BONDING, BRIDGING, AND LINKING SOCIAL CAPITAL IN AN ETHNICALLY

DIVERSE FISHERY: THE CASE OF HAWAIʽI’S LONGLINE FISHERY

A THESIS SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAIʽI AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

MASTER OF SCIENCE

IN

NATURAL RESOURCES AND ENVIRONMENTAL MANAGEMENT

MAY 2012

By

Michele Lee Barnes

Thesis Committee:

PingSun Leung, Chairperson Stewart Allen Steven Gray

Keywords: social network, social network analysis, ethnic diversity, social capital, fisheries, natural resource management

i

©2011 Michele Lee Barnes

All Rights Reserved

ii

DEDICATION

I dedicate this thesis to my fiancé Will, my mother Debbie, my father Keith, and my

sisters Christina, Danielle and Amanda; without whom I would not have had the

motivation, love, and support to complete this work.

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ACKNOWLEDGEMENTS

I am extremely thankful for my incredibly supportive adviser Dr. PingSun Leung, and for

my equally supportive and helpful colleague Shawn Arita, for cultivating my interest in

this project and for their help, feedback, and guidance throughout the production of this

thesis. I would also like to thank my committee members, Dr. Stewart Allen and Dr.

Steven Gray, for sharing their intimate knowledge and for challenging me to think in new

directions while supporting me throughout my thesis research. I am also very grateful for

Professor Alison Rieser of the Graduate Ocean Policy Certificate program, who inspired

and encouraged me throughout my master’s program. I am grateful for the funding

support from the Pelagic Fisheries Research Program for the larger project of which this

thesis is only a part. I would also like to acknowledge our hard working translators,

Sunny Bak and Jennifer Tran, as well as all of the fishermen who participated in this

study; this project could not have been completed without their participation and support.

I also thank my entire department and all of my colleagues at the University of Hawaiʽi at

Manoa, and particularly my office mates Cheryl Geslani-Scarton and Connie Winterstein,

who not only inspired me, but also supported and motivated me throughout my master’s

program. Lastly, I would like to thank my amazing family and fiancé, whose love,

encouragement and patience were key elements to my success in this endeavor.

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ABSTRACT

Social networks and social capital have recently been identified as key features in

facilitating collaborative arrangements which can enhance resource governance and

adaptability in social-ecological systems. Yet, how ethnic diversity among resource users

in a competitive pelagic fishery may affect social networks and social capital, and thus,

influence the potential for collaboration, has not been previously examined. To explore

this effect, a social network analysis of the population of resource users in Hawai‛i’s

longline fishery was performed, which is currently characterized by a division along

ethnic lines and competition over resource use. Results show that ethnicity significantly

influences social network structure and is responsible for a homophily effect, with higher

levels of bonding ties found within ethnic groups. This study provides empirical evidence

of the effect of ethnic diversity on social network capital among fishery resource users

and has implications for the success of potential collaborative management.

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TABLE OF CONTENTS

Page

Acknowledgements……………………………………………………………………....iii

Abstract…………………………………………………………………………………...iv

List of Tables………………………………………………………………………….....vii

List of Figures……………………………………………………………………….…..viii

List of Abbreviations..……………………………………………………………………ix

Chapter 1. Introduction…………………………………………………………………....1

Section 1.1 Context………………………………………………………………..1

Section 1.2 Objectives……….....………………………………...……………….3

Section 1.3 Thesis Organization..…………………………………..……..………3

Chapter 2. Literature Review……………………………………………………………...4

Section 2.1 Current Threats to Fisheries….…………..…………………………...4

Section 2.2 Adaptive Governance and Collaboration for Social-Ecological

Systems…………………………………………………………………….……..5

Section 2.3 Social Networks, Social Capital and Fisheries Governance………....8

Chapter 3. Methodology…………………………………………………….…….…..…14

Section 3.1 Study Area and Description………..………………………………..14

Section 3.2 Hypotheses…………………………………………………………..17

Section 3.3 Methods……..……………………………………………………….18

Chapter 4. Results and Discussion……………………………………………………….23

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Section 4.1 Overall Network Structure and Subgroup Analyses………………...23

Section 4.2 Linking Social Capital………………………………………………27

Section 4.3 Bonding and Bridging Social Capital………………………..……...30

4.3.1 Community Level Analyses………………………………………..33

Chapter 5. Summary and Conclusions………………………………………………...…38

Section 5.1 Summary……………………………………………………….……38

Section 5.2 Conclusions………………………………………………………….39

Section 5.3 Recommendations for Future Research……………………………..42

Appendix. A. Survey Questionnaire……………………………………...……………...44

References………………………………………………………………………………..50

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LIST OF TABLES

Table Page

1. The Effects of Social Capital on Fisheries Governance…………………11

2. Proportion of Ethnic Group Members Placed into Each Faction………..26

3. Relational Contingency Table Analysis Results………………………....27

4. Summary of Group Level Network Characteristics……………………...31

viii

LIST OF FIGURES

Figure Page

1. Conceptual Diagram of a Social-Ecological System ……………………..6

2. Network Configuration of all Hawai‛i Longline Vessel Owners and

Operators…………………………………………………...…………….24

3. Factions Network Configuration.......…………………………………….25

4. Network Configuration Highlighting Reciprocal Ties…………………..32

5. European-American Network Configuration………………………….…35

6. Vietnamese-American Network Configuration…………………….…....36

7. Korean-American Network Configuration………………………………37

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LIST OF ABBREVIATIONS

HLF Hawaii Longline Fishery

SNA Social Network Analysis

SES(s) Social-Ecological System(s)

NMFS National Marine Fisheries Service

WPRFC Western Pacific Regional Fishery Management Council

V-A Vietnamese-American

K-A Korean-American

E-A Euro-American

TAC Total Allowable Catch

EEZ Exclusive Economic Zone

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Chapter 1

INTRODUCTION

1.1 Context

Marine fisheries are an extremely important food source for many regions across the

globe and contribute significantly to the GDP of many island states (Mimura et al. 2007).

They also hold significant sociocultural and economic value for fishing communities and

coastal communities. However, marine fisheries are currently facing numerous threats to

their biological and ecological sustainability (Brander 2007), while fishing communities

are facing social and economic impacts due to increased fisheries regulations and reduced

catch (Lowe and Carothers 2008). Marine fisheries and the ecosystems on which they

depend are also facing significant climate-induced effects, which are likely to impact

further the already difficult undertaking of sustaining the social and economic value, as

well as the biological integrity, of marine fish stocks and their supporting ecosystems

(Roessig et al. 2004, Brander 2010, Cheung et al. 2010).

With increasing anthropogenic impacts and a projected escalation of climate-induced

effects on fisheries, there is a need for governance and policy strategies that can sustain

fishery ecosystems and their functions into the future. Though historically research and

emphasis in fisheries management have focused primarily on the biological aspects of

fisheries science, recent literature reflects a growing consensus that using a precautionary

and ecosystem-based approach to fisheries governance that also includes the social and

ecological dimension of the system is more likely to result in long-term sustainability

(Brander 2007, Martin et al. 2007, Cheung et al. 2009, Brander 2010). However, many

past and current approaches for managing resources and the ecosystems on which they

depend have been deemed unable to effectively take into account the complex social and

ecological processes embedded in natural resource systems (Dietz et al. 2003, Olsson et

al. 2004a, Folke et al. 2005, Armitage et al. 2008). In contrast to traditional management

schemes, adaptive management, which is facilitated by collaboration or co-management,

has been receiving increasing attention as a more realistic and promising approach for

addressing ecosystem complexity (Olsson et al. 2004a, Tompkins and Adger 2004,

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Walker et al. 2004, Armitage et al. 2008). Yet, adaptive governance and co-management

can be difficult to achieve in practice, due in part to collaborative barriers (Ostrom 1990,

Hahn et al. 2006). Thus, it becomes imperative to understand how collaborative barriers

can be overcome if we are to effectively address fish stock depletions, increased climate

change, and the social and economic impacts of resource depletion.

In an attempt to better understand how social factors may be affecting collaboration and

the broader arena of natural resource governance, there has been growing support for

adopting a social relational approach which expresses how relationships among and

between stakeholders can facilitate or inhibit communities from transforming the way

they govern natural resources (e.g., Janssen et al. 2006, Olsson et al. 2007, Hahn et al.

2008, Crona and Hubacek 2010, Bodin et al. 2011, Prell et al. 2011, Sandström 2011).

Results of recent studies employing this approach have found that social networks and

social capital can be crucial factors in facilitating collaboration and key components

impacting the success of adaptive natural resource governance (Adger 2003, Pretty 2003,

Grafton 2005, Bodin and Crona 2008). Yet, there has been little evidence of how

personal attributes of resource users affect social networks and social capital, and many

recent studies have identified the need for additional research in order to increase our

understanding of the factors influencing social networks and social capital in natural

resource settings (e.g., Bodin and Prell 2011).

To date, the effects of ethnic diversity on social capital in a common pool natural

resource setting have not been explored. Previous sociological research on ethnic

diversity and social capital suggests that ethnic diversity among resource users in a

competitive marine fishery is likely to have a significant impact on the level and

distribution of social capital; which, in turn, is likely to be influencing management

outcomes. In Hawai‛i’s longline fishery (HLF), there is an ethnically diverse participation

structure among fishers. The purpose of this thesis is to provide an understanding of how

this diversity among resource users in the HLF affects the level and distribution of social

capital, and to describe how these effects may be facilitating or impeding collaboration

and other key components of adaptive and effective resource governance.

