Bayesian decision analysis for evaluating management options to promote recovery of a depleted salmon population.
The endangered population of sockeye salmon (Oncorhynchus nerka) in Cultus Lake, British Columbia, Canada, migrates through commercial fishing areas along with other, much more abundant sockeye salmon populations, but it is not feasible to selectively harvest only the latter, abundant populations. This situation creates controversial trade-offs between recovery actions and economic revenue. We conducted a Bayesian decision analysis to evaluate options for recovery of Cultus Lake sockeye salmon. We used a stochastic population model that included 2 sources of uncertainty that are often omitted from such analyses: structural uncertainty in the magnitude of a potential Allee effect and implementation uncertainty (the deviation between targets and actual outcomes of management actions). Numerous state-dependent, time-independent management actions meet recovery objectives. These actions prescribe limitations on commercial harvest rates as a function of abundance of Cultus Lake sockeye salmon. We also quantified how much reduction in economic value of commercial harvests of the more abundant sockeye salmon populations would be expected for a given increase in the probability of recovery of the Cultus population. Such results illustrate how Bayesian decision analysis can rank options for dealing with conservation risks and can help inform trade-off discussions among decision makers and among groups that have competing objectives.
Pestes
Lynsey R
LR
School of Resource and Environmental Management, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
Peterman
Randall M
RM
Bradford
Michael J
MJ
Wood
Chris C
CC
D000818:Animals; D001499:Bayes Theorem; D001955:British Columbia; D003198:Computer Simulation; D003247:Conservation of Natural Resources / Q000379:methods*; D005398:Fisheries / Q000191:economics* / Q000379:methods*; D008962:Models, Theoretical; D011157:Population Dynamics; D012474:Salmon / Q000502:physiology*