An expert-based reference list of variables for characterizing and monitoring social-ecological systems

Abstract

The social-ecological system (SES) approach is fundamental for addressing global change challenges and to developing sustainability science. Over the last two decades, much progress has been made in translating this approach from theory to practice, although the knowledge generated is still sparse and difficult to compare. To better understand how SESs function across time, space, and scales, coordinated, long-term SES research and monitoring strategies under a common analytical framework are needed. For this purpose, the collection of standard datasets is a cornerstone, but we are still far from identifying and agreeing on the common core set of variables that should be used. In this study, based on literature reviews, expert workshops, and researcher perceptions collected through online surveys, we developed a reference list of 60 variables for the characterization and monitoring of SESs. The variables were embedded in a conceptual framework structured in 13 dimensions that were distributed throughout the three main components of the SES: the social system, the ecological system, and the interactions between them. In addition, the variables were prioritized according to relevance and consensus criteria identified in the survey responses. Variable relevance was positively correlated with consensus across respondents. This study brings new perspectives to address existing barriers in operationalizing lists of variables in the study of SESs, such as the applicability for place-based research, the capacity to deal with SES complexity, and the feasibility for long-term monitoring of social-ecological dynamics. This study may constitute a preliminary step to identifying essential variables for SESs. It will contribute toward promoting the systematic collection of data around most meaningful aspects of the SESs and to enhancing comparability across place-based research and long-term monitoring of complex SESs, and therefore, the production of generalizable knowledge.

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Copyright © 2020 by the author(s). Published here under license by the Resilience Alliance.
Pacheco-Romero, M., D. Alcaraz-Segura, M. Vallejos, and J. Cabello. 2020. An expert-based reference list of variables for
characterizing and monitoring social-ecological systems. Ecology and Society 25(3):1. https://doi.org/10.5751/ES-11676-250301
Research
An expert-based reference list of variables for characterizing and monitoring
social-ecological systems
Manuel Pacheco-Romero 1,2, Domingo Alcaraz-Segura 1,3,4, María Vallejos 3,5,6 and Javier Cabello 1,2
ABSTRACT. The social-ecological system (SES) approach is fundamental for addressing global change challenges and to developing
sustainability science. Over the last two decades, much progress has been made in translating this approach from theory to practice,
although the knowledge generated is still sparse and difficult to compare. To better understand how SESs function across time, space,
and scales, coordinated, long-term SES research and monitoring strategies under a common analytical framework are needed. For this
purpose, the collection of standard datasets is a cornerstone, but we are still far from identifying and agreeing on the common core set
of variables that should be used. In this study, based on literature reviews, expert workshops, and researcher perceptions collected
through online surveys, we developed a reference list of 60 variables for the characterization and monitoring of SESs. The variables
were embedded in a conceptual framework structured in 13 dimensions that were distributed throughout the three main components
of the SES: the social system, the ecological system, and the interactions between them. In addition, the variables were prioritized
according to relevance and consensus criteria identified in the survey responses. Variable relevance was positively correlated with
consensus across respondents. This study brings new perspectives to address existing barriers in operationalizing lists of variables in
the study of SESs, such as the applicability for place-based research, the capacity to deal with SES complexity, and the feasibility for
long-term monitoring of social-ecological dynamics. This study may constitute a preliminary step to identifying essential variables for
SESs. It will contribute toward promoting the systematic collection of data around most meaningful aspects of the SESs and to
enhancing comparability across place-based research and long-term monitoring of complex SESs, and therefore, the production of
generalizable knowledge.
Key Words: coupled human and natural systems; essential social-ecological variables; essential variables; long-term social-ecological
research; LTSER; place-based social-ecological research; social-ecological dimensions; social-ecological interactions; social-ecological
monitoring; social-ecological system framework; social-ecological system functioning
INTRODUCTION
The social-ecological system (SES) approach arose to formally
recognize that human and natural systems are intertwined and
interact across nested spatial and temporal scales (Berkes et al.
2000, Chapin et al. 2009). Currently, the SES approach is widely
acknowledged as crucial for addressing global change challenges
(Liu et al. 2007, Resilience Alliance 2007, Carpenter et al. 2009)
and as a basis for the development of sustainability science
(Ostrom 2009, Leslie et al. 2015). It provides new opportunities
to understand and manage critical feedbacks between nature and
society, which could lead to better ecosystem health, human well-
being and social equity in the distribution of benefits provided
by nature (Collins et al. 2011). However, the complex nature of
SESs (Levin et al. 2013) and their heterogeneity across the world
challenge place-based social-ecological research (Maass et al.
2016, Norström et al. 2017) and the production of generalizable
knowledge from these studies.
Over the past two decades, there has been evident progress in
moving the SES approach from theory to practice. First,
theoretical studies have defined the general characteristics of
SESs, explaining their complexity, dynamics, and emergent
properties (e.g., Holling 2001, Berkes et al. 2003, Liu et al. 2007,
Chapin et al. 2009). Second, conceptual frameworks were
developed to operationalize the SES concept for place-based
research (e.g., Scholz and Binder 2004, Redman et al. 2004,
Chapin et al. 2006, Ostrom 2009). Such frameworks have provided
lists of variables and components/dimensions of the SES,
including the assumed structural relations between these building
blocks, usually supported by a graphical representation
(Meyfroidt et al. 2018). Third, the most recent empirical studies
have dealt with place-based research through the development of
mapping approaches that characterize the diversity of SESs at
different spatial scales (e.g., Václavík et al. 2013, Hamann et al.
2015, Martín-López et al. 2017) or that analyze specific types of
SESs at the local scale, e.g., such as fisheries, estuaries, and forest
systems (Delgado-Serrano and Ramos 2015, Leslie et al. 2015).
Although these empirical studies have provided valuable
knowledge on SESs in diverse contexts, it is still difficult to
compare and extract general insights from them on how SESs
perform over time and across spatial scales (Václavík et al. 2016,
Magliocca et al. 2018).
Long-term monitoring provides a fundamental basis for
understanding the spatiotemporal dynamics of SESs. This has
been made explicit in some global research networks, such as the
International Long-Term Ecological Research Network (ILTER)
and the Program on Ecosystem Change and Society (PECS;
1Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería, Almería, Spain, 2Department of
Biology and Geology, University of Almería, Almería, Spain, 3Department of Botany, University of Granada, Granada, Spain, 4iecolab,
Interuniversity Institute for Earth System Research (IISTA), University of Granada, Granada, Spain, 5Instituto Nacional de Investigación
Agropecuaria (INIA La Estanzuela), Colonia, Uruguay, 6Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de
Agronomía, Universidad de Buenos Aires, Buenos Aires, Argentina

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Holzer et al. 2018). ILTER includes long-term social-ecological
research (LTSER) platforms based on the conceptual model of
the SES (Collins et al. 2011). These networks constitute
infrastructures for inter- and transdisciplinary research and data
collection that aim to produce knowledge for addressing the
complex environmental challenges that emerge from nature-
society interactions and to guide sustainability policies (Dick et
al. 2018, Mirtl et al. 2018). The main goal of PECS research is
the integration of place-based and long-term social-ecological
knowledge generated from case studies across the world to better
understand social-ecological dynamics (Carpenter et al. 2012,
Balvanera et al. 2017, Norström et al. 2017). In addition, the
World Network of UNESCO Biosphere Reserves introduced the
social-ecological approach into protected area management, as
well as the need to monitor changes in the biosphere resulting
from human-nature interactions (Holzer et al. 2018). Despite the
promising advances in long-term social-ecological monitoring by
these networks, one persistent challenge is the harmonization of
monitoring protocols to promote cross-site comparability. This
would foster more effective interoperability (Vargas et al. 2017)
and knowledge generalization from locally driven research
initiatives to broader contexts (Dick et al. 2018, Magliocca et al.
2018).
The systematic collection of standard datasets is the cornerstone
for enhancing our ability to study the spatial patterns of SESs
and their trajectories over time (Holzer et al. 2018). These datasets
should be based on a common core set of variables that contribute
to fostering a more comprehensive and comparable
characterization and monitoring of SESs (Ostrom 2009, Frey
2017). Only a few theoretical studies have dealt with the
identification of such common lists of key variables. In this sense,
Ostrom (2009) set the most important approach by proposing a
list of variables, which were organized in a multilevel nested
framework, to understand the sustainability of SESs. Subsequent
studies have further developed this list to make it more operational
for the empirical study of SESs (e.g., McGinnis and Ostrom 2014,
Delgado-Serrano and Ramos 2015, Frey 2017). However, the use
of Ostrom’s variables in place-based social-ecological research is
challenged because of some limitations. For instance, some
studies on specific SESs at local scales have reported difficulties
in understanding and standardizing the variables and collecting
the data (e.g., Basurto et al. 2013, Cox 2014, Delgado-Serrano
and Ramos 2015, Leslie et al. 2015). Likely because of these
constraints, only a few studies have used this approach for the
spatially explicit mapping of SESs (Dressel et al. 2018, Rocha et
al. 2020). To overcome these barriers to operationalization, a
standard list of variables should be useful in dealing with the
diversity of social-ecological contexts (McGinnis and Ostrom
2014, Frey 2017), the complex nature of SESs, and the availability
of data (Rocha et al. 2020). Finding a set of variables that meets
these requirements will enable the collection of datasets
worldwide to enhance place-based research on complex SESs as
well as the observation and tracking of long-term trends,
encouraging cross-system comparisons.
A promising initiative contributing to the development of core
lists of variables to make monitoring of the Earth system
comparable across sites is the identification of essential variables
(EVs). EVs constitute the minimum set of critical measurements
for the study, report, and management of a system and its changes
(Reyers et al. 2017, Guerra et al. 2019). Major steps have been
taken in the fields of biodiversity (Pereira et al. 2013), climate
(Bojinski et al. 2014), and oceans (Constable et al. 2016). However,
in transdisciplinary fields, only guidelines have been suggested
thus far to identify EVs. Reyers et al. (2017) proposed criteria for
the selection of EVs that link socioeconomic and environmental
concerns for monitoring sustainable development goals. Guerra
et al. (2019) defined a framework for identifying EVs that
characterize human-nature dynamics in the context of
conservation, and Balvanera et al. (2016) developed a pathway
for identifying essential ecosystem service variables. Hence, a
widespread consensus on a comprehensive list of EVs for SES
monitoring is still lacking, although recent studies have provided
valuable insights for identifying relevant variables. For instance,
Frey (2017) suggested that in addition to SES sustainability,
variables could also inform on other outcomes, such as resilience,
social equity, or economic efficiency. Holzer et al. (2018) proposed
that indicators collected across LTSER platforms might include
qualitative social, political, and economic variables, e.g., sense of
place, property ownership, or governance structures, to
understand trends in quantitative variables, e.g., population
density, ecosystem services, or biodiversity. Additionally, within
the LTSER context, Dick et al. (2018) highlighted the importance
of collecting social and biophysical data for addressing complex
challenges that emerge from nature-society interactions, e.g.,
climate change, biodiversity loss, or environmental hazards.
Additional studies that have developed spatially explicit maps of
SESs provide multiple examples of relevant variables from which
it is feasible to collect data to characterize SES dynamics (e.g.,
Alessa et al. 2008, Ellis and Ramankutty 2008, Václavík et al.
2013, Castellarini et al. 2014, Hamann et al. 2015, Martín-López
et al. 2017, Vallejos et al. 2020).
In summary, it is crucial to advance toward an established list of
relevant and feasible variables for characterizing and monitoring
SESs that can be used in science, policy, and management.
Developing such a list could foster a long-term coordinated social-
ecological monitoring network, allowing the intercomparability
of place-based social-ecological research (Redman et al. 2004,
Collins et al. 2011, Carpenter et al. 2012, Balvanera et al. 2017)
and strengthening the production of generalizable knowledge on
SESs across different regions of the world (Frey 2017). To our
knowledge, the few integrative lists of SES variables have been
built only from Ostrom’s (2009) approach, and difficulties have
been sometimes reported for their operationalization in empirical
research (Delgado-Serrano and Ramos 2015). To progress in the
development of a core set of integrative variables, it is important
to provide new insights into the fundamental traits to characterize
the functioning of SESs, i.e., how the system performs (Jax 2010).
For this purpose, it is necessary to compile the variables used in
previous studies and to incorporate the assessments of experts
working in inter- and transdisciplinary fields (Redman et al.
2004). In this study, we aimed to develop a reference list of
prioritized variables for characterizing and monitoring SESs. We
provide evidence about the potential most relevant variables based
on a comprehensive literature review, an iterative process driven
by expert workshops, and researcher perceptions collected
through online surveys.

