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Phil. Trans. R. Soc. B (2012) 367, 3158–3177
doi:10.1098/rstb.2011.0349
Research
Navigating challenges and opportunities of
land degradation and sustainable livelihood
development in dryland social –ecological
systems: a case study from Mexico
Elisabeth Huber-Sannwald*, Mónica Ribeiro Palacios,
José Tulio Arredondo Moreno, Marco Braasch, Ruth
Magnolia Martı́nez Peña, Javier Garcı́a de Alba Verduzco
and Karina Monzalvo Santos
División de Ciencias Ambientales, Instituto Potosino de Investigación Cientı́fica y Tecnológica (IPICYT ),
Camino a la Presa San José 2055, Lomas 4ta seccion, San Luis Potosı́ S.L.P., C.P. 78216, Mexico
Drylands are one of the most diverse yet highly vulnerable social – ecological systems on Earth.
Water scarcity has contributed to high levels of heterogeneity, variability and unpredictability,
which together have shaped the long coadaptative process of coupling humans and nature. Land
degradation and desertification in drylands are some of the largest and most far-reaching global
environmental and social change problems, and thus are a daunting challenge for science and
society. In this study, we merged the Drylands Development Paradigm, Holling’s adaptive cycle
metaphor and resilience theory to assess the challenges and opportunities for livelihood development in the Amapola dryland social – ecological system (DSES), a small isolated village in the
semi-arid region of Mexico. After 450 years of local social – ecological evolution, external drivers
(neoliberal policies, change in land reform legislation) have become the most dominant force in livelihood development, at the cost of loss of natural and cultural capital and an increasingly
dysfunctional landscape. Local DSESs have become increasingly coupled to dynamic larger-scale
drivers. Hence, cross-scale connectedness feeds back on and transforms local self-sustaining subsistence farming conditions, causing loss of livelihood resilience and diversification in a globally
changing world. Effective efforts to combat desertification and improve livelihood security in
DSESs need to consider their cyclical rhythms. Hence, we advocate novel dryland stewardship strategies, which foster adaptive capacity, and continuous evaluation and social learning at all levels.
Finally, we call for an effective, flexible and viable policy framework that enhances local biotic
and cultural diversity of drylands to transform global drylands into a resilient biome in the context
of global environmental and social change.
Keywords: complex adaptive systems; Drylands Development Paradigm; livelihood diversification;
resilience thinking
1. INTRODUCTION
Drylands form the largest biome complex on Earth,
covering about 41 per cent of the terrestrial surface
[1,2]. Life there has evolved under the most variable,
unpredictable and extreme of all global climates
considering the size, frequency and spatio-temporal
distribution of precipitation events [3,4], and the high
diurnal temperature fluctuations and incoming solar
* Author for correspondence ([email protected]).
Electronic supplementary material is available at http://dx.doi.org/
10.1098/rstb.2011.0349 or via http://rstb.royalsocietypublishing.org.
One contribution of 10 to a Theme Issue ‘Impacts of global
environmental change on drylands: from ecosystem structure and
functioning to poverty alleviation’.
radiation intensities [5]. This large biome has given
rise to unique ecosystem types with striking ecological
diversity, offering all fundamental ecosystem goods
and services for mankind and the many early cultures
originating in arid regions [6]. Drylands in Africa are
the cradle of humankind. High reciprocal adaptability
of drylands and their livelihoods to water scarcity has
shaped and strengthened land – human connections
for many millennia [7 – 9]. Hence, drylands carry one
of the longest legacies of any social – ecological system
(SES) on Earth.
Global drylands lie mostly (72%) in developing
countries and are home to over two billion people
[10] of whom 90 per cent depend on rural livelihoods
[9]. Global drylands are often common-pool resources
owned by national, regional or local governments,
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Factors of desertification
communal groups or private individuals [11]. Because
of their comparatively low productivity, global drylands have collective property rights regimes. This
tenure system remains hotly debated [12– 14] in the
context of resource exploitation and sustainable development [15]. While privatization has been proposed as
a viable alternative to collective property, commonpool resources in drylands essentially require local
adaptive governance institutions and innovative
public policy [16,17]. Over the last four to five decades, drylands have undergone loss of productivity
and biodiversity, and increasing drought frequency,
food insecurity, poverty, migration and social disintegration. These are key indicators of land degradation
[7,9], which may lead to desertification [18 – 21].
Desertification is an advanced stage of land degradation
(the reduction or loss of biological and economic
productivity) in arid, semi-arid and dry subhumid
areas, resulting from climatic variations and human
activities, among others [18] (Article 1 of United
Nations Convention to Combat Desertification,
UNCCD [21]). Fundamental changes in demographic,
economic, technological, climatic, policy, institutional
and cultural drivers influence human and biophysical
actions (e.g. livestock production, irrigation, deforestation, land-cover reduction), ultimately leading to
different syndromes of desertification [22–25]. Longterm mitigation efforts to combat desertification and
to rehabilitate or restore degraded or desertified
land [21] have rarely been successful. For instance,
government help programmes are economy- and/or
productivity-driven, not system-based [26].
We are charged with the daunting task of offering
timely solutions. We propose three pathways: (i) bring
new perspectives and efficient knowledge to strengthen
continuous science-policy dialogue; (ii) unfold translational models to frame policy-relevant research, policy
development and monitoring and assessment schemes;
and (iii) transform desertification into sustainable livelihood solutions. This quest requires an overarching,
multi-level approach to broaden and deepen our understanding of SES dynamics. We adopted existing
frameworks that were developed to increase our understanding of the unpredictable nature of complex
dynamic systems.
In the following sections, we first explore why global
drylands, after millennia of existence and adaptation
to strong climatic, socio-political and cultural changes,
have suddenly become vulnerable to land degradation
and, as a global biome, may be reaching the tipping
point to desertification (UNCCD [18]). We then
take the resilience perspective and examine how the
adaptive capacity of dryland social – ecological systems
(DSESs) may have changed to recover their structure,
function and identity after disturbance events (adaptive renewal cycle [27,28]) and in the light of rapid
directional global environmental and social change
[29]. With the Drylands Development Paradigm
(DDP; for details, see the electronic supplementary
material, appendix S1; [19]), we then identify potential underlying causes of dryland degradation with
the goal to halt, prevent and mitigate land degradation and to effectively restore affected systems. We
then apply the ecosystem services framework [30],
Phil. Trans. R. Soc. B (2012)
E. Huber-Sannwald et al.
3159
and highlight why ecosystem goods and services of
drylands are essential for building natural, social and
cultural capital as fundamental assets for the development of dryland livelihoods. We took a case study
approach that centres on the isolated rural community
of Amapola, located in the drylands of Mexico. Its
current livelihoods and degrading landscape have coevolved in a greatly fluctuating social–ecological
environment over the past 450 years, representing
dynamics found at regional and continental scales. We
addressed the following questions: (i) What biophysical
and socio-economic/cultural/political variables, processes and drivers have shaped the vulnerability and
resilience of Amapola and its livelihoods over the past
450 years? (ii) What critical issues and key emergent
properties of the Amapola SES need to be considered
to determine its adaptive capacity and longevity?
(iii) Based on what we have learned, how transferable
is this information to other DSESs regionally and globally? We discuss how our approach may serve for
the development of new adaptive models required to
effectively integrate research, monitoring, assessment,
policy development and capacity building [31].
(a) Drylands navigating between vulnerability
and resilience
DSESs have undergone innumerable transformations
for many millennia without losing their resilience
(i.e. the capacity to absorb shocks, readapt and sustain
overall structure, function and feedbacks within
favourable stability domains) [32 – 34]. However, over
the past half-century, DSESs have become astonishingly vulnerable to climate change [35 – 38], land
degradation [7] and desertification [7,39]. Some
DSESs have lost their resilience and crossed critical
thresholds beyond the possibility of returning to
favourable states [10,25]. This has put dryland systems on the global map as food, water, health and
environmental insecurity hot spots [40 –42].
Increasing dryland vulnerability to biophysical
change (i.e. susceptibility to harm [43,44]) is attributable to the following four main drivers. (i) Recent
anthropogenic climate changes are beyond the natural
rate and range of the broad inherent variability of dryland systems [45]. (ii) Humans have been eager to
eradicate high variability and unpredictability, two of
drylands’ unique inherent characteristics and sources
of resilience, in order to increase the overall low dryland productivity [9], to manage for more
continuous water supply [46] and to increase human
well-being. (iii) Globalization and the expansion of
neoliberal politics, particularly in developing countries
of Latin America [47], have significantly changed
environmental and production policies, and thus the
biophysical properties and dynamics of the world’s
drylands. (iv) Furthermore, trade liberalization and
global market-driven economies [47] have called for
regularizations in land rights and agrarian reforms
[48], and for replacements of local governance structures and knowledge systems with market-based
mechanisms [9,47]. Resulting losses of land productivity and biodiversity as well as increasing
vulnerability to poverty, migration and social
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Factors of desertification
disintegration have undermined the natural, social and
human capital of dryland rural livelihoods. Loss of
these assets and of adaptability to change eventually
leads to desertification [33].
