ESR 16:97-112 (2012)  -  DOI: https://doi.org/10.3354/esr00390

Forecasting cetacean abundance patterns to enhance management decisions

E. A. Becker1,*, D. G. Foley2, 3, K. A. Forney1, J. Barlow4, J. V. Redfern4, C. L. Gentemann5

1NOAA National Marine Fisheries Service, Southwest Fisheries Science Center, 110 Shaffer Road, Santa Cruz, California 95060, USA
2Joint Institute for Marine and Atmospheric Research, University of Hawaii, 1000 Pope Road, Honolulu, Hawaii 96822, USA
3NOAA National Marine Fisheries Service, Southwest Fisheries Science Center, 1352 Lighthouse Avenue, Pacific Grove, California, 93950, USA
4NOAA National Marine Fisheries Service, Southwest Fisheries Science Center, 3333 N. Torrey Pines Court, La Jolla, California 92037, USA
5Remote Sensing Systems Inc., 444 Tenth Street, Suite 200, Santa Rosa, California 95401, USA

ABSTRACT: Species−environment models are increasingly recognized as valuable tools for assessing protected species distributions and developing measures to reduce or avoid adverse impacts. Cetacean−habitat models can provide a finer spatial resolution than traditional abundance estimates, but model predictions are generally based on past observations rather than current or projected ocean conditions. We present and evaluate methods for near real-time and forecast models of cetacean distribution based on remotely sensed and modeled oceanographic data. Recent advancements in processing satellite-derived data (e.g. microwave/infrared blended sea surface temperature [SST] products) have virtually eliminated data gaps due to cloud cover, allowing short-term forecasts based on single-day snapshots of oceanic conditions. Ocean circulation models (e.g. the Regional Ocean Modeling System [ROMS]) allow medium-range forecast predictions of oceanic variables, including SST, chlorophyll and salinity. We developed habitat models for striped dolphin, fin whale and Dall’s porpoise using line-transect data collected from July to November 1991−2005 in the California Current Ecosystem. We incorporated daily blended SST data and monthly ROMS SST forecasts as input variables to predict relative species density in 2008. Forecast ability was assessed by the models’ ranked predictions across 8 geographic strata, and by visual inspection of predicted and observed distributions. For all 3 species, there was a significant correlation between model predictions using daily blended SSTs and actual survey observations (p < 0.05). Longer-term (3−4 mo) predictions also showed good concordance with observed sighting locations. Cetacean−habitat models that allow weekly to monthly forecasting of cetacean abundance can greatly enhance short-term decision-making and advanced mitigation planning.


KEY WORDS: Cetacean abundance · Habitat-based density model · Generalized additive model · GAM · California Current · Remote sensing · Fin whale · Striped dolphin · Dall’s porpoise


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Cite this article as: Becker EA, Foley DG, Forney KA, Barlow J, Redfern JV, Gentemann CL (2012) Forecasting cetacean abundance patterns to enhance management decisions. Endang Species Res 16:97-112. https://doi.org/10.3354/esr00390

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