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Fig. 1. Workflow. The main methodological steps are identified on the left, and their respective results are on the right. The boxes
group together the methodological steps to indicate the two main stages of this study: (1) the development of a list of variables
structured under a social-ecological system (SES) conceptual framework and (2) the prioritization of the list of variables.
METHODS
Developing a comprehensive list of social-ecological system
variables
The list of variables for characterizing and monitoring SESs was
developed in four steps (Fig. 1). First, we performed a literature
review to search for candidate variables. We also identified
candidate conceptual frameworks to structure the list of variables
and to depict the relationships among them. We searched Scopus
for journal articles and book chapters with the following terms
in their titles, keywords, or abstracts: “soci*-ecological system*”
and (“map*” or “framework”). Then, we followed a
“snowballing” approach (see van Oudenhoven et al. 2018) to
identify additional papers that explicitly developed SES maps,
SES conceptual frameworks, or were pivotal for understanding
SES functioning (Appendix 1). From this search, we registered
all variables and conceptual frameworks that were empirically
used or theoretically introduced to characterize SESs. Second, we
organized an initial workshop (November 2015) with experts on
Earth system dynamics (carbon, water, energy, nutrient cycling)
and sustainability science (ecosystem services, transdisciplinarity,
translational ecology; see participants in Appendix 2) to develop
a preliminary list of variables structured under an integrative
conceptual framework. Experts analyzed the candidate variables
and selected the most suitable framework. The variables were
classified into a nested scheme of three SES components, and
there were multiple dimensions within these components. Third,
to complete the list of variables and to validate the structure of
the dimensions and components, we conducted a preliminary
online survey targeted at researchers with experience in SES
science (August-December 2016; see acknowledgments). The
survey (Appendix 3) introduced the list of variables classified into
the dimensions and components and asked respondents to score
each variable from 0 to 5 according to its relevance for
characterizing and monitoring SESs. Scientists were also
encouraged to suggest the addition or deletion of variables and
to provide any other comments. These scores, suggestions, and
comments were analyzed during a second scientific workshop
(January 2017; see participants in Appendix 2) to improve the set
of variables and dimensions. We then launched a final online
survey (January-May 2017; Appendix 4) that was distributed to
a new group of researchers with similar expertise in SES science
(see acknowledgments). As in the preliminary survey, they were
asked to score each variable from 0 to 5 and to provide comments
and suggestions.
Prioritization of social-ecological variables
To prioritize the variables from the improved list, we conducted
a “relevance vs. consensus” analysis using the scores from the final
survey (Fig. 1) on the importance perceived by experts for each
variable for characterizing and monitoring SESs. The relevance
was evaluated as the mean of the scores assigned by the experts

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to each variable. The consensus was estimated as the difference
between the maximum standard deviation of the scores found
throughout the 149 variables and the standard deviation of the
score for each variable (low differences indicated low consensus
and high differences, high consensus). Then, the variables were
separately ranked according to their percentile for relevance and
consensus and grouped into five categories (four levels of priority
and one nonpriority). Priority level 1 (top priority) included
variables with relevance and consensus above the 90th percentile;
level 2 included variables between the 75th and 90th percentiles;
level 3 included variables with relevance above the 75th percentile
but consensus between the 50th and 75th percentiles and vice versa;
and finally, level 4 included variables with relevance and consensus
between the 50th and 75th percentiles. The nonpriority category
included variables with relevance and consensus below the 50th
percentile. Finally, to assess potential biases and gaps in the list
of variables, we analyzed the additional suggestions and
comments provided by researchers in both surveys (Fig. 1). This
analysis was performed by annotating key words and organizing
them through generalization in a conceptual map. We identified
recurrent key words (addressed five or more times by respondents)
as “featured topics.”
RESULTS
Variables and dimensions to guide the characterization and
monitoring of SESs
We developed a list of 149 variables structured in 13 dimensions
within the three components of the SESs: the social system, the
ecological system, and their interactions (Table A5.1, Appendix
5). We selected the Resilience Alliance conceptual framework
(Resilience Alliance 2007) in the first workshop as the most
pragmatic and illustrative framework to depict the structural
relations among the dimensions and to guide more coordinated
SES characterization and monitoring (Fig. 2). In the social
system, three dimensions (human population dynamics, well-
being and development, and governance) containing 36 variables
were identified. In the ecological system, five dimensions (organic
carbon dynamics, water dynamics, nutrient cycling, surface
energy balance, and disturbance regime) containing 51 variables
were identified. In the interactions between nature and people,
five dimensions (ecosystem service supply, ecosystem disservice
supply, ecosystem service demand, human actions on the
environment, and social-ecological coupling) containing 62
variables were identified. The featured topics derived from the
researchers’ comments in the preliminary online survey that
guided the development of the list of variables and dimensions
are shown in Fig. A6.1, Appendix 6, as well as in the conceptual
map in Appendix 7.
Prioritization of social-ecological variables based on scientist
scoring
The analysis of the final survey revealed a significant positive
linear relationship (n = 149; r = 0.82; p-value < 0.001) between
the average relevance for characterizing and monitoring SESs
obtained for each variable and the consensus observed across
respondents (Fig. 3). A positive slope lower than one (m = 0.33;
p-value < 0.001; root-mean-square error = 0.12) indicated that
relevance increased faster than consensus. By applying the
prioritization thresholds, 60 variables were considered relevant
because they were included at one of the four priority levels (Table
1). Ten variables were included under priority level 1 (highest
priority), representing the dimensions of nutrient cycling,
disturbance regime (ecological system component), ecosystem
service supply, human actions on the environment, and social-
ecological coupling (interaction component). Sixteen variables
were considered at priority level 2, adding new dimensions such
as well-being and development, governance (social system), water
dynamics (ecological system), and ecosystem service demand
(interaction component). Twenty-two variables constituted
priority level 3, incorporating the dimensions human population
dynamics (social system), organic carbon dynamics, and surface
energy balance (ecological system). Finally, level 4 (lowest
priority) added 12 variables, two of them belonging to the
dimension of ecosystem disservice supply (interaction
component). Thus, the prioritized variables represented all 13
dimensions proposed to characterize SES functioning, though we
found it remarkable that no variables in the social system
component reached priority level 1, reaching level 2 at the highest.
Overall, 25% of the variables assessed for the social system were
prioritized, 24% in the ecological system, and 48% for the
interaction component. To explore in detail the relevance and
consensus obtained for each variable, see Figs. A6.2 to A6.14 in
Appendix 6 and Appendix 8.
Fig. 2. Conceptual framework to guide the characterization and
monitoring of social-ecological systems (SESs). The framework
is structured in three components (social system, ecological
system, and interactions between them) and 13 dimensions of
SES functioning (modified from Resilience Alliance 2007).
Additional comments from the respondents
The analysis of respondents’ comments and suggestions in the
final survey allowed us to identify 14 featured topics indicating
potential biases and gaps in the list of variables (Fig. 4 and
Appendix 7). In the social system, several researchers emphasized
the importance of “social equity” and “living conditions” to
characterize the well-being and development dimension. In the
ecological system, “biodiversity” was the most featured topic,
which was considered the foundation for explaining the supply of
provisioning, regulating, and cultural ecosystem services.
Respondents also argued that the water dynamics dimension
should be mainly based on the characterization of the “water
balance,” with some additional variables concerning water and
soil salinity and seasonality. Within the interactions, the
importance of measuring the “strength of links between people
and nature” was the most addressed topic. Within this scope, other
related featured topics were “resource consumption patterns,” the

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Fig. 3. Relevance and consensus obtained by variables for characterizing and monitoring social-ecological
systems (SESs) in the final survey. Relevance was evaluated as the mean of the scores assigned by experts to each
variable. The consensus was estimated as the difference between the maximum standard deviation of the scores
found throughout the 149 variables and the standard deviation of the score for each variable (low differences
indicated low consensus and high differences, high consensus). Squares, circles, and plus signs identify the
variables belonging to the social system, ecological system, and interaction components, respectively. Horizontal
and vertical lines represent the 25th, 50th, 75th, and 90th percentiles of relevance and consensus. Boxes over the
grid illustrate the clustering of the variables by priority levels. The red box (priority level 1) includes those
variables with relevance and consensus above the 90th percentile; the green box (level 2) includes those variables
with both values between the 75th and 90th percentiles; the yellow box (level 3) includes those with relevance
above the 75th percentile but consensus between the 50th and 75th percentiles and vice versa; and the blue box
(level 4) includes variables with relevance and consensus between the 50th and 75th percentiles. At the bottom
right of the figure, the equation of the regression line, the significance of the line slope (p-value) and the root-
mean-square error (RMSE) are indicated, as are the number of variables (n), the Pearson’s correlation
coefficient (r), and its significance (p-value).
“cultural value of nature,” “cultural ecosystem service demand,”
“local ecological knowledge,” and the “beneficial human actions
on the environment.” Other highlighted issues were transversal
to the three SES components. Some researchers argued that all
“variables should reflect the underlying processes and functions”
occurring in SESs, instead of outcomes or symptoms of their
functioning. In addition, the need to consider more variables
related to “energy fluxes” as indicators of system complexity was
also suggested. Finally, researchers also stated that variable
relevance might be “context-dependent” and that SES complexity
makes it “difficult to assess some variables.” An extended version
of Fig. 4 with the whole list of topics is available in Fig. A6.15,
Appendix 6.
DISCUSSION
With this study, we contributed to the identification of a common
core set of relevant variables for the study and monitoring of SESs
by providing a reference list of 60 variables, which were structured
in 13 dimensions of SES functioning embedded in the social,
ecological, and interaction components of the SES (Fig. 2). The
use of such a nested framework contributes to understanding the
relationships among variables, aims to maintain the holistic
approach in the study of SESs, and promotes transdisciplinary
communication by acting as a boundary object (Ostrom 2009,
Meyfroidt et al. 2018, van Oudenhoven et al. 2018). The variables
were classified into four levels of priority according to researcher
consensus on their relevance (Fig. 3 and Table 1) to facilitate their
adaptation to the data availability, context, and sociopolitical
needs. The prioritization revealed the crucial role that social-
ecological interactions have in characterizing SES complexity
(Liu et al. 2007, Carpenter et al. 2009) but also showed that all
the dimensions of social-ecological functioning are necessary to
disentangle SES dynamics (Table 1). In general, the development
of reference lists of variables is an emerging need in sustainability
research to foster the collection of structured, long-term,
coordinated core datasets across SESs (Frey 2017, Holzer et al.