Current efforts to tackle and mitigate desertification
are mainly globally coordinated by the UNCCD. However, effective bottom-up governance and management
approaches are needed to achieve sustainable livelihood
development, which builds on the natural and social
capital as the essential pillar of dryland life-support systems [17,33]. Hence, knowledge of the dynamics of
natural and social capital constituents is needed to
understand their adaptive capacities and vulnerabilities
to change.
(b) Complex system characteristics of drylands
Drylands are characterized by nonlinear dynamics
with inherently high levels of uncertainty and unpredictability, high responsiveness to cross-scale
interactions, resilience, self-organization and emergent
properties. This behaviour classifies DSESs as complex [26,49 – 52] with a range of emerging functions
[53]. Complex systems by definition never reach a
long-term stable equilibrium state, but go through
Holling’s adaptive renewal cycles [27,54] after disturbance events. For instance, for a system to move from
state S2 to S1 (figure 1a), it will first go through an
adaptive cycle (see the electronic supplementary
material, appendix S2 and figure S2.1). It is important
to acknowledge that system breakdown does not imply
irreversible destruction but rather renewal for continuous development [55]. (For more details, see the
electronic supplementary material, appendix S2.)
After a collapse, the system enters an unspecified
state and releases all accumulated resources, energy
and/or information (see the electronic supplementary
material, figure S2.1: omega phase ¼ release phase)
before it readjusts. The process of reorganization (see
the electronic supplementary material, figure S2.1:
alpha phase ¼ renewal phase) is determined by the system’s memory (i.e. its historic path dependence), overall
system condition and new social and/or ecological
opportunities. Once reorganized, the system rapidly
exploits all available resources (see the electronic supplementary material, figure S2.1: r-phase ¼ growth
phase) and finally transits to the slow accumulation
and storage of certain resources and information (see
the electronic supplementary material, figure S2.1:
K-phase ¼ conservation phase) [27]. The longer a
system remains in the conservation phase, the more
internally connected and responsive it becomes to
small external shocks bringing the K-phase to an end;
a new adaptive cycle will initiate with the release phase.
The higher the diversity of options to respond to,
the higher a system’s capacity and potential to reorganize under new social – ecological conditions (i.e. the
higher a system’s resilience) [56]. In DSESs, the diversity constituting the natural, social and cultural capital
(figure 2) together enhances the system’s capacity to
reorganize in the same state (figure 1a). For instance,
in figure 1a, the system reorganizes in state S2, though
under changing environmental and socio-economic
conditions (figure 1a: moving along the time axis,
Phil. Trans. R. Soc. B (2012)
t1 2 tn). Alternatively, the system may shift to a new
state (figure 1a: from S2 to S1) in the same regime
(figure 1: system states S1 and S2 within regime X).
In case of adaptive capacity loss, the system is pushed
towards unstable conditions (figure 1b: the system
in state S1 is pushed towards a biophysical threshold, TBX/Y) before it crosses a threshold (figure 1b:
socio-economic threshold, TSZ/X) and shifts into a
new regime (figure 1b: after crossing TSZ/W, the
system enters regime Z). The capacity of a complex
system to navigate between a number of alternate
stable states within the same stability domain/regime
determines the resilience of a DSES (figure 1a: S1 and
S2 from time t0 to t1). Regime shifts may occur because
interactions of variables across scales within a single
domain (biophysical, social or economic) lead to drastic
changes in the properties and thus the responsiveness of
a system [57]. Alternatively, such shifts may be induced
by the interaction of social, ecological and economic drivers that may lead to the crossing of a series of thresholds
(cascading thresholds), and thus cause a series of regime
shifts [58]. Humans often interfere in the adaptive cycle
by adopting short-sighted management practices disregarding natural system dynamics (for more details, see
the electronic supplementary material, appendix S2).
(c) The Drylands Development Paradigm
The DDP [9,19,25,59] is a conceptual framework
based on five principles (for more details, see the electronic supplementary material, appendix S1) that best
responds to the research, monitoring and assessment
needs explained above. It allows the simultaneous analysis of the biophysical (ecological and climatic aspects,
natural capital), socio-economic/socio-cultural (social,
human, financial, infrastructure) and cross-cutting
(ecosystem goods and services) domains (figure 2) of
DSESs vulnerable to land degradation, drought and
desertification [18,42]. In particular, and unlike other
frameworks, the DDP permits an integrated analysis
of complex adaptive SESs considering past, present
and future policy, governance and management. Since
the DDP builds on complex systems and resilience
theory, this framework guides the search for key interrelated ecological and socio-economic variables and
processes that characterize system structure and
function. Unlike other frameworks and paradigms
developed to understand dryland degradation and
desertification, the DDP strongly emphasizes key
biophysical and socio-economic sources and the roles
of change, cross-scale interactions and overall system
dynamics. Also, it stresses the importance of system
memory (e.g. in the sense of traditional local knowledge
and social learning) and legacy (e.g. historic development of land-use change; path dependence) and their
implications for SES stewardship [9,19].
(d) Dryland ecosystem services couple social –
ecological systems
Dryland ecosystems are highly diverse, and this diversity contributes to sustain their multi-functionality and
the delivery of ecosystem goods and services to over
two billion people [7,60]. Long-term multi-functionality increases the resilience and resistance of drylands to
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E. Huber-Sannwald et al.
Factors of desertification
social subsystem
(a)
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biophysical subsystem
TB
TS
tn
TS
socio-economic,
socio-cultural,
socio-political
buffer
TB
ecological,
climate,
hydrological
buffer
t1
S1
S2
S3
TBW/Z
(b)
TSZ/X
TBW/Z
TSZ/X
TBX/Y
tn
TSZ/X
TBX/Y
t1
S1
regime Z
S2
regime X
S3
regime Y
Figure 1. Coupled Dryland Social-Ecological Systems (DSESs). (a) Resilience of DSESs. The social (left) and biophysical
(right) subsystems are coupled through a mutual basin of attraction, which represents the social –ecological landscape of
opportunities and constraints as a function of the social, cultural, physical, financial and natural capital. In response to unpredictable disturbance events between time t1 ! tn, the DSES has gone through three adaptive cycles of change and reorganized
in three different stable states (S1, S2 and S3) without having changed its fundamental structure, function and feedback mechanisms and without having crossed a biophysical (TB) or socio-economic (TS) threshold. (b) Loss of resilience in response to
past disturbance events (triggered by external or internal drivers) and as a consequence of decline in socio-economic and natural capital (slow variables); system states S2 and S3 in regime X are better adapted to the changing social –ecological conditions
than S1. However, S2 and S3 are increasingly vulnerable to disturbance, and the system approaches and finally reaches the critical TBX/Y (e.g. shrub encroachment, a slow variable, could be inhibiting grass recovery), which separates regime X from Y. In
this case, the DSES can temporarily recover in regime X, but continuing drought (slow variable) and a lack of rapid recovery of
the financial and social capital (slow variable; shown by the difference in height of vertical dotted lines at t1 and tn) ultimately
lead to massive death of livestock; by tn, the DSES finally crosses TSZ/X and enters a new regime Z (regime shift from X ! Z).
In regime Z, the DSES either continues as a highly degraded system, or it is intentionally transformed by human intervention
to generate new livelihood options (e.g. diversify by introducing rain-fed agriculture; ecotourism).
climate change and desertification. Most dryland ecosystems’ diversity and their goods and services, however,
are not protected in reserves or national parks but are
directly appropriated by people who are an integral
element of dryland landscapes [9] (figure 2). Many
external and internal biophysical and socio-economic
drivers will put high pressure on the natural capital of
DSESs and thereby impair dryland livelihoods (figure
2). Increasing water scarcity associated with climate
change and excessive extraction will be one of the
Phil. Trans. R. Soc. B (2012)
greatest threats for the long-term provisioning of ecosystem goods and services in drylands (figure 2; see the
electronic supplementary material, appendix S3, for a
detailed description of important ecosystem services in
drylands).
Redistribution of precipitation water in dryland landscapes is controlled by topography, vegetation cover,
vegetation composition, rooting depth, soil depth, soil
texture and structure and human interventions.
