RESEARCH ARTICLE

Integrating supply and demand in ecosystem service bundlescharacterization across Mediterranean transformedlandscapes

Cristina Quintas-Soriano . Marina Garcıa-Llorente . Albert Norstrom .

Megan Meacham . Garry Peterson . Antonio J. Castro

Received: 4 March 2018 / Accepted: 20 April 2019

� Springer Nature B.V. 2019

Abstract

Context Humans continually transform landscapes,

affecting the ecosystem services (ES) they provide.

Thus, the spatial relationships among services vary

across landscapes. Managers and decision makers

have access to a variety of tools for mapping

landscapes and analyzing their capacity to provide

multiple ES.

Objectives This paper characterizes and maps ES

bundles across transformed landscapes in southeast

Spain incorporating both the ecological and social

perspectives. Our specific goals were to: (1) quantify

ES biophysical supply, (2) identify public awareness,

(3) map ES bundles, and (4) characterize types of ES

bundles based on their social-ecological dimensions.

Methods Biophysical models and face-to-face social

surveys were used to quantify and map ES bundles and

explore the public awareness in a highly transformed

Mediterranean region. Then, we classified ES bundles

into four types using a matrix crossing the degree of

biophysical ES supply and the degree of social

awareness.

Results Results mapped seven ES bundles types

representing diverse social-ecological dynamics. ES

Electronic supplementary material The online version ofthis article (https://doi.org/10.1007/s10980-019-00826-7) con-tains supplementary material, which is available to authorizedusers.

C. Quintas-Soriano (&)

Social-Ecological Interactions in Agricultural Systems

Lab, Faculty of Organic Agricultural Sciences, University

of Kassel, Steinstraße 19, 37213 Witzenhausen, Germany

e-mail: cristina.quintas@uni-kassel.de

C. Quintas-Soriano � A. J. CastroBiology and Geology Department, Andalusian Center for

the Assessment and Monitoring of Global Change

(CAESCG), University of Almeria, 04120 Almerıa, Spain

e-mail: acastro@ual.es

C. Quintas-Soriano

FRACTAL Association, San Remigio 2, 28022 Madrid,

Spain

M. Garcıa-Llorente

Social-Ecological Systems Laboratory, Department of

Ecology, Universidad Autonoma de Madrid,

28049 Madrid, Spain

e-mail: marinaglloren@gmail.com

M. Garcıa-Llorente

Applied Research and Agricultural Extension Department,

Madrid Institute for Rural, Agricultural and Food

Research and Development (IMIDRA), Finca

Experimental el Encın, 28800 Alcala De Henares, Madrid,

Spain

A. Norstrom � M. Meacham � G. PetersonStockholm Resilience Center, Stockholm University,

Kraftriket 2B, SE-10691 Stockholm, Sweden

e-mail: albert.norstrom@su.se

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https://doi.org/10.1007/s10980-019-00826-7(0123456789().,-volV)( 0123456789().,-volV)

bundles mapped at the municipality level showed

mismatches between their biophysical provision and

the public awareness, which has important implica-

tions for operationalizing the bundles concept for

landscape planning and management. ES bundles

characterization identified four types of bundles

scenarios.

Conclusions We propose an ES bundles classifica-

tion that incorporates both their social and ecological

dimensions. Our findings can be used by land man-

agers to identify areas in which ES are declining as

well as priority areas for maximizing ES provision and

can help to identify conflicts associated with new

management and planning practices.

Keywords Dryland � Public awareness � Spain �Social-ecological system � Synergy � Trade-off

Introduction

Humans have transformed most of the Earth’s land

surface (Foley et al. 2005; Ellis 2011). While these

land transformations vary from place to place, their

effect has become a major driver of change at the

global scale. One major consequence is the spatial

shifts in both the supply of ecosystem services (i.e.,

benefits that people obtain from ecosystems) and the

beneficiaries of those services (Eigenbrod et al. 2011;

Castro et al. 2018a). For example, as more of the

Earth’s surface is converted into simplified and

intensive production landscapes there is a growing

spatial disconnect between the location of crop

production and where it is consumed (D’Odorico

et al. 2014; MacDonald et al. 2015). In the Mediter-

ranean region, the major land transformations are

urbanization, agriculture intensification, and rapid

changes in tourism activities and developments (Ni-

eto-Romero et al. 2014; Vigl et al. 2017). These

transformations have produced trade-offs among

ecosystem services [e.g., intensive agriculture pro-

duction from greenhouses and water supply coming

from groundwater and aquifer recharge (Quintas-

Soriano et al. 2014)], which result in conflicts among

stakeholder groups across different landscapes. The

concept of ecosystem services (ES) has been adopted

by managers and policy-makers worldwide as a way to

communicate the dependence of human well-being on

the benefits ES (Bastian et al. 2012). Nowadays,

managers and decision makers have access to a variety

of tools for mapping multiple ES or ecosystem service

bundles (Bennett et al. 2009; Balvanera et al. 2017).