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Table 1. List of prioritized variables for characterizing and monitoring social-ecological systems (SESs). The list is structured into 13 dimensions across the three
components of a SES (see Fig. 2). Priority level 1 includes variables with relevance and consensus above the 90th percentile; level 2 includes variables with both
values between the 75th and 90th percentile; level 3 contains those variables whose relevance was above the 75th percentile and consensus between the 50th and 75th
percentiles and vice versa; and finally, level 4 includes those variables with relevance and consensus between the 50th and 75th percentiles. An extended version of
this table including the nonpriority variable category, as well as examples and explanations for the variables, is available in Table A5.2, Appendix 5.
Component Dimension Priority variables (decreasing priority from 1 to 4)
Level 1 Level 2 Level 3 Level 4
Social
system
Human population
dynamics
· Population density
· Population distribution
Wellbeing and
development
· Access to drinking water
· Educational level
· Environmental quality
· Poverty
· Social equity
· Water sanitation
· Water scarcity
Governance · Current conflicts · Corruption level
· Political stability
Ecological
system
Organic carbon
dynamics
· Net primary productivity
· Organic carbon storage
· Ecosystem composition
by plant functional type
Water dynamics · Precipitation · Actual evapotranspiration
· Actual water deficit (or
excess)
· Soil water infiltration
capacity
Surface energy
balance
· Net solar radiation · Land surface temperature
Nutrient cycling · Nitrogen fixation · Soil phosphorus availability · Nitrogen deposition
Disturbance regime · Drought occurrence
· Flood occurrence
· Fire occurrence · Hurricanes/storms
occurrence
· Pest outbreaks occurrence
Interaction
s
Ecosystem service
supply†1
· Cropland production (P)
· Livestock production (P)
· Surface and groundwater
sources for drinking (P)
· Hydrological cycle and
water flow maintenance
(R)
· Surface and groundwater
sources for nondrinking
purposes (P)
· Local climate regulation (R)
· Pest and disease control (R)
· Pollination and seed
dispersal (R)
· Chemical conditions
maintenance of
freshwaters and salt
waters (R)
Ecosystem disservice
supply2
· Abiotic-economic (e.g.,
droughts, fires)
· Bioeconomic (e.g.,
biological invasions)
Ecosystem service
demand
· Appropriation of land for
agriculture
· Energy use level
· Water use level
· Water use for irrigated crops
· Material use level · Human appropriation of
net primary production
(HANPP)
Human actions on
the environment
· Land cover/land use
change
· Land use intensity
· Eutrophication of water bodies
· Land protection
· Pollution
· Soil erosion
· Anthropogenic water
management
· Net CO2 flux
· Territorial connectivity
Social-ecological
coupling
· Local natural capital
dependence
· Access to natural and
seminatural areas
· Biocapacity
· Import/export rates of
agricultural products
· Renewable energy use
†P = provisioning services; R = regulating services
1Haines-Young and Potschin (2013), 2 Shackleton et al. (2016) (see Table A5.2, Appendix 5)
Interactions
test
Well-being and

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2018). This will help to enhance our ability to study SESs over
time and across space, enabling cross-system comparisons and
the standardization of monitoring protocols.
Fig. 4. Featured topics (addressed by five or more respondents
in different dimensions) related to potential biases and gaps in
the list of variables identified from comments and suggestions
in the final survey. Black, white, and gray bars represent the
social system, ecological system, and interaction components,
respectively, while striped bars reflect issues that are transversal
to the whole conceptual framework. (See also these topics in
the conceptual map of Appendix 7).
Insights to address existing barriers in SES research
The list of variables presented in this study offered new
perspectives for addressing the main barriers, i.e., applicability to
place-based research, representativeness of SES complexity, and
feasibility for monitoring, detected in operationalizing existing
lists to assess SESs (e.g., Ostrom 2009, McGinnis and Ostrom
2014, Delgado-Serrano and Ramos 2015, Frey 2017). First,
regarding their applicability for place-based research, according
to van Oudenhoven et al. (2018), variables not only need to be
credible, i.e., scientifically sound based on expert judgment,
scientific literature, and a conceptual framework, but also
practically feasible for collection. For instance, Ostrom’s list of
variables, which was conceived to diagnose the sustainability of
SESs (Ostrom 2009), has sometimes been considered too abstract
and general to characterize concrete systems (Cox 2014, Delgado-
Serrano and Ramos 2015, Hinkel et al. 2015, Leslie et al. 2015).
To overcome such limitations, we emphasized the selection of
variables easily derivable from primary data that have been used
in previous research for the spatially explicit mapping of SESs
(Appendix 1; Table A5.3, Appendix 5). In addition, the list of
variables and the conceptual framework must offer certain
flexibility to be adapted to the diversity of contexts and scales of
analysis and to data availability (McGinnis and Ostrom 2014).
The Ostrom SES framework presents a hierarchical structure at
different levels (tiers), with variables and subvariables that could
be adapted depending on the type of SES (Delgado-Serrano and
Ramos 2015) but that lack any guidance on their relevance. In
our study, we not only hierarchically structured the variables
under the dimensions and components of SESs but also
distributed them into priority levels according to their agreed
relevance for characterizing SESs. By doing so, we provide
guidance for adapting variable selection according to the research
context while retaining consistency regarding the relevance and
representativeness of variables across SES dimensions.
Second, regarding their representativeness of SES complexity,
variables not only need to provide information on the different
“pieces” of the system but also must help to understand the
linkages among such “pieces” (Ostrom 2009). To achieve this goal,
embedding variables within a nested conceptual framework helps
to organize them across components and hierarchical levels while
depicting the structural relationships between them (Frey 2017,
Ostrom 2009, McGinnis and Ostrom 2014). For instance,
Ostrom’s SES framework uses an anthropocentric perspective of
SESs, where variables that are supposed to focus on the ecological
subsystem also have a social origin or reflect the interaction
between humans and nature (Binder et al. 2013). However, if most
variables make sense only if humans exist, it implies that there
exists an unbalanced representation among the social, ecological,
and interaction variables, which is acknowledged as a key
principle for addressing SES complexity (Liu et al. 2007,
Resilience Alliance 2007, Reyers et al. 2017). Our proposal
provides a scheme that categorizes all variables into 13 expert-
validated dimensions embedded into the three key components
of a SES, i.e., social system, ecological system, and interactions.
The variables for characterizing the ecological system followed
an “ecocentric” perspective (sensu Binder et al. 2013) and were
structured into five dimensions, where the system and its processes
were analyzed independently of their links to humans. For the
social system, our variables focused on understanding human
population dynamics, well-being and development, and
governance dimensions without considering ecological processes.
Finally, for the interactions between humans and nature, similar
to Ostrom (2009), our variables addressed the reciprocity between
the social and ecological systems (Binder et al. 2013). However,
we suggested a more detailed structure for the variables, which
we divided into five dimensions, depending on the type and
direction of the interactions: (a) from the ecological to the social
system (ecosystem service and disservice supply), (b) from the
social to the ecological system (ecosystem service demand and
human actions on the environment), and (c) bidirectionally
between the social and the ecological system (social-ecological
coupling). We recognize that relying on a single framework might
be unrealistic, but understanding and generalizing the complexity
of SESs requires common hierarchical analytical structures that
comprehensively integrate the multiple dimensions and
components of SESs (Reyers et al. 2017, Magliocca et al. 2018,
Meyfroidt et al. 2018).
Third, regarding the feasibility of the variables for long-term
monitoring (van Oudenhoven et al. 2018), our list facilitates SES
characterization at the system level, i.e., it focuses on the
macrolevels according to Binder et al. (2013) to integrate
properties of the SES components as a whole. Aggregated
variables at the system level have been clearly more used to
characterize, map, and track SESs than variables collected at the
individual level, i.e., variables focused on the microlevels
according to Binder et al. (2013) to measure properties of the SES
individual building blocks, e.g., plant, animal, individual
producer, user, or consumer (see examples in Table A5.3). In fact,
even those SES mapping strategies based on Ostrom’s framework,
which combines both system- and individual-level perspectives,
i.e., macro- and microlevels according to Binder et al. (2013), have

