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Sustainability science recognizes the importance of the integrated assessment of the ecological and social systems in land-use planning. However, most studies so far have been conceptual rather than empirical. We developed a framework to characterize the social-ecological systems heterogeneity according to its functioning through the identification of social–ecological functional types (SEFT). The SEFT framework builds on the plant, ecosystem and agent functional type approaches, taking a step forward to integrate the dimensions of social–ecological systems into an operational product to characterize administrative units in a hierarchical way. To illustrate this novel framework, we described the heterogeneity of SEFT in the Argentine Chaco by clustering administrative entities. This area is a global deforestation hotspot and has diverse social actors that harness ecosystem services in multiple, and sometimes contrasting and conflictive, ways which determines an urgent need for land-use planning. We combined data from national census and remote sensing to identify SEFT by clustering census tracts based on 17 input variables that integrate key human, ecological and interaction processes across landscapes. We identified three classes and eight subclasses of SEFT. Ecological variables defined the first level of heterogeneity (classes), while human variables and the variables of interactions between the human and ecological components defined a second level of heterogeneity (subclasses). The degree of anthropization and mean annual productivity were important variables to explain the first two axes in the ordination (32% of the total variance). This framework offers a conceptually novel and comprehensive approach to understand the spatial heterogeneity of social–ecological systems functioning, which could play a pivotal role to support conservation or land-use planning in rural areas.
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Social-Ecological Functional Types:
Connecting People and Ecosystems
in the Argentine Chaco
M. Vallejos,
* S. Aguiar,
G. Baldi,
M. E. Mastra
F. Gallego,
M. Pacheco-Romero,
D. Alcaraz-Segura,
and J. M. Paruelo
Laboratorio de Ana
´lisis Regional y Teledeteccio
´n (LART), IFEVA, Facultad de Agronomı
´a, Universidad de Buenos Aires, CONICET,
Buenos Aires, Argentina;
Departamento de Me
´todos Cuantitativos y Sistemas de Informacio
´n, Facultad de Agronomı
´a, Universidad
de Buenos Aires, Buenos Aires, Argentina;
Instituto Nacional de Investigacio
´n Agropecuaria, INIA La Estanzuela, Ruta 50 km 11,
Colonia, Uruguay;
´tedra de Ecologı
´a, Facultad de Agronomı
´a, Universidad de Buenos Aires, Buenos Aires, Argentina;
Instituto de
´tica Aplicada San Luis, Universidad Nacional de San Luis and CONICET, San Luis, Argentina;
Grupo de Estudio de
Agroecosistemas y Paisajes Rurales, Universidad Nacional de Mar del Plata, CONICET, Balcarce, Argentina;
Grupo de Ecologı
Pastizales, Instituto de Ecologı
´a y Ciencias Ambientales, Universidad de la Repu
´blica, Montevideo, Uruguay;
Departamento de
´a y Geologı
´a, Universidad de Almerı
´a, Almerı
´a, Spain;
Centro Andaluz para la Evaluacio
´n y Seguimiento del Cambio Global,
Universidad de Almerı
´a, Almerı
´a, Spain;
Departamen de Bota
´nica, Facultad de Ciencias, Universidad de Granada, Av. Fuente-
nueva s/n. 18071, Granada, Spain;
iecolab, Interuniversitary Institute for Earth System Research (IISTA), University of Granada, Av.
del Mediterra
´neo s/n. 18006, Granada, Spain
Sustainability science recognizes the importance of
the integrated assessment of the ecological and social
systems in land-use planning. However, most stud-
ies so far have been conceptual rather than empiri-
cal. We developed a framework to characterize the
social-ecological systems heterogeneity according to
its functioning through the identification of social–
ecological functional types (SEFT). The SEFT
framework builds on the plant, ecosystem and agent
functional type approaches, taking a step forward to
integrate the dimensions of social–ecological sys-
tems into an operational product to characterize
administrative units in a hierarchical way. To illus-
trate this novel framework, we described the
heterogeneity of SEFT in the Argentine Chaco by
clustering administrative entities. This area is a glo-
bal deforestation hotspot and has diverse social ac-
tors that harness ecosystem services in multiple, and
sometimes contrasting and conflictive, ways which
determines an urgent need for land-use planning.
We combined data from national census and remote
sensing to identify SEFT by clustering census tracts
based on 17 input variables that integrate key hu-
man, ecological and interaction processes across
landscapes. We identified three classes and eight
subclasses of SEFT. Ecological variables defined the
first level of heterogeneity (classes), while human
variables and the variables of interactions between
the human and ecological components defined a
second level of heterogeneity (subclasses). The de-
gree of anthropization and mean annual productiv-
ity were important variables to explain the first two
axes in the ordination (32% of the total variance).
This framework offers a conceptually novel and
comprehensive approach to understand the spatial
heterogeneity of social–ecological systems func-
tioning, which could play a pivotal role to support
conservation or land-use planning in rural areas.
Received 10 January 2019; accepted 21 June 2019
Electronic supplementary material: The online version of this article
( contains supplementary
material, which is available to authorized users.
Author’s Contribution MV, SA, GB, MEM, FG and JMP conceived of
or designed the study; MPR and DAS contributed new methods or
models; MV analyzed data, performed research and wrote the paper and
SA, GB, MEM, FG, MPR, DAS and JMP participated in the wring process.
*Corresponding author; e-mail:
Ó2019 Springer Science+Business Media, LLC, part of Springer Nature
Key words: social–ecological systems; land-use
planning; remote sensing; functional types; hier-
archical analysis.
Social–ecological functional types (SEFT) are
administrative units that share a similar social–
ecological functioning
SEFT were defined by clustering administrative
units based on variables that integrate key
human ecological and interaction processes
across landscapes
The degree of anthropization and mean annual
productivity were the variables that captured
most of the spatial variability of SEFT
Ecological variables (for example primary pro-
ductivity) defined a first level of heterogeneity
and human variables a second one
Solutions to environmental problems increasingly
require systemic perspectives that integrate key
aspects of the structure and function of ecological
and social systems (Holling 2001). Although the
full complexity of systems can never be character-
ized by maps (Hamann and others 2015), the def-
inition of relatively homogeneous units in terms of
its social–ecological functioning can contribute to
land-use planning by spatially characterizing social
and ecological dynamics together (Martı´n-Lo
and others 2017). Traditionally, rural planning has
characterized the spatial heterogeneity of land-
scapes through land zoning (that is, the delimita-
tion of homogeneous areas) mainly based on
biophysical properties (FAO 1996). Thus, human
dimensions were seldom considered and biophysi-
cal properties were generally related to ecosystems
structural attributes, whereas functional ones gen-
erally received less attention (Guerry and others
2015). The explicit spatial integration of these ne-
glected properties of social–ecological systems could
be an operational tool for understanding complex
systems and defining evidence-based policies for
sustainable development. This study develops a
conceptual framework to map social–ecological
systems according to their functioning. Using the
Argentine Chaco as a case study, we applied this
framework in order to identify social–ecological
functional types (SEFT) by clustering the smallest
possible administrative unit at two nested levels.
This area is a global deforestation hotspot and has
diverse social actors that harness ecosystem services
in multiple, and sometimes contrasting and con-
flictive, ways which determines an urgent need for
land-use planning.
Functional characterizations are increasingly being
considered a central aspect for sustainable man-
agement (Oliver and others 2015). According to
Jax (2010), the functioning of a system refers to its
‘performance’ or ‘behavior,’ which is regulated by
different mechanisms and interactions at multiple
spatial and temporal scales. Previous studies have
characterized functional types for ecological and
human systems separately. For ecological systems,
plant functional types were defined as groups of
plants with a similar response to environmental
conditions or with a similar effect on ecosystems
(Walker 1997), so its use has spread, for example,
to predict the responses of vegetation to global
change (Bonan and others 2002). In a higher level
of organization, ecosystem functional types were
defined as ecosystems that regardless of their
structure and composition have similar matter and
energy dynamics (Paruelo and others 2001), and
were useful for capturing the spatial and temporal
heterogeneity of ecosystem functioning, as well as
for improving the performance of general atmo-
sphere circulation models (Mu
¨ller and others
2014). For human systems, agent functional types
were theoretically defined as groups of agents with
a similar role and behavior regarding decision
making related to land-use change (Arneth and
others 2014). Although the usefulness of functional
approaches for different levels of organization is
widely acknowledged, a framework for classifying
social–ecological systems according to its function-
ing, to our knowledge, has not been developed yet.