Hence, any alteration of these fundamental dryland
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Factors of desertification
external
socio-economic drivers
external
biophysical drivers
population growth (VS),
globalization (VS), technology (VS),
subsidies (VF), neoliberal
policies (VS)
climate change (VS),
interannual variability (VF),
shrub encroachment (VS),
dam construction (VF)
internal
socio-economic drivers
S
E
property and use rights (VS),
demographic changes (VS), migration (VF),
education (VS), livelihood development (VS),
household income (VF),
access to resources (VF)
social capital
livestock (VF), stocking rate (VF),
cash crops (VF), disturbance regime (VS),
change in blue-green water flux (VS),
farm inputs (VF)
provisioning services
VF
VS
internal
biophysical drivers
milk, meat, wool, fuelwood, construction material, crops, freshwater,
wild terrestrial foods, agricultural fibre
cultural/social services
VS
VS
community organization,
social network
local governance
cultural capital
regulating services
water quality/quantity, ground and well
water recharge, blue-green water balance,
soil stability, climate–C sequestration
cultural connection to land,
creation of
environmental knowledge,
environmental learning
supporting services
perennial plant cover, biological soil
crust cover, soil organic
matter, soil formation, soil fertility
natural capital
Figure 2. Dryland social (S) –ecological (E) systems (DSES) are dynamically coupled through the quantity and quality of
assets (productive base) of social, cultural and natural capital, which consist of or are indirectly contributed by the supporting,
regulating and cultural/social services. Together and in response to internal and external socio-economic and biophysical drivers, these services determine the dynamics in the quantity and quality of the provisioning services. Hence, slow variables
(labelled VS) determine the variability in quantity and quality of natural resources (fast variables, labelled VF), which deliver
the provisioning ecosystem services to sustain livelihoods. The strength of coupling between social and ecological provisioning
services (the length of the white horizontal arrow) contributes to DSES resilience for the set of desirable stable states within a
certain regime (see figure 1).
ecosystem structures and functions will influence dryland capacity to deliver ecosystem goods and services
and to contribute to human well-being [30]. Rural dryland populations depend mainly on livestock and crop
production, and occasionally on wild terrestrial foods.
Thus, rural livelihoods draw on the many products
and assets that rangelands (food, fibre, freshwater, construction material) and rain-fed agriculture (staple
foods, cereals, legumes) provide (figure 2). Drylands
have high cultural diversity, suggesting that peoples
living in different drylands around the world have established strong identity, connections to and knowledge of
their lands and resources (figure 2; natural capital is
tightly coupled to cultural capital). The maintenance
of high biodiversity and the multi-functionality of ecosystems foster cultural identity and are the basis for
livelihood diversification.
For rural dryland systems to remain long-term,
multi-functional, sustainable life-support systems,
their landscapes must continuously and adaptively
provide multiple ecosystem goods and services. This
permits livelihood adjustment and development compatible with the inherent natural, cultural and social
capital (figure 2). Historic landscape transformations
and water management affect the water cycle and
thereby all fundamental ecosystem services directly
by diverting and retaining runoff water in dams for
Phil. Trans. R. Soc. B (2012)
irrigation or extraction, and/or indirectly by changing
land-use and cover type. One of the greatest challenges
drylands will be facing is to balance water for humans
and nature [40,61,62], in order to balance the provisioning and cultural services with the supplying
and regulating ecosystem services in the future [30]
(for more details, see the electronic supplementary
material, appendix S3).
2. MEXICAN CASE STUDY OF A DRYLAND
SOCIAL –ECOLOGICAL SYSTEM
Land degradation and desertification are extensive and
growing problems in Mexico, the fifth largest and third
most populated country in America. Close to 50 per
cent of Mexico’s land area is characterized by arid
and semi-arid climates [63], which host nearly 30
per cent of the national population [64]. Rural livelihoods depend on livestock production and rain-fed
agriculture [65]. Land degradation is causing approximately 2250 km2 of formerly productive farmland to
become abandoned annually, directly impacting twothirds of the country’s poor population [66].
In a recent assessment of soil degradation [64],
agriculture (39%) and overgrazing by livestock
(39%) were identified as the main drivers of land
degradation [67]. Rain-fed agriculture occurs on
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Factors of desertification
former semi-arid grasslands. These are low-input traditional farming systems, which include a diversity of
domesticated plant species (corn, bean, squash)
adapted to highly dynamic local environmental conditions and traditional cropping practices dating back
to 5000 BC [68,69]. Episodic droughts in the last century (in decadal cycles, [70]) have greatly impacted
livestock production and agriculture, thereby affecting
food security of rural livelihoods. In colonial times, climate variability, extreme droughts, agrarian crisis and
famine have influenced socio-political events such as
the Independence War and the Mexican Revolution
[65,71 – 73]. Mexican society’s responses to these
crisis events illustrate the resilience and adaptive
capacity of SESs in Mexico’s history [74]. Since the
agrarian land reforms (Articles 27) in 1917, Mexico’s
drylands have been common-pool resources (ejido)
under a collective property rights regime [75]. However, recent changes (1992) in national agrarian
policies introduced new land regularizations permitting individual ejido members to sell or rent their
land [48].
In the following sections, we present the case study of
Amapola, a small, isolated rural community whose
social–ecological legacy dates back to pre-colonial
hunter-and-gatherer societies. We will roughly reconstruct how changes in land property rights, land-use
policies, migration, droughts, urbanization, fluctuations
in global markets and government interventions have
led to the adjustment, collapse and renewal of the
current Amapola DSES.
Amapola is the most remote community within the
Sierra San Miguelito, 35 km southwest of San Luis
Potosı́ (latitude 218560 6000 N; longitude 1018100 000 W;
altitude 2105 (m)a.s.l.), an area of Mexico covered
by an extensive, semi-arid pine oak forest and open
rangeland [62] (see the electronic supplementary
material, figure S4.1). Founded in 1924 [62], Amapola is situated near the hydrological divide of the
Sierra San Miguelito. Exposed rocks in the area are
volcanic rhyolites; there are numerous lava domes
that contain localized tin. Soils are lithosols and xerosols in the hills and valleys, respectively, and are
shallow overall. Rainfall averages 480 mm yr – 1, dominated by torrential storms during July and September,
which create large volumes of runoff. The Amapola
region is characterized by a heterogeneous landscape
consisting of rocky hills, alluvial fans and valley floor.
(For detailed description and illustration, please see
the electronic supplementary material, appendix S4).
(a) Methods
The five principles (P1 – P5; see the electronic supplementary material, appendix S1) of the DDP [19]
guided our spatio-temporal analysis (DDP-P1; see
the electronic supplementary material, appendix S1)
of the Amapola DSES in three steps. In step 1, we
identified and characterized specific system states
(figure 1), for which we estimated the relative size of
the natural, social, cultural, human and built capital
(figures 2 and 3; DDP-P1, -P2 and -P5; see the
electronic supplementary material, appendix S1). In
addition, we identified key exogenous and endogenous
drivers that shaped DSES states and livelihood
Phil. Trans. R. Soc. B (2012)
E. Huber-Sannwald et al.
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development (figures 2 and 3; DDP-P1 and -P4; see
the electronic supplementary material, appendix S1).
Finally, we explored possible biophysical, socio-political and socio-economic crisis events at different scales
that may have caused a change in system state or
regime shift (figure 1; DDP-P1 – P4; see the electronic
supplementary material, appendix S1) considering the
past 450 years. Based on historical, political, economic, social and cultural contexts, we determined
specific periods corresponding to specific states or
regimes of the Amapola DSES (figure 3) with their
respective livelihoods considering the natural, social,
cultural (figures 2 and 3; DDP-P1,- P2 and -P5; see
the electronic supplementary material, appendix S1),
human and built capital. In step 2, we zoomed in on
the current state of the Amapola DSES. We analysed
the role of key exogenous and endogenous drivers
and how they interact with land-use/cover change
and current states of ecosystem goods and services
(natural capital) and livelihoods (social and cultural
capital; DDP-P5; see the electronic supplementary
material, appendix S1) in the Amapola DSES. Also,
we demonstrated how local-scale land-use dynamics
relate to regional-level land-use dynamics (DDP-P4;
electronic supplementary material, appendix S1) and
their consequences on regional hydrological function
considering an SES scenario of interacting public policies and climate change. In step 3, we integrated the
findings of steps 1 and 2 and describe emergent properties that help explain the socio-ecological functioning
and state of resilience of the Amapola DSES.
For the analysis of key external international,
national, regional socio-economic and socio-political
and socio-cultural drivers, we consulted archived historical information and databases on indigenous
tribes/cultures, land property rights, demography,
national, state and municipal institutions and associated
government programmes for sustainable rural development corresponding to Central Mexico [64,76– 83].
From these sources, we extracted dominant drivers
pertinent to the region and certain periods.
To identify historic internal/local drivers explaining
changes in land-use and economic practices, we consulted archived material, databases and aerial photos
(Registro Agrario Nacional, Instituto Nacional de Estatistica, Geografia y Informatica). To explore current
local drivers, we consulted government programmes,
agrarian and environmental laws, and conducted
semi-structured interviews of all Amapola households
(n ¼ 13). We collected information on family structure,
population age structure, migration, non-agricultural
labour, access to subsidies and government programmes, cropping systems, water-use types, social
organization, local environmental knowledge, cultural
ties, land-use history, local economy and land-use and
management decisions (DDP-P2; see the electronic
supplementary material, appendix S1: we evaluated
the decision-making process with respect to differential
responsiveness to fast and slow variables). For most
variables associated with Amapola community characteristics, we examined the frequency distributions
among households and community members.