The analysis of ES bundles across landscapes allows

for the inclusion and visualization of multiple ES

simultaneously, reflecting the multifunctionality of

landscapes and reducing the risk of policy failure by

focusing on inherently synergistic bundles of ES,

rather than individual ES (Turkelboom et al. 2018).

The concept of ES bundles emerged as one way to

understand relationships among multiple ES, in order

to avoid unwanted trade-offs and take advantage of

synergies between these services (Bennett et al. 2009).

The first quantitative studies of ES bundles defined

them as a set of ES that repeatedly appear together

across space or time (Raudsepp-Hearne et al. 2010).

Since then, several studies have used this biophysical

and spatio-temporal definition and identified ES

bundles in different landscapes (e.g., Maes et al.

2012; Turner et al. 2014; Crouzat et al. 2015; Derkzen

et al. 2015; Renard et al. 2015; Queiroz et al. 2015;

Yang et al. 2015). A second, and more recent,

approach to identify ES bundles has focused on the

socio-economic dimensions that can mediate relation-

ships between different ES (Plieninger et al. 2013;

Hicks and Cinner 2014). For example, Martın-Lopez

et al. (2012) explored the characterization of ES

bundles in Spain by assessing the socio-cultural

preferences of different stakeholder groups toward

different ES. Similarly, Hamann et al. (2015) used

national census data on the direct use of six provi-

sioning services to map ES bundles across South

Africa. However, despite this progress only one

previous study has evaluated ES bundles from a

supply–demand perspective (Baro et al. 2017) and no

study has simultaneously assessed and related the

biophysical and social dimensions of ES bundles in a

given landscape using social perceptions. A better

understanding of how ES bundle dynamics are

M. Meacham

e-mail: megan.meacham@su.se

G. Peterson

e-mail: garry.peterson@su.se

A. J. Castro

Social-Ecological Research Laboratory, Department of

Biological Sciences, Idaho State University, Pocatello,

ID 83209, USA

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influenced by the biophysical, ecological and social

components over space and time will improve our

ability to manage ES sustainably (Bennett et al. 2015).

For example, identifying spatial mismatches between

areas of ES bundle supply and demand—e.g., along

rural–urban gradients—can help guide decision mak-

ers on which areas need special conservation strategies

and at what institutional scales ES need to be

managed.

This paper characterizes and maps ES bundles

across transformed landscapes in southeast Spain by

incorporating both ecological and social dimensions.

Our specific goals are to: (i) quantify the ES biophys-

ical supply by examining ES diversity patterns and

mapping hot and cold spots of ES, (ii) identify the

public awareness by exploring the social importance

regarding ES, (iii) characterize and map ES bundles,

their synergies and mismatches using both the eco-

logical and social dimension, and (iv) classify types of

ES bundles based on their social-ecological

dimensions.

Methods

Study area

Spanish semi-arid ecosystems are among the driest

regions in continental Europe and have experienced

different types of land use transformations (Armas

et al. 2011). The most dramatic and well-known land

use change has been the rapid transition toward

intensive greenhouse horticulture, hosting the largest

concentration of greenhouses in the world (approx.

26,750 ha), also known as, ‘‘The Plastic Sea of

Almerıa’’ (Aznar-Sanchez et al. 2011) (Fig. 1a). This

specific transformation has been linked to severe

ecological, economic and socio-cultural consequences

(Aznar-Sanchez et al. 2011; Munoz-Rojas et al. 2011;

Requena-Mullor et al. 2018). Additionally, the main

job source in the area is the agribusiness sector, so the

population growth due to agriculture expansion has

promoted urban expansion and is accompanied by

massive tourism along the coast. However, this region

has also witnessed a large increase in the number and

size of protected areas (from 4% of the surface in 1980

to 20% in 2016) (Quintas-Soriano et al. 2016; Castro

et al. 2018b).

Ecosystem services mapping

Seven ES were spatially quantified and mapped,

including both intermediate and final services (Fisher

et al. 2009). Intermediate ES are the result of complex

interactions between ecosystem structure and pro-

cesses (e.g., water recharged by aquifers). Final ES

(e.g., food production) are produced from the combi-

nation of intermediate ES with other forms of capital

to provide benefits to human welfare (Castro et al.