As plant species, ecosystems and agents can be
grouped according to common functional charac-
teristics, social–ecological systems should be too.
Social–ecological systems are understood as sys-
tems where ecological and social components are
strongly coupled through multiple mechanisms of
interaction (Ostrom 2009). The social component
benefits from the services provided by the ecosys-
tem and, in turn, human agency modifies—directly
or indirectly—the functioning and structure of
ecosystems (Berkes and others 2003). As social–
ecological systems cannot be identified or found in
M. Vallejos and others
nature (they are defined by the observer under a
certain conceptual framework or question), their
spatial limits are strongly dependent on the specific
perspectives of the observer. Tracing these bound-
aries can be done in many different ways depend-
ing on the problem to be addressed and the data
availability (Martı´n-Lo
´pez and others 2017). Al-
though natural scientists frequently use landscape
units or pixels as entities, social scientists focus on
the individuals, households, farms or communities
as units of analysis and demographers or decision
makers base their analyses on administrative units
to achieve better management efficiency.
Most studies on social–ecological systems in the
literature have been conceptual rather than
empirical (Herrero-Ja
´uregui and others 2018).
However, there have been some efforts to develop
approaches for mapping them by combining bio-
physical and social attributes. At the global scale,
Ellis and Ramankutty (2008) used data on human
population density, land use and land cover to
derive a classification of anthromes (that is,
anthropogenic biomes). More recently, Va
and others (2013) integrated land-use intensity
indicators with underlying environmental and
socioeconomic factors to map global land system
archetypes. On a regional level, Alessa and others
(2008) identified social–ecological hotspots in the
Kenai Peninsula of Alaska, as areas where there
was a convergence between the social (human
perceptions of the biological value of the landscape)
and ecological (high net primary productivity)
space. Based on the ecosystem services framework,
Raudsepp-Hearne and others (2010) mapped the
supply of ecosystem service bundles (that is, groups
of services that appear together repeatedly) that
allowed to identify emerging social–ecological
dynamics across diverse landscapes in Quebec,
Canada. With a similar approach, Hamann and
others (2015) derived a classification based on the
direct use level of local ecosystem services by
households in South Africa, using national popu-
lation censuses. Although the spatial integration of
the human and ecological components has grown
in recent years, there is still a lack of operational
mapping approaches for characterizing the social–
ecological system from a functional perspective.
In this study, we adopted the social–ecological
system framework presented by the Resilience Al-
liance (2007, p. 8) as a basis to develop a more
detailed framework to characterize the functioning
of the social–ecological systems (see Figure 1). This
framework emphasizes the interdependence and
two-way feedbacks that exist between humans and
the environment. In this framework, the social–
ecological system is composed of three intercon-
nected components, which comprises two or three
key dimensions. Within the human component,
the population distribution is the way in which
people are spatially arranged in the territory,
whereas well-being and development integrate
aspects related to housing, work, income, educa-
tion, health status, governability and social con-
nections, among others (OECD 2015). Within the
ecological component, the natural capital refers to
the stock of natural resources, which can provide
people with free goods and services, while the
ecosystem functioning refers to the fluxes of energy
and matter that sustain the ecosystem over time
and space (Jax 2010). Within the interaction
component, the pressure on the environment re-
flects the impact of human activities on the
ecosystem; the ecosystem services are the benefits
people obtain from the natural environment (Mil-
lennium Ecosystem Assessment 2005), and the
territorial link reflects the degree of decoupling
between people and their surrounding environ-
ment (Liu and others 2007; Hamann and others
We define social–ecological functional types
(SEFT) as administrative units that share a similar
social–ecological functioning, that is, have similar
dynamics in terms of (a) the socioeconomic aspects
(human component), (b) biophysical aspects (eco-
logical component) and (c) the two-way interac-
tions between the previous subsystems
(interactions component). This approach can be
useful to explore complex systems, to understand
Figure 1. Conceptual diagram of the functional
framework for assessing social–ecological systems. Each
component (that is human, ecological and interactions) is
comprised of two or three dimensions (for example,
population distribution), which in turn should be
characterized by one or more variables (for example,
occupation of the territory) and its indicator (for
example, population density) [Adapted and modified
from Resilience Alliance (2007, p. 8)].
Social-Ecological Functional Types
the spatial heterogeneity of social–ecological sys-
tems, to provide better inputs for modeling and as a
tool to support conservation or land-use planning
(for example, guiding participatory processes to
regulate land use). The questions that guided this
research are: (1) How to characterize SEFT using
attributes of the social–ecological system function-
ing? (2) Which are the SEFT in the Argentine
Chaco at the census tract resolution? (3) How are
the attributes of social–ecological functioning cor-
related in the study area? (4) What are the spatial
patterns among the SEFT classes at different levels
of nesting?
Study Area
The Humid and Dry Chaco ecoregions (hereafter,
Chaco, see location in Figure A.1—Appendix
A—Supplementary Material) (Olson and others
2001) of Argentina have a great biological and
productive diversity (Baldi and others 2015), cul-
tural richness (Leake 2008) and agricultural
potential (Murray and others 2016), as well as high
levels of poverty and inequality (Paolasso and
others 2012). The social actors that occupy this
region include large-scale capitalized farmers,
small-scale farmers (‘puesteros’), indigenous com-
munities and mennonite colonists, among others
(Baldi and others 2015). These actors interact with
the environment in multiple ways by capturing
ecosystem services and modifying the environment
(for example, deforestation for industrial agricul-
ture, silvopastures for livestock farming, selective
logging or hunting and gathering). The rapid con-
version of native forests to annual crops and pas-
tures makes the Chaco region a global deforestation
hotspot (Hansen and others 2013; Vallejos and
others 2015). This transformation brought an in-
crease in the production of commodities (for
example, crops, meat) for the foreign markets, but
at the same time is compromising the supply of
multiple ecosystem services (Grau and others 2005;
Paruelo and others 2011; Mastrangelo and Laterra
2015), creating social inequalities (Laterra and
others 2019) and increasing social conflicts (Redaf
´ceres 2015; Aguiar and others 2016). De-
spite some legislation efforts (for example, Senado
´mara de Diputados de la Nacio
´n Argentina
2007, Law N°26,331), the territorial configuration
responds more to an economic rationality, than to
ecological, social and cultural criteria (Garcı´a Col-
lazo and others 2013; Aguiar and others 2018). The
rapid changes are affecting the configuration of the
territory, not only in the ecological aspects (natural
capital, ecosystem functioning), but also in the
human aspects (population distribution, well-being
and development) and in the interaction between
both (pressure on the environment, ecosystem
services and territorial link).
Data Sources and Preprocessing
The approach we followed to define SEFT consists
of integrating human, ecological and interaction
variables across rural landscapes to identify homo-
geneous administrative entities in terms of their
social–ecological functioning. The resolution of the
analysis was the ‘rural census tract,’ -that is, group
of farms—an administrative unit designed to carry
out the field surveys of agricultural censuses in
Argentina. These divisions are delimited according
to the way that population is distributed and settled
in rural areas (divisions contain an average of 300
households), and also by the accessibility and dis-
tance between households, achieving a mean area
of 180 km
. Census data at this resolution are the
finest and publicly available because the personal
data protection prohibits the distribution of census
data at finer resolutions in Argentina (Senado y
´mara de Diputados de la Nacio
´n Argentina 1968,
Law N°17,622). Because this study focused on
rural land-use planning, census tracts correspond-
ing to urban areas were eliminated. From 2188
rural census tracts, 22 were discarded due to
missing or incongruent information (for example,
area under irrigation larger than the total land), so
we retained 2166 units.
