maxdiff model). The list of the choice sets for BWS
satisfying BIBD is readily available (Louviere et al., 2015). While otherstated preference methods—including the contingent valuation method
and choice experiments using statistical analysis—have sample size re­quirements (e.g., de Bekker-Grob et al. (2015) as an example of such a
choice experiment), the calculation analysis does not have a strict
requirement. The questionnaire for the demand analysis takes the gen­eral form of a social survey with the addition of questions regarding
BWS, asking respondents to choose the best and worst from a choice set.
Keeping the method as simple as possible is critical in making it acces­
sible to the average practitioner (Ruckelshaus et al., 2015).
Fifth, the demand for ES improvements (Table 6) raises a concern
regarding the relationship between people and nature (Ives et al., 2017;
Restall and Conrad, 2015; Zylstra et al., 2014). The respondents
preferred ESs with less interaction with the Harima Sea (e.g., distur­bance prevention or moderation and food production) in receiving
benefits than ESs with more interaction, such as cultural ESs (Table 6).
For example, people do not need to interact with nature to benefit fromdisturbance prevention, which is improved by civil engineering or
moderation, and can eat fish that is available at a supermarket far awayfrom the Harima Sea. Most seafood is cut and packaged, and people may
not be aware of its origin. However, the lack of a human–natureconnection is a root cause of ecological crises and unsustainability(Uehara et al., 2019a). Therefore, managers should encourage residents
to interact with the Harima Sea to improve its ESs in the long term.
Sixth, overall, the narrative analysis substantiated the usefulness of
the ESs’ classification (Table 7). It captured most of the benefits fromnature. Most benefits were captured by the ESs’ classification; however,the narrative analysis elucidated the potential importance of “healing,”
which cannot be categorized into the ESs’ classification. The relation­
ship between the state of coastal zones and healing is worth investi­
gating. It highlights the importance of considering cultural ESs (e.g.,
spiritual services) that are specific to individual countries. Although weplaced intrinsic values outside the ESs by following the ESs’ classifica­tion adopted (Böhnke-Henrichs et al., 2013; Hattam et al., 2015), suchvalues could be part of or consistent with common ES approaches(Ruckelshaus et al., 2015; Stålhammar and Thorén, 2019).
Seventh, the comparison of the narratives before and after the ES
information was provided is instructive for management. The residents’recognition of ESs increased and was diversified (Table 7). The mentionof some ESs increased in the open-ended questions, implying thatproviding such information is effective in improving the residents’recognition of ESs.

Finally, the managers observed that the respondents seemed tomisunderstand the current conditions of the Harima Sea. For example,
while the respondents indicated that waste treatment and assimilation(ES7) was the second most important target for improvement, themanagers argued that the Harima Sea was too clean, lacking nutrient
salts, to cultivate fish. The Hyogo Prefectural government set a newwater quality standard that allows sewage plants and factories to drain
nitrogen (Kobe Shinbun, 2019). The demand analysis should be updated
by promoting the residents’ understanding of the Harima Sea.
4.1.3. Supply-side analysis
The supply-side ES trade-off and synergy combinations werecompiled based on the meetings and email communications with coastalzone managers of the Harima Sea and coastal zone management experts.
Similar to Cord et al.’s (2017) argument, the stakeholders hereinclaimed that whether the relationships between ES improvements aretrade-offs or synergy depends on the management practices that occur.
Moreover, this relationship is location specific. When asked to picture
the entire Harima Sea, the stakeholders said that the relationshipdepended on which part we were targeting. Moreover, “Air purification”(ES6) and “Climate regulation” (ES7) are not manageable at the local
level, necessitating multilevel governance on an international level(Saarikoski et al., 2018).
The procedure of the supply-side analysis should be further devel­
oped. One weakness herein is the limited involvement of scientists(particularly natural scientists). To continue updating the information,scientists should be involved in a systematic manner—for example, byapplying the Delphi method (Linstone and Turoff, 1975).
4.1.4. Asesmm
Per the managers and experts, the ASESMM has five main benefits.
First, it reveals how SESs should be managed from the demand side (howshould ESs be improved?) and supply side (how can ESs be improved?).
Second, the prioritization of ES improvement suggests what kind of ES
information should be collected specifically. This prioritization is crit­
ical given resource constraints (e.g., budgets and scientists). Third,
among the trade-offs involved in ES improvement, the ASESMM iden­tifies the trade-offs that should be especially avoided or considered. It
enables managers to focus on the trade-offs between ESs that are moreprioritized than the targeted ESs. There is no need to consider all the
combinations of trade-offs; however, a certain trade-off cannot always
be ignored to improve a certain ES. The order of ES management (Fig. 4)
could change as the provisions of ESs change as a result of ES manage­
ment. Notably, the managers, experts, and literature claim that a man­agement practice that could avoid such trade-offs might exist. Thus,
satoumi, a Japanese concept of SEPLS, could be realized in the Harima
Sea. Fourth, the ASESMM provides a platform for all stakeholders acrossdifferent organizations at multiple scales to discuss ES improvement in acomprehensive manner and on the same grounds. Linking a broad rangeof stakeholders is challenging (Folke et al., 2005). Because ES
improvement involves multiple sections (e.g., the division of fisheries,
education, or the environment) on multiple scales (e.g., municipal,
prefectural, national, or international), a platform that everyone canshare, such as the ASESMM, is both integral and effective. Finally, theASESMM is a practical tool for adaptive management because it isaccessible and updateable. As previously mentioned, given the changes
in demand for the richness (e.g., for fisheries) and cleanliness (e.g., forthe transparency) of water quality, the Hyogo Prefectural government
established a new water quality standard to increase the catch of fish in
the Harima Sea. The ASESMM could help managers adapt management
practices to such changes.
Nevertheless, there are three caveats. First, the ASESMM does not
aim to replace the complex decision-making process of SES management(Saarikoski et al., 2018) but to aid the process with additional infor­
mation. The managers noted that a decision involves multiple factors,
such as budgets and politics and their interactions, rather than a lucid
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Pareto improvement process. Second, the ASESMM is not perfect; it
includes a demand-side analysis based on residents’ inaccurate recog­
nition of benefits and lack of knowledge about ESs, which is expected to
improve through the iterative process of the ASESMM. For example, it
can be iterated periodically (e.g., every five years) and simultaneously towhen the management plan is revised. Finally, as indicated by the ex­perts and managers, the ASESMM varies depending on the target area. In
our experimental application, we asked the stakeholders to picture theentire Harima Sea. However, in reality, a management practice musttarget a smaller, more specific area.