2015). Intermediate ES included climate regulation,

water regulation, soil protection and habitat quality,

and final ES were food from traditional agriculture,

food from intensive agriculture and tourism. ES were

selected based on their importance in the study area,

data availability and their implication for the welfare

of local communities (Garcıa-Llorente et al. 2012;

Castro et al. 2014, 2015; Quintas-Soriano et al.

2014, 2016; Requena-Mullor et al. 2018). Table 1

and Online Appendix A summarizes the indicators,

data sources and models used to measure the ES.

Social perceptions of ecosystem services

The social importance of six ES was assessed by

conducting face-to-face surveys across the study area.

To test the suitability of the questionnaire design, we

conducted a preliminary survey in the study area

(Martın-Lopez et al. 2012; Castro et al. 2016a). We

randomly interviewed local respondents in the streets

compiling a total of 441 face-to-face valid question-

naires in 2012 at 26 sampling sites across the study

area (Fig. 1b). Individuals were sampled from the

population to encompass a wide range of the local

citizenry (e.g., residents, environmental experts, or

business workers) (Castro et al. 2016b). The sampling

frame was restricted to individuals over 18 years old.

We had no contact with any of the respondents prior to

the survey. The questionnaires included information

regarding: (1) respondent’s relationship with the study

area, (2) respondent’s perception of the importance of

ES for human well-being, (3) respondent’s perception

toward land use types, and (4) respondent’s general

environmental awareness as well as socio-demo-

graphic information. In this study, we used the data

of the second section to evaluate how local public

perceived the importance of ES. Respondent were also

asked to select the four most important ES from a total

of eight ES previously determined as important for the

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local public in the study area: traditional agriculture,

intensive agriculture, climate regulation, air quality,

water regulation, soil protection, tourism and local

identity (Online Appendix B). We estimated the

importance perceived for a given ES as the number

of respondents that selected that service as one of their

four most important ES. For this study, we only

employed the data for those services that were also

mapped with the biophysical models. Habitat quality

was not included in the survey because it was not

initially selected as an important service for the local

public in the study area.

Spatial scale and data analysis

ES delivery was assessed at the municipality level,

because this represents the smallest administrative

boundary for land management in Spain (Raudsepp-

Hearne et al. 2010; Queiroz et al. 2015). The case

study included 160 municipalities with a median size

of 75.48 km2, and that ranged in size from 1.5 to

600 km2 (See Fig. 1) (Quintas-Soriano et al. 2014;

Requena-Mullor et al. 2018). ES supply was first

averaged and standardized by the total area of each

municipality and then normalized to enable compar-

ison (Castro et al. 2014, 2015). The diversity of ES

present in each municipality was assessed using the

Simpson’s index (Oksanen et al. 2018). Each ecosys-

tem service value was normalized to be between 0 and

1 and each ES was given equal weight. Each

municipality was assessed using the same set of

ecosystem services. The Simpson’s index is a measure

of evenness and calculates where there is a more even

presence of all the ES verses where a municipality is

Fig. 1 Location of the case study delimiting the arid and semi-

arid ecosystems of Iberian Peninsula: a land use types

represented with different colors across 160 municipalities,

b population density across municipalities and sampling points

of the survey. Areas excluded within the study area boundaries

correspond with the highest mountain ranges that are not arid

and semi-arid ecosystems

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dominated by only one or a few ES. A municipality

supplying multiple ES would score higher than a

municipality only considered to provide habitat, for

example. Higher values indicate a more diverse set of

ES present in that municipality.We categorized the ES

into two categories, final ES and intermediate ES. We

then calculated the average supply of these two

categories of ES across all the municipalities. We

calculated the average supply of the two categories of

ES for the region as a whole and mapped each

municipalities’ residual from the regional average.

These residuals represent ‘‘hot’’ (higher production)

and ‘‘cold’’ (lower production) spots. Interactions

among ES were analyzed using a principal component

analysis (PCA) and cluster analysis. PCA identified

the main explanatory factors of the variability and

distribution of the ES across the municipalities.

Cluster analysis identified groups of municipalities

with similar provision levels of ES, or ecosystem

service bundle types. K-means clustering was used to

identify distinct types of bundles and the selection of

the number of clusters was based on cluster robustness

and knowledge of the social-ecological system (Raud-

sepp-Hearne et al. 2010; Queiroz et al. 2015). We used

flower diagrams to visualize biophysical ES bundles.

All bundles were calculated from normalized values

obtained for each service, and the area of flower petals

represents ES supply.

