Resilience and transdisciplinarity

The Sustainable Development Goals (SDG) summarized by the United Nations present an agenda encompassing 17 ambitious and interconnected proposals related to key challenges we shall face during the 21st century. Designing feasible strategies to pursuing SDGs at any scale—for example, cities, states, or countries—critically depends on our understanding of their interconnections. Trully integrative, science-based strategies need to be science-based. At this point, the concept of socioecosystem resilience is an idea that can robustly articulate different areas of knowledge studying the dynamics of complex systems to promote transdisciplinary knowledge, applications and innovations that will be the basis for the achievement of the SDGs.

Before presenting the resilience concept, we briefly define socioecosystem considering fundamental aspects of its structure and functioning. The socioecosystem is formed by (i) the set of biotic and abiotic elements that define ecosystem structure, which, in the Anthropocene, can not be dissociated from (ii) structures and processes built by humans (anthropogenic).antropogênicos. We herein adopt an approach to socioecosystems based on  Complexity Science.Here, the idea of complexity  refest to the fact that the key to understanding and managins socioecosystems is the interaction  structure and dynamics involving its multiple elements. Therefore,  network thinking is a natural approach enlightening the joint structure and dynamics of ecosystems, social organizations and economic enterprises.

We hereafter discuss socioecosystems as complex adaptive networks. Networks are formed by elements called nodes and their interactions -- the links. For example, in a plant-pollinator network there is one node representing each species of animal pollinator, which is linked to all the plant species it pollinates. The structure of this networks depicts the ecosystem provided by pollinators ensuring plant reproduction, promoting the forest structure persistence and contributing to agriculture. Similarly, the activities of different companies extracting timber within a given region can be depicted as network showing their impacts on the forest cover of multiple forest fragments, along with related commercial flows. Networks can therefore depict distinct systems often seen as dissociated but which actually share common elements, making it possible to integrate them into multi-level networks combining ecosystem and anthropogenic processes that are part of socioecosystem dynamics. 

The network approach allows synthetic and comparative descriptions of different types of socioecosystems and their properties, for example, size, connectance, topology, and associated dynamics. Among the multiple network properties that can be used to describe socioecosystems, a key attribute is their resilience. It is not our aim here to comprehensively review this concept, which has different meanings—for example, see the classic work of Holling (1996) on “engineering resilience” versus. “ecological resilience” for an entry on the broad conceptual discussion of resilience, stability, robustness, resistance and their relationships. We adopted a definition of socioecological resilience in line with Brazilian reality but also articulated with long-standing conceptualization efforts since Holling (1973) . Therefore, our approach represents a solid transdisciplinary framework to integrate theories of multiple equilibria, allowing the conceptual and methodological connection between different fields of Ecology and Economics.

We present here seven fundamental principles for promoting socioecological resilience. Our perspective combines the ideas presented by  Biggs et al. (2012) and  Moberg et al. (2016) considering the goals of DATAPB and the IDEAL IJL,., which take into account the ecological-economic and sociocultural reality of environmentally degraded and socially vulnerable territories of the Brazilian Northeast ( Berti-Equille & Raimundo 2022).

1. Promote diversity and functionality

The first fundamental principle for promoting socioecological resilience is acknowledging the value of sociobiodiversity and understanding the processes accounting for its integrity and functionality. Diversity is the raw material for adaptability and innovation. The diversity of species, interactions, habitats, land uses, cultural groups, institutions, companies and social movements are examples of the multiple human and non-human elements of socioecosystem diversity. These elements account for the socioecosystem ability to change in the face of disturbances and uncertainties. Human and non-human elements feedback with each other and shape adaptations and coadaptations involving biological diversity, sociocultural practices and production chains. In socioecosystems, diversity is closely linked to functionality, giving rise to innovations that articulate ecosystem functions, production processes and territorial dynamics. Thus, when we speak of socioecosystems under human governance, the ideas of adaptation and innovation are equivalent.  In the context of DATAPB and LMI IDEAL, the principle of promoting the diversity and functionality of socioecosystems is explicit in the line of inquiry "Protection and enhancement of agrobiodiversity, water resources and traditional knowledge".