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1.2 Objectives

This study systematically examines the social networks of resource users in Hawai‛i’s

longline fishery (HLF), which is currently characterized by a division along ethnic lines

and competition over resource use. The objective is to explore the impact of ethnic

diversity on group level bonding, bridging, and linking social capital among resource

users in the fishery. The specific objectives are as follows:

1. Perform a Social Network Analysis of the HLF.

2. Determine group level bonding, bridging, and linking social network capital for each

ethnic group and for all resource users in the fishery as a whole.

3. Explore the effects of ethnic diversity on the distribution of social capital and its

implications for collaboration and other key components of adaptive governance.

1.3 Thesis Organization

The thesis consists of five chapters including the introduction, literature review,

methodology, results and discussion, and summary and conclusion. Chapter 1 gives a

general overview of the context of the study, followed by the study objectives and the

organization of the thesis. Chapter 2 contains a literature review of the current threats to

fisheries; adaptive governance and collaboration for social-ecological systems; and social

networks, social capital, and ethnic diversity. Chapter 3 presents the study area and

hypotheses of the study, as well as the methods used. Chapter 4 presents and discusses

the results of the study, while Chapter 5 concludes the study and provides

recommendations for further research. Chapter 5 is succeeded with an appendix which

contains a copy of the survey instrument.

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Chapter 2

LITERATURE REVIEW

2.1 Current Threats to Fisheries

Marine fisheries are currently facing a variety of threats including high levels of

unsustainable fishing, habitat loss, incidental catch, loss of genetic diversity, introduced

invasive species, pathogens, and pollution (Brander 2007). Marine fisheries and the

natural environment on which they depend are also facing climate-induced impacts,

which are highlighted by many recent studies (Lehodey et al. 2003, Roessig et al. 2004,

Brander 2007, Cheung et al. 2009, Brander 2010, Cheung et al. 2010, Griffiths et al.

2010, Miller et al. 2010).

The impact of climate change is projected to affect fisheries directly, by affecting the

physiology, behavior, growth, development, reproductive capacity, mortality, and

distribution of the target species; and indirectly, by altering the productivity, structure,

and composition of ecosystems on which fish stocks depend for food and shelter

(Brander 2007). Considering that the global marine catch appears to have reached, or

even exceeded its biological limit, and most marine fishery resources in the world are

either fully exploited, over-exploited, or collapsed (Pauly et al. 2002, FAO 2008, Cheung

et al. 2010) changes in catch potential due to climate change are expected to strongly

affect global food supply (Cheung et al. 2010).

In fact, a recent study by Cheung and colleagues (2010) estimated that climate change

may lead to a large-scale redistribution of global fisheries catch potential, averaging out

at a 30–70% increase in high-latitude regions and a decline of up to 40% in the tropics.

According to their model, the impacts of climate change on fisheries appear to be most

extreme in the Indo-Pacific region, where a higher greenhouse gas emissions scenario

could result in up to a 50% decrease in a 10-year averaged maximum catch potential by

2055 (Cheung et al. 2010). These affects are likely to impact further the already difficult

undertaking of sustaining the social and economic value, as well as the biological

integrity, of marine fish stocks and their supporting ecosystems (Miller et al. 2010).

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The social effects caused by the decline in global fisheries are equally significant, though

they have not gained as much attention in the literature as the biological and economic

effects (Loquine 2010). Communities that rely on fisheries for social, cultural and

economic support are already experiencing noteworthy pressures resulting from fisheries

decline (Loquine 2010), which are expected to increase under amplified climate-change

scenarios. The growing pressure facing fishermen and fishing communities is not only

due to the obvious reason of the declines in stock, but also due an increase in regulations

that have been passed in attempt to mitigate biological and ecological impacts to

remaining stocks. Many fisheries have implemented a cap on the total number of vessels

that are able to obtain permits to fish, making it more difficult for new fishermen to enter

the industry in certain areas. Total allowable catches (TACs) are another form of

regulation that have been implemented in numerous fisheries, which is thought to have

increased competition (Costello et al. 2008) and stress (Rossiter and Stead 2003) among

resource users. Individual, market-based fishing rights have also become a popular

strategy in fisheries management, which have been shown to cause a number of

undesirable social impacts, such as unbalanced distributions of individual quotas,

marginalization of small-scale fishers, and labor displacement (Carothers 2008, Lowe and

Carothers 2008, Carothers 2011, Olson 2011).

2.2 Adaptive Governance and Collaboration for Social-Ecological Systems

With increasing anthropogenic pressures on fishery resources, growing social impacts

resulting from fisheries management schemes, and a projected escalation of climate-

induced impacts on fisheries, recent research has emphasized the need for innovative

governance initiatives that can sustain fishery ecosystems and their functions into the

future (i.e., Olsson et al. 2004a, Tompkins and Adger 2004, Folke et al. 2005, Brander

2007, Martin et al. 2007, Armitage et al. 2008, Cheung et al. 2009, Brander 2010, Miller

et al. 2010, Sandström 2011).

Historically research and initiatives in fisheries management have focused primarily on

the biological aspects of fisheries science. However, fisheries, like other natural resource

systems, are comprised of not only of a biological and ecological dimension, but also

6

include a social or human dimension. As described by Bodin et al. (2011), humans are

fundamentally embedded in natural-system processes, and are reliant on the ability of

ecosystems to generate ecological services to sustain societal development. In fact,

humans are often the dominant force in shaping the processes and structures of the

biophysical environment on which societies depend (Vitousek et al. 1997, Bodin et al.

2011). Thus, fisheries can be viewed as complex social-ecological systems (SESs)

including a biological component, an ecological component, and a social component, as

shown in Fig. 1 (Berkes and Folke 1998, Folke et al. 2002, Gunderson and Holling 2002,

Berkes et al. 2003).

Many current approaches for managing ecosystems are often unable to effectively take

into account these complex social and ecological processes, which often function at

various spatial and temporal scales (Olsson et al. 2004a, Tompkins and Adger 2004), and

adaptive management has been proposed as a more realistic and promising approach to

deal with ecosystem complexity (Olsson et al. 2004a, Tompkins and Adger 2004, Walker

et al. 2004, Armitage et al. 2008).

Figure 1. Conceptual diagram of a Social-Ecological System (SES) and the links between its components, adapted from Hahn et al. (2006). It consists of an ecosystem, the management of that ecosystem, and the formal and informal institutions underlying the management process, which include social norms and conventions.

Social Norms and Rules

External drivers, change

and surprise

Management, Actors, Organizations

Ecosystem functions and dynamics

Knowledge systems

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Closely tied with integrated learning, developing the capacity of individuals to effectively

learn from their experiences is a key aspect of building knowledge and skills that

facilitate adaptive management (Folke et al. 2005). In the context of ecosystem

management, this iterative process is often also referred to as social learning (Berkes

2009). Thus, adaptive management will be defined here as a management system that

focuses on achieving a governance process that is sensitive to the environment, but also

has the ability to continuously respond to social and environmental changes within the

ecosystem being managed (Sandström and Rova 2010b), which is facilitated by social

learning. Recognizing social-ecological systems as complex webs of interrelations among

their various components; adaptive governance, in this context, moves away from

addressing solely the biophysical and addresses the broader social context of creating

conditions for social coordination that facilitate ecosystem-based management (Hahn et

al. 2008).

Empirical research has shown that collaborative arrangements involving a variety of

actors from various sectors and user groups in the management process are more

successful at establishing adaptive natural resource governance than other types of

processes (Pinkerton 1989, Ostrom 1990, Rova 2004, Sandström and Rova 2010b). Often

referred to as co-management, these management systems are characterized by power and

responsibility sharing between resource users and governing bodies (Berkes 2009). The

theoretical basis is that co-management facilitates access to, and the transfer of, material

and immaterial resources, such as knowledge, scientific evidence, experiences,

legitimacy, etc. (Sandström and Rova 2010a). Due to the multi-actor, multi-purpose

context of resource use, effective natural resource management fundamentally relies on

the expertise, knowledge, and the willingness or potential for negotiation, conflict

resolution, collaboration, and coordination of actions among various stakeholders (Bodin

et al. 2011). Thus, by integrating resource users and local ecological knowledge into the

decision making process, a flexible governance structure well equipped for conflict-

resolution and problem solving is generated that allows for synergistic learning and the

capacity to respond to, as well as shape change (Carlsson and Berkes 2005, Folke et al.

2005).

8

However, as pointed out by Bodin and Crona (2009), co-management and adaptive

governance processes can be difficult to achieve in practice (Ostrom 1990, Hahn et al.

2006), and it is therefore crucial to understand how collaborative barriers can be

overcome if we are to effectively implement adaptive governance initiatives and increase

sustainability in SESs.

2.3 Social Networks, Social Capital and Fisheries Governance

Recognizing that social factors can significantly affect the natural environment, a social

relational approach has been encouraged as a theoretical and analytical framework for

revealing how critical social relations and their structural properties affect collaboration

and the broader concept of natural resource governance (Grafton 2005, Carlsson and

Sandström 2007, Sandström and Rova 2010b, Bodin et al. 2011, Crona et al. 2011,

Ramirez-Sanchez 2011, Sandström 2011). Acknowledging the embedded condition of

human actors, the social relational approach considers human actors as part of the social-

ecological system, and investigates patterns of relationships among actors within the

system to explore how they may be enabling or constraining actors or processes (Bodin et

al. 2011). These patterns of relationships are referred to as social networks, which

describe social organization and outline patterns of vertical and horizontal relationships,

or “ties,” among actors (Moore and Westley 2011).

The social-relational approach builds on themes from relational sociology, which views

the structure of relations (social networks) among actors as well as actor’s location within

the structure of a social network to have important perceptual, attitudinal, and behavioral

consequences (Emirbayer and Goodwin 1994). This approach attempts to deal with

practical problems indirectly by uncovering the role of patterned relationships among

actors within a system that may be enabling or constraining human action (Bodin et al.