For the classification of SEFT, we selected 17
descriptive variables, following the functional
framework for assessing social–ecological systems
described above (adapted and modified from the
Resilience Alliance 2007, p. 8), that is, we selected
variables and indicators according to the available
data (Table 1) to characterize the dimensions of the
three components of the framework (human, eco-
logical and interactions). We gathered data from
multiple governmental open-access databases, the
‘National Census of Population, Households and
Housing’ (INDEC 2001) and the ‘National Agri-
cultural Census’ (INDEC 2002). To characterize the
ecosystem functioning, we used the 2002 seasonal
dynamics of the enhanced vegetation index images
derived from MODIS 005.MOD13Q1 product (16-
day and 232 m resolution). Mean annual EVI and
the intra-annual coefficient of variation were used
as indicators of the primary productivity and its
seasonality (difference in C gains between the
growing and non-growing seasons), respectively.
M. Vallejos and others
These two indicators are descriptors of ecosystem
functioning (Alcaraz-Segura and others 2006; Vo-
lante and others 2012) and have also been suc-
cessfully used to quantify the provision of
ecosystem services (Paruelo and others 2016). All
variables where aggregated at the census tract res-
olution. We omitted the use of data from last Na-
tional Agricultural Census of Argentina (2008), as
Table 1. Variables Used for the Definition of Socio-ecosystem Functional Types at the Census Tract (CT)
Component Dimension Variable Indicator Metric Abbreviation Data source
Human Population
of territory
Population den-
Pop INDEC (2001)
and devel-
Quality of life Poverty Unsatisfied basic needs Pov INDEC (2001)
Employment Permanent labor #Permanent workers/
1000 ha
Lab INDEC (2002)
Number of
School Ministerio de
Ecological Natural cap-
Natural cover Remnant native
Native forest area/CT
area (%)
Nf INDEC (2002)
Productivity Primary produc-
tivity (mean)
Annual mean of EVI
(year 2002)
PPm MOD13Q1-
EVI - 2002
Seasonality Primary produc-
tivity (coeffi-
cient of
Annual CV of EVI
(year 2002)
PPs MOD13Q1-
EVI - 2002
Interactions Pressure on
the envi-
Surface with
Irrigated area/km
Irrig INDEC (2002)
Machinery Number of trac-
#Tractors/1000 ha Tract INDEC (2002)
Stocking rate Livestock unit (cattle
stock area (ha)
Cattle INDEC (2002)
Loss of natu-
ral cover-
Natural cover
Deforested area/CT
area (%)
Defor LART (2012)
Annual crops
Annual crops area/CT
area (%)
Crops INDEC (2002)
Forage crops
Forage crops area/CT
area (%)
Forage INDEC (2002)
Cattle breed-
ing activity
Cow pregnancy #Pregnant cows/Total
#cows (%)
Calf INDEC (2002)
Access Transport net-
work connec-
Km of roads/km
Roads Instituto Geo-
´fico Na-
cional (2015)
Type of man-
Legal type of
Area with legal type of
farmer ‘Physical
Person’/CT area (%)
Farmer INDEC (2002)
Land tenure Tenure regime Area with land tenure
regime ‘Owner’/CT
area (%)
Tenure INDEC (2002)
Social-Ecological Functional Types
it turned out to be unreliable due to a non-ex-
haustive survey (Giarracca and others 2008).
Statistical Analyses
We explored the Pearson correlation between the
17 variables to analyze the sign and magnitude of
the relationships between them. We standardized
the variables and performed a hierarchical cluster-
ing analysis using Ward’s method (Ward 1963)to
identify SEFT classes and subclasses, in two nested
levels of detail. Hierarchical clustering is useful to
recognize discontinuities in the dataset of multiple
variables, where the units of inferior-ranking
clusters (subclasses) are members of larger and
higher ranking (classes) (Legendre and Legendre
1998). Then, we performed a principal component
analysis to understand the multivariate structure of
data and to find which of the variables are more
important to describe the heterogeneity of the data
between the census tracts (Hair and others 2010).
Once the SEFT subclasses were defined, we map-
ped them using QGIS development team (2016).
Finally, we described each SEFT class and subclass
in terms of the indicators conforming each class.
Data analysis was done with R Core Team (2015),
using the following packages: ‘cluster’ (Maechler
and others 2015), ‘ggplot2’ (Wickham 2009), ‘cor-
rplot’ (Taiyun Wei 2013), ‘rgdal’ (Bivand and oth-
ers 2015), ‘raster’ (Hijmans 2015) and ‘sp’
(Pebesma and Bivand 2005).
Correlation Between Variables
Regarding the correlation between variables, of the
136 possible pairwise combinations, 75% were
significant (p<0.01). Of these, 66% presented
positive relationships, while the remaining 34%
were negative. The most significant positive corre-
lation was the occupation of the territory (Pop) and
the machinery (Tractors)(r= 0.63). The most sig-
nificant negative correlations were between the
natural cover (Nf) and the agricultural production
(Crops), and between the productivity (PPm) and its
seasonality (PPs)(r=-0.40, both) (Figure 2).
SEFT Classification
Rural census tracts were classified into three classes
and eight subclasses of SEFT by using two cutting
levels in the hierarchical clustering analysis. Class
1, 2 and 3 (defined by the first level) are composed
by 550, 634 and 982 census tracts, respectively.
Subclasses 1a, 1b, 1c, 2a, 2b, 3a, 3b and 3c (defined
by the second nested level) are composed by 157,
136, 257, 332, 302, 95, 252 and 635 census tracts,
respectively (Figure 3). The principal component
(PC) 1, (21.6% of the total variance), corresponds
to an axis that varies from less-transformed census
tracts with greater remnant native forests to more
transformed tracts with higher population density.
The PC 2, (10.9% of the total variance), corre-
sponds to a gradient that goes from tracts with
greater livestock pressure and higher mean annual
productivity to tracts with greater agricultural
activity and greater seasonality (Figure 4). Principal
component analysis showed that seven axes were
needed to explain 75% of the spatial variability
between the census tracts (see complete analysis in
Table B.1—Appendix B—Supplemental Material).
SEFT Mapping
We mapped the social–ecological functional types
in the Argentine Chaco at the census tract resolu-
tion (Figure 5). Not all the census tracts are repre-
sented on the map because some tracts were
discarded from the analysis (lack or inconsistency
of data). We also described SEFT classes and sub-
classes in terms of the variables used for their
classification (see Figure C.1 and C.2—Appendix
B—Supplemental material).
Class 1 (Agricultural functioning systems) occupied
census tracts in the western and central area of the
study region, coinciding with foci of agricultural
advance in the margins of the Dry Chaco, where
rainfall is greater. This class covers an area of
62,000 km
, and it has, on average, high popula-
tion density (Pop), high levels of well-being and
development (Schools &Roads), high land-use
intensity (Crops,Forage,Defor,Irrig &Tract), high
employment (Lab), high seasonality in the pro-
ductivity (PPs) and low natural cover (Nf). Subclass
1a (Small-scale intensive agriculture) was located
mainly in small-sized census tracts located in the
province of Tucuma
´n, where the sugarcane is the
predominant crop. This subclass had the highest
population density (Pop), employment (Lab),
machinery (Tract) and agricultural production
(Crops). Subclass 1b (Traditional middle-scale agricul-
ture) was located mainly in the central southwest-
ern region of the province of Chaco, where
immigrant settlers deforested for cotton production
in the mid-twentieth century and were then con-
verted to the soybean production. This subclass has
the highest loss of natural cover in the region (De-
for) in the region. Subclass 1c, (Expanding agri-
business), was located in new foci of agricultural
expansion in the eastern and western Dry Chaco.
