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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.