The SERV-ILEK Project: Social-Ecologically Resilient Visions using Integrated

Local Environmental Knowledge

Alex Webb1 and Kostas Alexandridis2

1UVI Masters of Marine and Environmental Science (MMES) Program; 2UVI Center for Marine and Environmental Studies (CMES); 3UVI College of Science and Mathematics/Computational Sciences

Introduction1

Theoretical Frameworks2

One of the primary difficulties in understanding and managing for environmental

sustainability within a systems context is the concept of uncertainty or surprise

(Holling and Meefe 1996; Berkes 2003). However; uncertainty is an inherent

component in all complex adaptive systems, such as a social-ecological system

(Holling 2001) (see fig. 1), and therefore should be embraced and anticipated .

Managing for environmental sustainability then, includes not only an

understanding of the normative values contextualizing it but also the ability to

identify and promote resilient solutions in the face of an ever changing and

dynamic world. This research project seeks to add to the rapidly growing

discipline of sustainability science by examining the multidimensionality of

collective community visions for the future. It will also investigate the

mechanisms and processes that inform those visions. Analysis of the data will

incorporate a hypothetical metal-learning framework (see fig. 2).

Human Systems

Natural Systems

Fig. 1: Illustrates the relationship and feedbacks both

within and between social and natural systems in SES

theory (NSF, 2012)

Fig. 2: Hypothetical relationship between

knowledge production/circulation and adaptive

governance (Sato, 2012)

DemographicsInstitutional

ArrangementsEconomy

Environment and

Resources

Health and Well-

being

Cultural

Properties

Infrastructure

and Services

Perceptions about

Environment

Methodological Design3

Triangulation of methodologies, both

qualitative and quantitative, were

implemented in order to give greater

breadth and depth of understanding to

the data gathered (Berg and Lune,

2004). These methodologies included:

Scenario Planning Focus Group

Exercises, an adapted Q-Method

ranking Scheme, and the development

of an analytic framework for drivers

within a SES.

Broad Research Question4

Methods of Analysis5

Methods

•Focus Group Exercises

•Adapted Q-Method

•Development of Analytic SES Framework

The broad research question of this study is: ‘What are the perspectives of specific

community stakeholder and institutional groups regarding the drivers, thresholds,

critical variables, tipping points and feedbacks that influence or are influenced by

environmental sustainability (ES) and the dynamics of social-ecological system

resilience (SESR)?’ Addressing this research question required a pluralistic and

multidimensional methodological approach in order to study the systemic complexity

of a focal SES system.

Each focus group discussion was transcribed verbatim into text documents that were

then analyzed using Semantic Network Analysis. During the Q-method ranking

scheme the analytic framework of drivers was used to code participants ranking of

critical system variables into categorical data. During that exercise participants also

coded each driver as it related to the meta-learning framework (Knowledge, Policy,

or Action).

Preliminary Results and Analysis6

In total 32 participants comprising 5 livelihoods based focus groups were sampled

during six month period resulting in 1,0181 statements adding up to 53,372 words.

During the Q-method exercise 176 ranked and coded statements were gathered. The

focus groups consisted of : 1) An MPA management team 2) Hospitality employees

3) Local NRM Employees 4) Members of a farming Co-op and 5) Environmental

conservation group.

Fig 3. The photo form this figure is of a focus group done

with members of a farming co-op.

Table 1. This table lists the eight categorical drivers used to operationalize the SES concept. (See Larson,

Alexandridis, 2009) These drivers were used to frame and code participants ranking of key, critical process

within the system.

References:Alexandridis, K. (2011). Identifying Social Community Resilience in Collective Semantic Knowledge Transformations. Paper presented at the Resilience 2011 - Resilience, Innovation and

Sustainability: Navigating the Complexities of Global Change. Second International Science and Policy Conference, University of Arizona, Tempe, AZ, USA, March 11-16,

2011.

Atkinson, R. and J. Flint (2001). "Accessing hidden and hard-to-reach populations: Snowball research strategies." Social Research Update 33(1): 1-4.

Berg, B. L. and H. Lune (2004). "Qualitative research methods for the social sciences.“

Berkes, F., Colding, J., Folke, C. (2003). Navigating social-ecological systems: building resilience for complexity and change, Cambridge Univ Pr.

Chermack, T. J. (2004). "Improving decision-making with scenario planning." Futures 36(3): 295-309.

Chermack, T. J. (2005). "Studying scenario planning: Theory, research suggestions, and hypotheses." Technological Forecasting and Social Change 72(1): 59-73.

D'Aquino, P., Page, C. L., Bousquet, F. o., and Bah, A. (2003). Using Self-Designed Role-Playing Games and a Multi-Agent System to Empower a Local Decision-Making Process for Land

Use Management: The SelfCormas Experiment in Senegal. Journal of Artificial Societies and Social Simulation, 6(3).

Holling, C. S. (2001). "Understanding the Complexity of Economic, Ecological, and Social Systems." Ecosystems 4(5): 390-405.

Holling, C. S. and G. K. Meefe (1996). "Command and Control and the Pathology of Natural Resource Management." Conservation Biology 10(2): 328-337.

Larson, S., and Alexandridis, K. (2009). Socio-Economic Profiling of Tropical Rivers. Canberra, ACT: Australian Government, Department of the Environment, Water, Herritage, and the

Arts, Land and Water Australia, National Water Commission, Tropical Rivers and Coastal Knowledge (TRaCK) Research Hub (ISBN: 978-1-921544-99-6). pp. 70.

Lempert, R. J., S. W. Popper, et al. (2003). Shaping the next one hundred years: new methods for quantitative, long-term policy analysis, Rand Corp.

Fig. 7 & 8: Graphs visualize the rankings and counts of the framework drivers by participants during the

adapted Q-Method portion of the scenario planning exercises. (n=176)

Funding for this research is provided by NSF VI-EPSCoR award no. 0814417. Additional

partial funding was supported by the initiative-based project EO-5 “Creation and Sustainable

Governance of New Commons through Formation of Integrated Local Environmental

Knowledge”, Research Institute for Humanity and Nature (RIHN), Japan.

Fig. 5 & 6: Illustrates the breadth of and relationship between topics discussed by participant describing

the key drivers of St. Thomas (top) (n=462) and concepts related to the resilience and preparedness for

the future (bottom) (n=147)

Fig 4. Map demonstrates

the range of where

participants work/live

across the island.