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only used system level metrics (e.g., Dressel et al. 2018, Rocha et
al. 2020). Several studies show that system-level characterizations
can better inform on social-ecological processes from local to
global scales (e.g., Václavík et al. 2013, Martín-López et al. 2017,
Levers et al. 2018, Vallejos et al. 2020) and could help to overcome
current limitations to upscale place-based research for the
coproduction of generalizable knowledge on SES (Balvanera et
al. 2017).
Potential biases and gaps in the list of variables
The analysis of the researchers’ comments revealed potential
conceptual biases introduced by the proposed framework during
the construction of the list of variables (Fig. 4). In the interaction
component, a majority of comments highlighted that
sociocultural values and identities might be underrepresented and
that the variables addressing the “strength of the links between
people and nature” and the “cultural value of nature” could be
enhanced, for instance, by incorporating the variable “local
ecological knowledge.” However, interestingly, cultural ecosystem
service variables (following the categories of the Common
International Classification of Ecosystem Services, CICES;
Haines-Young and Potschin 2013) were not prioritized by
researchers during the survey (Table A5.2, Appendix 5; Appendix
8). Although these findings may seem contradictory, they align
with new insights into the nature’s contributions to people (NCP)
paradigm (Díaz et al. 2018) and the plurality of values associated
with these contributions (UNEP 2015, Pascual et al. 2017). Under
the new NCP paradigm, culture plays a central role in defining
all links between people and nature (Díaz et al. 2018). Thus,
further lists of SES variables should expand the ecosystem service
supply dimension by giving culture and traditional/indigenous
knowledge a more transversal role across ecosystem services
categories, beyond the independent cultural category of CICES
and the Millennium Assessment (MA 2005). Furthermore,
enhancing the characterization of the cultural contexts and
identities goes further for the instrumental values of ecosystem
services and NCP by incorporating those values that emerge from
individual and collective relationships of humans with nature
(Chan et al. 2018). To address these “relational values,” new
variables, such as sense of belonging, responsibility toward
nature, or maintenance of traditions (Chan et al. 2016), may be
added to the list.
In the ecological system component, the explicit role of
biodiversity might also be underrepresented because many
comments suggested the addition of more biodiversity variables
or of a whole biodiversity dimension within this component.
Given the role of biodiversity in SESs as the natural capital that
supports social metabolism (Costanza et al. 1997) and the
biocentric conservationist tradition (Mace 2014), we agree that
biodiversity could be explicitly named in the framework.
However, we initially excluded the structural and compositional
biodiversity facets because of their slower response to
disturbances compared to functional variables (McNaughton et
al. 1989, Milchunas and Lauenroth 1995). Instead, we focused on
the functional aspects of biodiversity at the ecosystem level, such
as the candidates to become essential biodiversity variables for
the ecosystem function class (e.g., Pereira et al. 2013, Pettorelli et
al. 2018).
We are also aware of additional sources of potential
methodological biases. On the one hand, the way that the variables
were sorted in our framework during the survey could have
influenced respondents in assigning priority levels. By displaying
the variables sorted into dimensions, we aimed to facilitate the
completion of the survey. We are aware that a random display or
other sorting could have led to different variable scores. However,
this impact may have been low because there was no significant
correlation between the priority scores and variable order in the
online survey. On the other hand, because the field of expertise
of most respondents was sustainability science and ecology
(Appendix 9), the social variables might have received lower scores
than expected. Indeed, the social variables never reached the
highest priority level (level 1; Table A5.2, Appendix 5) despite
their importance for human well-being and for explaining the
form and intensity of human-nature interactions, e.g., education
and population density, respectively (Ellis and Ramankutty 2008,
Hamann et al. 2016). Most inter- and transdisciplinary efforts in
social-ecology and sustainability science come from ecology
(Lowe et al. 2009, Holzer et al. 2019), but a wide range of
perspectives still exist among ecologists for integrating concepts
and methods from social science. This disparity of perspectives
might be because some researchers consider ecology as a basic
science that studies wild nature (where people are only the
“ecological audience”), others see it as an instrument for guiding
ecosystem and species management (treating people as
“ecological agents”), and still others view it as a discipline that
considers human societies to be integrated in ecosystems (people
as “ecological subjects/objects”; Lowe et al. 2009, Mace 2014).
Indeed, these perceptions of ecology have been evidenced
throughout the development and implementation of the long-
term social-ecological monitoring network, which mainly
originated from ecological monitoring and research. Despite the
adoption of a new social-ecological paradigm, the network
continues to monitor primarily ecological processes, although it
is progressing toward incorporating economic and social data and
conducting more germane transdisciplinary research (Dick et al.
2018, Angelstam et al. 2019). In our study, the potential
coexistence of these three perceptions among the surveyed
researchers could be the basis of the lack of consensus around
the most relevant social variables. This highlights the need to
strengthen cooperation between natural and social scientists and
experts to lead to a truly integrated approach for long-term social-
ecological research (Dick et al. 2018). Finally, many scientists have
reported difficulties in scoring the variables without considering
a specific SES, arguing that variable relevance is context
dependent. Although biodiversity, climate, oceans, or sustainable
development goal variables may have more evident global
perspectives, this is not easily applicable to SES variables given
the place-based nature of SES research (Carpenter et al. 2012).
All these potential biases should be considered when using our
list of variables and formally analyzing them in future
assessments.
Toward the definition of essential variables for social-ecological
systems
The development of essential variables (EVs) that harmonize
global observation networks is a priority for tracking changes and
coordinating monitoring efforts (e.g., Pereira et al. 2013, Bojinski
et al. 2014, Constable et al. 2016). Despite the call from

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sustainability science to extend this systemic thinking to areas of
interaction between the social and the biophysical domains,
building a list of essential social-ecological system variables is still
needed (Reyers et al. 2017). The set of dimensions and variables
developed here can contribute to creating a common structure to
study SESs and to starting to work toward such essential variables.
Because the variables and dimensions were based on consensual
expert knowledge, their credibility, salience, and feasibility were
reaffirmed (van Oudenhoven et al. 2018). In addition,
fundamental steps in EV development were followed in the
codesign process (Reyers et al. 2017): (1) adoption, through an
expert-driven process, of a conceptual model of SESs
functioning, representing the social and ecological systems as well
as the interactions between them; (2) identification of the broad
categories and disaggregated inputs of candidate variables; (3)
refining and prioritization of variables based on the consensus
on their relevance; and all this by means of (4) an iterative
procedure fed by scientific expert knowledge obtained from
workshops and online surveys. However, given the preliminary
nature of our exercise, further work is needed to build a global
consensus around a set of EVs for the study of SESs. For instance,
new surveys should address the potential biases and limitations
outlined above, for instance (1) by explicitly considering the role
of biodiversity and of relational values about NCP; (2) by having
a greater and more balanced number of respondents (particularly
the inclusion of social scientists); and (3) by reporting on the most
frequently relevant variables in relation to specific place-based
social-ecological contexts.
To further develop EVs for SESs, finding common aspects and
variables among the existing lists could also help to establish a
baseline. Some variables suggested in Ostrom’s (2009) and Frey’s
(2017) lists were also relevant in our study. The most common
aspects were found for the interaction component. For instance,
the harvesting variable on Ostrom’s list was related to human
appropriation of net primary production, material use, water use,
or energy use on our list. Similarly, pollution patterns on Ostrom’s
list were related to eutrophication of water or net CO2 flux on our
list; constructed facilities on Ostrom’s list and accessibility on
Frey’s list were related to territorial connectivity, access to natural
areas, or anthropogenic water management on our list; and
importance of resources on Ostrom’s list and dependency on
resources on Frey’s list with dependence on local natural capital
on our list. In the social system, economic development and
socioeconomic attributes (Ostrom 2009) were associated with
poverty, educational level, or social equity variables on our list,
and number of actors (Ostrom 2009) with population density.
Similarly, governance-related variables, such as conflicts and
political stability, were included on both Ostrom’s list and our list,
while Frey (2017) considered conflict management as a crucial
aspect for the stability of rule systems and resource use. In the
ecological system, Ostrom’s (2009), Frey’s (2017), and our list
converged on including climate characteristics and primary
productivity or the regeneration rate of resources.
In addition, some of our prioritized variables from the ecological
and interaction components of SESs are related to six of the nine
major environmental challenges listed in the planetary
boundaries framework (Rockström et al. 2009, Steffen et al. 2015).
For instance, the monitoring of net solar radiation and net CO2
flux could provide information to assess “climate change” and
“atmospheric aerosol loading”; information on biological
invasions, pest outbreak occurrence, and ecosystem composition
by plant functional types to assess “changes in biosphere
integrity”; measuring nitrogen deposition and eutrophication of
water to evaluate interferences with “biogeochemical flows”; the
appropriation of land for agriculture and land use intensity for
“land-system change”; and finally, water use level and water use
for irrigated crops to assess “freshwater use.”
From a general perspective, additional steps should be given to
foster the institutionalization of the development and
implementation of essential SES variables (see Pereira et al. 2013,
Bojinski et al. 2014, Constable et al. 2016, Reyers et al. 2017). As
a first step, the compliance of the variables with the criteria to be
considered essential should be thoroughly checked, for instance,
to be (i) state variables, sensitive for long-term monitoring of
changes; (ii) representative for the system level, between primary
observations and indicators; (iii) flexible to adapt to multiple
monitoring programs; and (iv) feasible to observe and derive and
to be scaled to meet local, regional or subglobal needs. Second,
consensus should be built and coordinated to align the
development of the variable list with research and policy needs
by setting an open platform for scientist, policy maker, and
stakeholder cooperation. Third, the learning loop should be
optimized to refine and stabilize the list of EVs by establishing a
transparent process with specific targets and time lines to plan
the development of the list and track the updates. Finally, to
increase the global efficiency of Earth monitoring systems, the
interconnection of the EVs that may emerge from our list with
other sets of EVs (for biodiversity, climate, oceans, etc.) should
be coordinated.
CONCLUSION
The development of reference lists of variables is an emerging
need in sustainability research to foster the systematic collection
of comprehensive and coordinated datasets of SESs and to
enhance our ability to study SESs across time and space. These
lists of variables structured under a conceptual framework
provide a common language that facilitates comparisons and the
generalization of knowledge from empirical studies. Although the
development of such lists in specific fields of Earth systems
(climate, biodiversity, oceans) has progressed significantly in
recent years, integrative approaches for SESs are still scarce. With
this study, we contributed to the identification of a common core
set of variables for the characterization and monitoring of SESs.
Our 60-variable list gathered relevant traits and processes of the
SES from scientific literature reviews and expert knowledge. This
list was embedded in a framework of 13 dimensions across the
three key components of the SES (social system, ecological
system, and the interactions between them) to help maintain an
integrative approach when working with SESs. In addition,
variables were classified into priority levels to provide more
flexibility in their application to place-based research.
Throughout this process, new insights have arisen that could
contribute to overcoming existing barriers in the operationalization
of lists of variables in the study of SESs, such as the applicability
to place-based research, the capacity to deal with SES complexity,
or the feasibility for long-term monitoring of social-ecological
dynamics. Our list of variables may constitute a preliminary step
in the direction of identifying essential variables for SESs, whose
further development will provide an opportunity to boost the

