When, where, and what to fish? Cooperative information sharing and fishing behavior in Hawaii’s longline fishery

By Michele Barnes-Mauthe and Kolter Kalberg

Michele Barnes-Mauthe, University of Hawaii at Manoa, PhD Candidate

Michele Barnes-Mauthe, University of Hawaii at Manoa, PhD Candidate

Human behavior is profoundly affected by social interactions. In pelagic fisheries, individual fishers operate in a highly complex and dynamic environment and are faced with a high level of uncertainty on a daily basis. To cope with this complexity, fisher’s often rely on cooperating with others to make important decisions, such as when, where, and what to fish. When and how fishers cooperate can play a substantial role in determining how they will respond to social, political, and environmental change, and what the impacts of these responses will be on the sustainability of the fishery. Yet we generally have a very poor understanding of these social dynamics, with human behavior often considered the key source of uncertainty in fisheries.

In this project, University of Hawaii PhD candidate and NOAA affiliate Michele Barnes-Mauthe is leading an effort to link fisher’s cooperative information sharing networks to detailed data on catch, effort, and economic cost-earnings in order improve our understanding of these dynamics. Results indicate a high level of cooperation within ethnic groups, but relatively little cooperation across groups. This division and the manner in which individual fishers are connected to each other have been found to impact a diverse range of outcomes, including rates of shark bycatch and economic productivity. Future plans include using this information to build predictive models of fisher behavior over time and space to asses responses to different scenarios of change.

Graphical depiction of the Hawaii-based Longline Fishery (HLF) information sharing network, adapted from Barnes-Mauthe et al.,  2013

Graphical depiction of the Hawaii-based Longline Fishery (HLF) information sharing network, adapted from Barnes-Mauthe et al., 2013

This research develops a graphical depiction of the Hawaii-based Longline fishery (HLF) information sharing network. Each shape (i.e., node) represents an actor in the network, and the lines (i.e., edges) connecting them represent their cooperative information sharing relationships, or ties. Each node is color-coded by ethnicity, with European-American (E-A) represented by blue, Vietnamese-American (V-A) represented by yellow, and Korean-American (K-A) represented by red. The network was created in NetDraw (Borgatti, 2002) using the spring embedding algorithm with node repulsion, which uses iterative fitting to place nodes closest to those that they have the shortest path lengths to, while also separating nodes which may overlap in the network graph. The data used to develop this graph was collected by Michele Barnes-Mauthe and collaborators in 2011-2012 with the support of a grant from the Pelagic Fisheries Research Program. The project was approved by the University of Hawaii Institutional Review Board, and all social network data is held by Barnes-Mauthe and the University of Hawaii team.

Click the image below to view a video map illustrating the concentration of fishing effort over time and space as related to the HLF information sharing network for the deep-set fishery which primarily targets bigeye tuna.

Vessel operators are color coded by ethnic group as in the information sharing network graph. Initial points are scattered to protect fishers exact fishing location and privacy, while still depicting the spatiotemporal aspect of the information sharing network among and between the three ethnic groups.  The time-series spans from January 2008 to December 2012. The vertical line is the 150 degree west longitude and separates the Western and Central Pacific Ocean from the Eastern Pacific Ocean. The video map was developed through a two-step process using unique identifiers to link the social network data with Federal logbook data from the National Marine Fishery Service in order to maintain strict confidentiality of personal identifying information housed in both sets of data. We then used Stata analytical software to program twoway graphical outputs within a timelapse series of images.

For more information about this project, contact Michele Barnes-Mauthe at barnesm@hawaii.edu

For information regarding the programming syntax used to create the video graphics with StataMP 12, contact Kolter Kalberg at kolter.kalberg@noaa.gov

To learn more about the PIFSC Socioeconomics Program check out our website.

Project Lead:  Michele Barnes-Mauthe, University of Hawaii at Manoa, JIMAR

Collaborators:

  • Kolter Kalberg, JIMAR
  • PingSun Leung, University of Hawaii at Manoa
  • John Lynham, University of Hawaii at Manoa
  • Steven Allen Gray, University of Massachusetts Boston
  • Minling Pan, NOAA
  • Shawn Arita, USDA Economic Research Service

References:

Barnes-Mauthe, M., Arita, S., Allen, S. D., Gray, S. A., & Leung, P. 2013. The influence of ethnic diversity on social network structure in a common-pool resource system: implications for collaborative management. Ecology and Society 18(1):23.

Borgatti, Stephen P. 2002. NetDraw: Graph visualization software. Harvard: Analytic Technologies.

This entry was posted in Socioeconomics and tagged , , , , , , , , , , , , , , , , , , , . Bookmark the permalink.