We determined the percentage of respondents who

valued the social importance for each ES, estimating

the number of respondents who chose each ES as

important. In addition, we estimated the social

importance for each ES within each bundle by

calculating the average value for the set of munici-

palities included in each bundle. This information is

presented using flower diagrams for each bundle.

Finally, following Iniesta-Arandia et al. (2014), we

classified ES bundles into four types using a matrix

crossing the degree of biophysical ES supply and the

degree of social awareness. We expressed values for

each bundle using scatter plots for final and interme-

diate ES categories. We calculated the median of each

axis and used it as the cut values to decide which ES

bundles were perceived as highly important or having

Table 1 List of ecosystem services assessed, indicators and units

Ecosystem services

classification

Ecosystem

service type

Biophysical indicator or proxy Measure units Data source

Provisioning

Food from traditional

agriculture

Final Food production from traditional

agriculture practices

Tm/ha per year Statistical data from

Spanish department of

agriculture

Food from intensive

agriculture

Final Food production from greenhouses Tm/ha per year Statistical data from

Spanish department of

agriculture

Regulating

Climate regulation Intermediate Carbon sequestration Tm/ha per year InVEST, carbon

sequestration: climate

regulation model

Soil protection Intermediate Capacity of vegetation cover to soil

conservation and to avoid soil erosion

Tm/ha per year Andalusian department of

environment. USLE soil

equation

Water regulation Intermediate Groundwater recharge and water

supplied by reservoirs

hm3/ha per year APLIS model

Habitat quality Intermediate Spatial extent and quality of habitat for

the conservation of biodiversity

km2 InVEST, habitat quality

model

Cultural

Tourism Final Number of annual person-days of

photographs uploaded to the photo-

sharing website flickr

km2 InVEST, visitation:

recreation and tourism

model

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high biophysical supply. We used QGIS 2.12.1 to map

the spatial distribution of ES and R to conduct all

spatial analysis and produce all the figures (R Core

Team 2018).

Results

Ecosystem service supply

The provision of each ES showed distinct spatial

patterns across the study area (Fig. 2). Food produc-

tion from cultivated crops (both intensive and tradi-

tional agriculture) was delivered in distinct areas.

Food production from traditional agriculture (i.e., non-

greenhouse agriculture) was distributed in more

central and northern areas characterized by higher

altitude, irregular surfaces and slopes, corresponding

with household and small-scale agricultural activities

(called ‘‘minifundios’’). Food from intensive agricul-

ture (i.e., greenhouses) was mainly produced in

southern flat areas. Intermediate ES supply had a

markedly different spatial pattern. Climate regulation

was distributed extensively across the study area,

exhibiting the highest values in the south-west area. In

contrast, soil protection was mainly located in the

south-west, overlapping with the maximum values of

climate regulation. Maximum water regulation values

were located in the southwest and northwest parts of

the study area, in areas where recharge is promoted

and aquifers are located, whereas the southeast areas

offered the lowest values. Habitat quality presents

higher values in internal areas, concentrated in areas

with low population density. Finally, tourism was

primarily located in the more densely populated

municipalities along the coast (Fig. 2; See Online

Appendix A).

We found differences between the average ES

supply and distributions of hot and cold spots for final

and intermediate ES. Final and intermediate ES

showed opposite distributions (Fig. 3a, b). Final ES

were mainly located in the south coastal municipalities

(distributed in 45 municipalities). Hot spots of final ES

covered urban municipalities in coastal areas that are

Fig. 2 Distribution of the values obtained for individual ecosystem services across 160 municipalities in the study area. Darker shades

of blue represent a higher provision of the service. (Color figure online)

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densely populated and contain intensive agriculture

production. Conversely, intermediate ES covered a

greater number of municipalities (88 municipalities),

excluding most of the coastal municipalities. Hot spots

of intermediate ES were mainly located in the northern

and southwest areas. Most municipalities showed

varied sets of ES with high values of diversity across

the entire region (Fig. 3c). The lowest values of ES

diversity were mainly located in the north, corre-

sponding with the lowest values of food from tradi-

tional agriculture, as well as the densely populated

coastal municipalities. The highest values tended to be

located in municipalities in southern and eastern

coastal areas. Most of these municipalities presented

low population density and hot spots of intermediate

services.

Respondents and perceptions of ecosystem service

importance

The respondents (n = 441) reflected the average

characteristics of the local population for our study

site in terms of age, education, income, and gender

(See Online Appendix C). A slightly larger proportion

of men were surveyed (54.88% of male respondents

vs. 45.12% of women respondents). The vast majority

of respondents interviewed were local residents

(78.68%), while 21.32% constituted expert respon-

dents. 75% of respondents were younger than

50 years. Most of the respondents showed a high

level of education (42.63% with a university degree)

and mid-low levels of income (68.75% of respondents

with an income lower than 1500 euros per month).