consultation revealed the possibility of attaining satoumi, a Japanese
SEPLS, involving active human–nature interactions that enhance theproductivity and biodiversity of seascapes. In other words, management
using the ASESMM can enhance synergies while minimizing trade-offs
involving prioritized ESs in realizing a SEPLS, a desirable state of SES.
Because of the complex interactions of SES, trade-offs and synergies
among ESs are a critical concern in SEPLS management (Cord et al.,2017).
An ASESMM can be built using only spreadsheets and is expected to
be updated periodically to adapt to changes in SES, preferences, andupdated ES information. Although the managers and experts herein
supported the ASESMM’s practicality, more rigorous procedures for
supply-side analysis must be developed. For example, the ASESMM
should involve more diverse stakeholders including scientists with moreformalized procedures such as the Delphi method (Linstone and Turoff,1975). Overall, we believe that this approach is effective for adaptive
SES management. Moreover, it can be generally applied to SESs to assess
the management of both seascapes and landscapes.

4.2. Does the ASESMM contribute to sustainability?
The ASESMM is founded on Norton (2015) in that there is no single
sustainability and that the path to sustainability is open to various
possibilities. The ASESMM poses soft objectives that are adaptive to SESchanges to achieve sustainability. Therefore, an SES can remain sus­
tainable while continuously learning and adapting to changes.
Although we agree with Norton (2015), explicitly reflecting infor­
mation regarding the irreversibility of the ASESMM is essential. If
human and natural disturbances let an SES cross its threshold, ecosys­
tems may be lost or become expensive and physically difficult to restore(Walker et al., 2004; Wright et al., 2017). Management practices
changing an SES could also cross thresholds if they reflect peoples’ de­mands without considering them. Irreversible changes are critical but
not well reflected in the ES information available for instrumental use(Wright et al., 2017). For instance, seagrass (Zostera marina) in the Seto
Inland Sea, which includes the Harima Sea, decreased by 72% during the
1960–1990 period of rapid economic growth (Ministry of the Environ­ment, n.d.). Although no solid scientific data are available, an expert in
the Seto Inland Sea suggested that there may be a minimum area ofseagrass required to sustain the population and grow independently(personal communication, 2015). A loss of seagrass beds leads to the lossof various ESs (Himes-Cornell et al., 2018). In the ASESMM, information
regarding irreversibility could be reflected in management choices thatavoid crossing certain identified thresholds.

Funding
This work was supported by The Japan Society for The Promotion of
Science [grant number: 18H03432].
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgments
We would like to thank the coastal zone managers of the Harima Sea
and coastal zone management experts who helped us develop the
ASESMM. We would also like to thank Dr. Takahiro Ota for hiscomments.