Ecology and Society 25(3): 1
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long-term social-ecological research network. This could
strengthen our capacity to respond to global change challenges,
extend systemic thinking to the field of human-nature
interactions, and foster sustainability sciences through more
efficient operationalization of the social-ecological approach.
Responses to this article can be read online at:
http://www.ecologyandsociety.org/issues/responses.
php/11676
Acknowledgments:
We gratefully acknowledge the effort and ideas contributed by
workshop participants (Appendix 2), especially to José Paruelo,
Hugo Berbery, Howard Epstein, Julio Peñas, Antonio Castro,
Esteban Jobbágy, and Néstor Fernández, as well as the commitment
of those scientists who participated in the surveys (Appendix 9).
We are also grateful to the two anonymous reviewers for their helpful
comments, which substantially improved the manuscript. We thank
the Spanish Ministry of Economy and Business (Project
CGL2014-61610-EXP) for financial support, as well as the Spanish
Ministry of Education for the MPR fellowship (FPU14/06782).
This research was done within the LTSER platforms “The Arid
Iberian South East LTSER Platform,” Spain (LTER_EU_ES_027),
and “Sierra Nevada/Granada (ES- SNE),” Spain (LTER_EU_ES_010),
and contributes to the work done within the GEO BON working
group on ecosystem services.
Data Availability Statement:
The aggregate data that support the findings of this study are
available in the appendices of this paper. The individual responses
to the survey conducted in this study are not publicly available
because they contain information that could compromise the privacy
of research participants.
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Appendix 1. List of key references used for identifying variables and dimensions for
characterizing the social-ecological system (SES).
Key references on SES conceptual frameworks:
Binder, C., J. Hinkel, P. Bots, and C. Pahl-Wostl. 2013. Comparison of Frameworks for
Analyzing Social-ecological Systems. Ecology and Society 18(4).
Chapin, F. S., A. L. Lovecraft, E. S. Zavaleta, J. Nelson, M. D. Robards, G. P. Kofinas, S. F.
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address sustainability of Alaskan boreal forests in response to a directionally changing
climate. Proceedings of the National Academy of Sciences 103(45):16637–16643.
Delgado-Serrano, M. del M., and P. Ramos. 2015. Making Ostrom’s framework applicable to
characterise social ecological systems at the local level. International Journal of the
Commons 9(2):808–830.
MA. 2005. Ecosystems and Human Well-being: Synthesis. Island Press, Washington, DC.
McGinnis, M., and E. Ostrom. 2014. Social-ecological system framework: initial changes and
continuing challenges. Ecology and Society 19(2).
Ostrom, E. 2009. A General Framework for Analyzing Sustainability of Social-Ecological
Systems. Science 325(5939):419–422.
Redman, C. L., J. M. Grove, and L. H. Kuby. 2004. Integrating Social Science into the Long-
Term Ecological Research (LTER) Network: Social Dimensions of Ecological Change
and Ecological Dimensions of Social Change. Ecosystems 7(2):161–171.
Resilience Alliance. 2007. Assessing resilience in social-ecological systems: Volume 2
supplementary notes to the practitioners workbook.
Scholz, R., and C. Binder. 2004. Principles of Human-Environment Systems (HES) Research.
Pages 791–796 Complexity and integrated resources management. International
Environmental Modelling and Software Society, [2004]. Osnabrück.
Key references on SES mapping:
Alessa, L., A. Kliskey, and G. Brown. 2008. Social–ecological hotspots mapping: A spatial
approach for identifying coupled social–ecological space. Landscape and Urban
Planning 85(1):27–39.
Asselen, S. van, and P. H. Verburg. 2012. A Land System representation for global assessments
and land-use modeling. Global Change Biology 18(10):3125–3148.

Castellarini, F., C. Siebe, E. Lazos, B. de la Tejera, H. Cotler, C. Pacheco, E. Boege, A. R.
Moreno, A. Saldivar, A. Larrazábal, C. Galán, J. M. Casado, and P. Balvanera. 2014. A
social-ecological spatial framework for policy design towards sustainability: Mexico as a
study case. Investigación ambiental Ciencia y política pública 6(2).
Dittrich, A., R. Seppelt, T. Václavík, and A. F. Cord. 2017. Integrating ecosystem service bundles
and socio-environmental conditions – A national scale analysis from Germany.
Ecosystem Services 28:273–282.
Dressel, S., G. Ericsson, and C. Sandström. 2018. Mapping social-ecological systems to
understand the challenges underlying wildlife management. Environmental Science &
Policy 84:105–112.
Ellis, E. C., and N. Ramankutty. 2008. Putting people in the map: anthropogenic biomes of the
world. Frontiers in Ecology and the Environment 6(8):439–447.
Hamann, M., R. Biggs, and B. Reyers. 2015. Mapping social–ecological systems: Identifying
‘green-loop’ and ‘red-loop’ dynamics based on characteristic bundles of ecosystem
service use. Global Environmental Change 34:218–226.
Hamann, M., R. Biggs, and B. Reyers. 2016. An Exploration of Human Well-Being Bundles as
Identifiers of Ecosystem Service Use Patterns. PLOS ONE 11(10):e0163476.
Hanspach, J., J. Loos, I. Dorresteijn, D. J. Abson, and J. Fischer. 2016. Characterizing social–
ecological units to inform biodiversity conservation in cultural landscapes. Diversity and
Distributions 22(8):853–864.
Levers, C., D. Müller, K. Erb, H. Haberl, M. R. Jepsen, M. J. Metzger, P. Meyfroidt, T.
Plieninger, C. Plutzar, J. Stürck, P. H. Verburg, P. J. Verkerk, and T. Kuemmerle. 2018.
Archetypical patterns and trajectories of land systems in Europe. Regional Environmental
Change 18(3):715–732.
Martín-López, B., I. Palomo, M. García-Llorente, I. Iniesta-Arandia, A. J. Castro, D. García Del
Amo, E. Gómez-Baggethun, and C. Montes. 2017. Delineating boundaries of social-
ecological systems for landscape planning: A comprehensive spatial approach. Land Use
Policy 66:90–104.
Queiroz, C., M. Meacham, K. Richter, A. V. Norström, E. Andersson, J. Norberg, and G.
Peterson. 2015. Mapping bundles of ecosystem services reveals distinct types of
multifunctionality within a Swedish landscape. AMBIO 44(1):89–101.
Raudsepp-Hearne, C., G. D. Peterson, and E. M. Bennett. 2010. Ecosystem service bundles for
analyzing tradeoffs in diverse landscapes. Proceedings of the National Academy of
Sciences 107(11):5242–5247.
Renard, D., J. M. Rhemtulla, and E. M. Bennett. 2015. Historical dynamics in ecosystem service
bundles. Proceedings of the National Academy of Sciences 112(43):13411–13416.
Rocha, J., K. Malmborg, L. Gordon, K. Brauman, and F. DeClerck. 2020. Mapping social-
ecological systems archetypes. Environmental Research Letters 15(3):034017.

Sinare, H., L. J. Gordon, and E. Enfors Kautsky. 2016. Assessment of ecosystem services and
benefits in village landscapes – A case study from Burkina Faso. Ecosystem Services
21:141–152.
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M. G. Turner, P. H. Verburg, and F. Eigenbrod. 2017. Unpacking ecosystem service
bundles: Towards predictive mapping of synergies and trade-offs between ecosystem
services. Global Environmental Change 47:37–50.
Václavík, T., S. Lautenbach, T. Kuemmerle, and R. Seppelt. 2013. Mapping global land system
archetypes. Global Environmental Change 23(6):1637–1647.
Vallejos, M., S. Aguiar, G. Baldi, M. E. Mastrángelo, F. Gallego, M. Pacheco-Romero, D.
Alcaraz-Segura, and J. M. Paruelo. 2020. Social-Ecological Functional Types:
Connecting People and Ecosystems in the Argentine Chaco. Ecosystems 23(3): 471-484.
Other key references on SES science that inspired variable selection:
Arneth, A., C. Brown, and M. D. A. Rounsevell. 2014. Global models of human decision-making
for land-based mitigation and adaptation assessment. Nature Climate Change 4(7):550–
557.
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Duraiappah, A. Oteng-Yeboah, H. M. Pereira, C. Perrings, W. V. Reid, J. Sarukhan, R. J.
Scholes, and A. Whyte. 2009. Science for managing ecosystem services: Beyond the
Millennium Ecosystem Assessment. Proceedings of the National Academy of Sciences
106(5):1305–1312.
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Tscharntke. 2014. Implications of agricultural transitions and urbanization for ecosystem
services. Nature 515(7525):50–57.
Erb, K.-H. 2012. How a socio-ecological metabolism approach can help to advance our
understanding of changes in land-use intensity. Ecological Economics 76–341:8–14.
Fischer-Kowalski, M., F. Krausmann, and I. Pallua. 2014. A sociometabolic reading of the
Anthropocene: Modes of subsistence, population size and human impact on Earth. The
Anthropocene Review 1(1):8–33.
Foster, K. A., and W. R. Barnes. 2012. Reframing Regional Governance for Research and
Practice. Urban Affairs Review 48(2):272–283.
Frey, U. J. 2017. A synthesis of key factors for sustainability in social–ecological systems.
Sustainability Science 12(4):507–519.
Haines-Young, R., and M. Potschin. 2013. Common International Classification of Ecosystem
Services (CICES): Consultation on Version 4, August-December 2012.

Liu, J., T. Dietz, S. R. Carpenter, C. Folke, M. Alberti, C. L. Redman, S. H. Schneider, E. Ostrom,
A. N. Pell, J. Lubchenco, W. W. Taylor, Z. Ouyang, P. Deadman, T. Kratz, and W.
Provencher. 2007. Coupled Human and Natural Systems. AMBIO 36(8):639–650.
Shackleton, C. M., S. Ruwanza, G. K. Sinasson Sanni, S. Bennett, P. De Lacy, R. Modipa, N.
Mtati, M. Sachikonye, and G. Thondhlana. 2016. Unpacking Pandora’s Box:
Understanding and Categorising Ecosystem Disservices for Environmental Management
and Human Wellbeing. Ecosystems 19(4):587–600.