From this information, we extracted three
groups of variables: (i) those that form a key set of
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periods/capitals
Factors of desertification
drivers of livelihood development in Amapola
scale
pre-Hispanic before 1590s
built
human
hunters and
gatherers
cultural
social
natural
local
0%
50%
trade and
exchange
natural resource
availability
vast uninhabited
land
100%
international
colonial
1594–1917
national
Spanish Empire
hacienda land
tenure system
expansion of
railroad system
high charcoal
demand
mining permission to
extract tin and silver
built
human
regional
Hacienda Bledos
cultural
social
natural
mining labourers and
charcoal producers
0%
50%
100%
local
subsistence farming
on borrowed land
national
post-revolution
1917–1988
regional
built
human
dense pine-oak
forest
bronze industry
high charcoal
demand
proximity to
state capital
land reform
legislation
land property rights
communal use
of rangeland
high tin price
individual use
of agriculture land
diversifiers
cultural
social
natural
La Amapola foundation
0%
50%
100%
local
migration from
nearby communities
neoliberal
1988–present
international
dense pine-oak
forest
surface wells
neoliberal policies
NAFTA
global markets
new land property
rights
agrarian policies
national
price drop of
basic grain
built
human
cultural
social
natural
regional
0%
50%
change in
energy use
increase in price drop
livestock
of tin
demand
livestock
producers
100%
local
PROCAMPO
COUSSA
growth of
nearby cities
PROCEDE
agriculture land
privately owned
communal use
of rangeland
and forest
semi-proletarian
farmers
dam construction
Figure 3. Developmental trajectory of the Amapola DSES over the past 450 years considering four periods corresponding to
four adaptive cycles [27]. Each period is characterized by a certain combination and size of capital (horizontal bar diagrams),
local, regional, national, international drivers (box around text) and livelihoods (circle around text).
socio-economical and biophysical drivers at multiple
spatio-temporal scales (DDP-P1 and -P4; see the
electronic supplementary material, appendix S1); (ii)
those that explain slow and fast changes in the structure
and functioning of the Amapola DSES (DDP-P2; see
the electronic supplementary material, appendix S1);
and (iii) those that resulted in livelihood diversification
and associated land-use change.
Phil. Trans. R. Soc. B (2012)
(i) Spatio-temporal transformations of Amapola dryland
social – ecological system and the landscape-function
scenario
We assessed land-use change dynamics at the Amapola
landscape scale and compared those with larger scale
land-use change dynamics in the Sierra San Miguelito,
San Luis Potosı́ (see the electronic supplementary
material, appendices S4 and S5). With this upscaling
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281 718
282 717
(b)
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282 717
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2 438 559
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2 437 560
2 436 561
2 436 561
2 435 562
2 435 562
280 719
280 718
280 717
grassland
forest
cropland
abandoned cropland
Amapola limits
280 719
281 718
Wgs84
UTM 14n
500
0
3165
282 717
N
W
500
E
S
1000 m
Figure 4. Land-use/cover types of the Amapola watershed in the Sierra San Miguelito, San Luis Potosı́, Mexico, in (a) 1969
and (b) 2004, derived from aerial photography and visually classified polygons.
exercise, we demonstrate how transformations of rural
livelihoods are coupled to land-use change dynamics
and how they may feed back on landscape function.
For the Amapola landscape (for additional information, see the electronic supplementary material,
appendix S4), we used aerial photographs from 1969
and 2004 (the only aerial photographs available for the
area) to explore net legacy effects of external and internal
drivers on land-cover change and spatial reconfiguration
of land-use types in the Amapola region considering
different spatial and temporal scales (DDP-P1 and
-P4; see the electronic supplementary material, appendix S1). We digitized georeferenced orthophotos and
with visual classification created polygons with reference
points (with a Geographical Positioning System) taken
in the field for each land-use category: pine–oak forest,
grassland/rangeland and cropland and abandoned cropland. This analysis was performed with GIS IDRISI v.
3.2 (Clark Labs, Worcester, USA). For the Sierra
San Miguelito region (for additional information, see
the electronic supplementary material, appendix S5),
we generated thematic maps of the same land-cover
types as for Amapola for the years 1979, 1989, 1999
and 2009 using a combination of supervised and
non-supervised satellite images (Landsat) with the
GRASS-GIS software (Geographic Resources Analysis
Support System, v. 6.4.2 RC3). To scale runoff from
our experimental plots to the Amapola landscape level,
and to account for differential effects of land-use type
on hydrological processes, we multiplied the total area
(determined with IDRISI; figure 4) with the respective
variable (runoff volume, C and N content) for each
land-use type and year.
We simulated how the dominance of livestock-based
livelihoods at the scale of Sierra San Miguelito (stimulated by neoliberal agrarian policies and global
markets, and prolonged droughts and tree mortality)
[84] may cause irreversible regime shifts in DSES
function as a consequence of crossing the threshold
of critical soil depth (slow variable; figure 2b;
DDP-P2 and -P3; see the electronic supplementary
Phil. Trans. R. Soc. B (2012)
material, appendix S1). We parametrized the biogeochemical model BIOME-BGC [85] and incorporated
model outputs into thematic maps to determine
regional hydrological function (for details, see the
electronic supplementary material, appendix S4).
(ii) Current status of key dryland ecosystem services in the
Amapola social – ecological system
To analyse how farmers’ decision-making, past climate
conditions and historical regional socio-environmental
transformations (see previous section) have influenced
the natural capital of the Amapola SES, we examined
key supporting and regulating ecosystem services
(figure 2; DDP-P2; see the electronic supplementary
material, appendices S1 and S4) of the three land-use
types characterizing the Amapola landscape. To examine
whether a government agrarian subsidy programme
(PROCAMPO; see the electronic supplementary
material, appendix S6) influenced the provision of different supporting and regulating ecosystem services,
we compared crop management type (traditional—lowimpact tillage; technified—high-impact tillage) applied
to different crop types (corn, oat, abandoned).
Supporting services
In drylands, we consider soil characteristics to be
fundamental supporting services (figure 2). We determined soil fertility, soil organic matter content (SOM,
t ha – 1), soil organic carbon (SOC, t ha – 1), soil total
nitrogen (t ha – 1) and soil compaction (soil bulk density).
We randomly selected five plots per land-use type and
collected within each plot a composite sample (n ¼ 5)
at depths of 0–15 cm and 15–30 cm. To determine
potential nitrogen (N) mineralization rates as an indicator of soil fertility for each land-use and crop
management type, we collected three randomly distributed soil samples at 10 cm following the incubation and
extraction protocol described by Robertson et al. [86].
To determine soil depth, we measured the ‘A’ horizon
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Factors of desertification
in 50 gullies of the watershed and determined average
soil depth for each land-use type.
Regulating services
We consider perennial basal vegetation cover (%)
in grassland and forest sites fundamental in regulating water redistribution (figure 2). We used five 50 m
transects running parallel to the slope [87]. We also
determined the redistribution of each rainfall event
from August 2006 to July 2007 comparing the
three land-use types. Rainfall was collected in five
pluviometers spread across land-use types. To determine runoff, we installed five surface runoff plots
(1.60.5 m35 cm) connected to 50-litre collector
tanks [88] in each land-use type. Runoff was recorded
after each rain event during a whole year. Within
runoff plots, we determined water infiltration rates
during the dry season with a single-ring infiltrometer
[76]. To determine the impact of sediment erosion on
the loss of SOC and nitrogen, we collected one composite (n ¼ 5) sample (4 cm diameter 15 cm depth) of
the deposited sediment in eight earthen dams of the
Amapola watershed in March 2007 and in five samples
of water of each dam (total dissolved organic carbon).
Cultural services
Milpa is an ancient indigenous polyculture cropping
system which offers a wide variety of annual subsistence
crops (corn, beans, squash, corn fungus/huitlacoche,
chile) and thus contributes fundamentally to farmers’
food security (figure 2). Milpa is a traditional institution
that provides important cultural services such as cultural identity, local environment knowledge and
ornaments for religious purposes [89] (DDP-P5; see
the electronic supplementary material, appendix S1).
(b) Laboratory analyses
Composite soil samples were oven-dried at 708C
during 72 h and sieved (less than 2 mm for all the analyses). SOM was determined with the calcination
method (6008C for 2 h; [90]). For soil bulk density
(g cm – 3), we determined soil dry weight and core
soil volume [91]. Soil and sediment (deposited in
dams) organic carbon and total nitrogen were analysed
with an elemental analyser (Costech, modelo 1016),
and total dissolved carbon and nitrogen of dam
water (TOC, TON) were determined using a total
organic carbon analyser (TOC-VCSN, SHIMADZU).
Potential nitrogen mineralization rates were determined following the laboratory incubation and
extraction methods described by Robertson et al. [86].
(c) Statistical analysis
To identify past livelihoods, we discriminated between different groups of local, regional, national and
international drivers, and assigned a period-specific
descriptor to each livelihood (figure 3). To identify
current livelihoods, we matched qualitative and quantitative information of land-use type, management
practices, access to government subsidy programmes,
household age structure, income sources and level of
education for each household to information corresponding to key local, regional, national and international
Phil. Trans. R. Soc. B (2012)
drivers (figure 3). Based on frequency distribution
analysis of all categorical variables, we grouped households and farmers, which resulted in two livelihood
categories currently coexisting in Amapola.