5. Conclusions

Appendix A. Supplementary data

We developed the ASESMM as an instrumental tool for adaptive SES
management that adopts the notion of soft targets and sustainability
proposed by Norton (2015). The iterative application of the ASESMM
updates soft targets via learning and incremental processes. By reflecting
demand- and supply-side perspectives, the ASESMM helps managers
choose management practices for ESs’ improvement by avoiding tradeoffs with more prioritized ESs.
The ASESMM was developed and tested by applying it to a Japanese
coastal zone. The narrative analysis substantiated the comprehensive­
ness of the ESs’ classification in capturing the benefits of nature; most of
the benefits that the respondents recognized were categorized into theESs’ classification system. Although it captured most of the benefits, thenarrative analysis elucidated their intrinsic values and healing quality.
Further research is required on the relationship between ESs’ classifi­
cations and healing. Excluding monetary measures (Kelemen et al.,2014), the ASESMM enables diverse benefits to be captured on the samegrounds. Using BWS as opposed to other commonly used methods, suchas choice experiments (Kim et al., 2020), allows more flexibility for
capturing soft targets comprising many ESs for its simplicity. BWS istechnically more accessible than choice experiments comprising choice
sets, including multiple attributes and attending levels. While designingchoice sets in choice experiments requires sophisticated techniques (e.
g., D-efficient optimal orthogonal design (Holmes et al., 2017)), vali­
dated choice sets for BWS are readily available from previous studies(Louviere et al., 2015).
We consulted the managers of the study site and coastal zone man­agement experts to ensure the practicality of the ASESMM. The

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecoser.2021.101312.
References
Abson, D.J., von Wehrden, H., Baumgärtner, S., Fischer, J., Hanspach, J., Härdtle, W.,
Heinrichs, H., Klein, A.M., Lang, D.J., Martens, P., Walmsley, D., 2014. Ecosystemservices as a boundary object for sustainability. Ecol. Econ. 103, 29–37. https://doi.
org/10.1016/j.ecolecon.2014.04.012.
Aizaki, H., Nakatani, T., Sato, K., 2015. Stated Preference Methods Using R. Chapman &Hall/CRC, New York.
Allen, I.E., Seaman, C.A., 2007. Likert scales and data analyses. Qual. Prog. 40, 64–65.
Arias-Arévalo, P., Gómez-Baggethun, E., Martín-López, B., Pérez-Rincón, M., 2018.
Widening the evaluative space for ecosystem services: A taxonomy of plural values
and valuation methods. Environ. Values 27, 29–53. https://doi.org/10.3197/
096327118X15144698637513.
Arias-Arévalo, P., Martín-López, B., Gómez-Baggethun, E., 2017. Exploring intrinsic,
instrumental, and relational values for sustainable management of social-ecologicalsystems. Ecol. Soc. 22, 43. https://doi.org/10.5751/ES-09812-220443.
Avelino, J.E., Sasaki, J., Esteban, M., Salah, P., Jamero, M.L., Valenzuela, V.P., 2019.
Sustainability Evaluation of Marine Protected Areas Index (SEMPAI): A multi-criteria
decision-making method to determine the effectiveness of the El Nido-Taytay
Managed Resource Protected Area. Ocean Coast. Manag. 181, 104891 https://doi.
org/10.1016/j.ocecoaman.2019.104891.
Baral, H., Keenan, R.J., Stork, N.E., Kasel, S., 2014. Measuring and managing ecosystem
goods and services in changing landscapes: A south-east Australian perspective.
J. Environ. Plan. Manag. 57, 961–983. https://doi.org/10.1080/
09640568.2013.824872.
Bastian, O., Syrbe, R.-U., Rosenberg, M., Rahe, D., Grunewald, K., 2013. The five pillar
EPPS framework for quantifying, mapping and managing ecosystem services.
Ecosyst. Serv. 4, 15–24. https://doi.org/10.1016/j.ecoser.2013.04.003.
Berkes, F., Folke, C., 1998. Linking social and ecological systems for resilience andsustainability. In: Berkes, F., Folke, C. (Eds.), Linking Social and Ecological Systems:

12

T. Uehara et al.

Ecosystem Services 50 (2021) 101312
Folke, C., Carpenter, S.R., Walker, B., Scheffer, M., Chapin, T., Rockström, J., 2010.
Resilience thinking: Integrating resilience, adaptability and transformability. Ecol.
Soc. 15, 20. https://doi.org/10.5751/ES-03610-150420.
Folke, C., Hahn, T., Olsson, P., Norberg, J., 2005. Adaptive governance of socialecological systems. Annu. Rev. Environ. Resour. 30, 441–473. https://doi.org/
10.1146/annurev.energy.30.050504.144511.
Gu, H., Subramanian, S.M., 2014. Drivers of change in socio-ecological production
landscapes: Implications for better management. Ecol. Soc. 19, 41. https://doi.org/
10.5751/ES-06283-190141.
Guerrero, A.M., Shoo, L., Iacona, G., Standish, R.J., Catterall, C.P., Rumpff, L., de Bie, K.,
White, Z., Matzek, V., Wilson, K.A., 2017. Using structured decision-making to set
restoration objectives when multiple values and preferences exist. Restor. Ecol. 25,
858–865. https://doi.org/10.1111/rec.12591.
Haines-Young, R., Potschin, M., 2010. The links between biodiversity, ecosystem services
and human well-being. In: Raffaelli, D.G., Frid, C.L. (Eds.), Ecosystem Ecology: A
New Synthesis. Cambridge University Press, Cambridge, pp. 110–139.
Hattam, C., Atkins, J.P., Beaumont, N., Bӧrger, T., Bӧhnke-Henrichs, A., Burdon, D., de
Groot, R., Hoefnagel, E., Nunes, P.A.L.D., Piwowarczyk, J., Sastre, S., Austen, M.C.,
2015. Marine ecosystem services: Linking indicators to their classification. Ecol. Ind.
49, 61–75. https://doi.org/10.1016/j.ecolind.2014.09.026.
Himes-Cornell, A., Pendleton, L., Atiyah, P., 2018. Valuing ecosystem services from blue
forests: A systematic review of the valuation of salt marshes, sea grass beds andmangrove forests. Ecosyst. Serv. 30, 36–48. https://doi.org/10.1016/j.
ecoser.2018.01.006.
Himes, A., Muraca, B., 2018. Relational values : the key to pluralistic valuation of
ecosystem services. Curr. Opin. Environ. Sustain. 35, 1–7. https://doi.org/10.1016/
j.cosust.2018.09.005.
Holland, J.H., 1992. Complex adaptive systems. Daedalus. 121, 17–30.
Holmes, T.P., Adamowicz, W.L., Carlsson, F., 2017. Choice experiments. In: Champ, P.A.,
Boyle, K.J., Brown, T.C. (Eds.), A Primer on Nonmarket Valuation. Springer,
Dordrecht, Netherlands, pp. 133–186.
Hyogo Prefecture, 2016. Hyogo plan for the conservation of the Seto Inland Sea. Kobe
City.
Ives, C.D., Giusti, M., Fischer, J., Abson, D.J., Klaniecki, K., Dorninger, C., Laudan, J.,
Barthel, S., Abernethy, P., Martín-López, B., Raymond, C.M., Kendal, D., vonWehrden, H., 2017. Human–nature connection: A multidisciplinary review. Curr.
Opin. Environ. Sustain. 26–27, 106–113. https://doi.org/10.1016/j.
cosust.2017.05.005.
Karjalainen, E., Sarjala, T., Raitio, H., 2010. Promoting human health through forests:
Overview and major challenges. Environ. Health Prev. Med. 15, 1–8. https://doi.
org/10.1007/s12199-008-0069-2.
Kelemen, E., García-Llorente, M., Pataki, G., Martín-López, B., Gómez-Baggethun, E.,
2014. Non-monetary techniques for the valuation of ecosystem services. OpenNESS
Ref. Book. EC FP7 Grant Agreem, 308428.
Kim, H., Shoji, Y., Tsuge, T., Aikoh, T., Kuriyama, K., 2020. Understanding services fromecosystem and facilities provided by urban green spaces: A use of partial profile
choice experiment. For. Policy Econ. 111, 102086 https://doi.org/10.1016/j.
forpol.2019.102086.
Kitahara, Y., 2010. Meikyou Japanese Dictionary, 2nd ed. Taishukan, Tokyo.
Klain, S.C., Olmsted, P., Chan, K.M.A., Satterfield, T., 2017. Relational values resonate
broadly and differently than intrinsic or instrumental values, or the New Ecological
Paradigm. PLoS ONE 12, e0183962. https://doi.org/10.1371/journal.
pone.0183962.
Kleespies, M.W., Dierkes, P.W., 2020. Exploring the Construct of Relational Values: An
Empirical Approach. Front. Psychol. 11, 1–14. https://doi.org/10.3389/
fpsyg.2020.00209.
Kobe Shinbun, 2019. Is too clean not good for the Seto Inland Sea? The new water quality
standard for realizing “rich sea.” https://www.kobe-np.co.jp/news/sougou/
201912/p2_0012942544.shtml.
Ladle, R.J., Correia, R.A., Do, Y., Joo, G.J., Malhado, A.C.M., Proulx, R., Roberge, J.M.,
Jepson, P., 2016. Conservation culturomics. Front. Ecol. Environ. 14, 269–275.
https://doi.org/10.1002/fee.1260.
Langemeyer, J., Baró, F., Roebeling, P., Gómez-Baggethun, E., 2015. Contrasting valuesof cultural ecosystem services in urban areas: The case of park Montjuïc inBarcelona. Ecosyst. Serv. 12, 178–186. 10.1016/j.ecoser.2014.11.016.
Linstone, H.A., Turoff, M., 1975. The Delphi Method - Techniques and Applications.
Addison-Wesley, Boston, MA.
Lopes, R., Videira, N., 2016. A collaborative approach for scoping ecosystem services
with stakeholders: The case of Arrábida Natural Park. Environ. Manage. 58,
323–342. https://doi.org/10.1007/s00267-016-0711-5.
Louviere, J.J., Flynn, T.N., Marley, A.A.J., 2015. Best-Worst Scaling: Theory, Methods
and Applications. Cambridge University Press, Cambridge. 10.1017/
CBO9781107337855.
Lusk, J.L., Briggeman, B.C., 2009. Food values. Am. J. Agric. Econ. 91, 184–196.
Martin, D.M., Mazzotta, M., Bousquin, J., 2018. Combining ecosystem services
assessment with structured decision making to support ecological restoration
planning. Environ. Manage. 62, 608–618. https://doi.org/10.1007/s00267-0181038-1.
McFadden, D., 1973. Conditional logit analysis of qualitative choice behavior, in:Zarembka, P. (Ed.), Frontiers in Econometrics. Academic Press, New York, pp.
105–142.
Mehring, M., Ott, E., Hummel, D., 2018. Ecosystem services supply and demandassessment: Why social-ecological dynamics matter. Ecosyst. Serv. 30, 124–125.
https://doi.org/10.1016/j.ecoser.2018.02.009.
Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-Being: Synthesis.
Island, Washington, DC.