2. Manage biological and socioproductive connectivities

Our approach addresses socioecosystems as complex networks formed by biological, social and economic agents. Connectivity patterns of these networks are, therefore, another fundamental aspect we shall understand and manage. When we talk about biological connectivity, we are referring, for example, to forest restoration and rewilding efforts to functionally reconnect fragments of native habitats currently isolated within degraded landscapes dominated by  plantations and livestock and, consequently, more prone to biodiversity erosion and the collapse of ecosystem processes. Biological connectivity also refers to the networks of ecological interactions that describe a variety of biodiversity-based ecosystem services, such as pollination, seed dispersal and demographic control of herbivores. Socio-productive connectivity, in turn, refers to the structure of production processes and their relationships with labour organization, the optimization of which is a critical step to enable economic innovation associated with social inclusion. In the context of DATAPB and LMI IDEAL, we defined this line of inquiry as “Optimization of socioecological and bioeconomic connectivity for the restoration of environmentally degraded and socially vulnerable landscapes”.

3. Monitor and manage slow variables and feedabcks

Socioecological systems can assume a variety of configurations, i.e., they can have different states depending on how their elements interact. Some slowly changing variables describe fundamental properties of the socioecosystem that we intend to understand and transform. Biodiversity is an example of a slow variable that we may want to change by managing the structure and feedback of the socioecosystem. Biodiversity is a complex property of ecosystems, which we can decompose into different monitorable variables, such as species richness, the biological heterogeneity of the landscape or even the genetic diversity in populations of indicator species. Fostering mechanisms that generate positive feedback -- reinforcing the recovery of biodiversity and its functionalities, for example -- is another essential strategy for promoting socioecological resilience. By developing biodiversity-based products, such as ornamental flowers, fruits and industrial inputs derived from them, we will contribute to positive feedback reinforcing reforestation initiatives and the connectivity of biological diversity at the landscape scale. The management of diversity and connectivity demands monitoring slow variables that indicate whether the direction of transformation of the socioecosystem is approaching socially defined objectives. “Managing feedback and slow variables” is also a line of inquiry in the context of DATAPB and LMI IDEAL.

4. Promote experimentation and social learning

The socioecological approaches assumes public policies and projects for the transformation of territories should be based on Science. Thus, we should know the nature of the scientific method and its limitations. An essential view underlying the process of building socioecological resilience is that scientific knowledge about a system is always limited. Consequently, research and monitoring will always be necessary to support decisions and innovations. We must ensure that all the actors involved in socioecological governance have access to co-produced data. In the context of DATAPB and LMI IDEAL, this principle is part of our participatory research approach, which assumes that local communities, teachers and managers who participate in our projects are co-constructing knowledge with us.

5. Organize governance based on polycentrality

Public policies are often highly hierarchical and compartmentalized regarding their management. In contrast, socioecological governance requires network thinking and action associated with mechanisms supporting social learning, consensus building and decision-making by the multiple agents within the socioecosystem. The idea of polycentrality synthesizes such a network view of governance. Polycentrality implies coordinated, cooperative actions involving autonomous institutions that dialogue to establish consensual rules on the management of resources and other relevant strategies. The socioecological literature suggested that nested institutions that respond to different scales and complexity levels ensure higher adaptability when territories face disturbances and challenges. In the context of DATAPB and LMI IDEAL, the line of inquiry "multi-scale and polycentric approach to land management" focuses on this principle.

6. Promote social participation in decisionmaking

Social participation is a central aspect of governance that can help to consolidate the relationship between knowledge production, democracy and citizenship. It generates a sense of belonging and responsibility for the decisions taken. The involvement of local communities in the research process (principle 4) can include them more effectively in the knowledge production process and, consequently, in science-based decision-making. Integrating participatory research with governance and decision-making increases trust between institutions and civil society based on transparency and commitment to common interests. In the context of DATAPB and LMI IDEAL, social participation in research and governance also relates to using artificial intelligence and data science tools for participatory science, which opens up lines of inquiry and innovation that are as necessary as challenging.

7. Study and manage socioecological systems as complex adaptive systems (CASs)

A complex adaptive systems approach to the governance of socioecosystems because none of its elements can be managed in isolation. The study of complex systems focuses on interactions and their dynamics. This focus requires the actors involved in the governance process to exercise systemic thinking, assessing risks and uncertainties and pondering how institutional structures can be reshaped to better adjust to socioecological dynamics. The perspective of SACs allows the application of co-management and adaptive governance techniques. In this other article, we explore the idea of adaptability in biological, social and economic systems. This understanding is key for the convergence of knowledge production, normative systems and the set of values ​​and principles to converge into a governance scheme capable of promoting territorial transformation. The CAS approach to the study of socioecological systems is a principle of DATAPB and LMI IDEAL.

How to cite this text:

DATAPB, 2023. Resilience and transdisciplinarity: principles to construct diverse, functional and fair ecosystem. Available on http://datapb.ccae.ufpb.br.