Appendix 2. Workshop participants.
List of participants in workshop 1 - “Capturing the functioning of social-ecological systems”
Venue: University of Granada (Spain)
Dates: 18th – 20th November 2015
Surname / name
Institution
Area of expertise
Alcaraz-Segura, Domingo
Universidad de Granada
(Spain)
Remote sensing,
ecosystem ecology,
conservation biology
Blanco-Sacristán, Javier
Università degli Studi di
Milano-Bicocca (Italy)
Remote sensing,
ecosystem functioning
Berbery, Hugo
University of Maryland
(USA)
Land surface-atmosphere
interactions, climate
system, water and energy
budgets
Cabello, Javier
Universidad de Almería
(Spain)
Sustainability, ecology and
conservation, ecosystem
functions and services
Castro, Antonio
Universidad de Almería
(Spain)
Human-environment
relationships,
sustainability, social-
ecological systems
Epstein, Howard
University of Virginia
(USA)
Ecosystem functioning,
vegetation dynamics,
climate change, carbon
cycling, carbon-water
interactions, disturbances
regime
Fernández, Néstor
German Centre for
Integrative Biodiversity
Research – iDiv
(Germany)
Ecosystem functioning,
biodiversity and
conservation, ecological
modelling, remote sensing
Jobbágy, Esteban
Universidad Nacional de
San Luis (Argentina)
Ecosystem ecology,
human control of
ecosystem processes,
ecohydrology

Lourenço, Patricia
Universidade de Évora
(Portugal)
Ecosystem functioning,
remote sensing,
conservation biology
Oyonarte, Cecilio
Universidad de Almería
(Spain)
Soil science,
geochemistry, carbon
dynamics, climate change
Pacheco-Romero, Manuel
Universidad de Almería
(Spain)
Social-ecological systems,
sustainability
Paruelo, José
Universidad de Buenos
Aires (Argentina)
Ecosystem structure and
functioning, ecological
modelling, remote sensing,
ecosystem services
Peñas, Julio
Universidad de Granada
(Spain)
Conservation biology,
biodiversity, plant
ecology, biogeography
Pérez-Cazorla, Beatriz
Universidad de Almería
(Spain)
Ecosystem functioning,
remote sensing,
conservation biology
Requena-Mullor, Juan
Miguel
Boise State University
(USA)
Ecological modelling,
conservation biology,
remote sensing
Reyes, Andrés
Universidad de Almería
(Spain)
Ecosystem functioning,
remote sensing,
conservation biology
List of participants in Workshop 2 - “Towards the identification of Social-Ecological Functional
Types”
Venue: University of Buenos Aires (Argentina)
Dates: 6th - 11th February 2017
Surname / name
Institution
Area of expertise
Aguiar, Sebastián
Universidad de Buenos
Aires (Argentina)
Natural resource
management, territorial
planning, political
ecology, sustainability
Alcaraz-Segura, Domingo
Universidad de Granada
(Spain)
Remote sensing,
ecosystem ecology,
conservation biology

Bagnato, Camilo
Universidad de Buenos
Aires (Argentina)
Ecosystem functioning,
remote sensing, territorial
planning
Blanco-Sacristán, Javier
Università degli Studi di
Milano-Bicocca (Italy)
Remote sensing,
ecosystem functioning
Berbery, Hugo
University of Maryland
(USA)
Land surface-atmosphere
interactions, climate
system, water and energy
budgets
Cabello, Javier
Universidad de Almería
(Spain)
Sustainability, ecology and
conservation, ecosystem
functions and services
Epstein, Howard
University of Virginia
(USA)
Ecosystem functioning,
vegetation dynamics,
climate change, carbon
cycling, carbon-water
interactions, disturbances
regime
Fernández, Néstor
German Centre for
Integrative Biodiversity
Research – iDiv
(Germany)
Ecosystem functioning,
biodiversity and
conservation, ecological
modelling, remote sensing
Gallego, Federico
Universidad de la
República de Uruguay
(Uruguay)
Sustainability, natural
resource management,
social-ecological systems,
ecosystem services,
territorial planning
Jobbágy, Esteban
Universidad Nacional de
San Luis (Argentina)
Ecosystem ecology,
human control of
ecosystem processes,
ecohydrology
Pacheco-Romero, Manuel
Universidad de Almería
(Spain)
Social-ecological systems,
sustainability
Paruelo, José
Universidad de Buenos
Aires (Argentina)
Ecosystem structure and
functioning, ecological
modelling, remote sensing,
ecosystem services
Peñas, Julio
Universidad de Granada
(Spain)
Conservation biology,
biodiversity, plant
ecology, biogeography

Pérez-Cazorla, Beatriz
Universidad de Almería
(Spain)
Ecosystem functioning,
remote sensing,
conservation biology
Piñeiro, Gervasio
Universidad de Buenos
Aires (Argentina)
Biodiversity, ecosystem
ecology, sustainability,
natural resource
management
Vallejos, María
Universidad de Buenos
Aires (Argentina)
Sustainability, natural
resource management,
social-ecological systems,
ecosystem services,
territorial planning

Essential variables to describe the functioning of
Social-Ecological Systems
Introduction
We aim to integrate biophysical and social processes to produce a functional characterization and
mapping of social-ecological systems at the regional scale and landscape level. This survey aims to
agree on a set of 'Essential Social-Ecological Functional Variables' (ESEFVs) to be used in such
Participating Institutions
Appendix 3. Preliminary online survey

process.
A list of candidate variables is structured in three 'Components' of the social-ecological system (Social
System, Ecosystem and Interactions) and each Component into several 'Functional Dimensions'
(dimensions of the social system functioning, dimensions of ecosystem functioning, and dimensions of
the interactions between the social system and the ecosystems). Possible indicators are shown in
some cases only to exemplify, but the answers should focus on the variables (whatever the indicator
is).
*************************************************************************************************
We ask you to select and punctuate only those variables that you consider
essential to describe the functioning of social-ecological systems
*************************************************************************************************
We consider as essential those variables that encompass and integrate critical processes to
characterize the functioning of social-ecological systems. Following GEOBON approach for Essential
Biodiversity Variables, ESEFVs should be state variables, but useful for change monitoring. Also, they
should be coherent and appropriate for comparing across social-ecological systems diversity.
Spatially, these variables aim to target the ecosystem level and the human community level. Ideally,
they should be already available or technically feasible and economically viable for regional or global
implementation in monitoring programs, regional land-use planning, and sustainability and resilience
assessment. Please, feel free to visit 'E&SEFT Project' webpage (http://functionaltypes.caescg.org/)
to know about project goals, scientists involved, and other partners.
Personal data (optional)
In any case, your answers will be treated as confidential
1. First name:
2. Last name:
3. Institution/Department:
4. e-mail:
5. Area of expertise:
Selecciona todos los que correspondan.
Biophysical sciences
Social sciences
Sustainability Science
Environmental management / Territorial planning
Remote sensing
Biodiversity Science
Otro:

6. Tick if you want to be acknowledged in derived publications:
Selecciona todos los que correspondan.
Yes, include my name in the acknowledgments
7. Tick if you want to receive the results of this study:
Selecciona todos los que correspondan.
Yes, send to me the results of this study
COMPONENT 1. SOCIAL SYSTEM
Dimension 1a. Human population dynamics
(You are in: Component 1. Social System)
8. In your opinion, which variables that describe human population dynamics are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Population size
Population density
Population distribution (e.g.: %
rural population vs. % urban
population)
Age structure (e.g.: median age,
population ageing index)
Sex Ratio
Human migrations (e.g.: % of
inmigrants/emigrants in a
population)

9. Would you add/modify any variable of human population dynamics to better describe
social-ecological systems functioning? Please specify:
Dimension 1b. Well-being and development
(You are in: Component 1. Social System)
10. In your opinion, which variables that describe human well-being and development are
essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Life expectancy (e.g.: life
expectancy at birth)
Mortality (e.g.: infant mortality
rate)
Access to drinking water (e.g.:
distance to drinking water)
Electricity access
Water sanitation (e.g.: % of
houses using improved sanitation
facilities)
Overcrowding (e.g.: people/
home)
Employment (e.g.: economically
active population)
Economic level of the population
(e.g.: income per house/ per
capita)
Educational level of the population
(e.g.: illiteracy rate, % of
population with higher education,
school enrolment rate, out of
school rate for adolescents)
Social equality (e.g.: wealth
distribution, women participation in
goverment, women literacy rate)
Institutional diversity
Access to internet
Environmental quality (e.g.: air,
water and soil pollution levels)
Land protection (% of protected
area)

11. Would you add/modify any variable of social well-being and development to better
describe social-ecological systems functioning? Please specify:
COMPONENT 2. ECOSYSTEM
Dimension 2a. Carbon dynamics
(You are in: Component 2. Ecosystem)
12. Do you consider Net Primary Productivity as essential to characterize social-ecological
systems functioning?
Please, punctuate this variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Net Primary Productivity
13. Would you add/modify any variable of carbon dynamics to better describe social-
ecological systems functioning? Please specify:
Dimension 2b. Water dynamics
(You are in: Component 2. Ecosystem)

14. Do you consider evapotranspiration as essential to characterize social-ecological systems
functioning?
Please, punctuate this variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Evapotranspiration
15. Would you add/modify any variable of water dynamics to better describe social-ecological
systems functioning? Please specify:
Dimension 2c. Energy dynamics
(You are in: Component 2. Ecosystem)
16. In your opinion, which variables that describe energy dynamics are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Land surface energy balance
Land surface temperature
Albedo
17. Would you add/modify any variable of energy dynamics to better describe social-
ecological systems functioning? Please specify:
Dimension 2d. Nutrient cycling
(You are in: Component 2. Ecosystem)

18. In your opinion, which variables that describe nutrient cycling are essential to characterize
social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Nitrogen cycling
Phosphorus cycling
19. Would you add/modify any variable of nutrient cycling to better describe social-ecological
systems functioning? Please specify:
Dimension 2e. Disturbance regime
(You are in: Component 2. Ecosystem)
20. In your opinion, which variables that describe disturance regime are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Fire occurrence
Drought occurrence
21. Would you add/modify any variable of disturbance regime to better describe social-
ecological systems functioning? Please specify:
COMPONENT 3. INTERACTIONS

Dimension 3a. Ecosystem services supply
(You are in: Component 3. Interactions)
22. In your opinion, which variables that describe provisioning services supply are essential
to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Agricultural production
Livestock production
Wild plants, algae and their
outputs for food
Wild animals and their outputs for
food
Surface and ground water sources
for drinking
Surface and ground water sources
for non-drinking purposes
Fibres and other materials from
plants, algae and animals for
direct use or processing
Biomass-based energy sources

23. In your opinion, which variables that describe regulation & maintenance services supply
are essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Bio-remediation/ filtration/
sequestration/ storage/
accumulation by micro-organisms,
algae, plants, and animals (of
waste, toxics and other nuisances)
Mass stabilisation and control of
erosion rates
Hydrological cycle and water flow
maintenance
Ventilation and transpiration
Pollination and seed dispersal
Pest and disease control
Weathering, decomposition and
fixing rates (for soil formation)
Chemical conditions maintenance
of freshwaters and salt waters
Global climate regulation (by
reduction of greenhouse gas
concentrations)
24. In your opinion, which variables that describe cultural services supply are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Physical and experiential
interactions (with plants, animals,
landscapes, seascapes)
Intellectual and representative
interacions (scientific, educational,
heritage and cultural,
entertainment, aesthetic
contemplation)
Spiritual and/or emblematic
(symbolic, sacred and/or religious)
interactions
25. Would you add/modify any variable of ecosystem services supply to better describe
social-ecological systems functioning? Please specify:
Dimension 3b. Ecosystem disservices supply
(You are in: Component 3. Interactions)