Soil, vegetation and water variables were tested
for normality using Shapiro–Wilk’s test. For water
variables, such as runoff that included a time component, we applied a repeated-measures analysis of
variance with the mixed model (SAS). Land-use type
(forest, grassland, cropland) was the fixed effect,
while time (56 sampling dates) was the random
effect. Soil variables, including soil bulk density, soil
total N, SOM, SOC and soil depth, were obtained
once and analysed using a nested ANOVA with the
factor soil depth (0 – 15 cm, 15– 30 cm) nested within
the main factor land-use type (forest, grassland, cropland, abandoned cropland). To examine the effect of
PROCAMPO on soil quality (fertility, structure, stability) characteristics, we applied a two-way nested
ANOVA with soil depth (0– 15 cm, 15 – 30 cm)
nested within cropping type (with subsidy, without
subsidy) as the main factor. Analyses were performed
with SAS v. 8 (SAS Institute Inc., Cary, NC, USA).
We compared the condition of current supporting
and regulating ecosystem service variables (see the
electronic supplementary material, appendix S3, tables
S3.1–S3.3; figure 5) for the three/four land-use types by
using the values of the ecosystem service variables for
the forest ecosystem as reference values (100%). For databases used in this study, please consult doi:10.5061/
dryad.1c2b7.
3. RESULTS
Considering the past 450 years, the developmental
trajectory of the Amapola DSES can be divided into
pre-Hispanic, colonial, post-revolution and neoliberal
periods (figure 3). In each period, a distinct set of
dynamic external and internal socio-political, socioeconomic, socio-cultural and environmental drivers
acted and interacted across different spatial and
temporal scales leading to distinct livelihood developments. (For a detailed description of each of the four
periods, see the electronic supplementary material,
appendix S6.)
Over the past 24 years, the inhabitants of Amapola
have been exposed to a wide variety of external
drivers, and depending on their adaptive capacity
and willingness to change, they have developed one
of two livelihood types that currently coexist in the
community (figure 3).
Livestock producers base their living on farming
and livestock; they represent 47 per cent of the households; they are ejidatarios with land ownership rights
(for a more detailed description, see the electronic
supplementary material, appendix S6). Herd sizes fluctuate interannually between 40 and 80 animals per
household depending on annual precipitation and rangeland productivity. To reduce feed shortages, multicropping systems have been converted to corn and oat
monocultures, thereby sacrificing their essential subsistence crops. Government subsidies were used to buy
livestock, rent tractors for ploughing and occasionally
acquire vehicles to transport livestock and crops (feed
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potential n
mineralization
(%)
soil depth (%)
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80
60
40
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carbon (%)
forest
nitrogen (%)
grassland
cropland
soil infiltration
(%)
plant cover (%)
soil porosity
(%)
(b)
potential n
mineralization
(%)
soil porosity
(%)
SOM (%)
100
80
60
40
20
0
forest
carbon (%)
cropland without
subsidy
cropland with
subsidy
nitrogen (%)
Figure 5. (a) Effect of land-use change on key regulating and supporting ecosystem services for forest, grassland and cropland
systems. Spider diagram depicting relative changes in condition of key slow variables compared with the reference (100%)
forest ecosystem in the Amapola landscape, San Luis Potosı́, Mexico. (b) Effect of government subsidies on key regulating
and supporting ecosystem services of croplands. Spider diagram depicting relative changes in condition of key slow variables
compared with the reference (100%) forest ecosystem in the La Amapola landscape, San Luis Potosı́, Mexico.
sale) to local markets. This livelihood depends directly
on the fluctuation of local livestock prices and thus
global meat markets, while putting food security at risk.
Semi-proletarian farmers represent 53 per cent of
the farmers’ households; these are young farmers
between 30 and 40 years who make their living
growing staple foods (rain-fed) and herding livestock
(three to 25 goats per household). Compared with
livestock producers, this group has little access to government subsidies and cannot buy livestock. This
group favours the traditional milpa cropping system
mostly for self-consumption. They complement
income with non-farming activities outside the community in nearby cities. Temporary migration has
turned into a highly attractive alternative, which
allows income to be diversified and new consumeroriented lifestyles to be adopted (as learned from city
jobs; figure 3).
Migrants. This is not a livelihood, but considering
66 people migrated to the USA or nearby cities,
their influence on Amapola landscape function (natural capital) and social cohesion (social capital) is
considerable. With PROCEDE, as ejidatarios they
keep their land as heritage, to conserve land, community and family ties and for economic security. Since
migrants do not sell or rent their land, these agricultural plots are in all cases abandoned.
Livelihood development is tightly coupled to Amapola
landscape (3300 ha) transformation (figure 4). The
forested area is the dominant land-cover type (60%),
Phil. Trans. R. Soc. B (2012)
followed by secondary grasslands (30%) and agricultural
land (10%) (see the electronic supplementary material,
appendix S4 and figure S4.1). Between 1969 and
2004, 4 per cent of the forested area was converted to
grassland, and 5 per cent of the croplands was abandoned
(figure 4; see the electronic supplementary material,
appendix S4 and table S4.1). At the regional scale,
land-use change between 1979 and 2009 led to 6 per
cent deforestation (see the electronic supplementary
material, appendix S5 and figure S5.1).
After 450 years of co-adaptation between land and
people, the four land-use types currently shaping the
Amapola landscape differ in quantity and quality of
the ecosystem goods and services they provide. As in
other dryland regions, water scarcity has limited the
production of crops, forage, fuel wood and other provisioning services. However, despite these limitations,
several rural livelihoods have developed around the
fluctuating supply of ecosystem goods and in response
to newly emerging socio-environmental conditions,
restrictions and opportunities. A detailed description
of the four ecosystem service types of the four landuse types is presented in electronic supplementary
material, appendix S3.
Exploitative land use for several decades has
severely altered the supporting and regulating services
of the Amapola landscape. Livestock, drought and
overall shallow soils induced loss of vegetation cover
and soil erosion. When comparing key supporting
and regulating services and using the forest ecosystems
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as baseline, we found that SOM dropped in grasslands
by 26 per cent and 43 per cent, and in cropland by 34
per cent and 37 per cent at 0 – 15 and 15 – 30 cm
depth, respectively (figure 5a; see the electronic supplementary material, appendix S3 and table S3.1).
Grasslands suffered the greatest loss of SOC (74%
and 87%, respectively, for the two soil depths), while
both croplands and abandoned croplands showed
similar declines of around 60 per cent at both soil
depths (figure 5a; see the electronic supplementary
material, appendix S3 and table S3.1). Similarly,
total soil N suffered twice as much loss in grasslands
than in both cropland types. The potential nitrogen
mineralization rate was four times lower in grasslands and croplands than in pine– oak forests
(figure 5a; see the electronic supplementary material,
appendix S3 and table S3.1). For more details on
supporting and regulating services, see electronic
supplementary material, appendix S3.
To evaluate the influence of external drivers on
supporting and regulating ecosystem services, we
examined the effect of agriculture subsidy programmes
(PROCAMPO) on slow variables (figure 2). ‘Nonsubsidized’ agriculture plots exhibited 27 per cent,
30 per cent and 33 per cent greater SOM, SOC
and total soil N, respectively, than ‘subsidy’ plots
(figure 5b; see the electronic supplementary material,
appendix S3 and table S3.3). Also, potential N mineralization rates were substantially higher and soil
compaction overall lower in ‘non-subsidy’ than in
‘subsidy’ plots, highlighting the enormous benefits of
low-impact agricultural practices as well as a relatively
rapid change in slow variables (figure 5b; see the
electronic supplementary material, appendix S3 and
table S3.3). (For additional information, see the
electronic supplementary material, appendix S3.)
Some of the changes in supporting and most regulating services have to be seen in a landscape context.
Earth dams are sinks for runoff water and laterally
transported soil. The recent installation of 16 dams
in the Amapola landscape has led to a substantial
redistribution of runoff water and thus landscape
hydrological function. Hence, SOC and N transported
by water erosion from grasslands and forests resulted
in relatively high concentrations of total dissolved
organic C and N (9.18 and 1.43 mg l21, respectively)
in dam water. In dam sediments, organic C sinks corresponded to 14.7 t ha21, greater densities than those
observed in grasslands. In the case of N, we estimated
an average of 1.06 t ha21 corresponding to N densities
found in grassland soils.
At the regional scale of the Sierra San Miguelito, deforestation caused a 6.5 per cent decline in pine–oak forest
between 1979 and 2009 (see the electronic
supplementary material, appendix S5 and figure S5.1).