Management Practices and Social Mechanisms for Building Resilience. Cambridge
University Press, Cambridge, UK, pp. 13–20.
Blythe, J., Armitage, D., Alonso, G., Campbell, D., Carolina, A., Dias, E., Epstein, G.,
Marschke, M., Nayak, P., 2019. Frontiers in coastal well-being and ecosystemservices research: A systematic review. Ocean Coast. Manag. 105028 https://doi.
org/10.1016/j.ocecoaman.2019.105028.
Boerema, A., Rebelo, A.J., Bodi, M.B., Esler, K.J., Meire, P., 2016. Are ecosystem services
adequately quantified? J. Appl. Ecol. 54, 358–370. https://doi.org/10.1111/13652664.12696.
Böhnke-Henrichs, A., Baulcomb, C., Koss, R., Hussain, S.S., de Groot, R.S., 2013.
Typology and indicators of ecosystem services for marine spatial planning andmanagement. J. Environ. Manage. 130, 135–145. https://doi.org/10.1016/j.
jenvman.2013.08.027.
Bouwma, I., Schleyer, C., Primmer, E., Winkler, K.J., Berry, P., Young, J., Carmen, E.,Špulerová, J., Bezák, P., Preda, E., Vadineanu, A., 2018. Adoption of the ecosystemservices concept in EU policies. Ecosyst. Serv. 29, 213–222. https://doi.org/
10.1016/j.ecoser.2017.02.014.
Boyle, K.J., 2017. Contingent valuation in practice. In: Champ, P.A., Boyle, K.J.,
Brown, T.C. (Eds.), A Primer on Nonmarket Valuation. Springer, Dordrecht,
Netherlands, pp. 81–131, 10.1007/ 978-94-007-7104-8_4.
Braat, L.C., 2018. Five reasons why the Science publication “Assessing nature’s
contributions to people” (Diaz et al. 2018) would not have been accepted in
Ecosystem Services. Ecosyst. Serv. 30, A1–A2. https://doi.org/10.1016/j.
ecoser.2018.02.002.
Brown, J.D., 2011. Likert items and scales of measurement. Statistics (Ber). 15, 10–14.
Bryce, R., Irvine, K.N., Church, A., Fish, R., Ranger, S., Kenter, J.O., 2016. Subjective
well-being indicators for large-scale assessment of cultural ecosystem services.
Ecosyst. Serv. 21, 258–269. https://doi.org/10.1016/j.ecoser.2016.07.015.
Carifio, J., Perla, R., 2008. Resolving the 50-year debate around using and misusing
Likert scales. Med. Educ. 42, 1150–1152. https://doi.org/10.1111/j.13652923.2008.03172.x.
Cash, D.W., Clark, W.C., Alcock, F., Dickson, N.M., Eckley, N., Guston, D.H., Jäger, J.,
Mitchell, R.B., 2003. Knowledge systems for sustainable development. Proc. Natl.
Acad. Sci. USA 100, 8086–8091. https://doi.org/10.1073/pnas.1231332100.
Chan, K.M.A., Balvanera, P., Benessaiah, K., Chapman, M., Díaz, S., GómezBaggethun, E., Gould, R., Hannahs, N., Jax, K., Klain, S., Luck, G.W., MartínLópez, B., Muraca, B., Norton, B., Ott, K., Pascual, U., Satterfield, T., Tadaki, M.,
Taggart, J., Turner, N., 2016. Opinion: Why protect nature? Rethinking values andthe environment. Proc. Natl. Acad. Sci. 113, 1462–1465. https://doi.org/10.1073/
pnas.1525002113.
Chan, K.M.A., Gould, R.K., Pascual, U., 2018. Editorial overview: Relational values: What
are they, and what’s the fuss about? Curr. Opin. Environ. Sustain. 35, A1–A7.
https://doi.org/10.1016/j.cosust.2018.11.003.