26. In your opinion, which variables that describe ecosystem disservices supply are essential
to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Bio-economic (e.g.: biological
invasions, agricultural and
fisheries pests and diseases
incidence, red tydes)
Abiotic-economic (e.g.: droughts
and fires occurrence, siltation,
leaching of nutrients)
Bio-health (e.g.: human diseases
incidence from pathogens,
allergens)
Abiotic-health (e.g.: flood and
storm events occurrence )
Bio-cultural (e.g.: bird droppings
on outdoor sculptures, tree roots
cracking pavements)
Abiotic-cultural (e.g.: soil erosion
rates, mud/landslide scar events,
unpleasant odours from rotting
organic matter)
It is noted that this candidate variables express the incidence of different kinds of harmful events. For
simplicity, they have been classified according to their origin and primary dimension of human well-
being affected, following Shackleton et al. (2016) approach.
27. Would you add/modify any variable of ecosystem disservices supply to better describe
social-ecological systems functioning? Please specify:
Dimension 3c. Ecosystem services demand
(You are in: Component 3. Interactions)

28. In your opinion, which variables that describe the human capture of ecosystem goods and
services are essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Human Appropriation of Net
Primary Production (e.g.: Tn C
extracted/ha/year)
Material use level (e.g.: raw
materials consumed per capita/
per year)
Energy use level (e.g.: energy
consumed per capita/ per year)
Water use level (e.g.: water
consumed per capita/ per year)
29. Would you add/modify any variable of ecosystem services demand to better describe
social-ecological systems functioning? Please specify:
Dimension 3d. Human pressure on the environment
(You are in: Component 3. Interactions)
30. In your opinion, which variables that describe the human pressure on environment are
essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Isolation (e.g.: distance to main
roads, travel time to major cities)
Land use intensity
Carbon dioxide emissions
Pollution (toxic emissions and
spills)
31. Would you add/modify any variable of human pressure on environment to better describe
social-ecological systems functioning? Please specify:
Dimension 3e. Social-ecological coupling

Con la tecnología de
(You are in: Component 3. Interactions)
32. In your opinion, which variables that describe the degree of connection of a community to
its local environment are essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Weight of farming [industry,
services] sector in the economy
Population employed in farming
[industry, services] sectors
Land tenure structure (e.g.: %
communal lands)
Local natural capital dependence
(e.g.: % of final ecosystem
services consumed by the
population that are provided
directly by local environment)
Dependence on fossil energies
(e.g.: % of energy consumed
coming from fossil resources)
Renewable energy use (e.g.: % of
energy consumed coming from
renewable sources)
Non-ecosystem services demand
(e.g.: socioeconomic services like
hospitals, schools, culture,
internet)
Weight in the economy of the non-
ecosystem services market
Human perception of ecosystem
services
Access to natural or seminatural
areas (e.g.: distance to a natural
or seminatural area)
Human population ethnicity (e.g.:
% of indigenous population)
Local green initiatives (e.g.: in
agriculture, cities, touristic
activities, local companies)
Import [export] rates
Airports [ports] activity
33. Would you add/modify any variable of social-ecological coupling to better describe social-
ecological systems functioning? Please specify:

Essential variables to characterize the functioning of
Social-Ecological Systems
Introduction
This survey aims to collect expert opinions and knowledge about key variables to characterize social-
ecological systems functioning.
Participating Institutions
Appendix 4. Final online survey

The list of candidate variables is structured in three 'Components' of the social-ecological system
(Social System, Ecosystem and Interactions) and each Component into several 'Functional
Dimensions' (dimensions of the social system functioning, dimensions of ecosystem functioning, and
dimensions of the interactions between the social system and the ecosystem). Possible indicators are
shown in some cases only to exemplify, but the answers should focus on the variables.
We ask you to punctuate each variable according to its relevance to characterize the functioning of
social-ecological systems. A key aspect to deal with is the issue of context-dependence. We are
aware of the difficulties to assess the relevance of proposed variables without bearing in mind any
specific social-ecological system. However, we call for a common effort to identify those variables that
better explain the differences among social-ecological systems across the world.
We consider as essential those variables that encompass and integrate critical processes to
characterize the functioning of social-ecological systems. They should be coherent and appropriate
for comparing across social-ecological systems diversity. Spatially, these variables aim to target the
ecosystem level and the human community level. Ideally, they should be viable for regional or global
implementation in monitoring programs, regional land-use planning, and sustainability and resilience
assessment. Our final goal is to integrate both biophysical and social processes to produce a
functional characterization and mapping of social-ecological systems at the regional scale and
landscape level.
Please, feel free to visit the webpage of the E&SEFT Project: "Ecosystem & Socio-Ecosystem
Functional Types: integrating biophysical and social functions to characterize and map the
ecosystems of the Anthropocene” (http://functionaltypes.caescg.org/) to know more about project
goals, scientists involved, and other partners. In this webpage you can also learn more about the
variables included in this survey (selection process, definitions, etc.).
*Important: if you are viewing this survey through your mobile phone, we recommend that you use it in
horizontal position for better visualization.
Personal data (optional)
In any case, your answers will be treated as confidential
1. First name:

2. Last name:
3. Institution/Department:
4. e-mail:
5. Area of expertise:
Selecciona todos los que correspondan.
Biophysical sciences
Social sciences
Sustainability Science
Environmental management / Territorial planning
Remote sensing
Biodiversity Science
Otro:
6. Tick if you want to be acknowledged in derived publications:
Selecciona todos los que correspondan.
Yes, include my name in the acknowledgments
7. Tick if you want to receive the results of this study:
Selecciona todos los que correspondan.
Yes, send to me the results of this study
COMPONENT 1. SOCIAL SYSTEM
Dimension 1a. Human population dynamics
(You are in: Component 1. Social System)

8. In your opinion, which variables that describe human population dynamics are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Population density
Population distribution (e.g.: %
rural population vs. % urban
population)
Population size
Human migrations (e.g.: ratio of
inmigrantion/emigration)
Population growth rate by natural
increase
Population growth rate by
inmigration
Age structure (e.g.: median age,
population ageing index,
dependency ratio)
Sex Ratio
9. Would you add/modify any variable of human population dynamics to better describe
social-ecological systems functioning? Please specify:
Dimension 1b. Well-being and development
(You are in: Component 1. Social System)

10. In your opinion, which variables that describe human well-being and development are
essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Access to drinking water (e.g.:
distance to drinking water)
Water sanitation (e.g.: % of
houses using improved sanitation
facilities)
Water scarcity
Electricity access
Access to internet
Educational level of the population
(e.g.: illiteracy rate, % of
population with higher education,
school enrolment rate, out of
school rate for adolescents)
Employment (e.g.: employment
rate, unemployment rate)
Economic level of the population
(e.g.: household income, income
per capita)
Poverty (e.g. % of population with
unsatisfied basic needs)
Social equality (e.g.: wealth
distribution, women participation in
government, women literacy rate,
Gini Index)
Environmental quality (e.g.: air,
water and soil pollution levels)
Access to healthcare and other
basic social services (e.g.: % of
population receiving public
assistance)
Infant mortality rate
Life expectancy (e.g.: life
expectancy at birth)
Total fertility rate
Average household size (e.g.:
people per home)
Subjective well-being (e.g.: life
satisfaction)
Security (e.g.: crime rate)
Social trust (in government,
institutions)
11. Would you add/modify any variable of social well-being and development to better
describe social-ecological systems functioning? Please specify:
Dimension 1c. Governance

(You are in: Component 1. Social System)
12. In your opinion, which variables that describe regional governance are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Institutional diversity (degree of
polycentrism and nesting level in
government, with efficient
horizontal and vertical
coordination)
Agenda effectiveness (degree in
which the agenda is adequately
formulated and assessed to
achieve specific goals and have a
popular understanding)
Stakeholders participation in
decision making (degree of
stakeholders inclusiveness, with
an adequate leadership
arrangement and commitment to
group and purpose)
Internal capacity (degree of
sufficiency of resources -money,
information and expertise,
authority and legitimacy- to
achieve success on a specific
goal)
External capacity (skills and reach
of the government to connect to -
at both the national and
international levels- and secure
external resources to support
regional goals)
Implementation experience (level
of experience addressing regional
goals and degree of
institutionalization of these
experience in policies and
processes)
Political stability
Corruption level
Current conflicts (e.g.: armed
conflicts, political violence)
Candidate variables from 2 to 6 have been included following Foster & Barnes (2012) proposal of
indicators for regional governance.
13. Would you add/modify any variable of governance to better describe social-ecological
systems functioning? Please specify:

COMPONENT 2. ECOSYSTEM
Dimension 2a. Carbon dynamics
(You are in: Component 2. Ecosystem)
14. In your opinion, which variables that describe carbon dynamics are essential to
characterize social-ecological systems functioning?
Please, punctuate this variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Gross Primary Productivity (total
amount of carbon fixed in the
photosynthesis by plants in an
ecosystem)
Net Primary Productivity (net
productivity of organic carbon by
plants in an ecosystem, e.g.: Net
Ecosystem Exchange, Net Carbon
Flux, carbon acumulation rate)
Respiration (natural carbon
dioxide emissions by ecosystems)
Secondary productivity
(represents the formation of living
mass of a heterotrophic population
or group of populations)
Organic Carbon Storage (biomass
+ litter + soil organic carbon)
Radiation Use Efficiency (organic
carbon produced by unit of
absorbed solar radiation)
Ecosystem composition by Plant
Functional Types (plant
classification according to their
physical, phylogenetic and
phenological characteristics)

15. Would you add/modify any variable of carbon dynamics to better describe social-
ecological systems functioning? Please specify:
Dimension 2b. Water dynamics
(You are in: Component 2. Ecosystem)
16. In your opinion, which variables that describe water dynamics are essential to
characterize social-ecological systems functioning?
Please, punctuate this variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Precipitation (water + snow)
Snow precipitations
Snow storage
Horizontal precipitation (e.g.: fog,
dew, frost)
Extra-precipitation water
contributions (e.g.: surface or
groundwater inputs by rivers or
acuifers, respectively)
Potential evapotranspiration
Actual evapotranspiration
Potencial water deficit -or excess-
(due to climate conditions)
Actual water deficit -or excess-
(due to climatic and
ecohydrological conditions)
Evaporation - Transpiration ratio
Soil water infiltration capacity
Deep drainage (to aquifers)
Groundwater depth
Actual Soil Water Storage
Total water yield or "blue water"
(runoff + deep drainage)
Flows of green water (water in and
on soils and on vegetation
canopy)
Precipitation Use Efficiency
(organic carbon produced by unit
of precipitation or by unit of
evapotranspiration)
Vegetation water stress (e.g.
precipitation minus [potential or
actual] evapotranspiration)

