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Marine Ecology Progress Series

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MEPS 423:247-260 (2011)  -  DOI:

Risso’s and Pacific white-sided dolphin habitat modeling from passive acoustic monitoring

Melissa S. Soldevilla1,4,*, Sean M. Wiggins1, John A. Hildebrand1, Erin M. Oleson1,2,  Megan C. Ferguson3

1Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Dr. #0205, La Jolla, California 92093-0205, USA
2NOAA-NMFS-Pacific Islands Fisheries Science Center, 1601 Kapiolani Blvd. Ste. 1110, Honolulu, Hawaii 96814, USA
3National Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA Fisheries, 7600 Sand Point Way NE F/AKC3,  Seattle, Washington 98115-6349, USA
4Present address: Duke University Marine Laboratory, 135 Duke Marine Lab Road, Beaufort, North Carolina 28516, USA

ABSTRACT: Habitat characterization allows prediction of dolphin distributions in response to oceanographic processes and can be used to understand and predict effects of anthropogenic disturbances. Many habitat models focus on contemporary dolphin occurrence and environmental predictor data, but time-lagged oceanographic data may increase a model’s predictive power due to ecological successional processes. Using hourly occurrence of Risso’s dolphin Grampus griseus clicks and 2 types of Pacific white-sided dolphin Lagenorhynchus obliquidens clicks in autonomous passive acoustic recordings, we investigate the importance of time-lagged predictor variables with generalized additive models. These models relate dolphin acoustic activity from recordings at 6 sites in the Southern California Bight between August 2005 and December 2007 to oceanographic variables including sea surface temperature (SST), SST coefficient of variation (CV), sea surface chlorophyll concentration (chl), chl CV, upwelling indices, and solar and lunar temporal indices. The most consistently selected variables among the trial models evaluated during cross-validation were SST (100% of models) and SST CV (80%) for Risso’s dolphin clicks; solar indices (100%) and SST and SST CV (60% each) for Pacific white-sided type A (PWS A) clicks; and SST CV (100%), solar indices (100%) and SST (80%) for Pacific white-sided type B (PWS B) clicks. Best predictive models for Risso’s dolphins and PWS A clicks included time-lagged variables, suggesting the importance of ecological succession between abiotic variables and dolphin occurrence, while best models of PWS B clicks were for current conditions, suggesting association with prey-aggregating features such as fronts and eddies.

KEY WORDS: Risso’s dolphin · Grampus griseus · Pacific white-sided dolphin · Lagenorhynchus obliquidens · Habitat model · Generalized additive model · Passive acoustic monitoring · Southern California Bight

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Cite this article as: Soldevilla MS, Wiggins SM, Hildebrand JA, Oleson EM, Ferguson MC (2011) Risso’s and Pacific white-sided dolphin habitat modeling from passive acoustic monitoring. Mar Ecol Prog Ser 423:247-260.

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