Chaudhary, S., McGregor, A., Houston, D., Chettri, N., 2015. The evolution of ecosystemservices: A time series and discourse-centered analysis. Environ. Sci. Policy 54,
25–34. https://doi.org/10.1016/j.envsci.2015.04.025.
Cord, A.F., Bartkowski, B., Beckmann, M., Dittrich, A., Hermans-Neumann, K., Kaim, A.,
Lienhoop, N., Locher-Krause, K., Priess, J., Schröter-Schlaack, C., Schwarz, N.,
Seppelt, R., Strauch, M., Václavík, T., Volk, M., 2017. Towards systematic analyses of
ecosystem service trade-offs and synergies: Main concepts, methods and the roadahead. Ecosyst. Serv. 28, 264–272. https://doi.org/10.1016/j.ecoser.2017.07.012.
Costanza, R., 2015. Ecosystem services in theory and practice, in: Figgis, P., Mackey, B.,
Fitzsimons, J., Irving, J., Clark, P. (Eds.), Valuing Nature: Protected Areas andEcosystem Services. Australian Committee for IUCN, pp. 6–15.
Costanza, R., de Groot, R., Braat, L., Kubiszewski, I., Fioramonti, L., Sutton, P., Farber, S.,
Grasso, M., 2017. Twenty years of ecosystem services: How far have we come andhow far do we still need to go? Ecosyst. Serv. 28, 1–16. https://doi.org/10.1016/j.
ecoser.2017.09.008.
Costanza, R., de Groot, R., Sutton, P., van der Ploeg, S., Anderson, S.J., Kubiszewski, I.,
Farber, S., Turner, R.K., 2014. Changes in the global value of ecosystem services.
Glob. Environ. Chang. 26, 152–158. https://doi.org/10.1016/j.
gloenvcha.2014.04.002.
de Bekker-Grob, E.W., Donkers, B., Jonker, M.F., Stolk, E.A., 2015. Sample size
requirements for discrete-choice experiments in healthcare: a practical guide.
Patient-Patient-Centered Outcomes Res. 8 (5), 373–384.
de Groot, R.S., Alkemade, R., Braat, L., Hein, L., 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. https://doi.org/
10.1016/j.ecocom.2009.10.006.
Díaz, S., Pascual, U., Stenseke, M., Martín-López, B., Watson, R.T., Molnár, Z., Hill, R.,
Chan, K.M.A., Baste, I.A., Brauman, K.A., Polasky, S., Church, A., Lonsdale, M.,
Larigauderie, A., Leadley, P.W., van Oudenhoven, A.P.E., van der Plaat, F.,
Schröter, M., Lavorel, S., Aumeeruddy-Thomas, Y., Bukvareva, E., Davies, K.,
Demissew, S., Erpul, G., Failler, P., Guerra, C.A., Hewitt, C.L., Keune, H., Lindley, S.,
Shirayama, Y., 2018. Assessing nature’s contributions to people. Science 359,
270–272. https://doi.org/10.1126/science.aap8826.
Ellis, E.C., Pascual, U., Mertz, O., 2019. Ecosystem services and nature’s contribution topeople: Negotiating diverse values and trade-offs in land systems. Curr. Opin.
Environ. Sustain. 38, 86–94. https://doi.org/10.1016/j.cosust.2019.05.001.
Failing, L., Gregory, R., Higgins, P., 2013. Science, uncertainty, and values in ecologicalrestoration: A case study in structured decision-making and adaptive management.
Restor. Ecol. 21, 422–430. https://doi.org/10.1111/j.1526-100X.2012.00919.x.
Finn, A., Louviere, J.J., 1992. Determining the appropriate response to evidence of
public concern: The case of food safety. J. Public Policy Mark. 11, 12–25.