17. Would you add/modify any variable of water dynamics to better describe social-ecological
systems functioning? Please specify:
Dimension 2c. Surface energy balance
(You are in: Component 2. Ecosystem)
18. In your opinion, which variables that describe surface energy balance are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Net solar radiation (insolation)
Downward shortwave (visible [0.4-
0.8 µm] + near ultraviolet [0.4-0.3
µm] + near infrared [0.8-2.5 µm])
radiation
Upward shortwave (visible [0.4-0.8
µm] + near ultraviolet [0.4-0.3 µm]
+ near infrared [0.8-2.5 µm])
radiation (i.e. albedo)
Upward longwave radiation
(electromagnetic radiation)
Sensible heat, land surface
temperature
Downward longwave radiation
(thermal infrared [2.5-50 µm])
Latent heat flux (heat spent in
water evapotranspiration)
Snow heat flux
Deep ground heat flux
Air temperature
19. Would you add/modify any variable of surface energy balance to better describe social-
ecological systems functioning? Please specify:
Dimension 2d. Nutrient cycling
(You are in: Component 2. Ecosystem)

20. In your opinion, which variables that describe nutrient cycling are essential to characterize
social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Nitrogen fixation (atmospheric
nitrogen fixed by N-fixer
organisms, e.g.: Rhizobium)
Nitrogen deposition (wet and dry
deposition of ammonium, nitrate,
and particulate nitrogen)
Phosphorus deposition (e.g.:
aerosols and atmospheric dust,
etc.)
Gross nitrogen mineralization
(e.g.: rate of production of
ammonium in soils)
Net nitrogen mineralization (e.g.:
net rate of production of plant-
available nitrogen)
Soil phosphorus availability (e.g.:
concentrations of non-occluded
soil phosphorus)
Nitrogen status of plants (e.g.:
plant tissue nitrogen
concentrations)
Phosphorus status of plants (e.g.:
plant tissue phosphorus
concentrations)
21. Would you add/modify any variable of nutrient cycling to better describe social-ecological
systems functioning? Please specify:
Dimension 2e. Disturbance regime
(You are in: Component 2. Ecosystem)

22. In your opinion, which variables that describe disturance regime are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Drought occurrence [frequency,
severity, extension]
Fire occurrence [frequency,
severity, extension]
Flood occurrence [frequency,
severity, extension]
Herbivory (natural, not cattle
grazing) [frequency, severity,
extension]
Pest outbreaks occurrence
[frequency, severity, extension]
Hurricanes/ storms occurence
[frequency, severity, extension]
Landslides occurrence [frequency,
severity, extension]
Volcanic eruptions occurrence
[frequency, severity, extension]
23. Would you add/modify any variable of disturbance regime to better describe social-
ecological systems functioning? Please specify:
COMPONENT 3. INTERACTIONS
Dimension 3a. Ecosystem services supply
(You are in: Component 3. Interactions)

24. In your opinion, which variables that describe provisioning services supply are essential
to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Agricultural production
Livestock production
Surface and ground water sources
for drinking
Surface and ground water sources
for non-drinking purposes
Biomass-based energy sources
Fibres and other materials from
plants, algae and animals for
direct use or processing
Wild plants, algae and their
outputs for food
Wild animals and their outputs for
food
25. In your opinion, which variables that describe regulation & maintenance services supply
are essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Hydrological cycle and water flow
maintenance
Local climate regulation
Pollination and seed dispersal
Pest and disease control
Bioremediation
Chemical conditions maintenance
of freshwaters and salt waters
Mass stabilisation and control of
erosion rates
Ventilation (air renewal)
26. In your opinion, which variables that describe cultural services supply are essential to
characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Physical and experiential
interactions (with plants, animals,
landscapes, seascapes)
Intellectual and representative
interacions (scientific, educational,
heritage and cultural,
entertainment, aesthetic
contemplation)
Spiritual and/or emblematic
(symbolic, sacred and/or religious)
interactions

This candidate variables have been adapted from the Common International Classification of
Ecosystem Services (CICES) 4.3 version (‘class’ level of this classification for provisioning and
regulating services, and 'group' level for cultural services) (European Environment Agency, 2013).
27. Would you add/modify any variable of ecosystem services supply to better describe
social-ecological systems functioning? Please specify:
Dimension 3b. Ecosystem disservices supply
(You are in: Component 3. Interactions)
28. In your opinion, which variables that describe ecosystem disservices supply are essential
to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Bio-economic (e.g.: biological
invasions, agricultural and
fisheries pests and diseases
incidence, red tydes)
Abiotic-economic (e.g.: droughts
and fires occurrence, siltation,
leaching of nutrients)
Bio-health (e.g.: human diseases
incidence from pathogens,
allergens)
Abiotic-health (e.g.: flood and
storm events occurrence )
Bio-cultural (e.g.: bird droppings
on outdoor sculptures, tree roots
cracking pavements)
Abiotic-cultural (e.g.: soil erosion
rates, mud/landslide scar events,
unpleasant odours from rotting
organic matter)
It is noted that this candidate variables express the incidence of different kinds of harmful events. For
simplicity, they have been classified according to their origin and primary dimension of human well-
being affected, following Shackleton et al. (2016) approach.

29. Would you add/modify any variable of ecosystem disservices supply to better describe
social-ecological systems functioning? Please specify:
Dimension 3c. Ecosystem services demand
(You are in: Component 3. Interactions)
30. In your opinion, which variables that describe the human capture of ecosystem goods and
services are essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Water use level (e.g.: water
consumed per capita/ per year)
Water use for irrigated agriculture
(e.g.: water use per hectare/ per
year)
Energy use level (e.g.: energy
consumed per capita/ per year)
Material use level (e.g.: raw
materials consumed per capita/
per year)
Human Appropriation of Net
Primary Production (e.g.: Tn C
extracted/ per hectare/ per year)
Appropriation of land for
agriculture
Nature tourism (e.g.: number of
visitors to natural areas)
31. Would you add/modify any variable of ecosystem services demand to better describe
social-ecological systems functioning? Please specify:
Dimension 3d. Human actions on the environment
(You are in: Component 3. Interactions)

32. In your opinion, which variables that describe the human actions on the environment are
essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Land cover/Land use change
(e.g.: agriculturization,
urbanisation, land abandonment)
Land use intensity
Territorial connectivity (e.g.:
distance to main roads, travel time
to major cities)
Anthropogenic water management
(e.g.: water delivery, drainage and
storage systems)
Anthropogenic carbon dioxide
emissions (e.g.: per capita CO2
emissions, CO2 emissions by
sector of economic activity)
Net carbon dioxide flux (e.g.: CO2
emissions - CO2 sequestration)
Pollution (toxic emissions and
spills)
Eutrofization of water bodies
Soil erosion (by anthropogenic
practices)
Conservation tillage (sustainable
agricultural practices for soil
preservation)
Ecological restoration
Land protection (e.g.: % of the
territory declared as natural
protected area with a
management plan)
33. Would you add/modify any variable of human actions on the environment to better
describe social-ecological systems functioning? Please specify:
Dimension 3e. Social-ecological coupling
(You are in: Component 3. Interactions)

34. In your opinion, which variables that describe the degree of connection of a community to
its local environment are essential to characterize social-ecological systems functioning?
Please, punctuate each variable according to its relevance for being considered as 'Essential
Social-Ecological Functional Variable' (from 1 "less essential" to 5 "more essential")
Marca solo un óvalo por fila.
No essential 1 2 3 4 5
Local natural capital dependence
(e.g.: % of final ecosystem
services consumed by the
population that are provided
directly by local environment)
Import [export] rates of agricultural
and livestock products
Weight in the economy of the non-
ecosystem services market
(goods and services that do not
come directly from ecosystems,
e.g.: socioeconomic services like
hospitals, schools or culture,
internet, manufactured products,
technology)
Airports [ports] activity
Dependence on fossil energies
(e.g.: % of energy consumed
coming from fossil resources)
Renewable energy use (e.g.: % of
energy consumed coming from
renewable sources)
Weight of sectors in the economy
(agriculture vs. industry vs.
services)
Weight of traditional (vs. intensive)
agricultural and livestock sector in
the economy
Population employed by sectors
(agriculture vs. industry vs.
services)
Population employed in traditional
(vs. intensive) agriculture and
stockbreeding
Biocapacity (capacity of
ecosystems to meet people's local
demand and assimilate waste
products)
Land tenure (e.g.: % communal
lands vs. private lands vs.
government lands)
Access to natural or seminatural
areas (e.g.: distance to a natural
or seminatural area)
Human perception of ecosystem
services (awareness level of the
population about services
provided by local ecosystems)
Human population ethnicity (e.g.:
% of indigenous population)
Cultural attachment to nature
Local green initiatives (e.g.: in
agriculture, cities, touristic
activities, local companies)

Con la tecnología de
No essential 1 2 3 4 5
Non-ecosystem services demand
(goods and services that do not
come directly from ecosystems,
e.g.: socioeconomic services like
hospitals, schools or culture,
internet, manufactured products,
technology)
35. Would you add/modify any variable of social-ecological coupling to better describe social-
ecological systems functioning? Please specify:

Appendix 5. Tables
Table A5.1. Preliminary and enhanced lists of variables for characterizing and
monitoring SESs, structured into dimensions across the three components of a SES. The
preliminary list contains 77 variables structured into 12 dimensions and was generated
through literature review and an initial expert workshop. The improved list contains 149
variables structured into 13 dimensions and was the result of analyzing the preliminary
survey results (56 responses) in a second scientific workshop. This improved list was then
introduced in the final survey with the aim of using scientist scorings to prioritize the
variables.
Component
Dimension
Preliminary list (77 variables
in 12 dimensions)
Improved list (149 variables
in 13 dimensions)
Social system
Human population
dynamics
Population density
Population distribution
Population size
Human migrations
Age structure
Sex Ratio
Population density
Population distribution
Population size
Human migrations
Age structure
Sex Ratio
Population growth rate by
natural increase
Population growth rate by
immigration
Wellbeing and
development
Access to drinking water
Water sanitation
Electricity access
Access to internet
Educational level of the
population
Employment
Economic level of the
population
Social equity
Environmental quality
Mortality
Overcrowding
Life expectancy
Institutional diversity
Access to drinking water
Water sanitation
Electricity access
Access to internet
Educational level of the
population
Employment
Economic level of the
population
Social equity
Environmental quality
Infant mortality rate
Average household size
Life expectancy
-

Land protection
-
Water scarcity
Poverty
Access to healthcare and
other basic social services
Total fertility rate
Subjective wellbeing
Security
Social trust
Governance (not
included in 1st survey)
Institutional diversity
Agenda effectiveness1
Stakeholders participation
in decision making1
Internal capacity1
External capacity1
Implementation experience1
Political stability
Corruption level
Current conflicts
Ecological
system
Organic carbon
dynamics
(Carbon dynamics in 1st
survey)
Net Primary Productivity
Net Primary Productivity
Gross Primary Productivity
Respiration
Secondary productivity
Organic carbon storage
Radiation Use Efficiency
Ecosystem composition by
Plant Functional Types
Water dynamics
Evapotranspiration
Actual evapotranspiration