Mbaru, Emmanuel Kakunde (2018) Diffusion of a gear-based conservation innovation: adoption patterns and social - ecological outcomes. PhD thesis, James Cook University.

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Abstract

Conservation interventions are only effective if people use them. Thus, identifying motivations and barriers to the uptake of conservation interventions is critical. Yet, analysis of factors that hinder or promote conservation diffusion (spread of conservation interventions) processes has received little attention by conservation practitioners and policy makers. Consequently, many efforts to achieve sustainability fail to reach full potential.

Nearly all conservation interventions are characterized by the introduction of new ideas and practices. In line with this recognition, implementation of conservation can therefore benefit from a large body of social science research that explains how new ideas, practises, and technologies, i.e., innovations spread. Central to understanding how innovations spread among social systems, is the diffusion of innovations theory pioneered by Rogers. This thesis uses the diffusion of innovation lens to investigate the introduction of a conservation intervention in coastal Kenya.

Diffusion research show that peoples' adoption behaviour is typically influenced by social differentiations in terms of personal attributes, socioeconomic status, and communication behaviour (Rogers 2010). Though personal attributes and socioeconomic status are widely used to analyse adoption processes (Horst et al 2007, Knowler & Bradshaw 2007), there remains very limited empirical work emphasizing the effect of communication behaviour in conservation diffusion literature. In addition, there is a long-standing recognition that proper communication channels are critical in facilitating innovation transfer (Gladwell 2006, Nilakanta & Scamell 1990, Rogers 1995). Yet, no criteria currently exist in the conservation literature to identify characteristics and functions of key intermediaries needed to facilitate conservation transfer. Thirdly, after initial adoption, whether people maintain an innovation is largely determined by the impact it has on their lives. However, conservation diffusion studies rarely examine the impacts of conservation innovations on either people or ecosystems (Weeks et al 2010, Woodhouse & Emiel de Lange 2016). These critical knowledge gaps lend themselves for empirical investigation.

This thesis therefore aims to examine how people adopt conservation interventions and determine key social and environmental impacts of doing so. To address these aims, I ask two fundamental research questions: (i) "how does conservation interventions spread through societies?" (ii) "what are the consequences of conservation diffusion on people and environment?"

I provide answers to these questions by addressing the following interrelated specific objectives:

1. determine the factors that influence uptake (adoption) and spread (diffusion) of a conservation intervention over time (Chapter 3)

2. identify key stakeholders to facilitate conservation transfer (Chapter 4)

3. investigate impacts of conservation diffusion on people's wellbeing (Chapter 5)

4. examine impacts of conservation diffusion on the ecosystem (Chapter 6)

I explore these issues through a case study of a fisheries bycatch (incidental take) reduction initiative introduced in coastal Kenya (see details in chapter 2). Specifically, I study a modified basket trap retrofitted with escape gaps that allows juveniles and narrow-bodied, low value fish species (i.e. bycatch) to exit, while larger, wider-bodied target species are retained (Mbaru & McClanahan 2013). This intervention was introduced with the explicit aim to protect biodiversity by harvesting fish species at sizes that ensure sustainability of the local fishery (McClanahan & Mangi 2004). However, it was expected that improved catches over time will translate to positive sustainability outcomes, e.g., improved income and livelihoods that will continue to accrue over the long term.

Aside from the diffusion of innovations theory, this research further draws from a number of social science theories and emerging breakthroughs in functional ecology to provide a rigorous and deeper examination of the study aims highlighted above. Chapter 1 provides a general introduction about the different theoretical foundations and approaches that can be used to analyse conservation diffusion processes in light of the diffusion of innovations theory. Chapter 2 provides an overview of study sites and describes the methods used throughout the thesis, though each chapter will also have additional methods.

In chapter 3, I integrate theoretical foundations of the diffusion of innovations theory with novel breakthroughs in network science to offer a clearer understanding of the factors that shape conservation diffusion patterns over time. Unlike the majority of conservation diffusion studies, I explicitly measure communication behaviour via social networks and leverage recent advances in network modelling to simultaneously test the effect of social network structures and social influence on conservation diffusion while accounting for personal attributes and socioeconomic characteristics. I show that network processes contribute considerably to conservation diffusion – particularly in the early adoption stage – even when key socioeconomic factors are accounted for. By showing that communication behaviour is crucial during the early stages of the diffusion process, my results challenge decades of diffusion research suggesting commination behaviour is more important for late adoption. Overall, I demonstrate that harnessing the power and characteristics of social networks can help diffuse conservation interventions through target populations.