An increase in annual precipitation by 100 mm between
1979 and 2009 compensated for the negative effects of
deforestation on evapotranspiration and runoff (see the
electronic supplementary material, appendix S5 and
figure S5.2). Considering a decline in soil depth by
10 cm simulating prevalent laminar soil erosion at the
regional scale, and a 6.5 per cent land-use change by
deforestation, evapotranspiration and thus primary productivity declined significantly and runoff increased (see
Phil. Trans. R. Soc. B (2012)
the electronic supplementary material, appendix S5 and
figures S5.2 and S5.3).
4. DISCUSSION
Land degradation and sustainable livelihood development in global drylands are currently the most
daunting challenges the global policy and science
communities are facing (UNCCD; Millennium
Development Goals; United Nations Department of
Economic and Social Affairs, Division for Sustainable
Development). Global drylands are intrinsically
strongly coupled SESs whose complexity in the context
of desertification and dryland development is still poorly
understood [19]. It has been argued that our scientific
understanding of the interactions of global environmental change with the Earth system is much more
advanced than that related to complex social/cultural
responses to global environmental changes [92]. We
have increasing evidence that environmental subsystems
have reached or are close to reaching tipping points in
the near future [93]. However, we have no comparative
assessment of equivalent social tipping points considering environmental, economic or governance stressors.
Nevertheless, this knowledge gap is of considerable
concern as societal transformations in response to
global environmental or social change shift the context
of adaptation, resilience and future transformability
[94,95]. Hence, suitable frameworks that include
long-term integrative perspectives of SES dynamics
have recently been advocated to deepen our knowledge
of past breakdowns and historical collapses in SESs
[96], complex land system dynamics [94] and land
function [97] and resilience of past landscapes [98].
We used three complementary frameworks in an
empirically grounded, heuristic case study that
spanned 450 years. We explored the historical context
of the coevolution of the cultural, social and natural
system components and livelihood diversification of
the Amapola DSES. We identified key slow and fast
multi-scalar biophysical, socio-economic, socio-political and socio-cultural drivers that caused individually
or interactively sudden or continuous changes in the
internal Amapola DSES dynamics. These internal
dynamics included coupled transformations of livelihoods, land-use types and landscape function of the
Amapola DSES. In the following sections, we will
first synthesize key characteristics of Amapola’s complex adaptive system applying the three frameworks:
(i) the DDP [19] (see the electronic supplementary material, appendix S1); (ii) Holling’s adaptive
renewal cycle [27] (see the electronic supplementary
material, appendix S2 and figure S2.1) coupled to
the resilience/vulnerability landscape with multiple
basins of attraction and critical transitions between
regimes/domains [34] (figure 1; [54,99, 100]); and
(iii) an integrative ecosystem services model highlighting the strong interdependence between the social,
cultural and natural subsystems supporting adaptive
livelihood development in dryland regions (figure 2).
We will then propose a shift towards novel ‘next generation’ research, monitoring, assessment and policy
development to advance our knowledge for effective
DSES stewardship [9] in a rapidly changing world,
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Factors of desertification
and to respond to a recent call for an effective sciencepolicy dialogue by the Intergovernmental Platform on
Biodiversity and Ecosystem Services [101] in the context of interactive global environmental and social
changes [29].
(a) Linking metaphors and frameworks
towards understanding complex dryland
social – ecological systems
We reconstructed the dynamic trajectory of the Amapola DSES for the last 450 years focusing on the
coupled dynamics of the biophysical and social/cultural/economic/political dimensions of this system
(DDP-P1; see the electronic supplementary material,
appendix S1). In particular, we looked at the composition, size and quality of the total capital (DDP-P2
and -P5; see the electronic supplementary material,
appendix S1; figure 2) of the local Amapola DSES
(figure 3) as an integrated measure of system state
including livelihood development (figure 1a: S1 or S2
or S3) and its adaptive capacity to external or internal
drivers of change.
The Amapola DSES underwent four adaptive
cycles corresponding to the pre-Hispanic, colonial,
post-revolution and neoliberal periods. Transitions
between periods were either triggered by induced
system transformation [95], regime shift [34,95] or
by a shift between alternate states between or within
periods (figure 6). With the invasion of the Spaniards
and imposition of new ideologies (religion, culture,
governance), the native Guachichiles lost their cultural
identity (DDP-P2 and -P5; see the electronic supplementary material, appendix S1) and strong ties to
their territory (figure 2), triggering long-term loss of
social resilience and resistance and ultimately the collapse and end of the indigenous culture (Chichimeca
War; omega phase). A new social, political, economic
and land property rights context shifted the Amapola
DSES into a new regime (colonial period; figures 1b
and 3). Strong institutional, top-down governance
structures maintained the Amapola DSES for 350
years within this regime (figures 3 and 6).
Long-term exploitative mining and wood extraction
caused a net loss of the natural capital (DDP-P2; see
the electronic supplementary material, appendix S1)
of the Amapola DSES. The social capital was reorganized under the governance of Hacienda patrons
specializing workers as miners, charcoal producers
and wood extractors. The Hacienda SES experienced
rapid growth (r-phase) and accumulated wealth and
resources thanks to the abundant local human
and natural capital (DDP-P2; see the electronic supplementary material, appendix S1). The Hacienda
governance system maintained itself for centuries in
the conservation phase (K-phase) by maximizing
silver, tin and wood extraction (all fast variables) at
the cost of trust building, social and transformative
learning and allowing bottom-up adaptive governance
structures (all slow variables). Cultural capital never
recovered. In contrast, the ‘cosmic’ world view of the
indigenous people changed. As haciendas introduced
cognition, information, technology [103] (these are
all slow variables leading to shifts in fundamental
Phil. Trans. R. Soc. B (2012)
E. Huber-Sannwald et al.
3169
system characteristics) and infrastructure (a fast variable that enhances shift), this imposed changes in
values (for instance, change in perception of natural
resources), attitudes (loss of identity) and decisionmaking (shift from local to top-down governance;
DDP-P2, decline in key slow variables; see the electronic supplementary material, appendix S1).
Original local traditional knowledge was enriched
with newly acquired knowledge (DDP-P5; see the
electronic supplementary material, appendix S1).
Increasing social bottom-up organization among
workers, strengthened by frequent droughts and famine
together triggered the Mexican Independence (1821)
and Mexican Revolution (1917), and ultimately the collapse of the higher hierarchical socio-political Hacienda
system (DDP-P2; see the electronic supplementary
material, appendix S1; figure 3). With the fall of the
Hacienda land tenure system, a critical threshold was
crossed (DDP-P3; see the electronic supplementary
material, appendix S1; see also see the electronic
supplementary material, appendix S2 and figure
S2.1—‘revolt’). This had cascading effects [58] in the
socio-political arena of Mexico in the post-revolution
period (strong connectedness among scales, DDP-P4;
electronic supplementary material, appendix S1).
The Amapola DSES shifted state within the same
stability domain (figure 1; e.g. shift from S2 to S1)
and entered a new adaptive cycle at a local scale
(DDP-P4, see the electronic supplementary material,
appendix S1; figure 6) as it had maintained sufficient
social – ecological resilience. The phase of reorganization (alpha) took time. It was controlled by new
national land-reform legislation (DDP-P4; electronic
supplementary material, appendix S1), which resulted
in a new scheme of land property rights (DDP-P2; see
the electronic supplementary material, appendix S1).
This opened new options for local governance, land
use and local decision-making, etc. (build-up of resilience, DDP-P2; see the electronic supplementary
material, appendix S1). Change in local social – political governance structures during the post-revolution
period triggered livelihood diversification. Abundant
pine-oak forests were attractive common-pool
resources that triggered rapid in-migration by a
mixture of people from different communities and
the foundation of the Amapola community (see the
electronic supplementary material, appendix S6;
figure 3). After several decades of adaptation with
slow accumulation of capital (K-phase), a regime
shift in the global economic system (figure 6) triggered
a regime shift at the Amapola DSES in 1982.