M. Vallejos and others
Figure 2. Correlation analyses between descriptive variables. The correlation coefficients are represented by ellipses (the
more pronounced the shape of the ellipse, the greater the correlation) and colors (degrees of blue for positive correlations
and degrees of red for negative correlations, the legend for correlation coefficients is at the left). The correlation
significance is also shown (*are not significant correlations, pvalue >0.01).
Figure 3. Hierarchical cluster analysis to identify social–ecological functional types. Three classes and eight subclasses
were identified in the Argentine Chaco at the census tract resolution.
Social-Ecological Functional Types
This was the subclass with the highest livestock
industrial production (Forage) and the greatest
irrigated area (Irrig) of the whole region.
Class 2 (Extensive stockbreeding functioning systems)
is located mainly in the eastern portion of the study
region, coinciding with the Humid Chaco, where
agricultural aptitude is low due to the water surplus
during the rainy season, and the predominance of
low and flooded land. This class covers an area of
124,000 km
, and has, on average, high produc-
tivity (PPm), low seasonality (PPs), low deforesta-
tion rates (Defor), high proportion of cattle breeding
activities (Calf) and high stocking rates (Cattle).
Subclass 2a (Cattle breeding dominance) was located
in the eastern and southern part of the Humid
Chaco, where the floods are mostly of fluvial origin
(Paraguay and Parana
´Rivers floodplains). This
subclass had the highest proportion of cattle
breeding activities (Calf) of the whole region. Sub-
class 2b (Livestock farming dominance) was located in
the northern and eastern part of the Humid Chaco.
This subclass had the highest stocking rate (Cattle)
of the whole region. Within Class 2, it is observed
that subclass 2a has lower primary productivity
mean (PPm) and higher seasonality (PPs) than
subclass 2b, because in 2a, there is naturally more
pasture than in 2b. The deforestation rates are
lower in part because the amount of native forests
is lower too.
Class 3 (Forest functioning systems) occupy most of
the western zone of the study region, coinciding
with the central and southern portion of the Dry
Chaco, where rainfalls are low and the lands are
marginal for agriculture. This class covers an area of
340,000 km
, and has, on average, high native
forest area (Nf), low productivity (PPm), low pres-
sure on the environment (Irrig, Tract, Cattle, Defor),
low population density (Pop), low levels of well-
being and development (Pov, Lab, Schools) and low
access (Roads). Subclass 3a (Subsistence activities
dominance) was located in census tracts with pre-
dominance of indigenous communities or small
Figure 4. Principal component analysis (PCA) for the eight subclasses of SEFT. The first axis explains 21.6% of the
variability of the data, whereas the second axis, 10.9%. Pop: population density; Pov: unsatisfied basic needs (poverty);
Lab: permanent labor; School: number of schools; Nf: native forest area; PPm: mean net annual primary productivity; PPs:
annual seasonality of net primary productivity; Irrig: irrigated area; Tract: number of tractors per area; Cattle: stocking rate;
Defor: natural cover transformation; Crops: annual crops area; Forage: forage crops area; Calf: % cow pregnancy; Roads:
roads density; Farmer: legal type of farmer; Tenure: tenure regime.
M. Vallejos and others
farmers. This subclass had the lowest dominance of
physical person as legal type of farmer (Farmer).
This means that other legal types of farmer are
dominant (for example, societies, cooperatives,
nonprofit institutions, national public entities).
Subclass 3b (Low intensity livestock dominance) was
located in census tracts in the southern part of the
Dry Chaco, corresponding with the Arid Chaco.
This subclass had the lowest productivity (PPm)in
the whole region, and the highest proportion of
cattle breeding activity (Calf) of Class 3. Subclass 3c
(Incipient agriculture) was located in census tracts in
the northern part of the Dry Chaco, where agri-
culture is expanding. This subclass had the highest
productivity (PPm) and agricultural production
(Crops) within Class 3, but also has the highest le-
vels of structural poverty in the whole region (Pov).
Mapping SEFT allowed us to characterize and
understand the heterogeneity of the social–eco-
logical systems in the Chaco. We identified three
classes and eight subclasses, in a nested level of
Figure 5. Social–ecological functional types in the Argentine Chaco at census tract resolution (corresponding to the year
2002). Eight subclasses of SEFT are shown in the map: 1a, 1b, 1c, 2a, 2b, 3a, 3b and 3c. The polar diagrams at the right of
the figure illustrate the contribution of the 17 variables for each subclass of SEFT. All the variables are scaled from 0 to 1.
Variables: 1. Pop (population density); 2. Pov (poverty); 3. Lab (permanent labor); 4. School (number of schools); 5. Nf
(native forest area); 6. PPm (mean annual primary productivity); 7. PPs (annual seasonality of primary productivity); 8.
Irrig (irrigated area); 9. Tract (number of tractors per area); 10. Cattle (stocking rate); 11. Defor (natural cover
transformation); 12. Crops (annual crops area); 13. Forage (forage crops area); 14. Calf (% cow pregnancy); 15. Roads
(roads density); 16. Farmer (legal type of farmer); 17. Tenure (land tenure regime).
Social-Ecological Functional Types
detail at the census tract resolution. These classes
represent relatively homogeneous units in terms of
its social–ecological functioning. Our results
showed that ecological variables defined the first
level of heterogeneity (classes), while human and
interaction variables defined a second level of
heterogeneity (subclasses). This supports not only
the importance of considering the human dimen-
sion when zoning the territory but also suggest a
hierarchy of the controls that determine the spatial
distribution of SEFT.
Class 1 is associated with Agricultural functioning
systems, where there is a high pressure on the
environment, high seasonality of the productivity
and intermediate levels of well-being. Class 2 is
associated with Extensive cattle functioning systems,
where there is a high livestock production, a lower
pressure on the environment in relation to the first
class, high mean annual productivity and low sea-
sonality of the productivity. Class 3 is associated
with Forest functioning systems, where there is a high
native forest area, high structural poverty, pre-
dominance of de-capitalized producers and low
mean annual productivity. Class 2 and 3 are clearly
associated with biogeographical areas (Humid and
Dry Chaco, respectively), while Class 1 is inter-
spersed mainly over Class 3, reflecting the advance
of agriculture mainly in the Dry Chaco. In this re-
gion, agriculture is expanding over areas with less
suitability, and land clearing dynamics are associ-
ated with the proximity to already cleared areas,
defining a frontier-advancement pattern which
suggests the prevalence of a contagion process
(Volante and others 2016). In the last two decades,
agriculture has drastically expanded into the Chaco
ecoregion, due to favorable political and economic
factors in Argentina, increasing soybean prices and
new genetically modified varieties, between other
aspects (Pengue 2005;Ca
´ceres 2015; Piquer-Ro-
drı´guez and others 2018). Although it was not
possible to capture the current state of social–eco-
logical systems in this study, we can assume that
the expanding agri-business (that is, Subclass 1c)
has spread out in many census tracts of the study
area in the present.