Almost 30% of respondents had professional jobs

(e.g., science and engineering professionals, health

professionals, teaching professionals or business and

Fig. 3 a Average supply, b hot and cold spots, and c diversityvalue of ES across 160 municipalities. Services diversity was

calculated by a derivation of the original Simpson’s diversity

index. Hot spots (represented by an increasing gradient of red)

are municipalities with a high delivery of a given type of service

relative to the region as a whole, while cold spots (represented

by a decreasing gradient of blue) are municipalities where the

delivery of a given type of service is relatively low. (Color

figure online)

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administration professionals) and almost 20% of them

had a job in the service and sales sector. 30% of

respondents exhibited a more localized sense of place,

indicating a primary affinity for their own city and

regional areas (25.4% and 39.23% respectively),

whereas 26.53% reported a sense of place correspond-

ing to the national scale. Finally, proactive environ-

mental behavior (measured by membership in an

environmental association) was low (3.7%), but

respondents indicated that they usually visit natural

protected areas (81.63%) (Online Appendix C).

Overall, respondents identified final ES (i.e.,

tourism and traditional agriculture) as the most

important services for human well-being in the study

area (Fig. 4). Tourism was the ES considered by the

local public as the most important (74. 83% respon-

dents chose it from the ES panel; Online Appendix B)

followed by traditional agriculture (61.90%). Respon-

dents recognized intermediate ES (e.g., water regula-

tion and climate regulation) with a medium level of

importance (44.90% and 44.67% of respondents

respectively). A smaller number of respondents

selected intensive agriculture and soil protection

services (41.50% and 35.60% respectively), indicating

that they were considered less important by the public.

Ecosystem service bundles

The PCA analysis identified two gradients that

explained the variation of ES across the study area

(Online Appendix D). The first PCA component

explained 28% of the variance and corresponds to a

gradient of population density, from high to low, and

with a gradient of altitude, from low-lying coastal

areas to more mountainous-forest inland areas with

higher altitudes. Coastal areas are urban and have a

higher population density, while inland areas are more

rural and have less populated municipalities. The

second PCA component, 23% of the variance, corre-

sponds to a gradient from urban and agricultural areas

describing areas supplying final services, to more

natural areas with a greater supply of intermediate

services. Cluster analysis grouped the 160 municipal-

ities into seven groups of ES bundles (Fig. 5). The

number of municipalities present in each group varied

from 4 to 64. We named these bundle groups based

upon the dominant ES and land cover.

Traditional mosaic croplands (bundle 1, n = 14

municipalities; Fig. 5) grouped traditional agricultural

areas where is located crops with the highest annual

yield (See Figure A2 in Online Appendix A). Within

this bundle medium levels of other final ES (e.g.,

intensive agriculture and tourism) were supplied.

Tourism, soil protection and climate regulation were

perceived with high levels of importance, while

intensive agriculture was perceived as the least

important service. Urban touristic areas (bundle 2,

n = 5 municipalities) corresponded with highly pop-

ulated urban municipalities and with more recreational

opportunities related to beach and coastal tourism.

Within this bundle, tourism and intensive agriculture

were considered as the most important ES by locals.

Fig. 4 Level of importance

perceived towards

ecosystem services across

the study area given by

locals’ respondents

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Balanced multifunctional areas (bundle 3, n = 5

municipalities) grouped medium values of supply of

intermediate ES and higher values of social percep-

tions toward final ES (tourism and traditional agricul-

ture). Climate regulating areas (bundle 4, n = 13

municipalities) grouped municipalities with high val-

ues of intermediate ES supply, especially climate

regulation. Final ES exhibited low supply levels in this

bundle, and low-levels of perceived importance.

Habitat and water regulation conservation areas

(bundle 5, n = 21 municipalities) covered scrub-

grassland, forest and mosaic areas with agricultural

lands corresponding with regions with high average

altitude. These areas exhibited high levels of interme-

diate ES supply. We have no information regarding

the perceived values of the ES in this bundle because

no survey respondents inhabitant municipalities

within this bundle. Greenhouses production (bundle

6, n = 4 municipalities) covered municipalities with

greenhouses, specifically located in the ‘‘Campo de

Dalıas’’ (Fig. 5). This area is located in a coastal

platform with low altitude and above the ‘‘Sierra de

Gador’’ aquifer that allow the provision of water for

the production of horticulture. Respondents associated

with this bundle recognized intensive agriculture and

tourism as the most important ES. Soil protection was

generally perceived as not important.Habitat quality

areas (bundle 7, n = 64 municipalities) included

municipalities in depopulated rural areas covering

scrublands, forest and agricultural areas, with high

level of habitat quality, and medium levels of others

intermediate ES. Traditional agriculture and tourism

were considered as the most important ES, while

intensive agriculture as the least important service.

