Table 1:
Drivers of social tie formation with select examples.
| Drivers | Definition | Select examples | Evidence in small scale fisheries | Contextually relevant drivers | |||
|---|---|---|---|---|---|---|---|
| Structurally drivenˆ | Establishment and maintenance of social ties are driven by existing direct and indirect connections (Rivera et al. 2010; Lusher et al. 2012) | • | Preferential attachment – those who have more social ties will tend to accrue additional ties more rapidly (Barabási and Albert 1999) | • | Triadic closure is one way to capture bonding ties – which have been repeatedly noted as important in SSF (e.g. Ramirez-Sanchez and Pinkerton 2009; Alexander et al. 2015) | • | Triadic Closure |
| • | Triadic closure – i.e. one is likely to be friends with the friends of their friends (Granovetter 1973) | ||||||
| Attribute driven+ | Establishment and maintenance of social ties are driven by similarities (or differences) in the attributes of actors (Rivera et al. 2010; Lusher et al. 2012) | • | Homophily – i.e. tie formation between two individuals who share some commonality (e.g. age, gender, education, etc.) (McPherson et al. 2001). | • | Gear based homophily (Crona and Bodin 2006; Cox et al. 2016) | • | Gear based homophily |
| • | Kinship based homophily (Ramirez-Sanchez 2011)* | • | Leaders/wardens | ||||
| • | Certain attributes can make particular actors more/less sought after, for example being a state actor during decision-making processes in developing new policies (Ingold and Fischer 2014). | • | Ethnic based homophily (e.g. Barnes-Mauthe et al. 2013a)* | ||||
| • | Leaders as individuals being sought after (e.g. Alexander et al. 2015; Crona et al. 2017) | ||||||
| Exogenous contextual factors | Cultural, social, geographic, and/or ecological environments of individual actors drive the establishment and maintenance of social ties | • | Involvement in other types of social networks (e.g. an information sharing network and a resource sharing network) (Lusher et al. 2012) | Involvement in other types of social networks | • | Geographic Proximity | |
| • | Coop membership† Geographic Proximity | ||||||
| • | Geography and space brings and holds people together (Rivera et al. 2010; Lusher et al. 2012) | • | Fishing grounds (e.g. Maya-Jariego et al. 2016)* | ||||
| • | Landing sites (no empirical work to date) | ||||||
ˆ Rivera et al. (2010) refers to these as relational drivers while Lusher et al. (2012) refers to this suite of drivers as self-organization. +Rivera et al. (2010) refers to these as assertive drivers. *Not contextually relevant. †Captured by geographic proximity via landing site as most members are from just 1 of the 7 landing sites.
Figure 1:
Bluefields Bay Special Fishery Conservation Area and associated landing sites (Made by D. Campbell).
Figure 2:
Information-sharing network among fishers in Bluefields Bay, Jamaica. Each circle represents a fisher, and the lines between them represent their information-sharing ties. Panel 2A illustrates the basic structure of the social network among fishers. Panels 2B–D each include one of the attributes of interest; (B) leadership; (C) landing site; and (D) gear type. The network visuals were generated using the Fruchterman-Reingold layout algorithm in the package igraph in R, where nodes are placed on the plane using this force-directed layout algorithm.
Table 2:
Exponential random graph model configurations used for Models 1–5.
 |
*Included as a control factor.
Table 3:
Descriptions of the sequential exponential random graph models (ERGM) developed and associated hypotheses.
