loading page

Recommendations for the Formulation of Grazing in Marine Biogeochemical and Ecosystem Models
  • +1
  • Tyler Rohr,
  • Anthony Richardson,
  • Andrew Lenton,
  • Elizabeth Shadwick
Tyler Rohr
Australian Antarctic Partnership Program, Hobart, TAS, Australia, Australian Antarctic Partnership Program, Hobart, TAS, Australia, Australian Antarctic Partnership Program, Hobart, TAS, Australia

Corresponding Author:[email protected]

Author Profile
Anthony Richardson
Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, BioSciences Precinct (QBP), St Lucia, Queensland, Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, BioSciences Precinct (QBP), St Lucia, Queensland, Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, BioSciences Precinct (QBP), St Lucia, Queensland, Australia
Author Profile
Andrew Lenton
Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, Hobart, TAS, Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, Hobart, TAS, Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, Hobart, TAS, Australia
Author Profile
Elizabeth Shadwick
Australian Antarctic Partnership Program, Hobart, TAS, Australia, Australian Antarctic Partnership Program, Hobart, TAS, Australia, Australian Antarctic Partnership Program, Hobart, TAS, Australia
Author Profile

Abstract

For nearly a century, the functional response curves, which describe how predation rates vary with prey density, have been a mainstay of ecological modelling. While originally derived to mechanistically describe specific, terrestrial interactions on a two dimensional plane, they have more recently been adopted to characterize the mean state of three dimensional aquatic systems in marine biogeochemical, size-spectrum, and population models. This translation, however, has further abstracted the functional response from first principles and led to a large divergence in its formulation across models. Marine ecological modellers disagree over the qualitative shape of the curve (e.g. Type II vs. III), whether its parameters should be mechanistically or empirically defined (e.g. disk vs. Michaelis-Menten scheme), and the most representative value of those parameters. This leaves modellers with little sense of best practice and models liable to bias. As a case study, we focus on marine biogeochemical models, providing a comprehensive theoretical, empirical, and numerical road-map for interpreting, formulating, and parameterizing the functional response when used to prescribe zooplankton specific grazing rates on phytoplankton. After providing a detailed derivation of each of the canonical functional response types explicitly for aquatic systems, we review the literature describing their parameterization. We find that empirical values and those used in models vary hugely, ranging over three to four orders of magnitude. Next, we conduct a suite of 0-D NPZ simulations to isolate the sensitivity of phytoplankton population size and stability to the grazing formulation. We find that the disk parameterizations scheme is much less sensitive to it parameterization than the Michaelis-Menten scheme, and confirm that the Type II response is susceptible to instabilities and extinction events. Finally, after considering the numerical sensitivity of the functional response in the context of ecological reality, we recommend using a type III rather than the type II response, employing a Michaelis-Menten rather than disk parameter scheme, and testing a large range of values, particularly low ones, to parameterize the half saturation concentration in optimization search routines. While we focus specifically on the grazing formulation in marine biogeochemical models, we believe these recommendations are robust across a much broader range of ecosystem models when seeking to represent the mean state of a complex trophic system constrained by limited observations.