How does the accuracy of fisher knowledge affect seahorse conservation status?

Authors


Correspondence
Kerrie P. O'Donnell, Project Seahorse, Fisheries Centre, University of British Columbia, 2202 Main Mall, Vancouver, BC V6T1Z4, Canada. Tel: +604 827 5137
Email: odonnell@zoology.ubc.ca

Abstract

Despite a growing interest in incorporating fisher knowledge into quantitative conservation assessments, there remain practical impediments to its use. In particular, there is some debate about the accuracy of fisher knowledge. In this study, we report an attempt to quantify assumptions about how accurately fishers report past events (retrospective bias). Then we examine how the assumption we make about retrospective bias affects the characterization of changes in the fishery and extinction risk. We link fisher interviews and fisher logbooks to establish a catch rate (catch per unit of effort) trend for the history of a data-poor, small-scale seahorse fishery in the Philippines. We find that fishers perceive historic declines in fishing rate that are not apparent in more recent logbook trends, and the extent of the decline (and therefore extinction risk) hinges on assumptions we make about the accuracy of fisher recall. Scenarios that ignore retrospective bias result in the most severe declines and the most worrying extinction risk classifications. Furthermore, the historic baseline set by interviews suggests that relying on recent decades of data alone may underestimate extinction risk for our study species, and others that have been historically exploited. Attempting to link interviews with logbooks also illustrates differences between fisher-derived datasets: retrospective interviews may exaggerate early fishing rates and capture less variability than logbooks. In addition to being the first seahorse fishery reconstruction, our work contributes to the emerging interest in how fisher knowledge can guide conservation assessment. Future studies that incorporate fisher knowledge into quantitative assessments require (1) clearly stated assumptions about fisher knowledge bias; (2) clear criteria to compare fisher knowledge collected with different methods; (3) evaluation of the impact of assumptions on assessments.

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