The degree of anthropization and mean annual
productivity were important variables to explain
the first two axes in the ordination (32% of the
total variance). Biophysical and social variables
showed a low-to-moderate level of covariation,
evidencing their high complementarity for the
definition of the SEFT classes. In fact, the seven of
axes needed to explain 75% of the spatial vari-
ability in the principal component analysis reveals
the complex and multidimensional nature of so-
cial–ecological systems. Although in this case the
reduction in dimensionality was not possible, we
support the use of multivariate analyses in order to
understand the structure of the data and, hence,
the importance of each variable for the definition of
When studying the correlation of variables at the
census tract resolution, we observed that census
tracts located in disadvantaged environments (that
is, with low productivity) were also those that
showed less pressure on the environment (low
population density, technification level and natural
cover transformation). Moreover, we observed that
in these areas, where agri-business agriculture is
not feasible and the natural cover has not been
replaced, the levels of well-being and development
are low (high poverty, low employment and edu-
cational infrastructure). This reflects a clear con-
nection between land quality and poverty,
suggesting a long-term ‘accumulation by dispos-
session’ process (Harvey 2004;Ca
´ceres 2015),
where smallholders are on poor land because they
have been expelled by large farmers. This process of
marginalization of the less capitalized stakeholders
in lower-quality lands entails a potential risk of
poverty traps (Aghion and Durlauf 2005), as mar-
ginal producers make use of the surrounding forest
intensifying the degradation of natural resources,
which in turn feeds back the level of poverty
(Duraiappah 1998). This situation is difficult to
overcome without an appropriate redistribution of
wealth, and the implementation of incentives for
marginalized lands. The results presented here
could be used to identify the areas prone to poverty
traps to apply specific interventions oriented to-
ward promoting rural development and halting
environmental degradation.
The identification of SEFT using administrative
units as entities is useful for land-use planning
because it represents the space where interests and
problems relevant to local stakeholders connect
with decision makers (Martı´n-Lo
´pez and others
2017). Studying social and ecological aspects to-
gether at lower resolutions (for example, land-
holding level) also matters because of its direct link
with the unit in which stakeholders make deci-
sions. This would require the collection of primary
data from interviews and field surveys, and the use
of high spatial resolution satellite information. The
choices over scale, extent and resolution critically
affect the type of patterns that are observed, be-
cause patterns that appear at one level of resolution
or extent may be lost at lower or higher scales
(Gibson and others 2000). So, the observed pat-
terns cannot be extrapolated to other scales
M. Vallejos and others
(Peterson and others 1998). Complex systems must
be analyzed and managed by performing a simul-
taneous analysis at various scales and approaches
(Viglizzo and others 2005; Cumming and others
2006). At a regional scale, coarse resolutions are
enough to answer questions about the general
patterns of interaction between social and natural
systems, but to study smaller geographic areas,
more detailed resolutions will be required (Gibson
and others 2000).
Despite the complexity of social–ecological sys-
tems, it is possible to assess them in a degree of
simplicity necessary for understanding, but also
with the required complexity to develop policies for
sustainability (Holling 2001). Nevertheless, con-
ceptualizing the systems functioning is a matter of
both scientific knowledge and values. The defini-
tion of the system limits, the selection of variables
and the classification methods are always depen-
dent on the observer and his/her specific interests
or problems to be tackled. Depending on the
objectives and resolution of the study other criteria
for the selection of variables may be used. Once the
classification has been conducted and distinct
modes of functioning were identified, the question
of which of those modes of functioning is prefer-
able (or desirable) is still a matter of evaluation by
human observers (Theobald and others 2005).
Each mode of functioning can be considered as
‘proper functioning’ for its kind of system and state,
or not. Each productive system has ‘winners’ and
‘losers’ among the parts involved and impacted,
and also in terms of the services that human may
derive from the system (Jax 2010). Although the
definition of SEFT is not absolutely neutral (since
the choice of the unit to be classified, the variables
for its classification and also the number of classes
are made by humans), the advantage of using SEFT
for understanding the social–ecological hetero-
geneity of the territory lies in the transparency of
the process. Therefore, it could be a useful tool to
guide participatory processes in land-use planning
and to define sustainable policies in more trans-
parent and integrative ways (for example, seeking
for consensus with regard to the expansion of
particular activities in a SEFT class), as a function of
the overall performance or functioning of the sys-
Land-use planning implies a public policy that
must reconcile the process of economic develop-
ment and the conservation of natural capital
through the regulation of land use, with the ulti-
mate goal of increasing the human well-being and
the equity in the distribution of cost and benefits
associated with land use. The identification and
mapping of SEFT constitutes a social–ecological-
based zoning process, where territorial units have a
similar behavior or functioning in terms of its so-
cial–ecological vulnerability, adaptability and resi-
lience (Chapin and others 2009). The framework
developed here might help decision makers to
understand the social–ecological context to estab-
lish restrictions, incentives or to promote an effec-
tive spatial arrangement of activities. For example,
the area of the triple border of the Santiago del
Estero, Chaco and Santa Fe provinces shares the
same biophysical characteristics. However, provin-
cial boundaries define different SEFT due to dif-
ferences in the human aspects or the interaction
between the biophysical and ecological compo-
nents (Figure 5). Whereas in eastern Santiago del
Estero SEFTs associated with an expanding agri-
business were dominant, in western Chaco domi-
nant SEFT were associated with more traditional
middle-scale agriculture. In northern Santa Fe, in
contrast, dominant SEFT were associated with
subsistence activities or cattle breeding. All these
SEFT present contrasting socio-ecological arrange-
ment and, consequently, different potential roles or
functions in the region (for example, providing
commodities for exports, meat or areas for conser-
vation). The presences of distinct SEFT would also
indicate differences in stakeholders and actual or
potential conflicts. Such discrimination clearly
indicates the need of different intervention policies.
In short, the use of SEFT facilitates a more inclusive
understanding of the territory in a comprehensive
and transparent framework and could be useful to
identify areas within which to develop similar
management policies in accordance with the
objectives of the land-use planning. Improving ac-
cess to scientific information could help decision
makers anticipate potential consequences of rural
land-use change and in doing so, avoid unintended
ecological and social effects. Finally, the developed
framework can be applied to understand the
heterogeneity, complexity and trends of social–
ecological systems in other regions of the world.
This research was supported by Consejo Nacional
de Investigaciones Cientı´ficas y Te
´cnicas (Ar-
gentina), Universidad de Buenos Aires (Argentina).
This work was carried out with the aid of a grant
from the Inter-American Institute for Global
Change Research (IAI) CRN III 3095, which is
supported by the US National Science Foundation
(Grant GEO-1128040), and an associated comple-
mentary project financed by CONICET. The project
Social-Ecological Functional Types
CGL2014-61610-EXP, which is supported by Di-
´n General de Investigacio
´n Cientı´fica y Te
nica, Ministerio de Economı´a y Competitividad
(Spain) also provided fundings for the research. We
would also like to thank anonymous referees and
the editors, who have made valuable comments to
a previous version of the manuscript, and to Dr.
Ignacio Gasparri who also made useful comments.
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M. Vallejos and others
... Here, we used a holistic perspective that incorporates multiple dimensions of SESs across the social and ecological system, as well as the interactions between them (Liu et al., 2007;Reyers et al., 2017). In line with recent SES studies (e.g., Dressel et al., 2018;Rocha et al., 2020;Vallejos et al., 2020), we used an existing conceptual SES framework to organize indicators and to characterize the identified types with the aim of promoting: 1) comprehensiveness (Meyfroidt et al., 2018;Rocha et al., 2020); 2) knowledge comparison and generalization (Dressel et al., 2018;Partelow, 2018); and 3) the credibility and salience of the analysis (van Oudenhoven et al., 2018). We suggest that the combination of a temporal dimension and comprehensiveness represents a major step towards capturing the full complexity of SES mapping to inform resource management and landscape planning (Hamann et al., 2015;Levers et al., 2018). ...
... Overall, our approach can thus contribute to enhancing SES archetype analyses by 1) integrating different levels of abstraction (Oberlack et al., 2019) that keep the context specificity of regional SES diversity while linking to Table B.5). Background colour for SESs follows the colour code of Fig. 3A, while line colour indicates the specific SECH affecting to each SES, following the colour code of Fig. 3B. globally recognisable, generic archetypes; 2) generating more potentially comparable and transferable insights across scales and contexts (Eisenack et al., 2019;Sietz et al., 2019); and 3) improving the usefulness and adaptability of archetypes to support territorial and resource use planning (Hamann et al., 2015;Sietz et al., 2017;Vallejos et al., 2020), considering SESs as units of management at regional scale . ...
