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A biophysical representation of seagrass growth for application in a complex shallow-water biogeochemical model

Author

Listed:
  • Baird, Mark E.
  • Adams, Matthew P.
  • Babcock, Russell C.
  • Oubelkheir, Kadija
  • Mongin, Mathieu
  • Wild-Allen, Karen A.
  • Skerratt, Jennifer
  • Robson, Barbara J.
  • Petrou, Katherina
  • Ralph, Peter J.
  • O’Brien, Katherine R.
  • Carter, Alex B.
  • Jarvis, Jessie C.
  • Rasheed, Michael A.

Abstract

Seagrasses are a critical component of the healthy functioning of many coastal marine ecosystems. Capturing the dynamics of seagrass communities requires both a detailed representation of processes such as seagrass nutrient uptake and photosynthesis, as well as models of light penetration, water column and sediment biogeochemical processes and other ecosystem characteristics that determine the environmental state. Here we develop a new two-state, 13-parameter seagrass model with the aim of providing sufficient detail to represent light and nutrient limitation, but simple enough to be coupled into a 60 state variable biogeochemical model. The novel formulation is built around a nitrogen-specific leaf area parameter, Ω, that is well-constrained and is used in calculating both the rate of photosynthesis and the fraction of the seafloor covered by seagrass, Aeff, where Aeff=1−exp(−ΩSGA) and SGA is the aboveground areal seagrass biomass. The model also contains terms for the uptake of nutrients from multiple layers of varying-porosity sediments, translocation of organic matter between leaves and roots, respiration and simple mortality terms. The model is applied to Gladstone Harbour, a macro-tidal sub-tropical estuary in northeast Australia, and is able to simulate realistic spatial seagrass distributions. A simplified form of the model is derived, which can be used to predict seagrass light-limited growth based on five measurable species-specific parameters (maximum growth rate, mortality rate, compensation irradiance, leaf blade angle and nitrogen-specific leaf area). The steady-state percent coverage of seagrass achieved at varying light levels and mortality intensity is calculated as a means of understanding the dynamics of the new seagrass model.

Suggested Citation

  • Baird, Mark E. & Adams, Matthew P. & Babcock, Russell C. & Oubelkheir, Kadija & Mongin, Mathieu & Wild-Allen, Karen A. & Skerratt, Jennifer & Robson, Barbara J. & Petrou, Katherina & Ralph, Peter J. &, 2016. "A biophysical representation of seagrass growth for application in a complex shallow-water biogeochemical model," Ecological Modelling, Elsevier, vol. 325(C), pages 13-27.
  • Handle: RePEc:eee:ecomod:v:325:y:2016:i:c:p:13-27
    DOI: 10.1016/j.ecolmodel.2015.12.011
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    References listed on IDEAS

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    1. Gustafsson, Malin S.M. & Baird, Mark E. & Ralph, Peter J., 2013. "The interchangeability of autotrophic and heterotrophic nitrogen sources in Scleractinian coral symbiotic relationships: A numerical study," Ecological Modelling, Elsevier, vol. 250(C), pages 183-194.
    2. Mongin, Mathieu & Baird, Mark, 2014. "The interacting effects of photosynthesis, calcification and water circulation on carbon chemistry variability on a coral reef flat: A modelling study," Ecological Modelling, Elsevier, vol. 284(C), pages 19-34.
    3. Baird, Mark E. & Suthers, Iain M., 2007. "A size-resolved pelagic ecosystem model," Ecological Modelling, Elsevier, vol. 203(3), pages 185-203.
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    1. Baird, Mark E. & Mongin, Mathieu & Rizwi, Farhan & Bay, Line K. & Cantin, Neal E. & Soja-Woźniak, Monika & Skerratt, Jennifer, 2018. "A mechanistic model of coral bleaching due to temperature-mediated light-driven reactive oxygen build-up in zooxanthellae," Ecological Modelling, Elsevier, vol. 386(C), pages 20-37.
    2. Khangaonkar, Tarang & Nugraha, Adi & Premathilake, Lakshitha & Keister, Julie & Borde, Amy, 2021. "Projections of algae, eelgrass, and zooplankton ecological interactions in the inner Salish Sea – for future climate, and altered oceanic states," Ecological Modelling, Elsevier, vol. 441(C).

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