Ecosystem service bundles characterization:

matches and mismatches between the supply

and demand

ES bundles were characterized using their biophysical

supply level and public awareness (i.e., social impor-

tance) (Fig. 6). The characterization distinguished

between final and intermediate ES. We identified four

types of bundles configurations: (1) coupled bundles

as those with a high level of ES supply and ES public

awareness, (2) bundle mismatch type A as bundles

Fig. 5 ES bundles identified by k-means clustering for the

study area. N is the number of municipalities grouped in each

bundle. Flower diagrams show the biophysical supply and social

perceptions (importance perceived) for each ecosystem service

within each bundle. The flower diagrams are dimensionless, as

they are based on normalized data for each service, and a higher

surface area of a petal indicates the higher production and

perception of a particular service. Flower diagrams are

comparable among bundles inside biophysical supply or

importance perceived, but not between them

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with a high supply level but low public awareness, (3)

bundle mismatch type B as bundles with high public

awareness and low levels of ES supply, and (4) bundle

mismatch type C as bundles with low levels of both

public awareness and supply level (Fig. 6). Two

bundles were identified as coupled bundles for both

supply and demand of ES (i.e., bundle 2 for final ES

and bundle 1 for intermediate ES). However, most

bundles were identified in the quadrant describing the

mismatch type A (i.e., high levels of biophysical

supply but a low level of public awareness): bundles 1

and 6 for final ES, and bundles 2, 3, 4 and 7 for

intermediate ES. Only final ES bundles were identified

as mismatch type B (i.e., bundles 4 and 7 for final ES).

These areas indicate low values of biophysical supply

and high values of social awareness. Finally, bundle

mismatch type C, indicating low levels of both public

awareness and supply level, were found for bundle 3

for final ES, and bundle 6 for intermediate ES. It is

important to note that no data was collected about ES

social importance in bundle 5 (for both final and

intermediate ES). This bundle is defined only with

ecological data, with a high supply of habitat quality

and water regulation services, thereby it’s not possible

to develop a bundle characterization.

Discussion

Understanding the spatial distribution of ES bundles

across transformed landscapes is critical for the

success of landscape management and planning

(Opdam 2013). This study offers a comprehensive

approach for understanding the distribution of ES

bundles, including both the social and biophysical

factors that shape them. Our approach illustrates both

which ES are more or less abundant across specific

landscapes, as well as the level of public awareness

across municipalities. This combination defines the

social-ecological context that managers need to

incorporate into land decision-making.

Spatial distribution and ecosystem service bundles

characteristics

Spatially explicit mapping exercises are considered

one of the tools for ES planning and implementation in

decision-making (Goldstein et al. 2012; Bateman et al.

2013). This study proposes an analysis of ES bundles

and characterization incorporating the biophysical

factors that defines their supply as well as the public

awareness across administrative boundaries. Different

patterns were identified based on the spatial distribu-

tion of ES categories. For instance, final services (i.e.,

food from intensive agriculture, food from traditional

agriculture and tourism) were mainly localized in

municipalities along the coastline, whereas interme-

diate services (i.e., water regulation or carbon seques-

tration) were scattered across inland municipalities

(Fig. 2). The spatial supply of final and intermediate

ES was also identified by the ES hot and cold spots

analysis. The supply of intermediate ES is driven by

ecological features and management practices within

specific landscapes (Reyers et al. 2013; Queiroz et al.

Fig. 6 ES bundles characterization based on the supply level and public awareness for final and intermediate ES

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2015), while the supply of final ES is mainly

associated with intensive management practices as a

result of high social demand and population growth in

coastal areas (Castro et al. 2014). For instance, the

social demand for tourism is linked with urban and

highly populated areas, as has been shown in previous

studies in Spain (Martın-Lopez et al. 2012) and

Denmark (Turner et al. 2014). This may be explained

by the type of tourism studied which is related to sun

and beaches. This is the opposite of the more rural

ecotourism located on internal areas in our case study

(Garcıa-Llorente et al. 2012, 2016; Quintas-Soriano

et al. 2016).