13

T. Uehara et al.

Ecosystem Services 50 (2021) 101312
Skourtos, M., Kontogianni, A., Harrison, P.A., 2010. Reviewing the dynamics of
economic values and preferences for ecosystem goods and services. Biodivers.
Conserv. 19, 2855–2872. https://doi.org/10.1007/s10531-009-9722-3.
Soto, J.R., Escobedo, F.J., Khachatryan, H., Adams, D.C., 2018. Consumer demand forurban forest ecosystem services and disservices: Examining trade-offs using choice
experiments and best-worst scaling. Ecosyst. Serv. 29, 31–39. https://doi.org/
10.1016/j.ecoser.2017.11.009.
Stålhammar, S., Thorén, H., 2019. Three perspectives on relational values of nature.
Sustain. Sci. 14, 1201–1212. https://doi.org/10.1007/s11625-019-00718-4.
Tyner, E.H., Boyer, T.A., 2020. Applying best-worst scaling to rank ecosystem andeconomic benefits of restoration and conservation in the Great Lakes. J. Environ.
Manage. 255, 109888 https://doi.org/10.1016/j.jenvman.2019.109888.
Uehara, T., Hidaka, T., Matsuda, O., Sakurai, R., Yanagi, T., Yoshioka, T., 2019a.
Satoumi: Re-connecting people to nature for sustainable use and conservation ofcoastal zones. People Nat. 1, 435–441. https://doi.org/10.1002/pan3.10047.
Uehara, T., Mineo, K., 2017. Regional sustainability assessment framework for integrated
coastal zone management: Satoumi, ecosystem services approach, and inclusive
wealth. Ecol. Ind. 73, 716–725. https://doi.org/10.1016/j.ecolind.2016.10.031.
Uehara, T., Niu, J., Chen, X., Ota, T., Nakagami, K., 2016. A sustainability assessment
framework for regional-scale Integrated Coastal Zone Management (ICZM)
incorporating Inclusive Wealth, Satoumi, and ecosystem services science. Sustain.
Sci. 11, 801–812. https://doi.org/10.1007/s11625-016-0373-5.
Uehara, T., Sakurai, R., Tsuge, T., 2020. Cultivating relational values and sustaining
socio-ecological production landscapes through ocean literacy: a study on Satoumi.
Environ. Dev. Sustain. 22, 1599–1616. https://doi.org/10.1007/s10668-018-02268.
Uehara, T., Tsuge, T., Onuma, A., 2019b. Applying three distinct metrics to measurepeople’s perceptions of resilience. Ecol. Soc. 24, 22. https://doi.org/10.5751/ES10903-240222.
Uehara, T., Tsuge, T., Ota, T., 2018. Long-term evolution of preferences for conservation
projects in the Seto Inland Sea, Japan: A comprehensive analytic framework. PeerJ6, e5366.
UNU-IAS, Bioversity International, IGES, UNDP, 2014. Toolkit for the Indicators ofResilience in Socio-Ecological Production Landscapes and Seascapes. United Nations
University Institute for the Advanced Study of Sustainability, Tokyo.
Walker, B., Holling, C.S., Carpenter, S.R., Kinzig, A., 2004. Resilience, adaptability andtransformability in social-ecological systems. Ecol. Soc. 9, 5.
Willits, F.K., Theodori, G.L., Luloff, A.E., 2016. Another look at Likert scales. J. Rural Soc.
Sci. 31, 6.
Wright, W.C.C., Eppink, F.V., Greenhalgh, S., 2017. Are ecosystem service studies
presenting the right information for decision making? Ecosyst. Serv. 25, 128–139.
https://doi.org/10.1016/j.ecoser.2017.03.002.
Yanagi, T., 2012. Japanese Commons in the Coastal Seas: How the Satoumi Concept
Harmonizes Human Activity in Coastal Seas with High Productivity and Diversity.
Springer Science & Business Media.
Zylstra, M.J., Knight, A.T., Esler, K.J., Le Grange, L.L.L., 2014. Connectedness as a coreconservation concern: An interdisciplinary review of theory and a call for practice.
Springer Sci. Rev. 2, 119–143. https://doi.org/10.1007/s40362-014-0021-3.