... Yet, finer granulation and/or multi-scale analyses could usefully extend this approach and resolve surprising outcomes such as urban areas (likely red-loop systems), that were embedded within wider green-loop systems. Third, although a comprehensive set of variables is needed to identify and characterize SESs and SECHs (Levers et al., 2018;Rocha et al., 2020;Vallejos et al., 2020), the resulting complexity can make it challenging to clearly interpret them. Developing a base set of essential socialecological variables could further facilitate interpretation and crosscomparison (Cox et al., 2020;Pacheco-Romero et al., 2020). ...
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Archetype analysis is a key tool in landscape and sustainability research to organize social-ecological complexity and to identify social-ecological systems (SESs). While inductive archetype analysis can characterize the diversity of SESs within a region, deductively derived archetypes have greater interpretative power to compare across regions. Here, we developed a novel archetype approach that combines the strengths of both perspectives. We applied inductive clustering to an integrative dataset to map 15 typical SESs for 2016 and 12 social-ecological changes (1999–2016) in Andalusia region (Spain). We linked these types to deductive types of human-nature connectedness, resulting in a nested archetype classification. Our analyses revealed combinations of typical SESs and social-ecological changes that shape them, such as agricultural intensification and peri-urbanization in agricultural SESs, declining agriculture in natural SESs or population de-concentration (counter-urbanization) in urban SESs. Likewise, we identified a gradient of human-nature connectedness across SESs and typical social-ecological changes fostering this gradient. This allowed us to map areas that face specific sustainability challenges linked to ongoing regime shifts (e.g., from rural to urbanized systems) and trajectories towards social-ecological traps (e.g., cropland intensification in drylands) associated with decreasing human-nature connectedness. This provides spatial templates for targeting policy responses related to the sustainable intensification of agricultural systems, the disappearance of traditional cropping systems and abandonment of rural lands, or the reconnection of urban population with the local environment, among others. Generally, our approach allows for different levels of abstraction, keeping regional context-specificity while linking to globally recognisable archetypes, and thus to generalization and theory-building efforts.
... We considered the results at two nested spatial levels of detail (1 st level corresponds to social-ecological regions or SER and 2 nd level to social-ecological land systems or SELS) because findings at different levels can complement each other and improve analysis robustness (Sietz et al. 2017, Vallejos et al. 2020. The authors analyzed the clustering outputs (spatial layout, cluster's statistics, and method's performance metrics) at successive dendrogram cuts in relation to their territorial knowledge to agree on the optimal number of clusters. ...
... Consideramos los resultados del agrupamiento en dos niveles de detalle espacial anidados (1er nivel corresponde a las Regiones Socio-Ecológicas -SER-y el 2do nivel a los Sistemas Territoriales Socio-Ecológicos -SELS-) ya que los diferentes niveles aportan información complementaria y mejoran la robustez del análisis (Sietz et al. 2017, Vallejos et al. 2020. Los autores analizamos los productos del agrupamiento (distribución espacial, estadísticas de los grupos, y métricas de desempeño del método) en cortes sucesivos del dendrograma (árbol) en relación a su conocimiento territorial para acordar en el número óptimo de grupos. ...
Humans place strong pressure on land and have modified around 75% of Earth’s terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world.
... Despite recent efforts, there is no consensus on how to analytically delineate and define SES boundaries, or zones with consistent social and ecological characteristics, organized by a common set of system variables, and defined by diverse sets of socio-ecological dynamics (Jones et al., 2019;Quintas-Soriano et al., 2022). Current SEZ mapping efforts (e.g., Cruz-Cardenas et al., 2017;Dressel et al., 2018;Hamann et al., 2015;Hanspach et al., 2016;Kok et al., 2016;Martín-L opez et al., 2017;Vallejos et al., 2019;Zhang et al., 2011) have used a similar three-step analytical approach (Appendix S1: Figure S1). Briefly, these are as follows: (1) collection of spatially referenced social and bio-geo-physical data, followed generally by application of dimension-reduction methods (e.g., principal components analysis [PCA]); (2) cluster analyses to separately identify social and ecological system types; and (3) synthesis of social and ecological classification types into conjoined SEZs, based on spatial overlap. ...
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Policy interest in socio‐ecological systems has driven attempts to define and map socio‐ecological zones (SEZs), that is, spatial regions, distinguishable by their conjoined social and bio‐geo‐physical characteristics. The state of Idaho, USA, has a strong need for SEZ designations because of potential conflicts between rapidly increasing and impactful human populations, and proximal natural ecosystems. Our Idaho SEZs address analytical shortcomings in previously published SEZs by: (1) considering potential biases of clustering methods, (2) cross‐validating SEZ classifications, (3) measuring the relative importance of bio‐geo‐physical and social system predictors, and (4) considering spatial autocorrelation. We obtained authoritative bio‐geo‐physical and social system datasets for Idaho, aggregated into 5‐km grids = 25 km2, and decomposed these using principal components analyses (PCAs). PCA scores were classified using two clustering techniques commonly used in SEZ mapping: hierarchical clustering with Ward's linkage, and k‐means analysis. Classification evaluators indicated that eight‐ and five‐cluster solutions were optimal for the bio‐geo‐physical and social datasets for Ward's linkage, resulting in 31 SEZ composite types, and six‐ and five‐cluster solutions were optimal for k‐means analysis, resulting in 24 SEZ composite types. Ward's and k‐means solutions were similar for bio‐geo‐physical and social classifications with similar numbers of clusters. Further, both classifiers identified the same dominant SEZ composites. For rarer SEZs, however, classification methods strongly affected SEZ classifications, potentially altering land management perspectives. Our SEZs identify several critical regions of social–ecological overlap. These include suburban interface types and a high desert transition zone. Based on multinomial generalized linear models, bio‐geo‐physical information explained more variation in SEZs than social system data, after controlling for spatial autocorrelation, under both Ward's and k‐means approaches. Agreement (cross‐validation) levels were high for multinomial models with bio‐geo‐physical and social predictors for both Ward's and k‐means SEZs. A consideration of historical drivers, including indigenous social systems, and current trajectories of land and resource management in Idaho, indicates a strong need for rigorous SEZ designations to guide development and conservation in the region. Our analytical framework can be broadly applied in SES research and applied in other regions, when categorical responses—including cluster designations—have a spatial component.
... Em cada escala, a complexidade do sistema aumenta conforme aspropriedades emergentes entram em cena (instituições, normas, fenômenos biofísicos). Esta é uma maneira muito simples de conceituar a agricultura e fenômenos relacionados em múltiplas escalas, como nas abordagens dos sistemas agrários (COCHET, 2012), sistemas sócio-ecológicos(VALLEJOS et al., 2020;WITTMAN et al., 2017) e agroecologia(FRANCIS et al., 2003;MÉNDEZ;BACON;COHEN, 2013).Intrínseca a esta visão é a compreensão da agricultura como um fenômeno sócioecológico. Análogo ao conceito de ecótono na Ecologia 23 , pensamos na agricultura como um socioecótono, algo que emerge da tensão entre o ser humano e a natureza exterior, entre o sistema social e o sistema natural, o meio ambiente e a sociedade. ...