Similarly, food production from greenhouse horti-

culture shows a high spatial clustering defined as

agribusiness and associated with increasingly static

productivity, increasing capacity for innovation and

the stimulation of new business formations (Porter

2000; Schouten 2011). This landscape exhibits the

largest concentration of greenhouses in the world, that

produce and export vegetables to respond to the high

levels of national and international demand (Aznar-

Sanchez et al. 2011; Egea et al. 2017), representing

19% of the local economy (Sanchez-Picon 2005). The

high spatial clustering can be explained by the

suitable topographical conditions of the area, and

mirrors previous studies that show the predominance

of agriculture in certain areas with flat topography

(Raudsepp-Hearne et al. 2010; Turner et al. 2014).

Furthermore, the greenhouse production also benefits

from the groundwater supplies of an aquifer system

located in nearest mountains (Vallejos-Izquierdo et al.

2008; Quintas-Soriano et al. 2014; Castro et al. 2015).

Our results show that greenhouse horticulture produc-

tion does not co-occur with other ES supply, high-

lighting the negative impact or trade-offs on other ES

such as water regulation or habitat quality (e.g., see

Castro et al. 2015; Quintas-Soriano et al. 2016)

(Fig. 2).

Food production from traditional agriculture was

distributed more widely across less populated rural

areas, and associated to intermediate ES. This type of

agriculture is related to subsistence farming practices

at small scales. Cultural services such as the main-

taining of local breeds and varieties, local identity,

local ecological knowledge, and regulating services

such as water regulation and erosion control are often

linked to traditional agriculture (Sayadi et al. 2009;

Garcıa-Llorente et al. 2012).

We found the lowest ES diversity in the north of the

study area, coinciding with the lowest supply of

traditional agriculture, mainly in areas affected by

rural abandonment. Previous studies showed that the

study area traditionally had a high agrarian tradition

and currently is in an abandonment process (Fischer

et al. 2012; Iniesta-Arandia et al. 2015). Moreover,

this region has promoted subsistence agriculture

practices with very limited production for self-suffi-

ciency of the local residents of the mountains and rural

areas. However, in accordance with other studies

(Sayadi et al. 2009; Quintas-Soriano et al. 2016), our

results highlight food from traditional agriculture as

the second most important ES. Garcıa-Llorente et al.

(2012) also showed the importance of traditional

agriculture due to its role in the supply of cultural

services such as aesthetic values, local identity and

local ecological knowledge, and regulating services,

such as erosion control, hydrological regulation and

water quality.

Bridging the gap between ecological and social

perspectives in ES management

Efficient and sustainable ES management requires a

detailed knowledge about drivers and underlying

mechanisms of ES trade-offs and synergies (Bennett

et al. 2009), as well as the incorporation of social

values of local public. To date, there have been few

attempts to merge ecological (i.e., supply) and socio-

cultural (i.e., demand) dimensions in an ES bundle

analysis (i.e., see Baro et al. 2017 and Dittrich et al.

2017). This study presents a novel approach by

incorporating both ecological and social factors that

shape the supply and demand of ES bundles. However,

this integrating represents both a methodological

challenging, but also a limitation in order to evaluate

the validity if the assessment. One of the limitations

found here is in combining biophysical and social

factors to define ES bundles. The spatial scale used in

the analysis of ES bundles (i.e., municipality level)

(Raudsepp-Hearne et al. 2010; Renard et al. 2015;

Queiroz et al. 2015) requires normalizing and stan-

dardizing ES supply, resulting in loss of spatial

accuracy. We advanced on this limitation by using

GIS models (e.g., InVEST) instead of using proxies of

ES supply (Raudsepp-Hearne et al. 2010; Queiroz

et al. 2015), however, the averaging and standardiza-

tion process of ES supply at the municipality level

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Landscape Ecol

remains a methodological challenge that requires a

solution and new protocols (Carpenter et al. 2009;

Martın-Lopez et al. 2012; Castro et al. 2014). Also, the

difficulty to incorporate two different-nature data (i.e.,

ecological and social) promote a gap regarding the

scale level used: social perceptions are linked to

individuals of the local public, and this information is

difficult to translate to a specific spatial scale. More-

over, social perceptions can be identified at the same

time for a specific landscape unit, land use or a whole

region. Because of all that, to homogenize and

translate all this complexity to a similar spatial unit

that ecological data, makes the methodology as one of

the major challenges in the transdisciplinary research

(Castro et al. 2014; Quintas-Soriano et al.

2014, 2018a, b).

The four types of ES bundles identified exemplified

the complexity of social and ecological factors that

operate in transformed landscapes. Only two bundles

(i.e., bundle 2 for final ES, and bundle 1 for

intermediate ES) were characterized as ‘‘coupled

bundles’’ where the supply level and public awareness

were both high. In addition, three types of mismatched

bundles were found corresponding with areas where

the ES supply and public awareness showed different

levels of de-coupling. For instance, medium and high

values of ES supply with low public awareness,

mismatch type A, (Fig. 6) demonstrates a need for

increased of social awareness of those ES. We found

that most bundles were identified within this category.