Ministry of Agriculture, Forestry and Fisheries, 2019. The fishery and aquafarming
production statistics [WWW Document]. http://www.maff.go.jp/j/tokei/kouhyou/
kaimen_gyosei/index.html (accessed 1 March 2021).
Ministry of the Environment, G. of J., n.d. Setouchi Net. https://www.env.go.jp/water/
heisa/heisa_net/setouchiNet/seto/ (accessed 1 March 2021).
Ministry of the Environment Japan, 2019. 4. Harima-Nada. Tokyo. https://www.env.go.
jp/council/09water/y0915-13/ref03_1-3.pdf (accessed 1 March 2021).
Norton, B.G., 2015. Sustainable Values, Sustainable Change: A Guide to Environmental
Decision Making. University of Chicago Press, Chicago.
Oteros-Rozas, E., Martín-López, B., González, J.A., Plieninger, T., López, C.A., Montes, C.,
2014. Socio-cultural valuation of ecosystem services in a transhumance socialecological network. Reg. Environ. Chang. 14, 1269–1289. https://doi.org/10.1007/
s10113-013-0571-y.
Potschin-Young, M., Haines-Young, R., Görg, C., Heink, U., Jax, K., Schleyer, C., 2018.
Understanding the role of conceptual frameworks: Reading the ecosystem service
cascade. Ecosyst. Serv. 29, 428–440. https://doi.org/10.1016/j.ecoser.2017.05.015.
Restall, B., Conrad, E., 2015. A literature review of connectedness to nature and its
potential for environmental management. J. Environ. Manage. 159, 264–278.
https://doi.org/10.1016/j.jenvman.2015.05.022.
Reyers, B., Biggs, R., Cumming, G.S., Elmqvist, T., Hejnowicz, A.P., Polasky, S., 2013.
Getting the measure of ecosystem services: A social-ecological approach. Front. Ecol.
Environ. 11, 268–273.
Ruckelshaus, M., McKenzie, E., Tallis, H., Guerry, A., Daily, G., Kareiva, P., Polasky, S.,
Ricketts, T., Bhagabati, N., Wood, S.A., Bernhardt, J., 2015. Notes from the field:
Lessons learned from using ecosystem service approaches to inform real-world
decisions. Ecol. Econ. 115, 11–21. https://doi.org/10.1016/j.ecolecon.2013.07.009.
Saarikoski, H., Primmer, E., Saarela, S.-R.R., Antunes, P., Aszalós, R., Baró, F., Berry, P.,
Blanko, G.G., Goméz-Baggethun, E., Carvalho, L., Dick, J., Dunford, R., Hanzu, M.,
Harrison, P.A., Izakovicova, Z., Kertész, M., Kopperoinen, L., Köhler, B.,
Langemeyer, J., Lapola, D., Liquete, C., Luque, S., Mederly, P., Niemelä, J.,
Palomo, I., Pastur, G.M., Peri, P.L., Preda, E., Priess, J.A., Santos, R., Schleyer, C.,
Turkelboom, F., Vadineanu, A., Verheyden, W., Vikström, S., Young, J., 2018.
Institutional challenges in putting ecosystem service knowledge in practice. Ecosyst.
Serv. 29, 579–598. https://doi.org/10.1016/j.ecoser.2017.07.019.
Sandifer, P.A., Sutton-Grier, A.E., Ward, B.P., 2015. Exploring connections among
nature, biodiversity, ecosystem services, and human health and well-being :Opportunities to enhance health and biodiversity conservation. Ecosyst. Serv. 12,
1–15. https://doi.org/10.1016/j.ecoser.2014.12.007.
Schlüter, M., Hinkel, J., Bots, P.W.G., Arlinghaus, R., 2014. Application of the SES
framework for model-based analysis of the dynamics of social-ecological systems.
Ecol. Soc. 19, 36. https://doi.org/10.5751/ES-05782-190136.
Schulz, C., Martin-ortega, J., 2018. Quantifying relational values — why not? Curr. Opin.
Environ. Sustain. 35, 15–21. https://doi.org/10.1016/j.cosust.2018.10.015.
Schröter, M., Stumpf, K.H., Loos, J., van Oudenhoven, A.P.E., Böhnke-Henrichs, A.,
Abson, D.J., 2017. Refocusing ecosystem services towards sustainability. Ecosyst.
Serv. 25, 35–43. https://doi.org/10.1016/j.ecoser.2017.03.019.

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