Premium
Long time horizon for adaptive management to reveal predation effects in a salmon fishery
Author(s) -
Walsworth Timothy E.,
Schindler Daniel E.
Publication year - 2016
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.1417
Subject(s) - predation , productivity , abundance (ecology) , ecosystem , fishery , ecology , predator , fisheries management , trophic level , biology , adaptive management , maximum sustainable yield , apex predator , fishing , economics , macroeconomics
Predator–prey interactions shape ecosystem structure and function, potentially limiting the productivity of valuable species. Simultaneously, stochastic environmental forcing affects species productivity, often through unknown mechanisms. The interacting effects of trophic and environmental conditions complicate management of exploited ecosystems and have motivated calls for more holistic management via ecosystem‐based approaches, yet the limitations to these approaches are not widely appreciated. The Chignik salmon fishery in Alaska is managed to achieve maximum sustainable yield for sockeye salmon, though research suggests that predation by less economically valuable, and thus not targeted, coho salmon during juvenile rearing limits the productivity of sockeye salmon. We examined the relationship between historical sockeye salmon recruitment and coho salmon abundance observed in the Chignik system and could not detect a clear effect of coho salmon abundance on sockeye salmon productivity, given existing data. Using simulation models, we examined the probability of detecting a known predation effect on sockeye salmon recruitment in the presence of observation error in coho salmon abundance and stochasticity in sockeye salmon recruitment. Increased recruitment stochasticity reduced the ability to detect predator effects in recruitment, an effect further strengthened when low frequency environmental variation was added to the system. Further, increased observation error biased estimates of predator effects towards zero. Thus, in systems with high observation error on predator abundances, estimates of predation effects will be substantially weaker than true effects. We examined the effects of stochasticity on the ability of an adaptive management program to learn about ecosystem structure and detect an effect of management actions intended to release a prey species from its predators. Simulation models revealed that even under scenarios of large predation effects on sockeye salmon, stochastic recruitment masked detection of an effect of increased coho salmon harvest for nearly a decade. These results highlight the challenges inherent in ecosystem‐based management of predator–prey systems due to mismatched timescales of ecosystem dynamics and the willingness of stakeholders to risk losses in order to test uncertain hypotheses. It is critical for stakeholders considering EBFM (ecosystem‐based fisheries management) and adaptive management strategies to be aware of the potential timelines of perceiving ecosystem change.