Details

Autor(en) / Beteiligte

• Robson, Barbara J.
• Arhonditsis, George B.
• Baird, Mark E.
• Brebion, Jerome
• Edwards, Kyle F.
• Geoffroy, Leonie
• Hébert, 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
• Steven, Andy

Titel

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

Ist Teil von

• Environmental modelling & software : with environment data news, 2018-02, Vol.100, p.74-81

Ort / Verlag

Oxford: Elsevier Ltd

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. •Current practices in parameterising aquatic ecosystem models can be improved.•We present a new resource to inform specification of parameter values.•An online tool helps modellers find, filter & transform process rate and trait data.•Parameter distributions are presented in a probabilistic framework.•This will improve connection between observational science and modelling.