9
expertise from the natural and social sciences, and platforms of communication,
negotiation, and decision-making that facilitate the formation of learning communities (similarly to Davidson-Hunt and O’Flaherty 2007; Bautista et al. 2017). The
purpose of such learning communities is the sharing of scientific, local, indigenous,
and technical knowledge, and ethics, wisdom, and worldviews, to ensure equal dialogue among all involved stakeholders. Utilizing and simultaneously protecting the
wealth of natural resources and ecosystem goods and services upon which humanity
depends calls for novel, holistic, transboundary designs, analysis, and knowledge
co-production (Daily 1997; MEA 2005; Chapin III et al. 2009b). This integrated
approach is fundamental for co-management, collaborative governance, and adaptive
policy development (Bautista et al. 2017).
With this perspective, in 2002, a global multidisciplinary think tank of dryland
specialists developed the Drylands Development Paradigm (DDP) (Stafford Smith
and Reynolds 2002; Reynolds et al. 2007). The DDP is an integrative framework for
the analysis, restoration, mitigation, and/or prevention of dryland SES as affected by
degradation and/or desertification (Reynolds et al. 2007) and for policy development.
The DDP is based on complex adaptive systems theory (CAS) (Ashby 1962; von
Bertalanffy 1968), in that systems consist of interconnected elements conferring a
particular structure following the underlying rules of the specific purpose or function
of a system (Meadows 2008). Elements and processes may change at different rates,
either “slow” or “fast,” and at and across different spatial and/or temporal scales. The
CAS has three key properties: (1) order is emergent not pre-determined, where the
system adjusts and self-organizes after disturbance events; (2) historic impacts are
irreversible in that current dynamics are linked to and influenced by past events
(legacy effects); (3) based on (1) and (2) the future of CAS is unpredictable and the
past lays the foundation for future changes (Chapin III et al. 2009a; Curtin 2015).
When studying SES, we need to ask the questions what causes overall system
dynamics, internal connectedness, SES contexts and feedbacks of system components in response to internal and external long-term drivers (e.g., climate change,
human population growth), stressors (e.g., mega-drought, emigration, fluctuations
in markets or commodity prices, policy change), or pulsed trigger events (e.g.,
extreme weather or natural hazards, sudden access to electricity, communication
technology), and how do system elements (resources, species, social actors) disperse, migrate, or interact across socio-ecological systems (Biggs et al. 2012). Do
the variables and processes change slowly or rapidly and are they connected to the
dynamics of events occurring at other times or places (Folke et al. 2009)?
For relevant research questions, clear understanding is required of the spatial and
temporal dimensions of connectivity in SES. This implies understanding the connections between landscape units, habitats, species, social groupings, generations,
knowledge types, institutions, and policies, among others (Biggs et al. 2015).
Comprehending the functional integrity of SES as CAS is daunting yet crucially
important, as the younger generations’ tolerance to and perception of environmental
degradation is changing such that the threshold of acceptance of environmental condition is declining, a psychological and sociological phenomenon laconically coined
shifting baseline syndrome (SBS) (Pauli 1985). Hence, understanding SES dynamics and the degree of land degradation and potential human’s preventive, reactive,
1 Introduction: International Network for the Sustainability…
expertise from the natural and social sciences, and platforms of communication,
negotiation, and decision-making that facilitate the formation of learning communities (similarly to Davidson-Hunt and O’Flaherty 2007; Bautista et al. 2017). The
purpose of such learning communities is the sharing of scientific, local, indigenous,
and technical knowledge, and ethics, wisdom, and worldviews, to ensure equal dialogue among all involved stakeholders. Utilizing and simultaneously protecting the
wealth of natural resources and ecosystem goods and services upon which humanity
depends calls for novel, holistic, transboundary designs, analysis, and knowledge
co-production (Daily 1997; MEA 2005; Chapin III et al. 2009b). This integrated
approach is fundamental for co-management, collaborative governance, and adaptive
policy development (Bautista et al. 2017).
With this perspective, in 2002, a global multidisciplinary think tank of dryland
specialists developed the Drylands Development Paradigm (DDP) (Stafford Smith
and Reynolds 2002; Reynolds et al. 2007). The DDP is an integrative framework for
the analysis, restoration, mitigation, and/or prevention of dryland SES as affected by
degradation and/or desertification (Reynolds et al. 2007) and for policy development.
The DDP is based on complex adaptive systems theory (CAS) (Ashby 1962; von
Bertalanffy 1968), in that systems consist of interconnected elements conferring a
particular structure following the underlying rules of the specific purpose or function
of a system (Meadows 2008). Elements and processes may change at different rates,
either “slow” or “fast,” and at and across different spatial and/or temporal scales. The
CAS has three key properties: (1) order is emergent not pre-determined, where the
system adjusts and self-organizes after disturbance events; (2) historic impacts are
irreversible in that current dynamics are linked to and influenced by past events
(legacy effects); (3) based on (1) and (2) the future of CAS is unpredictable and the
past lays the foundation for future changes (Chapin III et al. 2009a; Curtin 2015).
When studying SES, we need to ask the questions what causes overall system
dynamics, internal connectedness, SES contexts and feedbacks of system components in response to internal and external long-term drivers (e.g., climate change,
human population growth), stressors (e.g., mega-drought, emigration, fluctuations
in markets or commodity prices, policy change), or pulsed trigger events (e.g.,
extreme weather or natural hazards, sudden access to electricity, communication
technology), and how do system elements (resources, species, social actors) disperse, migrate, or interact across socio-ecological systems (Biggs et al. 2012). Do
the variables and processes change slowly or rapidly and are they connected to the
dynamics of events occurring at other times or places (Folke et al. 2009)?
For relevant research questions, clear understanding is required of the spatial and
temporal dimensions of connectivity in SES. This implies understanding the connections between landscape units, habitats, species, social groupings, generations,
knowledge types, institutions, and policies, among others (Biggs et al. 2015).
Comprehending the functional integrity of SES as CAS is daunting yet crucially
important, as the younger generations’ tolerance to and perception of environmental
degradation is changing such that the threshold of acceptance of environmental condition is declining, a psychological and sociological phenomenon laconically coined
shifting baseline syndrome (SBS) (Pauli 1985). Hence, understanding SES dynamics and the degree of land degradation and potential human’s preventive, reactive,
1 Introduction: International Network for the Sustainability…