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The issue of the transition of agricultural systems to states of strong multifunctionality has been addressed. The concept of multifunctionality has been used to situate and analyze agriculture in the context of broader social changes towards sustainability, highlighting and valuing functions of agriculture beyond primary production (and the productivist logic), and recognizing it as an activity promoting sustainable development. According to the normative view of multifunctionality (MF), the transition trajectories of rural establishments can be conceptualized as multifunctional trajectories within a spectrum bounded by productivist and non-productivist action and thinking, ranging between weak, moderate, and strong MF. Strong MF is characterized by well-developed social, economic, cultural, moral, and environmental capitals, and can be realized through diversified, agroecological, and localized systems. Given this, the main question of the project is: how can rural establishments transition to states of strong multifunctionality that simultaneously promote adequate livelihoods, food sovereignty, and natural resource conservation? Objective: To explore the inter-relationships between human, socio-economic and agro-environmental factors and the quality of multifunctionality of agroecosystems. Methodology: This work is a mixed-methods research in the form of a case study. An a priori conceptual model was developed based on the following concepts: multifunctionality of agriculture; livelihoods approach; agroecology. Based on this model, the phenomenon of multifunctionality of agriculture, and its expression in the context of the municipality of Iperó/SP will be investigated. From a socio-ecological perspective, socio-economic, human and agro-environmental factors of the landscape/territory will be analyzed, to infer about their inter-relationships and their influences on the level of landscape multifunctionality. Data collection will be through participant observation and interviews with farming families, heads of non-family establishments and other local actors relevant to the study (rural extension technicians, representatives of institutions that support agriculture and rural development, managers and public agency officials). Conclusion: this study contributed to the development of an integrative conceptual model that highlighted the factors that influence the performance of multifunctionality. The expression of multifunctionality in a family agroecosystem is a complex process, influenced by several internal and contextual factors and mechanisms.
... These data could provide deep insights into how deforestation frontiers advance and help to identify typical, recurrent patterns of deforestation frontiers. Recently, detecting and assessing such archetypical human-environment interactions has become a major focus in sustainability science 18,19 , including for identifying static land systems 20 or driver/outcome constellations 8,21,22 . In this study we have pioneered the use of archetype analyses to identify the major, recurring patterns in global deforestation frontiers. ...
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Tropical dry woodlands are rapidly being lost to agricultural expansion, but how deforestation dynamics play out in these woodlands remains poorly understood. We have developed an approach to detect and map high-level patterns of deforestation frontiers, that is, the expansion of woodland loss across continents in unprecedented spatio-temporal detail. Deforestation in tropical dry woodlands is pervasive, with over 71 Mha lost since 2000 and one-third of wooded areas located in deforestation frontiers. Over 24.3 Mha of deforestation frontiers fall into what we term ‘rampant frontiers’. These are characterized by drastic woodland loss and conditions favourable for capital-intensive agriculture, as seen in the South American Chaco and Southeast Asia. We have found many active and emerging frontiers (~59% of all frontiers), mostly in the understudied dry woodlands of Africa and Asia, where greater frontier monitoring is needed. Our approach enables consistent, repeatable frontier monitoring, and our global frontier typology fosters comparative research and context-specific policymaking. Agricultural expansion is responsible for tree loss in tropical dry woodlands, but the dynamics of such loss are not well understood. This study presents a global, high-resolution assessment of deforestation dynamics in dry woodlands and provides a tool for consistent monitoring.
... Specifically, the identification and mapping of SES archetypes as typologies of cases (sensu Oberlack et al 2019) allows to translate the SES concept into the territory and make it spatially explicit, by delineating territorial units that share similar social, ecological, and human-nature interaction patterns (e.g. SES archetypes by Rocha et al 2020, socio-ecological functional types by Vallejos et al 2020). SES maps can work as templates for decision-makers to develop more integrative and sustainable models of territorial management that consider the coupling between human and natural systems (Oberlack et Recently, the identification of SES archetypes as typologies of cases has moved towards more integrative perspectives that consider the multidimensional aspects of human-nature interactions. ...
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The spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives guided by SES conceptual frameworks and reference lists of variables. However, hardly any studies have investigated how to empirically identify the most relevant set of indicators to map the diversity of SESs. In this study, we propose a data-driven methodological routine based on multivariate statistical analysis to identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Taking Andalusia (Spain) as a case study, we applied this methodological routine to 86 indicators representing multiple variables and dimensions of the SES. Additionally, we assessed how the empirical relevance of these indicators contributes to previous expert and empirical knowledge on key variables for characterizing SESs. We identified 29 key indicators that allowed us to map 15 SES archetypes encompassing natural, mosaic, agricultural, and urban systems, which uncovered contrasting land sharing and land sparing patterns throughout the territory. We found synergies but also disagreements between empirical and expert knowledge on the relevance of variables: agreement on their widespread relevance (32.7% of the variables, e.g., crop and livestock production, net primary productivity, population density); relevance conditioned by the context or the scale (16.3%, e.g., land protection, educational level); lack of agreement (20.4%, e.g., economic level, land tenure); need of further assessments due to the lack of expert or empirical knowledge (30.6%). Overall, our data-driven approach can contribute to more objective selection of relevant indicators for SES mapping, which may help to produce comparable and generalizable empirical knowledge on key variables for characterizing SESs, as well as to derive more representative descriptions and causal factor configurations in SES archetype analysis.
... The Argentine Dry Chaco (ADC) is bio-culturally rich, being originally inhabited by 25 indigenous groups of 6 language families, then settled by Spanish descendants in the late 19th century, followed by the arrival of European immigrants in the early 20th century, and in the last decades by extra-regional capitalized farmers who acquired large tracts of land (Morello et al., 2005). Nowadays, these historical inhabitants coexist, sometimes in conflict (Morello et al., 2005;Aguiar et al., 2016), with capitalized farmers, which determines the high social diversity of the ADC (Baldi et al., 2015;Vallejos et al., 2019). Although the ADC has the highest proportion of rural population in Argentina, its population density is relatively low (Paolasso et al., 2012). ...
Dry forests are among the most threatened ecosystems globally, due to agricultural expansion driven by the increasing demand for food, fibers, and energy in developed and emerging countries. Among these, the forests of the South American Gran Chaco are one of the global deforestation hotspots. The Argentine Dry Chaco has been the focus of several studies that assess the factors that drive forest conversion. However, these studies do not describe the causal relationships among these drivers and seldom use existing theory to select drivers. Here we employ a theory-driven approach to test the relative merits of alternative and complementary hypotheses to explain the drivers and mechanisms explaining the unequal spatial distribution of forest loss and maintenance in the Argentine Dry Chaco from 2000 to 2010. Using structural equation modeling, we quantified the direct and indirect effects of multiple drivers and compared the explanatory power and parsimony of these alternative hypotheses, i.e. the biophysical, infrastructure, socio-demographic, institutional, and the integration of them. For both forest loss and maintenance, the model containing infrastructural drivers had the best balance between parsimony and explanatory power. Integrated models, comprising a combination of drivers, had the highest explanatory power (R² = 0.81 for forest maintenance, and R² = 0.58 for forest loss). We show that biophysical constraints operate directly and indirectly: soil suitability had direct effects on forest cover maintenance, while precipitation affected it both directly and indirectly through influencing the institutional (land tenure) and infrastructure (road density). Indigenous communities positively affected forest maintenance both directly and indirectly mediated by non-private land tenure. Our results suggest that disentangling the structure of the relationships among drivers could increase our capacity for understanding and steering land-use change. Furthermore, policies for halting deforestation might increase their effectiveness by accounting for the mechanisms that underlie forest loss and maintenance.
... However, social, and ecological issues have recently been studied in an integrated way in semiarid Chaco. This research includes analysis of natural resources appropriation by peasant people (Arístide 2014) and sustainable use of forest carried out by indigenous people and peasant (Matteucci et al. 2016), evaluation of appropriation of ecosystem services by different social actors (Vallejos et al. 2020), assessment of trade-offs between social and ecological goals (Mastrangelo and Laterra 2015), development of a framework to characterize socioecological systems (Vallejos et al. 2019), and sustainability assessment through the integration of indicators of different domains (Seghezzo et al. 2020). ...