2011).

These patterns of relationships among actors are often referred to as social capital.

However, the meaning behind the term social capital is not always clear when it is

utilized and there is not exactly a consensus in the literature about its origins and

applications. The most widely accepted definition is given by Putnam (2001), who

9

defines social capital as “…connections among individuals - social networks, and the

norms of reciprocity and trustworthiness that arise from them” (pg. 19). In line with this

definition, Coleman’s theory of social capital (1988) is adopted for this study, which

refers directly to the ties and relations both within and between social networks, and

highlights the importance of social bonds and norms for people and communities. Thus,

social capital can be conceptualized as arising via relations and ties within and between

social networks, the existence of which has been found to lower transaction costs and

facilitate cooperation and collaboration by lubricating relations of trust, encouraging

reciprocity and exchanges, and facilitating the establishment of common rules, norms and

sanctions among stakeholders (Pretty 2003).

Due to these observed effects, social capital has been identified as a critical factor in

achieving collaborative and adaptive natural resource governance (Adger 2003, Pretty

2003, Grafton 2005, Bodin and Crona 2008). There are three different types of social

capital that have been identified as important: bonding, bridging, and linking social

capital (Putnam 2001, Narayan 2002, Crona and Bodin 2006, 2011). Bonding social

capital involves strong social linkages, or “strong-ties,” within groups of like-minded

individuals that are often characterized by dense, localized networks (Grafton 2005).

Bridging social capital characterizes weaker linkages across somewhat similar, but

different groups or social networks (Grafton 2005). Linking social capital refers to

linkages across incongruent groups or networks at varying hierarchical levels, such as

connections between resource users and management officials (Grafton 2005).

In the past few years the effect of social capital on natural resource governance has been

empirically tested and theoretically advanced by case studies examining its role in

achieving sustainable natural resource management and governance. For example, higher

levels of bonding social capital within a system, which is also referred to as higher levels

of social cohesion, have been shown to increase possibilities for joint action and

collaboration (Bebbington and Perreault 1999, Olsson et al. 2004a, Hahn et al. 2006,

Sandström and Rova 2010a, Sandström and Rova 2010b) and to facilitate the

development of knowledge and understanding among stakeholders (Conley and Udry

2001, Olsson et al. 2004a, Olsson et al. , Isaac et al. 2007, Conley and Udry 2010,

10

Sandström and Rova 2010a, Sandström and Rova 2010b). Bonding social capital was

also shown by Bodin and Crona (2009) to endorse trust and reciprocity within

communities, thus facilitating consensus building and effective conflict resolution, all

very important aspects of adaptive resource governance.

Strong ties inherent in bonding social capital are particularly beneficial in the context of

fisheries due to the common-pool nature of the resource. Fisheries resources can be added

or depleted by the level of harvesting; harvesting is rivalrous because a fish that is taken

by one vessel prevents it from being caught by another; and the ability to exclude others

from harvesting is limited due to the mobility of the species. Therefore, trust and

cooperation, which previous research suggests can be derived from social capital (e.g.

Granovetter 1985, Coleman 1990, Pretty and Ward 2001, Hahn et al. 2006), among

fishermen is vital in encouraging individual fishers to observe standards, rules, and

sustainable fishing practices, thus decreasing externalities for individual fisherman

(Grafton 2005).

Bridging social capital has also been found to be important for natural resource

governance and sustainable fisheries outcomes. Though bridging ties characteristic of

bridging social capital tend to be weaker than those that make up bonding social capital,

these ties have the ability to link heterogeneous groups or networks of people into a

larger network. Sociological and organizational studies have shown that these types of

bridging ties bring with them an inherent diversity of ideas and perspectives that improve

the capacity for the development of innovative solutions to complex problems, and can

thus enhance adaptive capacity (Bodin and Crona 2009). For example, the existence of

key bridging ties, or bridging social capital, across heterogeneous groups can allow

access to external resources and diverse knowledge which can be essential for resource

governance (Crona 2006, Hahn et al. 2006, Newman and Dale 2007, Bodin and Crona

2009, Ramirez-Sanchez and Pinkerton 2009, Sandström and Rova 2010b). Furthermore,

key actors forming bridging ties among smaller groups, or subgroups, may be capable of

connecting and mobilizing these subgroups toward a common goal (Bodin and Crona

2009, Ramirez-Sanchez 2011). It has also been shown that bridging ties can also foster

11

trust amongst previously unconnected groups or heterogeneous actors which can further

facilitate collaborative processes (Woolcock 2001, Bodin and Crona 2009).

In an excellent review on the effects of social capital on fisheries governance,

summarized in Table 1, Grafton (2005) argues that effective fisheries governance greatly

depends on the effectiveness of the rules or norms established, which in turn, is highly

influenced by social capital. He identifies five key aspects of fisheries that greatly impact

the effectiveness of the rules or norms, which are (1) the ability to monitor fisher

behavior, (2) rates and change of resource use, (3) the level of interaction between fishers

and their families, (4) the ability to exclude outsiders, and (4) collective support for

monitoring and enforcement; all of which are greatly influenced by bonding, bridging

and linking social capital (Grafton 2005). For example, bonding social capital plays a key

role in promoting community interest over personal gain when fishers are less able to

monitor each other; whereas bridging social capital facilitates access to outside networks,

which can promote resilience and increase adaptive capacity in the face of change or

external shocks (Grafton 2005). Links to outside governing agencies in the form of

linking social capital can provide increased access to scientific knowledge or the ability

to access the force of the state to exclude outsiders from fishing community resources

(Grafton 2005). Linking social capital can also help to ensure stakeholder interests are

represented in the management and policy arena, and can facilitate stakeholder

understanding and cooperation in regards to management initiatives.

Table 1. The effects of social capital on fisheries governance. Aspect of fisheries governance Type of social capital Bonding social

capital Bridging social capital

Linking social capital

Conflict resolution X X X Rule compliance X X X Knowledge creation, diffusion and exchange

X X

Enhanced flexibility to change X X Rent-seeking behavior X X Management options with uncertainty

X X X

Note: Table adapted from Grafton (2005). ‘X’ indicates the governance factor (row) is likely increasing in the number and quality of connections of the given type of social capital (column).

12

Overall, social networks and social capital have been acknowledged as a common and

significant variable in a variety of different cases where stakeholders have acted

collectively to manage natural resources in an effective manner and to deal with complex

natural resource problems (Bodin and Crona 2009). However, all social networks are not

equal in terms of generating social capital. In fact, empirical studies have shown that

social networks with low structural cohesion, particularly those with the existence of

various subgroups, can pose a challenge to collaboration and joint action due to an “us-

them” attitude that may develop under such circumstances (Krackhardt and Stern 1988,

Borgatti and Foster 2003). In social network research, these subgroups are characterized

by having significantly more ties between the group members (ie. bonding social capital)

than bridging ties between group members and others outside of the group ties (ie.

bridging social capital) (Bodin and Crona 2009). Moreover, a very high degree of

bonding social capital in a densely connected network may result in the homogenization

of knowledge and experiences, which can affect natural resource governance negatively

if attitudes, beliefs and practices not conducive to managing resources sustainably are

compounded throughout the group (Oh et al. 2004, Bodin and Norberg 2005, Bodin et al.

2006).

Thus, the degree of bonding, bridging, and linking social capital among resource users

have important management implications and can affect natural resource governance

processes and the long-term sustainability of natural resource systems. This impact has

been clearly identified in the literature. However, there have been few studies on the

factors that affect the level and distribution of social capital among resource users in

SESs. An exception is a recent study by Ramirez-Sanchez and Pinkerton (2009), which

examined the role of resource scarcity and kinship, friendship and acquaintance attributes

on bonding, bridging, and linking social capital patterns of fisher’s networks in seven

coastal communities in Loreto, BSC, Mexico. The study area consisted of seven rural

fishing communities, whose relationships were largely that of kin, relations through

marriage, and close friends (or “fictitious kin”) (Ramirez-Sanchez and Pinkerton 2009).

The authors found the presence of bonding, bridging and linking social capital that

extended beyond kinship ties both within and between communities, and in fact found a

13

higher level of friendship ties rather than kinship ties linking social capital over the

network as a whole; but found resource scarcity to be an ambiguous indicator of social

capital (Ramirez-Sanchez and Pinkerton 2009). The fishermen in this study, though from

different communities, were of the same ethnic background and spoke the same language,

which may have facilitated the assembly of social capital via friendship ties.

To date, the distribution of bonding, bridging, and linking social capital among resource

users in a natural resource system characterized ethnic diversity has not been examined.

In pelagic fisheries, fishermen group-level social capital, and the establishment of trust

and agreed upon norms it typically brings with it, may be particularly important due to

the competitive nature of fishing and the difficulty in monitoring individual fisherman.

This can become even more complex when resource users themselves, all operating

within a single fishery, are comprised of ethnically diverse backgrounds with different

language capabilities. Previous sociological research on social networks has shown that

racial and ethnic differences can create strong divides between individuals (McPherson et

al. 2001). In fact, race and ethnicity are largely considered the number one cause of

homophily in social networks, which has substantial implications on the level and quality

of information different actors receive, the attitudes and beliefs they form, and the

interactions they experience (McPherson et al. 2001). Thus, ethnic diversity among

resource users in a competitive pelagic fishery is likely to impact the level and

distribution of bonding, bridging, and linking social capital; which in turn, is likely to be

affecting the potential for collaboration and adaptive governance.

