Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 7510
Title: Use of mixing models for Humboldt squid diet analysis: reply to Field et al. (2014)
Author: Todd W. Miller, K. L. Bosley, Junya Shibata, Richard D. Brodeur, Koji Omori, Robert L. Emmett
Publication Year: 2014
Journal: Marine Ecology Progress Series
Volume: 500
Pages: 287-290
Keywords: Dosidicus gigas,stable isotopes,Trophic level,Source production,

Field et al. (2014; Mar Ecol Prog Ser 500:281-285) comment on our application of a Bayesian isotope-mixing model (SIAR) to examine the relative contribution of prey from different regions to Dosidicus gigas diet, and point out that our model violated assumptions of D. gigas feeding. We agree in part with their position that the use of SIAR for assessing contributions of sources from different regions for an omnivorous species may be unreliable. However, the results from our study and from the prevailing literature and data indicate that the D. gigas we collected in the Northern California Current (NCC) isotopically matched the NCC baseline and were isotopically distinct from prey resources in the Southern California Current. Field et al.’s (2014) comments on the distribution and abundance of D. gigas in the NCC missed results from the primary literature which show that D. gigas and their purported prey are predominantly distributed along the shelf-slope waters of the NCC, well within the offshore extent of our study. The discrepancy of not finding myctophids as significant sources to D. gigas diet in our study may lie in the fact that our isotope values of myctophids came from adults only, and that smaller conspecifics with lower relative δ15N values would have shown a greater contribution from this trophic group. The conclusion we reached of lower trophic level feeding by D. gigas in our study relative to previous diet studies remains valid.


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Theme: Recovery and rebuilding of marine and coastal species
Foci: Develop methods to use physiological, biological and behavioral information to predict population-level processes.