The precursors of governance in the Maine lobster fishery

doi:10.1073/pnas.0702241104

The precursors of governance in the Maine lobster fishery

*School of Marine Sciences, University of Maine, Orono, ME 04469; and
^‡Maine Department of Marine Resources, Boothbay Harbor, ME 04538

Edited by Elinor Ostrom, Indiana University, Bloomington, IN, and approved July 11, 2007 (received for review March 12, 2007)

Abstract

Collective action is more likely to occur and to be effective when it is consistent with the self-interest of the affected individuals. The Maine lobster fishery is an instructive example of biological and technological circumstances combining with individual self-interest to create conditions favorable to collective action. The model describes the way social structure emerges from the adaptive behavior of competing fishers. Fishers compete in two ways: in a scramble to find the lobsters first and by directly interfering in other fishers' ability to compete, i.e., by cutting their traps. Both forms of competition lead fishers to interact frequently and to self-organize into relatively small groups. They learn to restrain their competitive behavior toward their neighbors but do not extend that same restraint to nonneighbors. Groups work within well defined boundaries, contact one another frequently, actively exchange information about the resource, and, most importantly, depend on continuing mutual restraint for their economic well-being. These self-organizing, competitive processes lay the foundation for successful collective action, i.e., mutual agreements that create the additional restraint required for conservation. The modeling approach we use is a combined multiagent and classifier systems simulation. The model allows us to simulate the dynamic adaptation (learning) of multiple individuals interacting in a complex, changing environment and, consequently, provides a way to analyze the fine-scale processes that emerge as the broad social–ecological patterns of the fishery. Patterns generated by the model are compared with patterns observed in a large dataset collected by 44 Maine fishers.

Footnotes

^†To whom correspondence should be addressed. E-mail: jwilson{at}maine.edu
Author contributions: J.W., L.Y., and C.W. designed research, performed research, contributed analytic tools, analyzed data, and wrote the article.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/cgi/content/full/0702241104/DC1.
↵ § Since the late 1980s, the lobster fishery has enjoyed record levels of abundance and harvests (Maine Department of Marine Resources; www.maine.gov/dmr/rm/lobster/lobdata.htm). This period was preceded by ≈40 years of relatively stable harvests. It is not the argument of this paper that the current abundance is attributable to the social organization described here. It is more likely that overfishing removed lobster predators and competitors and, thereby, released the lobster population from restraints that previously contained the population. Management before the current boom, however, did seem to have protected lobsters from the fate of the other major species in the system, but the events in the rest of the ecosystem have turned lobsters into a monoculture that is potentially subject to disease and the instabilities of an eroded system. These problems cannot be addressed by lobster management alone.
↵ ¶ At least two other models address similar learning questions in fisheries from a very different perspective. Allen and McGlade (7) use a systems-modeling approach; Dreyfus-León (8) uses neural networks.
↵ ‖ In the real fishery, traps are fished in groups referred to as a string and are located in the same neighborhood. A string may consist of 5–25 traps. Placing a string generally involves a single location decision, at least at the scale employed in the model.
↵ ** Entry is driven by average fleet profits. At entry, each fisher is endowed with a bank account that buffers the initial costs of learning or other temporary declines in profitability; the exhaustion of the account at any time is taken as a signal to exit.
↵ †† Holland (10) uses the example of a checkers game in which, say, a triple jump is set up by several prior moves. The credit given to the decision that implements the triple jump also has to be shared with the prior decisions that made it possible. The entire strategy has to be learned.
↵ ‡‡ In the CS model, as long as the environment is uniform, fishers evolve multiple rules. However, each rule yields exactly the same feedback and, consequently, there is no preference for one over the other. Each has an equal probability of being chosen at any time, and together they function as if there were a single random rule (SI Appendix 2).
Abbreviation:

CS,

classifier system.