A MODEL LINKING ENERGETIC EFFECTS OF WHALE WATCHING TO KILLER WHALE (Orcinus orca) POPULATION DYNAMICS

David E. Bain
Friday Harbor Labs, University of Washington, Friday Harbor, WA 98250

The southern resident killer whale (Orcinus orca) population declined from 98 to 79 animals between 1995 and 2001. I employed three models to test the null hypothesis that whale watching does not affect killer whale population dynamics through energetic mechanisms (increased energy expenditure due to avoidance responses and reduced energy acquisition due to impairment of acoustic foraging efficiency). A generalized logistic growth model was used to calculate the implications for population growth rate of changes in the energy balance of the population. The second model estimated changes in detection range of echolocation clicks due to noise exposure. The third considered how reduced echolocation range might affect foraging efficiency. I combined these models with changes in behavior measured in field studies and levels of whale watching traffic experienced by southern residents to estimate population-scale effects. The models indicate that population-level effects are negligible for killer whale populations well below carrying capacity, but become important when food is limited. Missed prey due to noise is potentially a more important mechanism than excess energy expenditure. The high value of the shape parameter (z) of the killer whale population growth model suggests that the maximum net productivity level is over 80% of K, rather than the NMFS default of 50-60%. Southern residents are currently below their maximum net productivity level and should be considered a depleted stock. The best estimate of the effect on carrying capacity of whale watching at current levels is about 3% due to excess energy expenditure, but insufficient data are available to reliably estimate the impact of noise. Clearly, more data are needed to determine whether the actual impact of whale watching that this model predicts exceeds "insignificant levels approaching zero." However, the precautionary principle supports mitigating potential impacts until data become available to indicate that the regulations are unnecessary.