| Model | Description | Hypotheses |
|---|---|---|
| Model 1 Random model | The random model suggests that ties are both random and uniformly distributed across all fishers (Bernoulli model). Accordingly, the model only includes the general edge configuration – alternatively referred to as the density parameter | Baseline |
| Model 2 Triadic Closure | The triadic closure model builds on Model 1 through the inclusion of the alternating triangle (ATA) configuration and suggests that there is a structurally induced propensity of friends of a friend also being friends resulting in clustering and cohesion | H1 |
| Model 3 Triadic Closure + Homophily | The homophily model builds on Model 2 through the addition of a suite of gear-based homophily parameters. The resulting model considers the propensity for fishers to establish and maintain ties with others using the same fishing gear (e.g. spear gun, net) | H1 H2 |
| Model 4 Triadic Closure + Homophily + Geographic Proximity | The geographic proximity model includes a series of parameters concerning landing sites. Building on Model 3, this model considers the propensity of geographic proximity to drive the establishment and maintenance of ties between fishers (or alternatively, drives fishers apart) | H1 H2 H3a H3b |
| Model 5 Triadic Closure + Homophily + Geographic Proximity + Leadership | The leadership model adds the warden activity parameter and suggests that certain actors (i.e. wardens) will have more direct ties than the average actor | H1 H2 H3a Hb H4 |
Table 4:
Results from nested exponential random graph models.
| Model 1 Random model | Model 2 Triadic Closure | Model 3 Triadic Closure + Homophily | Model 4 Triadic Closure + Homophily + Geographic Proximity | Model 5 Triadic Closure + Homophily + Geographic Proximity + Leadership | |
|---|---|---|---|---|---|
| General parameters | |||||
| Edge | −3.9846 (0.088)* | −4.7151 (0.118)* | −4.6725 (0.148)* | −5.2685 (0.164)* | −4.3228 (0.18)* |
| Triadic Closure | |||||
| ATA | – | 1.2087 (0.085)* | 1.048 (0.096)* | 0.9088 (0.091)* | 0.8402 (0.102)* |
| Homophily | |||||
| Hook and line interaction | – | – | 0.3871 (0.319) | 0.1735 (0.351) | 0.1849 (0.356) |
| Net activity+ | – | – | −1.2387 (0.592)* | −1.1896 (0.587)* | −1.1757 (0.584)* |
| Net interaction | – | – | 3.4265 (1.576)* | 2.6555 (1.437)* | 2.6565 (1.457)* |
| Spear gun interaction | – | – | 1.0461 (0.162)* | 1.2324 (0.198)* | 1.3197 (0.218)* |
| Multi-gear interaction | – | – | 0.1979 (0.192) | 0. 2174 (0.2) | 0.1331 (0.21) |
| Geographic Proximity | |||||
| Landing site 1 interaction | – | – | – | 1.3195 (0.175)* | 1.4524 (0.192)* |
| Landing site 2 interaction | – | – | – | 3.5227 (0.62)* | 3.7323 (0.623)* |
| Landing site 3 interaction | – | – | – | 1.6137 (0.188)* | 1.4161 (0.209)* |
| Landing site 4 interaction | – | – | – | 2.6917 (0.383)* | 2.8058 (0.419)* |
| Landing site 6 interaction | – | – | – | 2.0963 (0.31)* | 2.0015 (0.326)* |
| Landing site 7 interaction | – | – | – | 1.7764 (0.231)* | 1.9387 (0.226)* |
| Leadership | |||||
| Warden activity | – | – | – | – | 0.8948 (0.21)* |
| Mahalanobis distance | 26,189,363 | 1,656,063 | 100,893 | 6,963 | 4,203 |
Parameter Estimate (Standard Error); *Reject null hypothesis of parameter=0, p<0.05: +included as a control factor.
| Total # of fishers surveyed | 130 | |
| Total # of fishers surveyed from target landing sites | 122 | |
| Total Alters* not surveyed | 106 | |
| Alters* outside the network boundaries | ||
| Organizations/agencies | 7 | |
| Individuals from organizations/agencies | 8 | |
| Fishers from other landing sites | 13 | |
| Total outside network | 28 | |
| Total inside network | 41 | |
| Unknown | 34 | |
| Low-end total (total from target landing site + inside network) | 163 | 74.8% |
| Upper-end total (total from target landing site + inside network + unknowns) | 197 | 59.5% |
*Alters refers to the names of individuals and/or organizations that a person being surveyed identifies as sharing or receiving information from.