title: Assessing strategies to minimize unintended fitness consequences of aquaculture on wild populations.
abstract: Artificial propagation programs focused on production, such as commercial aquaculture or forestry, entail strong domestication selection. Spillover from such programs can cause unintended fitness and demographic consequences for wild conspecifics. The range of possible management practices to minimize such consequences vary in their control of genetic and demographic processes. Here, we use a model of coupled genetic and demographic dynamics to evaluate alternative management approaches to minimizing unintended consequences of aquaculture escapees. We find that, if strong natural selection occurs between escape and reproduction, an extremely maladapted (i.e., nonlocal-origin, highly domesticated) stock could have fitness consequences analogous to a weakly diverged cultured stock; otherwise, wild population fitness declines with increasing maladaptation in the cultured stock. Reducing escapees through low-level leakage is more effective than reducing an analogous number of escapees from large, rare pulses. This result arises because low-level leakage leads to the continual lowering of wild population fitness and subsequent increased proportional contribution of maladapted cultured escapees to the total population. Increased sterilization efficacy can cause rapid, nonlinear reductions in unintended fitness consequences. Finally, sensitivity to the stage of escape indicates a need for improved monitoring data on how the number of escapees varies across life cycle stages.
authors.affiliation: Department of Environmental Science and Policy, University of California Davis Davis, CA, USA.
authors.forename: Marissa L
authors.initials: ML
authors.lastname: Baskett
authors.forename: Scott C
authors.initials: SC
authors.lastname: Burgess
authors.forename: Robin S
authors.initials: RS
authors.lastname: Waples
keywords: Salmo salar; aquaculture; contemporary evolution; domestication selection; migration load; quantitative genetic model