Towards evidence-based parameter values and priors for aquatic ecosystem modelling

Robson, Barbara J., Arhonditsis, George B., Baird, Mark E., Brebion, Jerome, Edwards, Kyle F., Geoffroy, Leonie, Hebert, Marie-Pier, van Dongen-Vogels, Virginie, Jones, Emlyn M., Kruk, Carla, Mongin, Mathieu, Shimoda, Yuko, Skerratt, Jennifer H., Trevathan-Tackett, Stacey M., Wild-Allen, Karen, Kong, Xiangzhen, and Steven, Andy (2018) Towards evidence-based parameter values and priors for aquatic ecosystem modelling. Environmental Modelling & Software, 100. pp. 74-81.

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Abstract

Mechanistic models rely on specification of parameters representing biophysical traits and process rates such as phytoplankton, zooplankton and seagrass growth and respiration rates, organism sizes, stoichiometry, light, temperature and nutrient responses, nutrient-specific excretion rates and detrital stoichiometry and decay rates. Choosing suitable values for these parameters is difficult. Current practise is problematic. This paper presents a resource designed to facilitate an evidence-based approach to parameterisation of aquatic ecosystem models. An online tool is provided which collates relevant, published biological trait and biogeochemical rate observations from many sources and allows users to explore, filter and convert these data in a consistent, reproducible way, to find parameter values and calculate probability distributions. Using this information within a traditional or Bayesian paradigm should provide improved understanding of the uncertainty and predictive capacity of aquatic ecosystem models and provide insight into current sources of structural error in models.

Item ID: 58061
Item Type: Article (Research - C1)
ISSN: 1873-6726
Keywords: Parameterisation, Biogeochemical rates, Biological traits, Aquatic ecosystems, Parameter priors, Seagrass, Phytoplankton, Zooplankton, Remineralisation
Funders: eReefs
Date Deposited: 17 Apr 2019 09:22
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050206 Environmental Monitoring @ 100%
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