For instance, intermediate services such as water

regulation, climate regulation and soil protection were

found with the lowest perceived importance. These

results highlight the difficulty of the local public to

recognize the value of regulating services. This is

especially true in arid and semi-arid landscapes where

extremely arid lands are linked to unproductive

ecosystems and thus, are undervalued by the general

public (Castro et al. 2011; Lopez-Rodrıguez et al.

2015; Rodrıguez-Caballero et al. 2018). Those results

highlight that more effort is needed by policymakers

and researchers to raise awareness about the key role

of arid landscapes in the provision of intermediate

services. Our results also show how different areas

(defined by ES bundles) showed different levels of

social importance of ES, which emphasizes that

stakeholders’ perspectives are influenced by social,

economic, institutional, and ecological factors

(Turkelboom et al. 2018). Therefore, we argue that

better understanding of stakeholders’ preferences for

ES should be at the core of ES bundle analysis.

Actions in management and decision-making must

recognize the importance of incorporating the public

awareness for conservation strategies. The type B of

bundle mismatch is characterized by medium–high

levels of social awareness and low ES supply. As

previously shown by Anderson et al. (2015), this

bundle type highlights that the value (demand) placed

on a service is not necessarily connected to the

quantity (supply) of the service. In our case study, this

bundle type was localized in internal rural areas

affected by rural abandonment and the loss of

traditional agriculture practices. This result is consis-

tent with the trend of urban expansion observed in

Spain over the last four decades (Garcıa-Llorente et al.

2016). Consequently, we call to implement actions

that promote public participation in environmental

planning and decision-making (Miller 2005) and to

reconnect urban dwellers with rural areas (Garcıa-

Llorente et al. 2016). Further, there is a need to

combine formal knowledge with historically and

culturally consolidated experiential knowledge, tradi-

tions and practices to address the complexity of

ecosystem and biodiversity conservation objectives

(Martın-Lopez and Montes 2015). Finally, the type C

of bundle mismatch describes areas with low ES

supply and low social awareness. Bundles 3 for final

ES and 6 for intermediate ES were in this category. For

instance, the greenhouse production bundle represents

an area with low values of intermediate services where

there is also a lack of public awareness, which

emphasizes the need for raising social awareness

regarding the invisible processes that maintain multi-

functional landscapes. As pointed out by Castro et al.

(2014), our results are consistent with the need of

preserving intermediate ES (e.g., aquifer recharge) to

maintain the provision of final ES (e.g., food produc-

tion). Our study identified ES bundles with different

levels of decoupling, highlighting the need of new

land management strategies that address those mis-

matches in order to achieve a more sustainable use of

ES. It is clear that new strategies to increase social

awareness and reverse the bundles mismatches found

are needed. For instance, some key recommendations

are the promotion a culture of shared responsibility

among multiple stakeholders to launch real action at

the administration level for specific environmental

problems, and train and work more closely with

123

Landscape Ecol

managers and decision-makers to integrate the ES

framework and specifically the ES bundle approach

into the day-to-day management of natural spaces

(Quintas-Soriano et al. 2018a). Understanding and

characterizing ES bundles is a key challenge for

implementing ES approaches in land management and

planning. Our study demonstrates the importance of

integrating biophysical and socio-cultural dimensions

when mapping ES bundles. Our findings can be used

by land managers to identify areas in which ES are less

supply and low perceive as well as priority areas for

maximizing ES provision, and can help to identify

conflicts associated with new management and plan-

ning practices (Castro et al. 2018a). Incorporating both

ecological and social dimensions in the assessment of

ES brings ES trade-offs and bundle analysis closer to

the real-world realities spatial planners and decision-

makers are working with.

Acknowledgements Funding for the development of this

research was provided by the Andalusian Center for the

Assessment of Global Change (CAESCG) (GLOCHARID

project). C.Q.S. was funded by a grant from the University of

Almeria to perform a short research stay at the Stockholm

Resilience Centre to develop the present study. A.J.C. was

partially funded by the NSF Idaho EPSCoR Program and by the

National Science Foundation under award number IIA-

1301792. A grant from the Spanish National Institute for

Agriculture and Food Research and Technology (INIA), which

is co-funded by the Social European Fund provided support for

M.G.L. Special thanks for JuanMiguel Requena for his valuable

help and advices about R. This research is a contribution to the

Programme on Ecosystem Change and Society (www.pecs-

science.org).

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