14

Chapter 3

METHODOLOGY

3.1 Study Area and Description

Hawaiʽi’s longline fishery (HLF) presents an ideal case to examine the effects of ethnic

diversity on group level bonding, bridging, and linking social capital due to the

ownership of vessels being divided along ethnic lines; with roughly one-quarter owned

and operated by Korean-Americans (K-A), while the remaining vessels are split between

Vietnamese-American (V-A) and Euro-American (E-A) owners and/or operators (Allen

et al. 2012). The HLF is a limited-entry multimillion dollar pelagic fishery, targeting

predominantly bigeye tuna (Thunnus obesus), yellowfin tuna (Thunnus albacares) and

swordfish (Xiphias gladius), and is the dominant commercial fishery sector in the

Hawaiian Islands. The fishery is comprised of approximately 120 active vessels, nearly

all home ported in Honolulu on the island of Oʽahu at one of three piers, and is capped at

164 vessels (Allen et al. 2012).

The HLF is currently characterized by top-down management and is governed by various

regulatory policies enacted by either the Western Pacific Regional Fishery Management

Council (WPFRC) or the National Marine Fisheries Service (NMFS). To decrease the

likelihood of interactions and conflict with small-scale fishermen, longline vessels are

restricted to fishing at least 25-50 miles off shore, and typically fish both inside and

outside of the U.S. Exclusive Economic Zone (EEZ), which extends 200nmi offshore

(Allen and Gough 2006). Due to concerns over the status of bigeye tuna, there is a total

allowable catch (TAC) set for the fishery as a whole. Moreover, concerns about longline

interactions with endangered sea turtles led to multiple regulations on swordfish fishing

vessels, whose lines are set in shallower water, and thus are more likely to interact with

sea turtles. The regulations restrict longline swordfish fishing completely in waters south

of Hawaii (from 0˚ to 15˚) and enacted seasonal closures in areas 145˚W to 180˚ (Allen

and Gough 2007). Furthermore, there is a cap set on sea turtle interactions and every

swordfish fishing vessel is required to carry an on-board observer (Allen and Gough

2007).

15

Due to the pelagic nature of the fishery, the enforcement of regulations, particularly those

regarding bycatch, is largely dependent on the observer program. However, there is only

20% observer coverage on vessels targeting tuna, which are subject to regulations

regarding longline interactions with marine mammals and other bycatch related issues.

Moreover, previous research on the observer program in Hawaiʽi suggests that observer

coverage is not always effective in monitoring fishing activity at sea (Allen and Gough

2007). Therefore, we assume that enforcement capacity, which is a key aspect of fishery

governance as described by Grafton (2005), is generally low. Therefore, trust and socially

accepted rules and norms are particularly important in this case since vessel owners and

operators, as resource extractors and therefore primary decision makers, have direct

effects on the sustainability of the fishery.

Previous ethnographic research on the HLF has shown that formal and informal social

networks exist among longline owners and captains, but that most interaction occurs

among individuals of the same ethnicity (Allen et al. 2012). In this preliminary

assessment of social networks among HLF fishers, the V-A community was characterized

as a cohesive group containing several family ties with relatively strong solidarity; the K-

A group was characterized as being made up of at least two fairly separate groups with

some family ties but lower sense of solidarity among fishers; while the E-A group was

characterized by less defined social networks, potentially including several subgroups

and a lack of unity overall (Allen et al. 2012). Moreover, differences among ethnic

groups were found to exits in regards to operational practices and attitudes toward

regulations (Allen et al. 2012). However, this previous case study did not systematically

identify social networks for each individual in the longline owner/operator population or

determine the level and extent of bonding, bridging and linking social capital, which is

the aim of the present study.

Building on this previous research, the objective of this study is to collect explicit social

network data from resource users in order to examine the effects of ethnic diversity on

social capital, both by comparing group level social capital for each ethnic group, and by

examining group level social capital for the fishery as whole. How ethnic diversity

among resource users in a fishery characterized by competitive interaction among

16

individual fishers may affect the measures of bonding, bridging, and linking social capital

has not been identified in previous studies. However, empirical research on the effect of

ethnic diversity on social and economic outcomes have found that ethnic diversity is

generally associated with fragmentation and lower levels of trust across groups (Alesina

and La Ferrara 2002). This implies lower levels of bonding social capital across ethnic

divides, which the previous ethnographic work in the HLF alludes to. Moreover,

fragmentation and lower levels of trust across ethnic groups is thought to be inflated

under conditions of competition, which often gives rise to conflict (Poteete and Ostrom

2004). This aspect of competition may be particularly pertinent in this case due to the

implementation of a TAC for bigeye tuna in 2009. Recent research has suggested that

establishing a TAC on a key harvested resource in a fishery without allocating secure

rights to part of the quota for each vessel may be encouraging a more intensely

competitive environment where actors “race to fish” in order to outcompete others

(Costello et al. 2008). Since the TAC was established by the WPRFC the quota has been

hit prior to the end of the season every year, suggesting not only a highly competitive

environment, but also a potential governance issue with biological, social, and economic

consequences.

In relation to public goods, ethnic diversity has been found to be associated with a

decreased ability to impose social sanctions on actors not adhering to norms, which can

lead to collective action failures (Miguel and Gugerty 2005). This implies lower levels of

bonding social capital as well as lower levels of bridging social capital. A recent study by

Romani (2003) on the role of social proximity suggested that minority groups are less

likely to benefit from extension services in social-ecological systems, which could be

viewed as linking social capital. His results also showed that, unlike their neighbors

belonging to the dominant ethnic group, minority groups benefited significantly from

exchanging information amongst themselves (Romani 2003), which implies high levels

of bonding social capital within ethnic minority groups. Romani’s findings appear to be

corroborated by the previous ethnographic study on the HLF by Allen et. al (2012), but

will be ultimately determined with further investigation into the social network structure

of the HLF.

17

3.2 Hypotheses

Building on this theoretical and empirical evidence, the hypotheses for this study are as

follows:

H1: There will be an observed division along ethnic lines in the social networks of the

HLF.

H2: Bonding social capital will be found primarily among resource users of the same

ethnicity, and will be more prevalent among the V-A and K-A group.

H3: There will be higher levels of linking social capital for the E-A group of fishermen,

which in this case is considered to be the dominant ethnic group, than for the V-A group

and the K-A group of fishermen.

Hypothesis one will be determined in order to corroborate previous sociological results of

a homophily effect caused by ethnic differences generally found in social networks with

diverse actors and to verify the findings of previous ethnographic research which

observed divisions along ethnic lines (i.e., Allen et al. 2012). To an extent this hypothesis

also examines the level of bridging social capital, hypothesizing that there will be much

lower levels of relations across ethnic groups than within the groups. This would imply

low levels of bridging social capital for the fishery as a whole, which would suggest a

low capacity for collaboration and decreased success of social sanctions. Hypothesis two

attempts to further corroborate the existence of a homophily effect and determine the

level of bonding ties across ethnic groups. Hypothesis three tests the level of linking

social capital identified between fishers and other important actors in the fishery system,

such as industry leaders, management and government officials, and the scientific

community, hypothesizing that the ethnic minority groups will have lower levels of this

type of linking social capital than the dominant ethnic group. The dominant ethnic group,

in this case, refers to the E-A group primarily due to this population being the largest in

Hawaiʽi of the three groups (24.7% of Hawaiʽi’s population compared to 1.8% Korean

18

and 0.7% Vietnamese1) as well as the primary language of this group being the dominant

language spoken in the state of Hawaiʽi, where the fishery is located.

3.3 Methods

Social Network Analysis (SNA) is regarded as one of the best developed tools for

performing social relational research (Wasserman and Faust 1994), and has been utilized

by numerous studies looking at social relations and social structure in natural resource

management (e.g., Crona and Bodin 2006, Bodin and Crona 2008, Prell et al. 2009,

Ramirez-Sanchez and Pinkerton 2009, Sandstrom and Rova 2009, 2010). SNA is a

quantitative method that incorporates graph theory and the use of sociograms to elicit,

visualize and analyze social relations and social networks among individuals or groups.

By providing a formalized articulation of relational data by explicitly mapping out how

actors, or nodes, are connected, SNA is able to generate a rich set of sociometrics for

analysis and extrapolation. Thus, SNA was chosen as the primary methodology for this

study.

To perform the SNA, a structured survey questionnaire was developed and administered

to the primary decision makers for each vessel operating in the HLF in order to collect

information on their relevant fishery-related social linkages. Acknowledging that some

vessels are run by an owner-operator, while some are run by a hired captain, and all

owner-operators, owners, and captains are involved in various management decisions

concerning resource use in the fishery, this study sought to survey the entire population

of all vessel owners and operators in order to get a complete picture of the HLF’s

resource user social networks and overcome the statistical sampling issues that have

commonly posed issues for other random-sample-based data collection methods in the

social network literature. The survey questionnaire was pre-tested with members of each

ethnic group in order to reduce any misinterpretation of wording due to differing cultural

understandings and to diminish potential errors in data collection. Face-to-face interviews

were performed in the native language of each respondent beginning in May, 2011 and

ending in January, 2012 in order to complete the survey questionnaires for all owners and

1 Demographics obtained from the 2010 report of the U.S. Census Bureau

19

operators currently residing in Hawaiʽi. A limited number of survey questionnaires were

sent and returned via mail and email to owners currently living on the mainland U.S. The

response rate was 91.2% for the entire fishery (145 of the 159 owners and operators),

93.3% for fishers in the V-A community (70 of 75 owners and operators), 89.7% for the

E-A community (52 of 58 owners and operators), and 88.5% for the K-A community (23

of 26 owners and operators). Though there are a total of 14 fishers (out of 159) not

represented in this study, only three vessels from each community (nine vessels total)

were not represented. This is due to some of the vessels having an owner and an operator,

and either the owner or the operator not being available when the other individual was

available. When this was the case, the owner or operator we did survey typically stated

that it was not necessary to speak with the other fishers associated to the same vessel

because their networks included the same people. Furthermore, nine of the fishers not

surveyed were identified by at least one, but typically a handful of other fishers, making

it possible to infer their network, while the other five were not identified. Therefore, we

treat our data as the population of longline fishers rather than a sample, and the five

fishers not identified by others we classify as isolated fishers. This classification was

corroborated by a key informant firmly embedded in the HLF, who stated that these five

fishers often fish and stock their vessel in California on the mainland U.S., only coming

to Hawaiʽi periodically and operating alone when they do.

