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Predicting fish diet composition using a bagged classification tree approach: a case study using yellowfin tuna (Thunnus albacares)

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Abstract

We provided a classification tree modeling framework for investigating complex feeding relationships and illustrated the method using stomach contents data for yellowfin tuna (Thunnus albacares) collected by longline fishing gear deployed off eastern Australia between 1992 and 2006. The non-parametric method is both exploratory and predictive, can be applied to varying size datasets and therefore is not restricted to a minimum sample size. The method uses a bootstrap approach to provide standard errors of predicted prey proportions, variable importance measures to highlight important variables and partial dependence plots to explore the relationships between explanatory variables and predicted prey composition. Our results supported previous studies of yellowfin tuna feeding ecology in the region. However, the method provided a number of novel insights. For example, significant differences were noted in the prey of yellowfin tuna sampled north of 20°S in summer where oligotrophic waters dominate. The analysis also identified that sea-surface temperature, latitude and yellowfin size were the most important variables associated with dietary differences. The methodology is appropriate for delineating ecosystem-level trophic dynamics, as it can easily incorporate large datasets comprising multiple predators to explore trophic interactions among members of a community. Broad-scale relationships among explanatory variables (environmental, biological, temporal and spatial) and prey composition elucidated by this method then serve to focus and lend validity to subsequent fine-scale analyses of important parameters using standard diet methods and chemical tracers such as stable isotopes.

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Acknowledgments

The authors wish to acknowledge Ross Sparks and the three anonymous reviewers who kindly reviewed this manuscript. We acknowledge Alexandre Aires-da-Silva for making the R code and resources available for producing the maps in this paper. We thank Christine Patnode for assistance with the graphics. We thank skippers from the Eastern Tuna and Billfish fishery for help in collecting the yellowfin samples, Matt Lansdell and S. Ridoch (CSIRO) for taxonomic support and Scott Cooper (CSIRO) for structuring the database that housed this information. We thank Alistair Hobday, Klaus Hartman, Jason Hartog and Sophie Bestley for developing and making available the SDODE interface for exploring global datasets. Finally, we acknowledge the funding support from CSIRO through the Julius Award that was granted to the first author.

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Authors and Affiliations

  1. CSIRO Mathematics, Informatics and Statistics, Private Bag 2, Glen Osmond, SA, 5064, Australia

    Petra M. Kuhnert

  2. Inter-American Tropical Tuna Commission, 8604 La Jolla Shores Drive, La Jolla, CA, 92037-1508, USA

    Leanne M. Duffy & Robert J. Olson

  3. CSIRO Marine and Atmospheric Research and Wealth from Oceans Flagship, GPO Box 1538, Hobart, TAS, 7001, Australia

    Jock W. Young

Authors
  1. Petra M. Kuhnert
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  2. Leanne M. Duffy
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  3. Jock W. Young
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  4. Robert J. Olson
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Correspondence to Petra M. Kuhnert.

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Communicated by M. A. Peck.

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Kuhnert, P.M., Duffy, L.M., Young, J.W. et al. Predicting fish diet composition using a bagged classification tree approach: a case study using yellowfin tuna (Thunnus albacares). Mar Biol 159, 87–100 (2012). https://doi.org/10.1007/s00227-011-1792-6

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