ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 23:1-22 (2014) - DOI: https://doi.org/10.3354/esr00548 Theme Section: Geospatial approaches to support pelagic conservation planning and adaptive management Predicting seasonal density patterns of California cetaceans based on habitat models Elizabeth A. Becker1,2,*, Karin A. Forney1, David G. Foley3,4, Raymond C. Smith5, Thomas J. Moore6, Jay Barlow6 1Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 110 Shaffer Rd, Santa Cruz, California 95060, USA 2Ocean Associates Inc., 4007 N. Abingdon Street, Arlington, Virginia 22207, USA 3Institute of Marine Sciences, University of California at Santa Cruz, 1000 Shaffer Rd, Santa Cruz, California 95060, USA 4Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 1352 Lighthouse Ave, Pacific Grove, California 93950, USA 5Environmental Research Institute, UCSB, Santa Barbara, California 93106, USA 6Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Dr., La Jolla, California 92037, USA *Corresponding author: ebecker77@cox.net ABSTRACT: Temporal variability in species distribution remains a major source of uncertainty in managing protected marine species, particularly in ecosystems with significant seasonal or interannual variation, such as the California Current Ecosystem (CCE). Spatially explicit species–habitat models have become valuable tools for decision makers assisting in the development and implementation of measures to reduce adverse impacts (e.g. from fishery bycatch, ship strikes, anthropogenic sound), but such models are often not available for all seasons of interest. Broad-scale migratory patterns of many of the large whale species are well known, while seasonal distribution shifts of small cetaceans are typically less well understood. Within the CCE, species–habitat models have been developed based on 6 summer-fall surveys conducted during 1991 to 2008. We evaluated whether the between-year oceanographic variability can inform species predictions during winter-spring periods. Generalized additive models were developed to predict abundance of 4 cetacean species/genera known to have year-round occurrence in the CCE: common dolphins Delphinus spp., Pacific white-sided dolphin Lagenorhynchus obliquidens, northern right whale dolphin Lissodelphis borealis, and Dall’s porpoise Phocoenoides dalli. Predictor variables included a combination of temporally dynamic, remotely sensed environmental variables and geographically fixed variables. Across-season predictive ability was evaluated relative to aerial surveys conducted in winter-spring 1991 to 1992, using observed:predicted density ratios, nonparametric Spearman rank correlation tests, and visual inspection of predicted and observed distributions by species. Seasonal geographic patterns of species density were captured effectively for most species, although some model limitations were evident, particularly when the original summer-fall data did not adequately capture winter-spring habitat conditions. KEY WORDS: Cetacean abundance · Habitat-based density model · California Current · Remote sensing · Common dolphin · Pacific white-sided dolphin · Northern right whale dolphin · Dall’s porpoise Full text in pdf format NextCite this article as: Becker EA, Forney KA, Foley DG, Smith RC, Moore TJ, Barlow J (2014) Predicting seasonal density patterns of California cetaceans based on habitat models. Endang Species Res 23:1-22. https://doi.org/10.3354/esr00548 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 23, No. 1. Online publication date: January 28, 2014 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2014 Inter-Research.