In chapter 4, I draw on social network theory and methods to develop specific criteria for selecting stakeholders who are best placed in social networks (i.e., key players) to facilitate four key conservation objectives: (1) rapid diffusion of conservation information, (2) diffusion between disconnected groups, (3) rapid diffusion of complex knowledge or initiatives, or (4) widespread diffusion of conservation information or initiatives over a longer time period. After identifying the key players for the four distinct diffusion related conservation objectives, I then test whether the socioeconomic attributes of the key players I identified match the ones typically selected by conservation NGOs and other resource management agencies to facilitate conservation diffusion (i.e., current players). Results show clear discrepancies between current players and key players, highlighting missed opportunities for progressing more effective conservation diffusion. The chapter concludes with a novel, practical, and nuance approach to identify a set of ‗key players' better positioned to facilitate diffusion related conservation objectives, thereby helping to mitigate the problem of stakeholder identification in conservation diffusion processes.

The focus of chapter 5 is to investigate the effects of adoption or non-adoption of the conservation intervention on people's wellbeing, i.e., an umbrella term that encompasses good social relations, freedom of choice, and basic materials for a good life (MEA 2005). Here, I use the wellbeing framework (Gough & McGregor 2007) to capture how the conservation innovation may impact multiple dimensions (material, relational, subjective) of people's wellbeing. I use panel data (i.e., follow the same individuals over time) to study these three dimensions of wellbeing before the intervention, during the short term (i.e., one year after the introduction), and in the medium term (i.e., about two years after the introduction) for those that adopt the innovation (adopters), those that don't adopt (nonadopters), and in control villages, where the intervention was not introduced. Overall, my findings indicate that adoption of the conservation intervention did no harm to the associated human communities. Indeed, I show modest improvements in material and subjective livelihood wellbeing for adopters relative to controls over time. However, the variations I find in wellbeing experiences (in terms of magnitude of change) among adopters, nonadopters, and controls across the different domains over time affirm the dynamic and social nature of wellbeing. Findings emphasize the need for environmental policy to use multiple indicators of wellbeing in addition to baselines in future evaluation research.

The focus of chapter 6 is to assess the impact of the conservation intervention on environment. Previous attempts have been made to understand the effects of escape slot trap fishing on the marine environment (Condy et al 2015). However, most of this work tends to focus on species abundances and catch composition (Gomes et al 2014). Yet, the growing interest in an ecosystem-based approach has stressed maintaining and sustaining ecological functions (Henriques et al 2014). Moreover, in multi-species coral reef fisheries fishing gears are known to exhibit some degree of overlap in the species they capture (McClanahan & Mangi 2001). Depending on the level and type of overlap, these interactions can potentially retard critical pathways associated with gear-based conservation interventions (McClanahan & Kosgei 2018). Against this background, I employ a trait-based approach to assess functional selectivity of the escape slot trap. In addition, I quantify overlaps in catch composition between escape slot traps and other gear types that operate concurrently in the same reefs. These are hook and line, speargun, gillnet, beach seine, basket trap, and a combination of other nets. Overall, I show that using escape slot traps has the potential to lead to environmental improvements. Fish assemblages in escape slot traps are more functionally redundant (tendency of species to perform similar functions) and a vast majority constitute the least breadth of functional diversity. However, I find that two-thirds of the catch released by escape slot traps is targeted by other gear types. Thus, given the extent of overlaps in species selectivity between gears, switching to escape slot traps may not achieve conservation targets in the Kenyan multi-species coral reef fishery unless other gear types are also simultaneously excluded. These results call for caution when assessing ecological implications of gear-based conservation innovations particularly in gear-diverse coral reef fisheries where competitive interactions between gears are eminent.

Together, this body of work advances the current state of knowledge about analysing patterns and outcomes of conservation diffusion over time. The stakeholder selection criteria developed in chapter 4 can be applied to facilitate widespread adoption and diffusion of simple initiatives such as rapid environmental awareness campaigns as well as more complex initiatives that seek to implement behaviour change to improve conservation outcomes. This work further provides a more comprehensive way to look at conservation outcomes and can help draw policy attention to the nonmaterial impacts of conservation. Trait-based approaches can provide a concrete platform for ecosystem-based management approaches in tropical multi-species fisheries.

Item ID:	58935
Item Type:	Thesis (PhD)
Keywords:	conservation, diffusion, key players, social network analysis, social-ecological systems
Related URLs:	https://researchonline.jcu.edu.au/50674/
Copyright Information:	Copyright © 2018 Emmanuel Kakunde Mbaru.
Additional Information:	Publications arising from this thesis are available from the Related URLs field. The publications are: Chapter 4: Mbaru, Emmanuel K., and Barnes, Michele L. (2017) Key players in conservation diffusion: using social network analysis to identify critical injection points. Biological Conservation, 210. pp. 222-232.
Date Deposited:	17 Jul 2019 02:42
FoR Codes:	05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050202 Conservation and Biodiversity @ 50% 16 STUDIES IN HUMAN SOCIETY > 1608 Sociology > 160802 Environmental Sociology @ 50%
SEO Codes:	96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960507 Ecosystem Assessment and Management of Marine Environments @ 50% 96 ENVIRONMENT > 9607 Environmental Policy, Legislation and Standards > 960701 Coastal and Marine Management Policy @ 50%
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