Useless Arithmetic? Lessons Learned From Aquatic Biogeochemical Modelling

Session: 31. - Evaluation of the Current State of Ecological Modeling and Future Perspectives

George Arhonditsis, University of Toronto Scarborough, georgea@utsc.utoronto.ca
Michael Brett, University of Washington, mtbrett@u.washington.edu
Kenneth Reckhow, Duke University, reckhow@duke.edu
Duncan Boyd, Ontario Ministry of the Environment, kilbride.boyd@gmail.com
Gurbir Perhar, University of Toronto Scarborough, g.perhar@gmail.com
Christopher Wellen, Ryerson University, christopher.wellen@ryerson.ca
Weitao Zhang, University of Toronto Scarborough, tonyzhang_ca@yahoo.com
Dong-Kyun Kim, University of Toronto Scarborough, dkkim1004@gmail.com
Alex Neumann, University of Toronto Scarborough, alex.gudimov@mail.utoronto.ca
Vincent Cheng, University of Toronto Scarborough, vi.cheng@utoronto.ca
Yuko Shimoda, University of Toronto Scarborough, yshimoda@utsc.utoronto.ca
Noreen Kelly, University of Toronto Scarborough, noreen.kelly@utoronto.ca
Tianna Peller, McGill University, tiannapeller@gmail.com

Abstract

The credibility of the scientific methodology of numerical models and their adequacy to form the basis of public policy decisions have been frequently challenged. The first part of my talk provides evidence that there is still considerable controversy among modelers and the resource managers about how to develop, evaluate, and interpret mathematical models. Our arguments are that models are not always developed in a consistent manner, clearly stated purpose, and predetermined acceptable model performance level, and the potential users select models without properly assessing their technical value. The second part of this presentation argues that the development of novel methods for rigorously assessing the uncertainty underlying model predictions should be a top priority of the modeling community. We also critically evaluate the mathematical representations of key physiological processes (e.g., growth strategies, nutrient kinetics, settling velocities) as well as biotic group-specific characterizations typically considered in the literature. We argue that the most prudent strategies are the gradual incorporation of complexity, where possible and relevant, along with an open dialogue on how we can mathematically depict the interconnections among different biotic subunits or even how we can frame the suitable data collection efforts.

1. Keyword
mathematical models

2. Keyword
risk assessment

3. Keyword
eutrophication