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Forest replacement and degradation driven by crop expansion and livestock intensification are some of the main global socio-ecological threats, severely affecting the dry Chaco region (main dry forest in America). By involving stakeholders, whose actions are decisive in dealing with the problem under analysis, we assessed the interactions among processes of multiple dimensions and spatial scales, currently controlling communal forest degradation in 11 peasant communities of Taboada, Ibarra and Salavina departments, in Santiago del Estero province, Argentina. Then, by reconstructing historical processes undergone by these communities over the last century, we analyzed how different system settings have conducted to the system collapse (forests and community loss) or strengthened its adaptive capacity facing natural disturbances (droughts) and anthropogenic stressors (economic shocks, land disputes). This work unveils system attributes related to native resource management and economic diversification on the farm, family and community structure, and social networking with peasant organizations and other institutions, crucial for building social–ecological resilience. Alternative trajectories are shown towards degradation (throughout a downward spiral, often followed by peasant exodus and deforestation) or restoration. Our results would explain why forest (protection) law and state subsidies aimed at sustainable management have been insufficient and suggest some clues to reorient them.
... This region also has high natural biodiversity coexisting with diverse cultures (Leake, 2008). Rural activities are carried out by rural actors of different size and composition, including capitalized farmers, small-scale farmers and indigenous communities Vallejos et al., 2020). Most of the native human populations of the Chaco do not possess formal land tenure rights and are being evicted due to agricultural expansion (Busscher et al., 2020). ...
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Deforestation control is one of the major challenges worldwide. The aim of this study was to analyse deforestation under the Forest Law in the Argentine Dry Chaco ecoregion a decade after its enactment and to assess compliance with forest protection standards in this region. For this purpose, we overlapped the provincial land zoning maps with an annual plot level deforestation database and, for some provinces, with the rural cadastral cartography. Deforestation exceeding the values allowed by the Forest Law and the provincial zonings during this period totalized 722,782 ha (28% of the total deforested area in this period), of which 59,732 ha were deforested in high conservation value areas, 644,396 ha in medium conservation value areas and 18,654ha in low conservation value areas. While Santiago del Estero was the province with the highest deforested area in medium conservation value areas, Córdoba was the province with the highest deforested area in high conservation value areas. Our results are an important step towards identifying discrepancies between the legal objectives and the observed results and represent an input to think about solutions to improve the environmental governance of the region.
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Sustainable intensification (SI) has become a central issue in both academic and political-institutional debates. Questions mostly center on the term’s conceptual scope. In this article, we outline an operational definition of SI based on (1) a more explicit characterization of the intensification process that describes the intensity/magnitude of the management interventions generating stress or disturbances in the system, (2) a description of the relative change in sustainability based on quantifying ecosystem services supply changes among alternative uses, and 3) the definition of “impact functions” of a given management intervention as the relationships between the level of supply of given ES (or a “bundle” of ES) and an indicator of the intensification process.
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Latin America exhibits one of the highest rates of biodiversity and ecosystem services (ES) loss worldwide along with a remarkable asymmetry in the access to ES benefits (ecosystem services inequality, ESI hereafter). The objective of this manuscript is to propose and validate a conceptual model to understand the links between ESI and ecosystem services supply. First, previous ES frameworks were expanded to acknowledge the role of the unequal access to ES on socio-ecological system dynamics. Second an ESI conceptual model was posed to testing feed-back mechanisms between ESI and natural capital. Finally, independent information and expert opinions on ten case studies of five Latin American countries were used to quali-quantitatively validate the ESI model. The most rated ESI impacts were landscape and seascape transformations driven by the markets, overuse of natural capital, ecosystems degradation, and biodiversity loss. This study highlights that ESI may enhance the vulnerability of the socio-ecological systems, describing a self-reinforcing mechanism that differentially affects the well-being of the most economically disadvantaged beneficiaries (ESI traps). However, while the occurrence of ESI traps was inferred for half of the examined cases, remaining cases suggest that potential ESI traps did not operate, or that they were dampened by governance mechanisms.
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En un complejo escenario ambiental, productivo y socioeconómico, el 28 de noviembre de 2007 fue sancionada en Argentina la Ley Nacional Nº 26.331 de “Presupuestos Mínimos de Protección Ambiental de los Bosques Nativos" (conocida como "Ley de bosques") con el propósito de proteger los bosques nativos a escala nacional. En este artículo nos proponemos realizar una síntesis crítica de la información disponible acerca de esta ley a diez años de su sanción, con una aproximación que toma en cuenta aspectos ambientales, económicos y sociales. Caracterizamos el desempeño de esta ley en la Región Chaqueña en cuanto a diferentes dimensiones, identificamos sus principales desafíos y describimos una serie de propuestas que desde el sector de Ciencia y Técnica pueden contribuir a su (re)diseño e implementación en el contexto de las actualizaciones de los Ordenamientos Territoriales de Bosques Nativos provinciales. Para ello, integramos información disponible proveniente de distintas fuentes, tales como normativas (nacionales y provinciales), literatura científica, informes de organismos estatales y de ONG y artículos periodísticos. La Ley de Bosques instaló en la opinión pública de nuestro país la problemática vinculada a la pérdida de bosques nativos y se ha posicionado como el principal instrumento de política forestal nacional para su protección. Si bien hubo una reducción en las tasas de deforestación en la región Chaqueña, no existen evidencias certeras de que esta reducción se deba a su aplicación. La Ley de Bosques en la Región Chaqueña presenta una serie de desafíos para mejorar su desempeño en cuanto a su efectividad, equidad y legitimidad social. En este trabajo se presentan diez observaciones que emergen de la revisión realizada. Por otro lado, se esbozan una serie de propuestas de investigación y acción en torno a la ley vinculadas a esas observaciones.
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An increasing number of studies demonstrate the need of applying a social-ecological system approach for landscape planning. However, there is a lack of empirical research that operationalizes the concept of social-ecological system for landscape planning through the characterization of social-ecological interactions. In this study, we develop a methodological framework to delineate the boundaries of social-ecological systems and to characterize their main social-ecological units in a spatially explicit way. Social-ecological units represent the interactions between the biophysical and socioeconomic subsystems at local scale. The methodology is structured in four phases: (1) ecological regionalization, i.e. identification and mapping of consistent ecological units based on bio-physical variables; (2) socioeconomic regionalization, i.e. identification and mapping of homogeneous groups of municipalities based on socioeconomic variables; (3) identification of social-ecological systems boundaries and characterization of social-ecological units; and (4) validation of the social-ecological systems boundaries with key informants through participatory mapping. By applying the proposed methodological framework to three different Mediterranean cultural landscapes, we define the boundaries of social-ecological systems and illustrate how social and ecological subsystems interact at local scale. We conclude that the proposed methodological framework is useful to operationalize the concept of social-ecological systems in landscape planning.
Agricultural expansion and intensification in South America's dry forests and grasslands increase agricultural production, but also result in major environmental trade-offs. The Pampas and Chaco regions of Argentina have been global hotspots of agricultural land-use change since the 2000s, yet our understanding of what drives the spatial patterns of these land-use changes remains partial. We parameterized a net returns model of agricultural land-use change to estimate the probability of agricultural expansion (conversions of woodlands to either cropland or grazing land) and agricultural intensification (conversion of grazing land to cropland) at the 1-km scale for the years 2000 and 2010. Uniquely, our model allowed us to quantify the importance of underlying causes (i.e., changes in agricultural profit) and spatial determinants (i.e., soil fertility, distance to markets, etc.), for Argentina's prime agricultural regions as a whole. We found that cropland and grazing land expansion into woodlands was much less sensitive to changes in profit-related factors than agricultural intensification. Profit-related variables, were a particularly strong cause of intensification in the Pampas, where cropland profits rose by 29% (compared to 18% in the Chaco). This suggests that further conversions of grazing land to cropland in the Pampas and Chaco is likely as long as agricultural demand, and thus returns to agriculture, continue to be high. The moderate impact of profit-related factors on affecting woodland conversion rates also suggests a limited potential of economic policies that affect marginal profits (e.g., taxes or subsidies) to alter deforestation rates and patterns in major ways. Policies that target socioeconomic variables not included in our profit-focused framework (e.g., capital availability), area-based interventions (e.g., land zoning), or less-profit oriented actors (e.g., via community-based management) might be more effective in addressing deforestation rates in the Chaco.