The survey questionnaire consisted of three sections. The first section asked general

questions about the respondents fishing experience, experience fishing and living in

Hawaiʽi, as well whether or not they frequently discuss or share information regarding

different aspects of fishing with other stakeholders in the fishery and how valuable they

would say this information exchange is to their fishing success. How valuable

respondents feel their information exchanges are to their fishing success was asked in an

attempt to separate purely social interactions from informative interactions where useful

knowledge regarding the fishery is shared. The second part of the survey asked

respondents to nominate at least five, but up to ten people with whom they share useful

information regarding different aspects of fishing within the HLF that they feel is

valuable for their fishing success. Respondents were also asked to rate how valuable they

20

feel the information exchange is with each person they have nominated on a scale from

very valuable, to valuable, to not valuable. Relations initially reported by fishers but then

rated not valuable were not included in this analysis. The last part of the survey collected

general socio-demographics, information on fishers membership and activity in fishing

organizations and associations, and specifically, ethnicity. Please see Appendix A for a

complete draft of the survey2.

To test the first hypothesis (H:1), that there will be an observed division along ethnic

lines among fishers, sociometrics aimed at identifying components or subgroups within

networks were calculated. First a component analysis was performed, which identifies the

number of networks in the overall network that are not connected to each other via any

tie. If there are no ties present between ethnic communities and completely separate

network components made up of fishers of solely the same ethnic backgrounds were

present, this would be the most extreme case of homophily. Another more relaxed way of

identifying subgroups is by looking for factions within the network. This analysis

optimizes a cost function which measures the degree to which a network consists of

subgroups or clique-like structures, and finds the optimal arrangement of actors into

factions by maximizing similarity to the ideal type and measuring how well the data fits

this ideal type (Hanneman and Riddle 2005). The number of factions can be set to any

number, allowing us to test how well the HLF network fits a three faction model. Lastly,

a relational contingency table analysis was performed to gauge the level of homogeneous

vs. heterogeneous ties among actors distinguished by the ethnicity attribute. Relational

contingency table analysis finds the ratio of measured versus expected relations within

and between groups, where the expected number of relations equals the relations

expected to exist by chance alone in a network of equal size and number of ties.

Relational contingency table analysis was also used to test the last hypothesis (H:3),

where the emphasis was on the observed vs. expected relations between each group of

fishers and industry leaders, government or management officials, or the scientific

community.

2 Note that only part of the information in the survey was used for this study.

21

To evaluate the extent of bonding social capital within each ethnic community (H:2),

Crowe’s (2007) framework for conceptualizing social capital within a community

network as a matter of degree was adopted, which also allows us to determine the level of

bridging social capital within communities. Crowe (2007) classifies four different types

of network structures, two which she defines as bonding structures, and two as bridging

structures. Following this framework, bonding structures can either be represented by a

complete network, in which nearly every member is connected to every other member, or

a fractional network, which is characterized by two or more densely connected groups

that are not connected to each other; whereas bridging structures are either represented by

a coalitional network, which is made up of densely connected groups that are connected

to one another in a non-redundant way, or a bridging network, which is a sparsely

connected network (Crowe 2007). In this study there was only one group of two actors

(one E-A and one V-A fisher) that were not connected to the rest of the network, which

was only the case when viewing solely the E-A network (when viewing the HLF as a

whole, these two nodes were connected via the V-A network). Therefore fractional

networks were not considered.

To determine which network structure each community most represented, k-cores and

cut-point network metrics were used. A k-core analysis locates parts of the network that

form subgroups in a way that each member of the subgroup is connected to at least k

number of other actors in the subgroup. The value of k for each group is determined by

finding the maximum amount of actors with whom each actor accesses for information

and the lowest reported value of k is used in order to facilitate comparisons across the

groups. Following the work of Crowe (2007) and Ramirez-Sanchez and Pinkerton (2009)

isolated actors were not considered. Cut-points are an actor in a network, whom, if

removed, would fragment a network into two or more sub-networks. By utilizing this

framework, each community network was categorized as one of the three relevant

community networks (complete, coalitional or bridging) distinguished by Crowe (2007)

in order to provide insight into the extent and level of bonding or bridging social capital.

To test whether bonding social capital is found primarily among actors of the same

ethnicity the proportion of reciprocal ties, which are thought to be a key feature of

22

bonding social capital, found between ethnic communities was determined and compared

to the extent of reciprocal ties in the overall HLF network.

23

Chapter 4

RESULTS AND DISCUSSION

4.1 HLF Overall Network Structure and Subgroup Analyses

The data was analyzed in UCINET6’s suite of social network programs (Borgatti et al.

2002), and visualized in NetDraw (Borgatti 2002), which provides multi-dimensional

scaling/hierarchical clustering techniques that help to generate a rich visual mapping of

the HLF’s social networks. A dataset was compiled for the entire HLF network where all

relations identified as being either valuable or very valuable, including ties to industry

leaders, management or government officials, and the scientific community, were

included. This network is depicted graphically in Fig. 2, where each node was placed by

an algorithm that uses iterative fitting to place points on the smallest path length closest

to each other. The node repulsion option was also used, which separates objects that

would otherwise be placed very close together, which makes the graph a bit easier to read

without losing important information on node placement. Ethnic affiliation was not

determined for industry leaders that were not also fishermen or for management and

government officials/members of the scientific community since the focus was on the

ethnic diversity of the fishers themselves. For the rest of this paper, all graphs will be

depicted using the above criteria.

Looking at the overall network structure of the HLF depicted in Fig. 2, a few interesting

things emerge. The first observation one may have is that all nodes in the overall network

are connected to one single network structure; meaning that every fisher is somehow

connected to every other fisher (though it may be through a very indirect relationship).

This observation is verified by the component analysis, which classifies all nodes as

falling within a single component. Thus, no group of fishers is completely fragmented or

isolated from any other group. Another thing one may notice is that the nodes seem to be

placed in groups determined by the ethnicity attribute, and there appears to be many more

ties within groups than between groups. This suggests a homophily effect, though not as

extreme as it would be if the groups has formed completely separate components.

24

Figure 2. Network configuration of all relations identified by the population of vessel owners and operators in the HLF generated in NetDraw (Borgatti 2002). Nodes (representing actors) with the smallest path lengths to each other are placed closest together by an algorithm that uses iterative fitting. Node color and shape represent the actors title and ethnicity affiliation: V-A (yellow), E-A (blue), K-A (red), N/A (grey); vessel owner (square), vessel operator (circle), vessel owner and operator (rounded square), industry leader (box), government or management official/member of the scientific community (triangle),vessel owner and industry leader (circle in square).

The second step of exploring the possible homophily effect was to employ the factions

analysis set to three factions, the results of which are depicted graphically in comparison

to the overall network in Fig. 3. One can see from the depiction of the results that the

algorithm appears to have placed the majority of the nodes with the same ethnicity into

the same faction. This is in fact correct. The exact proportions of each ethnic group that

were placed into each faction can be found in Table 2, which shows that approximately

92% of the E-A community was placed into faction 1, while the remaining 8% was

placed into faction 2; 96% of the K-A community was placed into faction 2, while the

remaining 4% was placed into faction 1; and 77% of the V-A community was placed into

25

faction 3, while the remaining 22% and 1% were placed into faction 2 and faction 1

(respectively). The fitness of this partition of nodes is reflected by the proportion correct

value of 0.69, meaning that this partition follows 69% of the observed relations. These

results corroborate previous ethnographic research and support our first hypothesis that

there are observed divisions along ethnic lines among the fishers in the HLF, but the

proportion correct value of 0.69 and 23% of the V-A community not being placed into

the same faction as the rest of the V-A community suggests that the network structure

may be more complex than the three faction model allows. This can easily be confirmed

when viewing the structure of the HLF network (Fig. 3, A), where it is apparent that

although there are much more ties among fishers of the same ethnicity, there is certainly

not a complete lack of ties between ethnic communities.

A. B.

Figure 3. HLF factions network compared to the general HLF network configuration of all relations identified by the population generated in NetDraw (Borgatti 2002). In the general overall network (A) (also depicted in Fig. 2) nodes represent actors and color and shape represent the actor’s title and ethnicity affiliation: V-A (yellow), E-A (blue), K-A (red), N/A (grey); vessel owner (square), vessel operator (circle), vessel owner and operator (rounded square), industry leader (box), government or management official/member of the scientific community (triangle),vessel owner and industry leader (circle in square). In the factions network depiction (B) each color represents a different faction: faction one (blue), faction 2 (red), faction 3 (yellow); shapes are the same as the figure on the left. Nodes with the smallest path lengths to each other are placed closest together by an algorithm using iterative fitting.

26

Table 2. Proportion of ethnic group members placed into each faction. Final proportion correct = 0.69. E-A K-A V-A

Faction 1 0.92 0.04 0.01

Faction 2 0.08 0.96 0.22

Faction 3 0.00 0.00 0.77

The last sociometric analysis performed to gauge the division along ethnic lines was the

relational contingency table analysis. The results of this analysis, presented in Table 3 as

the proportion or observed vs. expected relations, clearly support the first hypothesis,

revealing that there are significantly higher amounts of ties within groups than would be

expected in a network of the same size and number of ties under a model of

independence. Results also show that there are significantly less ties between groups

(bridging ties) than would be expected, with all the proportions of observed vs. expected

relations falling well below the expected average. Results of this analysis are particularly

striking when considering the low level of bridging ties present between the V-A and K-

A community. Although the K-A community did identify a relatively small number of

ties with the V-A community (a proportion of 0.11 of the expected ties to this group),

there were no reported ties between these groups originating from the V-A community.

