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Marine Biology

, 164:73 | Cite as

Plankton supports the majority of fish biomass on temperate rocky reefs

  • Lisa Truong
  • Iain M. Suthers
  • Derrick O. Cruz
  • James A. Smith
Original paper

Abstract

Coastal temperate rocky reefs are economically valuable and highly diverse, yet the trophodynamics of these productive systems are understudied. Quantifying the trophic linkages that support fish assemblages on these reefs is valuable for understanding how these assemblages may change due to changes in benthic and pelagic primary production. The goal of this study was to quantify the relative importance of primary sources of nutrition (here, macroalgae, phytoplankton, zooplankton, and detritus) for supporting fish assemblages on shallow rocky reefs. We constructed a general food web that traces the food sources supporting fish biomass on shallow temperate reefs near Sydney, Australia, using species composition data from 17 reefs based on field sampling and the Reef Life Survey (reeflifesurvey.com), and combined this with dietary information sourced from field collections and published literature. Planktivorous fish typically dominated reef fish assemblages, comprising an average of 41% of the total fish biomass (ranging from 12 to 71% among sites). Our food web analysis showed that, on average, 56% (±5% s.e.) of the total fish biomass was ultimately supported by phytoplankton (and 53% from zooplankton), in comparison to 31% (±4%) supported by macroalgae and 12% (±2%) by detritus. This result highlights the dominance of zooplanktivorous fish on temperate reefs and also their importance as prey for piscivores. Our findings demonstrate the importance of understanding the coastal dynamics of plankton and planktivory for predicting the response of temperate reefs and their fish assemblages to a changing climate.

Keywords

Macroalgae Fish Assemblage Reef Fish Trophic Group Fish Biomass 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors recognise the data shared by the Reef Life Survey (reeflifesurvey.com). This article is Sydney Institute of Marine Science Contribution #202.

Compliance with ethical standards

Funding

This research was funded by an Australian Research Council Linkage Project (Grant Number LP120100592).

Conflict of interest

All authors declared that they have no conflict of interest.

Ethical approval

This research was done under the University of NSW Animal Care and Ethics Committee (ACEC) approval #10/15B.

Supplementary material

227_2017_3101_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (PDF 1082 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Lisa Truong
    • 1
    • 2
  • Iain M. Suthers
    • 1
    • 2
  • Derrick O. Cruz
    • 1
    • 2
  • James A. Smith
    • 1
    • 2
  1. 1.Evolution and Ecology Research Centre, and School of Biological, Earth and Environmental SciencesUniversity of New South WalesSydneyAustralia
  2. 2.Sydney Institute of Marine ScienceMosmanAustralia

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