When in the late 1980s international socio-political
decision-makers called for a shift in the global economy
paradigm from a welfare to a neoliberal regime resulting in globalization, free-trade agreements (NAFTA),
and so on, the critical slow-variable global economy
crossed a threshold with socio-political, socio-economic
and socio-cultural and environmental repercussions at
all geographical scales (figure 6). This regime shift was
induced at a late stage of the K-phase of the global adaptive cycle of the economy. Rather than waiting for a
global economic crisis (as in 1929), the regime shift
was induced and the global economy transformed to
the neoliberal model, which has proved to be highly
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pre-industrial
revolution
earth system
—climate
—biodiversity
—biome function
—human population
post-industrial revolution/
anthropocene
RS
the great acceleration
RS
capitalism
feudalism
neoliberalism
international
global economy
AS
scale
RS
viceroyalty of New Spain/
estate system (Haciendas)
national
governance/
economy
a
dictatorship
(Porfiriato)
k
AS
RS
RS
W
r
local
livelihoods
Mexican republic
neoliberal state
Mexican republic
welfare state
hunters/gatherers
labourers
RS
ranchers
semiproletarians
diversifiers
AS
RS
AS
PROCAMPO
pre-Hispanic
colonial
post-revolution
COUSSA
neoliberal
Figure 6. Hierarchical adaptive cycles (panarchy; see also electronic supplementary material, appendix S2) at different organizational scales ranging from local dryland livelihoods, national Mexican governance/economy, to global economy and Earth
system dynamics (y-axis) for the pre-Hispanic, colonial, post-revolution and neoliberal periods (x-axis). External drivers of
local livelihood development in the Amapola DSES correspond to system changes at higher organizational (e.g. national or
global economy in response to human population growth) and spatial (biome function in response to climate change)
scales. System changes at certain scales (y-axis) may be state changes (alternative state; AS) within a regime, or regime
shifts (RS; see also figure 1). A change of phase in the adaptive cycle at one scale (e.g. r ! K-phase: expansion of New
Spain into North America) may trigger a phase change (e.g. K ! V phase: annihilation of Guachichiles and hunter/gatherer
livelihood) and lead to a regime shift (e.g. shift to labourers livelihood) at a lower scale. Alternatively, 350 years of poor
labourer and peasant livelihoods (e.g. a long K-phase in colonial times) and 30 additional years of dictatorship and suppression
under Porfirio Dı́az (1876–1910) stirred up Mexican peasant and worker groups. The Mexican Revolution (1910–1920)
pushed the national governance system across a threshold resulting in the Mexican Republic (change at the higher scale is
triggered through ‘revolt’ mechanism at the lower scale; see also electronic supplementary material, appendix S2). Since
the post-revolution period, local adaptive cycles of the Amapola DSES have become increasingly coupled to the adaptive
cycles in the global economy (from capitalism to neoliberalism) triggering regime shifts at the national level (from a welfare
state to a neoliberal state). By intensifying cross-scale interactions, local systems have become increasingly coupled to
higher hierarchical scales and increasingly de-coupled from local conditions (see thin arrows connecting adaptive cycles
across scales). Earth system changes associated with global environmental and social changes following the industrial revolution have caused the crossing of global biophysical thresholds [93], leading to climate change and biodiversity loss. These
global changes are directional and rapid [102], and feed back on nonlinear system dynamics at the local scale.
vulnerable considering recent years’ food (2007) and
economic crises (2010).
Globalization, trade liberalization, change in the
national land tenure systems, elimination of producer
price support of basic crops and the increasing import
of staple foods [76,104–107] led to the shift from a
welfare to a neoliberal state [108]. Hence, local transformations of the Amapola DSES towards a new
regime were induced by new international and national
socio-economic policies and supported by national
government help and subsidy programmes. Policy
shifts from price subsidies to cash transfer programmes
(PROCAMPO and OPORTUNIDADES; see the
electronic supplementary material, appendix S6) were
Phil. Trans. R. Soc. B (2012)
rural development and poverty alleviation programmes
(intended to avoid local collapse; extension of the
K-phase). However, by replacing subsistence crops
with forage crops, redundancy (and resilience) in
the production system was removed, and livestock
production fully controlled and maximized. New
agrarian policies strengthened livestock producer livelihoods (see the electronic supplementary material,
appendix S6), which specialized in a single commodity
maintaining the Amapola SES in the K-phase at the
cost of its natural capital and thus ecological resilience,
further diminishing potential future options (i.e. desirable alternative stable states within the current
regime). This livelihood is highly vulnerable to surprise
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Factors of desertification
events (animal disease, massive soil loss, change in
markets, shifts in economic models, etc.).
Livestock farmers are relatively well organized
socially; however this organization is based on fast
external drivers (markets, government programmes,
etc.). Local environmental knowledge (DDP-P5; see
the electronic supplementary material, appendix S1)
and socio-cultural ties (slow variable) to the traditional
milpa production system were increasingly neglected
with severe consequences on long-term food security.
Overall, livestock farmer livelihood had negative
impacts on key dryland supporting and regulating ecosystem services (figure 2; see the electronic
supplementary material, tables S3.1 – S3.3; land degradation as an emergent landscape-scale property).
The semi-proletarized livelihood seems more
resilient and resistant to environmental or economic
surprises, as it diversifies between livestock production, low-input agriculture and wage labour.
However, this livelihood is ‘young’ and in the earlyto mid-K-phase of the adaptive cycle, as it is still
exploring alternatives and opportunities. Overall, the
combination of exogenous drivers and local biophysical system conditions led to a general decline in the
diversity (decline in resilience) of productive activities,
and thus to livelihood diversification.
A thorough analysis of the supporting, regulating
(DDP-P2; see the electronic supplementary material,
appendix S1) and provisioning (fast variables) ecosystem services of the Amapola landscape suggests the
command-and-control approach for livestock production is in the process of moving the Amapola
DSES towards a biophysical threshold (figure 1b), as
the natural capital is increasingly deteriorating thereby
putting the Amapola life-support system at risk
(approaching the biophysical threshold feeds back on
the socio-economic outcomes; figure 1b). Grasslands
in the Amapola landscape are the land-use type producing the most economically valuable provisioning
services (forage) at the enormous cost of most of the
regulating and supporting services (figure 5; see the
electronic supplementary material, appendix S3,
tables S3.1-S3.3). If current livelihoods persist in the
future, it is expected that the semi-proletarian farmers
will eventually convert into a migrant group (nonlinear
unpredictable trajectory of system development). This
phenomenon has become rather frequent in many
rural areas of the world [109] (emergent property at
a large spatial scale). This will allow livestock farmers
to increase stocking rates and to expand the rangelands
into the forest ecosystem with obvious environmental
consequences (pushing the system at larger spatial
scales into an increasingly vulnerable K-phase) risking
desertification [21], and the loss of the life-support
system of Amapola. This trend of extending rangeland
into forest can be observed in the Sierra
San Miguelito, where an increase in shrubland
vegetation in the former grassland suggests shrub
encroachment and forest deterioration as a consequence
of overgrazing (see the electronic supplementary
material, appendix S5, figure S5.1).
Many of the slow variables (loss of soil, soil fertility,
soil cover) are close to reaching biophysical thresholds
(DDP-P3; see the electronic supplementary material,
Phil. Trans. R. Soc. B (2012)
E. Huber-Sannwald et al.
3171
appendix S1; livestock producer livelihood triggers
cascading effects on thresholds [58]). However, loss
of fundamental supporting and regulating services
will trigger land degradation at the larger scale
(DDP-P4; see the electronic supplementary material,
appendix S1) and loss of the multi-functionality of
the landscape (landscape trap, [110]). The high cost
associated with a command-and-control approach
(focusing only on provisioning services) can be
demonstrated for the Amapola landscape (electronic
supplementary material, table S3.1, S3.2). Based on
observed local annual runoff for forest and grassland
sites, we estimated that a 4 per cent conversion of
forest to grassland corresponds to 86 300 m3 annual
loss of water by surface runoff. Considering soil C and
N pools, a 4 per cent deforestation at the landscape
level is equivalent to losing 11 855 and 435 t ha21 of C
and N, respectively. This is alarming, as Amapola represents one of a universe of similar communities in
Mexico’s drylands. When applying the previous exercise
to the regional scale of Sierra San Miguelito (1240 km2)
using the biogeochemical model BIOME-BGC [85]
(see the electronic supplementary material, appendix
S5 for a detailed description), we showed that 3 per
cent deforestation (electronic supplementary material,
figure S5.2) of pine–oak forest between 1989 and
2009 triggered almost one million cubic metres increase
in annual runoff (electronic supplementary material,
figures S5.2 and S5.3). When simulating soil loss (one
of the most critical slow variables in drylands) for 30
years, total evapotranspiration and net primary productivity decreased on average by 5.4 per cent, while
runoff increased by 12.5 per cent (electronic supplementary material, figure S5.3). Hence, an increase
in the establishment of dams and the massive redistribution of soil and water caused by erosion have
fundamental implications for the long-term multifunctionality of landscapes as providers of a diversity of
ecosystem goods and services [110].
With Holling’s adaptive cycle metaphor and the
DDP analytical framework, we were able to identify
key processes that explain the resilience and vulnerability of the Amapola SES dynamics. Having gone
through four adaptive cycles in its 450 year history, the
Amapola DSES has a long and complex socio-cultural
and biophysical legacy [94] having crossed socio-cultural, socio-political and socio-economic thresholds
[19,94]. The Amapola SES has maintained itself
because of the surprisingly high resilience of its biophysical and social systems. However, during its recent
history, the Amapola SES has become increasingly
coupled to external national (policies), international
(policies) and global (climate) drivers, at the cost of its
natural, social, human and cultural capital (cascading
thresholds, [58]; figures 2 and 6).
(b) Navigating sustainable coupled dryland
social – ecological systems for research,
monitoring, assessment and policy
development—insights from emergent
system properties
It has been acknowledged that only a small set of key
biophysical and socio-economic variables (slow
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E. Huber-Sannwald et al.