This observed homophily effect based on ethnic association among resource users was

somewhat expected and has important implications for fishery management and the

overall adaptability of the fishery. As previously discussed, homophily in networks can

have substantial impacts on the level and quality of information that different actors

receive, the attitudes and beliefs they form, and the interactions they experience

(McPherson et al. 2001). Moreover, this separation of actors into somewhat separate

groups may be responsible for an “us-them” attitude among stakeholders that can pose

challenges to collaboration and the potential for joint-action. Fishers were not explicitly

asked to express their attitudes concerning fishers of the different ethnic communities

when the field work was being completed for this study. However, while surveying

fishers for this study it was not uncommon for respondents to speak openly about fishers

27

from each ethnic background as being part of separate group of fishers all together,

suggesting that an “us-them” attitude does exist in the HLF to a certain extent.

Nonetheless, links across ethnic communities do exist in the HLF, and these ties may

have the potential to build trust across ethnic communities and bring subgroups together

behind a common goal under the right circumstances.

Table 3. Relational contingency table analysis results*. Values are reported as the proportion of observed vs. expected number of ties within and between groups. E-A K-A V-A Ind. Leaders/Govt. Officials** E-A 1.91 0.37 0.13 0.35 K-A 0.07 4.00 0.11 0.08 V-A 0.09 0.00 3.25 0.98 *All values are significant at the 0.001 level. ** This group also includes management officials and members of the scientific community.

4.2 Linking Social Capital

Results concerning the last hypothesis (H:3); that there will be higher levels of linking

social capital for the dominant ethnic group, the E-A community, than for the minority

ethnic groups, the K-A and V-A community; are also provided by the relational

contingency table analysis (Table 3). Concerning the K-A community, this hypothesis is

supported by this analysis, which identified a much lower level of linking ties to industry

leaders/government officials/members of the scientific community (grouped together for

this analysis) for the K-A community (0.08) than the E-A community (0.35). Though this

type of linking social capital was expected to be less prevalent among the K-A

community, this community reported a strikingly low proportion of these types of ties,

and in fact, the proportion of 0.08 represents only one single tie among the whole

community (see Fig. 2 for a depiction of this tie, which is in the lower middle section of

the graph). These results imply that the K-A group may be rather fragmented in regards

to linking social capital, in addition to being somewhat fragmented from the V-A

community, which could certainly be obstructing stakeholder cooperation and

collaboration in the HLF. This also suggests that the interests of K-A fishers may not be

adequately represented in the management and policy arena, and that K-A fishers may be

28

somewhat isolated from certain resources such as technological innovations and scientific

knowledge.

In contrast to the findings for the K-A community, there was little evidence to support

hypothesis 3 for the V-A community. In fact, the V-A community reported more ties to

industry leaders, management or government officials, and members of the scientific

community than any other group in our analysis, reporting 98% of the average number of

expected ties. These results differ from previous work done on linking social capital

among ethnically diverse resource users (i.e., Romani 2003), which suggested that ethnic

minority groups tend to have less access to extension services in natural resource settings,

which implies lower levels of linking social capital. This is certainly an interesting result

for a variety of reasons. One reason this is interesting is due to the fact that members of

the E-A community rather than the V-A community occupy the majority of positions in

the Hawaii Longline Association (HLA), which were classified as industry leaders in the

present study. The HLA is a trade association which works to represent the HLF, and

every vessel selling fish to the auction in Honolulu is required to join the HLA and pay

dues of two cents per pound of fish sales. (In Honolulu, longline fishers are required to

sell their fish to the auction rather than directly to suppliers, indirectly causing most

vessel owners to become members of HLA.) The HLA has a functioning body of

leadership as well as fisher representatives that act as board members, and regular

meetings are held to discuss important issues regarding the fishery as well as how the

member dues will be used. Though all fishers are invited to the meetings, most fishers are

unable to attend due to often being at sea when the meetings are held (HLF fishers

typically only come into port for 3-4 days before returning to sea again unless major

vessel repairs are needed). This makes it important that fishers have a board member

present at HLA meetings to ensure their interests are represented and that information

regarding policy or management issues is disseminated back to each fishing community.

In the survey used for this study, respondents were asked if they were a member of HLA

and if they were presently a board member or officer in the association, or if they had

been in the past. Results show that 100% (77) of V-A fishers reported being a member of

the HLA, but only 4% (3) reported being a board member or officer either currently or in

29

the past; while 88% (52) of the E-A fishers surveyed reported being members of HLA,

and 17% (9) of them reported holding a position as a board member or officer either

currently or in the past. In regards to the K-A community, only 72% (18) reported being a

member of HLA, while only one of those members reported serving as a board member

or officer.

With such a high proportion of E-A community members having direct involvement with

the HLA, it is interesting to note that the V-A fishers reported a much higher rate of ties

to industry leaders and government/management officials. One explanation for this is that

while some members of the E-A community reported having ties to officials involved

with HLA as well as supply store owners, gear maintenance and technology experts, fish

auction officials, and government or management officials, many of these ties were only

identified by a single fisher in the E-A community rather than by multiple fishers. In

contrast, members of the V-A community reported similar ties to the same type of actors

listed above, but also identified ties to members of the coast guard, customs and border

protection, and crew agents, and in many cases, ties to these actors were identified by

more than one fisher in the V-A community. Members of the V-A community also

identified ties to industry leaders involved with VAK Fisheries, which was originally

developed as a cooperative to expand business services to all Hawaii’s longline vessels.

Though the cooperative sought to attract members from all fisher communities, according

to our survey membership is largely made up of vessels from the V-A community, with

only one member of the E-A community and three members of the K-A community

reporting an affiliation with VAK. Nonetheless, VAK members are afforded resources

and ties to government and management officials via the organization, which not only

aids in restocking and refueling vessels when they arrive in port, but also helps fishers

with business filings, permits, and licenses, particularly for those with limited English

speaking and reading skills (Lu and Nguyen 2008). Therefore, VAK Fisheries managers

and affiliates were classified as industry leaders for the purposes of this study, as were

other supply store owners and workers who operate in a similar fashion, and ties to

managers and affiliates of VAK were reported solely in the V-A community.

30

4.3 Bonding and Bridging Social Capital

Table 4 provides a descriptive summary of group level characteristics concerning

bonding and bridging social capital found for the overall network and for each ethnic

group considered in this study. The table includes the number of actors and ties within

each group, the average degree of each group (the average number of ties identified by

actors in each group), as well as the percent of ties reported in each group that were

reciprocated. This is followed by the number of actors in the largest component, as well

as the number of isolated fishers in each community. Following the framework of Crowe

(2007), isolated fishers were reported, but not included in these analyses. Results of the k-

core and cutpoints analyses are also offered in Table 4, which were used to classify each

community as having either a bonding or bridging network structure.

Overall, the HLF network was classified as a coalitional structure. This classification was

largely made due to the observed division along ethnic lines in the HLF network (see Fig.

2), as well as the moderate rate of cutpoints (0.06) in the HLF network. As previously

stated, the number of cutpoints equals the number of actors, whom, if removed, would

fragment the network into two or more completely separate networks. Though the HLF

network has a high order of k-core, where k = 8, which is an indicator of network

cohesion, all of the 48 fishers in this order are from the V-A community (see the V-A

community k-core results three columns over), which had a strong impact on the k-core

analysis for the HLF network as a whole. Coalitional networks are a type of bridging

network. Thus, the network structure of the HLF may be facilitating access to external

resources and diverse knowledge, which is thought to increase the overall adaptability of

the fishery. The central idea is that novel information flows to actors through weak ties

rather than strong ties because close friends tend to have the same information and

knowledge, whereas acquaintances connect individuals to a wider world (Granovetter

2005). Though there is an overall lack of bonding ties across groups inherent in

coalitional structures, dense bonding groups connected to each other in non-redundant

ways can form within coalitional structures wherein a level of trust and norms is possible

across groups (Crowe 2007).

31

Table 4. Summary of group level network characteristics.

Entire HLF E-A K-A V-A HLF network data Total actors 179 79 33 94 No. of ties 895 229 83 581 Fishers only network* Total actors --- 60 25 77 No. of ties --- 189 73 542 Avg. degree 5.00 3.26 2.92 7.04 No. of components 1 2 1 1 % of reciprocal ties 16.23% 21.94% 10.61% 18.08% Largest component Number of actors 179 60 25 77 Isolated actors 5 1 0 0 Indicators of network cohesion Largest k-core 8 4 4 8 No. of actors in largest k-core 48 34 16 48 Proportion in 4-core and higher 0.75 0.58 0.64 0.99 Indicators of structural holes No. of cut-points 10 7 0 1 No. of blocks 20 11 1 2 Proportion of cut-points to total points

0.06 0.09 0.00 0.01

Estimated network configuration Coalitional Bridging Complete/

Bonding Complete/ Bonding

* Fisher’s only network, which was used to calculate all ethnic group metrics, consists of ethnic group members only and does not include industry leaders, government or management officials, and members of the scientific community. However, in this case, the ability for trust and norms to develop across groups is likely to

be impacted by the observed homophily effect, and may rest on the level of reciprocal

ties reported across groups. As highlighted in Fig. 4, there was only one reciprocal tie

between ethnic groups that was reported, which was between a E-A owner/operator and a

K-A captain. In comparison, 16% of all ties reported by fishers in this study were

reciprocated. Thus, bonding social capital in the form of reciprocal ties between groups is

almost nonexistent. The lack of reciprocal ties across groups in this case may imply a low

32

level of trust across groups, despite the possibility for trust and norms to develop in

coalitional network structures. To an extent a low level of trust across ethnic groups in

this fishery was recently reported by Allen and colleagues (2012). Perceptions of distrust

concerning fishers in different ethnic groups were also expressed by a number of

individuals who participated in this study. Though specifically gauging trust across

groups was not the aim of the present study, fishers often spoke openly about this topic

when asked if there was anyone in the other ethnic communities with whom they often

share information.

Figure 4. Network configuration of the HLF highlighting reciprocal ties, classified by thick red lines, generated in NetDraw (Borgatti 2002). Nodes (representing actors) with the smallest path lengths to each other are placed closest together. Node color and shape represent the actor’s title and ethnicity affiliation: V-A (yellow), E-A (blue), K-A (red), N/A (grey); vessel owner (square), vessel operator (circle), vessel owner and operator (rounded square), industry leader (box), government or management official/member of the scientific community (triangle),vessel owner and industry leader (circle inside square). Actors with reciprocal ties between groups (only one tie in this network – connecting a K-A captain and an E-A owner/operator) are circled in black.

33

4.3.1 Community Level Analyses

Fishers only networks were used to calculate all metrics at the ethnic community level,

since including ties to industry leaders, government and management officials, and

members of the scientific community would have skewed the results of community level

metrics. As reported in Table 4, the E-A community was classified as a bridging

structure, whereas the K-A community and the V-A community were classified as

complete or bonding structures. Depictions of each community network including all

reported ties (A) and fisher’s only networks (B) can be found in Fig.’s 5, 6 and 7. The

difference between the bonding and bridging classification is most obvious when

comparing the results of the E-A and V-A community, where the E-A community only

has 0.58 of its members in the 4-core and higher order, while the V-A community has

0.99 of its members in the 4-core and higher order. Furthermore, the E-A community has

7 cutpoints and 11 blocks, meaning that if these 7 actors were removed from the E-A

community, the E-A network would be fragmented into 11 completely separate networks,

while the V-A community only has 1 cutpoint and 2 blocks.

The bridging nature of the E-A community can be seen when viewing this network in

Fig. 5 (B), which shows that there are quite a few actors who are only sparsely connected

to the overall network, while the bonding nature of the V-A community is easily detected

when viewing the V-A fishers network in Fig. 6 (B), which is made up of very densely

connected actors. These findings support previous ethnographic research done on the

HLF which inquired about the social networks of the fishery via semi-structured

interviews as a part of a larger research project (i.e., Allen et al. 2012). In this study,

Allen et al. (2012) found that although some members of the E-A community are closely

tied with a small unit of others, some operate independently, and many feel that there is a

lack of solidarity within their community. They also found that the V-A community

demonstrated a strong kinship between fishers in their community, with many immediate

and extended family members owning and/or working together on a number of vessels or

assisting others when as needed (Allen et al. 2012).

34

Comparatively, the K-A community (Fig. 7) has no identifiable cutpoints, which was a

major factor in its classification. Although there are only 64% of its members included in

the highest order of k-core in this analysis, there is reason to believe that many more ties

exist in this community. In general, K-A fishers were hesitant about sharing information

about their ties to other fishers in their community when participating in the survey, and

often times would report only a few ties, and then state that they didn’t feel comfortable

giving more information. This is also consistent with previous research, which found that

members of the K-A community were often reluctant to share information (Allen et al.

2012). However, the current analysis of the K-A community network structure contrasts

with the previous inquiry into fishers social networks by Allen et al. (2012), which found

that there were two separate social networks of individuals in the K-A community. As

shown in Fig. 5, members of the K-A community currently form a single network, which

is corroborated by our group level metrics (Table 4) that classify the K-A community as

one single, cohesive component with no cutpoints. One possible explanation for this

observed difference in social structure is that members of one of the two previously

observed groups may have recently decided to exit the fishery, potentially causing the

remaining fishers to form ties with other K-A fishers. In the current study, only 24 K-A

owned vessels were identified as currently in operation, while previous studies had

reported a much higher number of K-A owned vessels. Additionally, many members of

the K-A community that were contacted for this study reported to us that they had sold

their vessel and were no longer operating in the fishery.

35

A.

B.

Figure 5. E-A network configuration depiction of all ties identified (A), and of ties between members of the E-A community only (B). Nodes (representing actors) with the smallest path lengths to each other are placed closest together. Node color and shape represent the actor’s title and ethnicity affiliation: V-A (yellow), E-A (blue), K-A (red), N/A (grey); vessel owner (square), vessel operator (circle), vessel owner and operator (rounded square), industry leader (box), government or management official/member of the scientific community (triangle),vessel owner and industry leader (circle inside square).

36

A.

____________________________________________________________________

B.

Figure 6. V-A network configuration depiction of all ties identified (A), and of ties between members of the V-A community only (B). Nodes (representing actors) with the smallest path lengths to each other are placed closest together. Node color and shape represent the actor’s title and ethnicity affiliation: V-A (yellow), E-A (blue), K-A (red), N/A (grey); vessel owner (square), vessel operator (circle), vessel owner and operator (rounded square), industry leader (box), government or management official/member of the scientific community (triangle),vessel owner and industry leader (circle inside square).

37

A.

B.

Figure 7. K-A network configuration depiction of all ties identified (A), and of ties between members of the EKA community only (B). Nodes (representing actors) with the smallest path lengths to each other are placed closest together. Node color and shape represent the actor’s title and ethnicity affiliation: V-A (yellow), E-A (blue), K-A (red), N/A (grey); vessel owner (square), vessel operator (circle), vessel owner and operator (rounded square), industry leader (box), government or management official/member of the scientific community (triangle),vessel owner and industry leader (circle inside square).

38

Chapter 5

SUMMARY AND CONCLUSIONS

5.1 Summary

In this thesis the current threats to fisheries and the importance of taking into account the

social aspects of natural resource systems in order to increase management effectiveness

were discussed. Adaptive governance and co-management, facilitated by collaboration

across stakeholder groups, were presented as innovative and promising management

strategies that may be more able to deal with ecosystem complexity than traditional

management schemes. Social networks and social capital and their relation to adaptive

governance and collaboration were then discussed, and many recent studies highlighting

their importance in facilitating collaboration and the broader arena of effective natural

resource governance were presented.

Three different types of social capital were identified as important: bonding, bridging,

and linking. Bonding social capital has been shown to increase trust, cooperation,

reciprocity, and the ability to enforce social norms in natural resource settings; all

important factors in facilitating collaboration. Bridging social capital can also be a key

component facilitating collaboration, which connects different groups of stakeholders and

can provide access to external resources and diverse knowledge. Lastly, linking social

capital can be highly important for facilitating the transfer of scientific knowledge and

local ecological knowledge, for ensuring stakeholder interests are represented in the

management and policy arena, and for facilitating stakeholder understanding and

cooperation in regards to management initiatives. These effects have been empirically

tested and theoretically advanced by many recent studies. Yet, how stakeholder attributes

impact the level and distribution of social capital among stakeholders has not received as

much attention in the literature.

In this study, the effects of ethnic diversity among resource users on the level and

distribution of bonding, bridging, and linking social capital in Hawaiʽi’s longline fishery

were analyzed and discussed. Results revealed that ethnic diversity is responsible for a

39

homophily effect among fisher networks of the HLF, where there exists a much higher

proportion of ties within fisher groups of the same ethnicity than between groups. This

result supported the first hypothesis that there would be an observable division along

ethnic lines. Higher levels of bonding social capital were also found to exist among fisher

groups of the same ethnicity, and was nearly absent between groups, which supported the

second hypothesis that bonding social capital would be found primarily among resource

users of the same ethnicity. However, ethnic fisher groups are not completely isolated

from one another and bridging ties were found to exist across groups. Thus, the overall

network structure of all fishers in the HLF was classified as a coalitional network. The E-

A fisher community was classified as having a bridging network structure due to a high

level of cutpoints and low structural cohesion overall. In contrast, the K-A and V-A

communities were classified as bonding network structures due to low levels or an

absence of cutpoints and higher structural cohesion. These results also supported the

second hypothesis, which stated that bonding social capital would be more prevalent in

the K-A and V-A communities than in the E-A community.

Results of the relational contingency table analysis did not fully support the third

hypothesis, which stated that there would be higher levels of linking social capital for the

dominant ethnic group, the E-A community, than for the minority ethnic groups, the V-A

and K-A community. Interestingly, results show that the V-A community reported a

much higher level of linking social capital ties than any other group, though the E-A

community did have a higher proportion of these types of ties than the K-A community.

Additionally, results from this analysis suggest that the K-A community is somewhat

fragmented concerning linking social capital, with only one reported tie to industry

leaders, government or management officials, and the scientific community.

5.2 Conclusions

With increasing fisheries decline on a global scale coupled with the looming threat of

climate change impacts on fish stock abundance and distribution, there is a clear and

imminent need for innovative and adaptive governance strategies that can increase the

long-term sustainability of fisheries resources and their supporting ecosystems. The

40

success of adaptive governance is highly influenced by collaboration among

stakeholders, which, in turn, is impacted by the relations both within and between

relevant stakeholders. From a management perspective, the structure of stakeholder social

networks and the existence of social capital can facilitate or impede the diffusion of

innovations, management approaches, and regulations (Mueller et al. 2008). Thus,

understanding not only network structure of resource users, but also the factors

influencing the level and distribution of social network capital can enable more effective

resource governance than can enhance sustainability.