Factors of desertification
variables) determines the underlying dynamics, resilience or vulnerability of SESs to disturbance events
[26,111]. While it is important to identify, examine,
monitor and assess the changing conditions [25,112]
of these variables, it is also important to ask which
fundamental emergent properties of SESs besides
these variables can provide conclusive integrative
information and insight into the long-term dynamics
and functioning of SESs for a new, integrated
science-policy process [31]. With our multi-framework
analysis of a complex DSES, we identified the following emergent properties and processes as fundamental
to enhancing dryland system resilience, adaptability
and transformability [95]. (i) Managing ecosystem
service packages instead of single provisioning (livestock production) or supporting (C sequestration)
services enhances the multi-functionality of landscapes
and thus biophysical resilience and adaptive capacity
under future environmental change conditions [7,113].
(ii) Human appropriation of water interferes strongly
with natural hydrological fluxes at the cost of ecosystem function. Since water is becoming increasingly
scarce, a shift in water resource management from a
pure runoff-based to an integrated blue (constituting
runoff) / green water (constituting soil moisture,
transpiration—sustaining all ecosystem services) flowbased paradigm has been advocated as a resilient
approach [40,61] to better balance water between
nature and people and to increase food security in
the face of global environmental and social change
[114]. (iii) Household livelihoods depend on balanced
natural, social and human capital (figure 2). Livelihood development at the community and landscape
scale needs to be able to diversify in response to cultural, biotic and policy diversity to enhance social
resilience and stimulate social innovation [115]
coupled to multi-functional and biophysically resilient
landscapes [116]. (iv) Food security in drylands is a
matter of local adaptive governance strategies [117]
considering (i)– (iii) and the fostering of traditional
knowledge including cognition of legacy. (v) Dryland
dynamics need to be seen as a cyclical rhythm of
coupled systems that are hierarchically connected
across scales (panarchy). Some of the complex
system dynamics result from cross-scale interactions
and hierarchical nesting of subsystems both in the biophysical (plot – landscape – watershed) and social (local
governance, community organization, land property
rights systems, institutional) contexts [118], but also
from going through hierarchically linked adaptive
cycles (panarchy) operating at different scales in time
and space [27]. Drivers are variables (slow or fast) of
other (coupled) systems that have undergone state
changes or regime shifts themselves, by going through
higher or lower level adaptive cycles, and thereby
couple system dynamics at all scales. Managing systems (including the development of policies) requires
the understanding of slow and fast variables and processes and their inter-connectedness (figure 6 and
electronic supplementary material, S2.1). These are
system-specific properties of SESs that can be identified when adopting the adaptive cycle metaphor
coupled to the basin of attractions (figure 1) and the
DDP. These emergent properties are outcomes of
Phil. Trans. R. Soc. B (2012)
this analysis, and when developing management or
new policies, these system properties should be targeted.
This requires long-term monitoring of interacting
variables and coupled processes across scales—an integration of different knowledge systems considering
observational, modelling and empirical approaches.
(c) Challenges and opportunities
Complex systems research is needed that considers the
dynamic interrelations and feedback that couple the
biophysical and social subsystems, key slow variables
and processes providing resilience and how they interact with fast variables on which humans most
frequently base management and governance
decisions. New research is needed on emergent properties that addresses the following questions: Can we
identify a priori emergent properties of the DSES
and consequently examine, monitor and assess its
changing condition as a central objective of complex
system analysis (rather than a result of complex
system analysis)? Are emergent properties more tangible assets and communication tools to more efficiently
inform policy and decision-makers? Can we identify
key emergent properties for DSESs and other SESs
(coastal areas, forest ecosystem) that are fundamental
for novel adaptive governance models, stewardship
strategies and/or institutional capacity?
The Amapola case study is widely representative
for rural SESs not only in Mexico, but also in Latin
America and globally. SESs similar to the Amapola
community exist worldwide as they share many
characteristics including highly ecologically diverse
ecosystems extremely vulnerable to livestock production controlled by global markets, sharing of
common-pool resources, extensive land degradation
coupled to desertification and drought, poverty and
migration. In recent decades, drivers influencing the
decision-making of these communities have shifted
directionally from local to global by the adoption of
neoliberal economies which feed back on fundamental
assets of local SESs [116]. This trend is widespread in
rural regions of Latin America as they increasingly
depend on and respond to global market dynamics,
and increasingly ignore the production limitations of
local small household farms [118,119]. This is of
great concern as rural environments are the longterm basis for the development of farming-based livelihoods [120], which perhaps are the most frequent
livelihoods in developing countries. A trend towards
proletarianization has been explained by the lack or
scarcity of private land [121] and neoliberal policies
that focus only on economic growth [122].
We took a heuristic case study approach where we
linked an empirical multi-scalar, interdisciplinary
study with several frameworks related to resilience, complex systems and sustainable development theory. The
DDP functioned as an umbrella framework that defined
the social–environmental scope and spatio-temporal
dimensions of this study. While the DDP is highly flexible and adaptable to a wide variety of SESs, it also
provides clear-cut guidelines for the analysis of SESs
targeted towards effective pathways to transform desertification into sustainable development [19]. To our
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Factors of desertification
knowledge, this is one of the first studies to explore
path-dependent relationships of a local coupled
DSES. We focused our analysis on the behaviour and
responsiveness of livelihoods as key indicators of the vulnerability and resilience of DSESs in the context of
cross-scale demographic, socio-cultural, socio-political,
socio-economic and environmental change. From pathdependent analysis, we learned livelihoods need to
maintain local options for diversification. Local environmental knowledge, cultural heritage and high biotic
and cultural diversity are fundamental elements to
increase livelihood security and diversification. Only
by considering the cyclical rhythm of past and future
SES trajectories, can we learn about the adaptability, transformability and resilience of DSESs [95]. We
will have to focus on these emergent phenomena of
complex systems when developing policies, management, governance or stewardship strategies for SESs
[33]. Fostering resilience thinking [95] increases
capacity for innovation, creativity and cross-lateral
multi-stakeholder learning and opens new windows of
opportunity in times of crisis and uncertainty. In the
new area of transition towards a resilient planet [123],
rather than operating within the safety margins
of control and aiming at maximizing productivity of
market-driven commodities, we have to transform our
mindsets and start learning to induce shift and transformation and thereby increase options and longevity
of sustainable livelihoods in times of foreseeable crises
and breakdown.
Global environmental and social changes have
made dryland livelihoods highly vulnerable. Climate
change, land-use change, loss of biodiversity and
degradation of ecosystem services as well as globalization, neoliberal politics, monetary crisis, global
markets, industrialization and urbanization are all
global change drivers directly or indirectly affecting
DSESs. It is the biophysical or social subsystem that
is responding to external drivers of change; however,
since SESs are coupled, the response to multiple drivers may be amplified [94]. Thus, it is important to
understand the nature of SES responses and feedback
to multi-causal continuous or abrupt changes in
system dynamics that may lead systems to local
thresholds or global tipping points. Global change drivers are systemic, in that they affect the functioning of
the whole Earth system (global warming), or accumulative, in that they occur first at local or regional scales
(land-use change), yet when increasing in impact
potentially affect the whole Earth system [29]. In
recent decades, local and regional land degradation in
drylands has emerged as a global phenomenon, strongly
interacting with global climate change, globalization, neoliberal policies and global markets, together
exerting a continuous impediment for dryland development. Desertification is the most advanced stage of land
degradation, when SESs have lost the elements and
interconnections to sustain an adaptive life-support
system for livelihood development in a world increasingly affected by global environmental and social
change. Actively inducing the transformation of a
degraded or desertified system in a new regime could
offer windows of opportunities for system development.
However, this intervention requires sound scientific
Phil. Trans. R. Soc. B (2012)
E. Huber-Sannwald et al.
3173
understanding, great skill in leadership, novel governance structures and continuous dialogue between
scientists and policy-maker representatives of the two
billion people living in the largest complex SES on
Earth. Future research and policy development need
to catalyse new and continuous dialogues and effectively
engage local to global stakeholders in a new sciencepolicy process to jointly tackle the daunting challenge
to transform the process of desertification into sustainable development [31].
We would like to thank Fernando T. Maestre Gil and Roberto
Salguero Gomez, as Guest Editors, for having invited us to
contribute to this Dryland Theme Issue. We thank all
members, in particular James F. Reynolds and Jeffrey
E. Herrick, of the 10 ARIDnet workshops held between
2004 and 2010 in Latin America, where many of the ideas
explored in this study were stimulated. We thank community
members of Amapola for their eager participation in this
research. E.H.S. thanks Jorge Melquiades Hernández
Martı́nez for inspiration throughout the process of writing
the manuscript. We thank Marı́a del Rocio Perales Ruiz for
assistance in the graphical design of figure 1. We gratefully
acknowledge valuable inputs from three anonymous
reviewers, Fernando T. Maestre Gil and R. Salguero Gómez.
We thank Jennifer Eckerly Goss for editing the manuscript.
M.R.P., R.M.M.P., M.B. and J.G.A. thank CONACYT for
graduate student scholarships. E.H.S. acknowledges research
support by SEMARNAT (Projects 410 and 23721).
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