In this analysis of resource user’s social networks in Hawai‛i’s longline fishery (HLF), a

homophily effect was observed along ethnic lines as well as an absence of bonding ties

across ethnic divides. This suggests a lack of trust and reciprocity across groups in the

HLF, which is likely to be impeding the ability to enforce social norms among resource

users. In pelagic fisheries, the ability to enforce social norms is particularly important due

to enforcement capacity being relatively low. The lack of bonding social capital across

the fishery as a whole is also likely to be negatively impacting the potential for consensus

building and conflict resolution, having an overall negative impact on collaboration

among resource users.

However, ethnic fisher groups in the HLF are not completely isolated from one another

and bridging ties do exist across groups, which may be facilitating access to external

resources and diverse knowledge. Bridging ties such as these are thought to increase the

overall adaptability of social-ecological systems because they allow stakeholders a

greater variety of resources and ideas when faced with change or external shocks. Also,

actors occupying key positions in the overall HLF network structure may be able to foster

trust across groups and facilitate joint-action. However, actors occupying these key

positions in the HLF may potentially impede potential collaboration if they feel

obstructing the transfer of sound information or access to resources across groups

benefits their own self-interest, or if they are simply not interested in fostering trust

across groups or acting as a catalyst for joint-action. Though the interests of these actors

is not easily hypothesized, in this case one might assume that the competitive nature of

fishing is likely to be affecting how they behave in regards to their advantageous

41

positions between the different HLF social networks, and that self-interest may hold a

greater weight than the potential for collaboration and cooperation. It is also possible that

these actors are unaware that they occupy central positions in the HLF network structure

that connect different groups of actors. In any event, this analysis shows that although

fisher’s tend to have a much higher proportion of relations with other fishers of the same

ethnicity, all networks of the HLF are connected via a few key ties and actors. Identifying

these actors and bringing them into the decision making process would most likely

benefit the long-term sustainability and adaptability of the fishery since these actors may

be more able to influence fisher understanding and cooperation in regards to policies and

regulations and aid in disseminating technological and scientific information across the

diverse communities of the HLF. This type of collaboration would also ensure that

fisher’s interests are represented in the management arena, which may further influence

cooperation among resource users.

In this analysis of fisher’s social network capital, diverse network structures were found

among ethnic communities. The E-A community reflects more of a bridging network

structure, while the K-A and V-A communities reflect more of a bonding network

structure. These findings are consistent with previous sociological research on ethnicity

and social networks, and suggest that though there may be a low ability to enforce social

norms across the fishery as a whole, social norms may be more easily enforced within the

K-A and V-A community, where higher levels of trust and reciprocity are present. Joint-

action and collaboration are also more likely to be achieved within the K-A and V-A

communities, whereas fishers in the E-A community may be faced with the same barriers

to enforcing social norms and to fostering joint action within their community that are

likely to be impacting the fishery as a whole. On the other hand, due to the tight bonding

structure of the K-A and V-A communities, information and resources circulating within

these communities may become redundant; whereas the E-A community, made up of a

weaker bridging structure, may be benefiting from increased access to non-redundant and

diverse information, such as information on technological innovations.

This study provides a unique result concerning linking social capital and minority ethnic

groups that contrasts with previous sociological research on ethnicity and social capital.

42

Though it was assumed, based on previous theoretical and empirical research, that the

dominant ethnic group (the E-A community) would have a higher level of linking social

capital, the opposite was found to be true in this case in regards to the V-A community.

There are various reasons why the V-A community may have reported more links to

industry leaders, government or management officials, and the scientific community than

the E-A community, including the V-A community’s involvement with the recently

developed organization VAK Fisheries. However, the precise reasons for this unique

result can only be revealed with further research. In contrast, the K-A community appears

quite marginalized in regards to linking social capital, and may benefit significantly from

further outreach. These results suggest that ethnic diversity among stakeholders does not

always negatively impact linking social capital for ethnic minority groups. Each natural

resource setting may be unique, and resource managers and policy makers should take

steps to build linking social capital ties to all resource user groups to ensure they are both

represented in the management arena and provided access to resources and scientific

information. Reaching this level of engagement with each diverse group of resource

users, particularly in marine fisheries, could help to increase the rate of adoption of

sustainable fishing practices and facilitate understanding and cooperation among resource

users in regards to management and policy initiatives, generally having a positive effect

on the sustainability of the resource and its supporting ecosystem.

5.3 Recommendations for Future Research

This study is only one example of the effects of ethnicity diversity among resource users

on social network capital in a natural resource system, and there are a limited number of

studies looking at the effects of other stakeholder attributes on social networks and social

capital in the natural resource management literature. In order to achieve generalizable

results for various natural resource settings, much more research is needed in this area.

Future research could test whether the hypotheses used in this study are also relevant in

other natural resource settings. Additional research on the effects of other stakeholder

attributes on social networks and social capital are equally important.

43

In Hawai‛i’s longline fishery (HLF), future research could seek to explicitly determine

how resource user’s social networks coincide with trust across groups, as well as how

they impact stakeholder perceptions and attitudes regarding the fishery system. Do

divisions along ethnic lines and specific network structures affect stakeholder

perceptions, and if so, how? Future research could also attempt to analyze the impact of

social networks and social capital on stakeholder’s decision-making, or more specifically,

on their resource use and extraction. This information would surely add to our

understanding of not only the impacts on social networks and social capital, but also the

effects of social networks and social capital in competitive common pool natural resource

settings.

This study looked primarily at social capital at the community level, but social capital

also differs within groups and communities. Other future research on the HLF could

examine and quantify social capital at the individual level, allowing a more thorough

examination of the dispersion of social capital both within and across groups. Finally,

future research could examine the relationship between social networks and economic

outcomes for individual fishers, paying particular attention to group level social capital

and the impact of the homophily effect apparent in the HLF. Does community level

social capital impact economic outcomes for individual resource users, and if so, how?

Considering economic outcomes typically play an important role in influencing resource

user’s decision-making, this research could provide valuable insight for natural resource

managers, economists, and policy-makers on the economic implications of social

organization in natural resource settings.

44

APPENDIX

A. Survey Instrument

Date of interview: ________________ Surveyor’s name: __________________________

HLF Social Network Analysis Survey Questionnaire Owner-Operator Survey

Many fishermen have a network of other fishermen and people connected to the fishing industry with whom they share information and talk about gear, vessel maintenance, regulations, ways of dealing with bycatch, what the fish are doing, and other fishing topics. Sometimes these conversations with others just pass the time and it's enjoyable to talk story, and sometimes they provide valuable information that can contribute to vessel performance. We're interested in knowing a little about you and who is in your network of fishermen. First we’d like to ask a few questions about you and your experience with fishing. 1. How long have you been involved in the fishing industry? ______years 2. How long have you lived in Hawaii? ______years 3. How long have you been involved in the Hawaii Longline Fishery? ______years 4. Most fisherman have a network of friends that are also involved in the same fishery such as other captains, owners, or supply store owners whom they rely on to share useful information about fishing. In general, how often do you share useful information about fishing within your network? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) 5. In general, how important is the information you get from other fisherman to your fishing success? Very important Important Somewhat important Not important 6. Are you involved in decisions for the vessel(s) you own and/or operate concerning the following topics? (check all that apply) fish activity hiring of crew/captain site catch/location fishery regulations weather conditions vessel technology/maintenance gear type bycatch/turtle activity Now we’d like to learn more about your network and explore with you how your network may help you with fishing. We’d like you to begin by identifying five or more people with whom you regularly exchange information with or get advice from about fishing, and then we’d like to learn a little more about each of them. I have a list of longline owners, captains, and supply store owners if that helps; otherwise you could just start with the person you probably talk to the most and we can go on from there. I. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________

45

B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable II. ________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________ B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable III. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________ B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain

46

D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable IV. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________ B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable V. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________ B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable VI. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________

47

B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable VII. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________ B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable IIX. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________ B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain

48

D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable IX. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________ B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable X. _________________________ professional acquaintance friend family member strength of relationship (circle one): very strong strong weak very weak A. How did you meet this person? family member through fishing from a friend from a family member other:_____________________ B. How often do you share useful information about fishing with this person? Not often (1-3 times/yr.) Sometimes (1-3 times/mo.) A lot (1-3 times/wk. or more) C. What do you typically talk to this person about? (check all that apply) gear type weather conditions site catch/location fish activity bycatch/turtle activity vessel technology/maintenance fishery regulations hiring of crew/captain D. In general, how valuable would you say the information that you get from this person is to your fishing success? Very valuable Somewhat valuable Not valuable We greatly appreciate you taking the time to talk to us. We’d now like to conclude the survey with a few general follow-up questions. 7. If you were the owner or operator of a vessel within the Hawaii Longline Fishery five years ago, are the people you mentioned in this survey the same people you would have identified if we had talked with you then? (circle one)

49

exactly the same mostly the same somewhat the same completely different 8. Which commercial fishing organizations do you belong to? HLA VAK Other: ___________________ 9. How active are you in the organization? have served as officer or board member active member not active 10. Which supply store do you typically go to, if any? (check all that apply) POP Pacific Fishing and Supply VAK HiSea Other:__________________ Interviewee Name: ________________________ Contact (phone): ______________________ Age: ______ Ethnicity: _________________ Highest level of Education: _________________ 11. Please list the vessels in the Hawaii Longline Fishery you own/operate or have owned/operated (within the past 5 years), along with the approximate dates of ownership and dates you served as captain. Vessel Name Dates Owned

From: End Dates Captained From: End

1

2

3

4

5

6

7

5. Are you (or were you) the primary decision maker for the vessels listed above? If not, please indicate who you believe to be the primary decision maker for each vessel. ____________________________________________________________________________________________________________________________________________________________

Thank you for your help with this project.

50

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