Professor, PhD Dag L. Aksnes
Mechanistic modeling in biological oceanography
A major endeavor in ecology is to gain understanding of how biological processes, organisms, and ecosystems respond to environmental changes. Such responses are mapped in experimental and field studies and often equations/models are fitted to the observations. The choice of equation, however, is often ad hoc and without mechanistic content. Ecosystem models rest on a number of such equations and this reduces the explanatory as well as the predictive power. Thus it is a challenge to derive formulations that are founded on hypotheses about causality. Such models need to be confronted with, rather than fitted to, observations, modified, and tested again. In such stepwise manner general explanatory models might emerge.
An example: Michaelis-Menten type saturation curves are widely used for observed phenomena such as growth rate, food intake, microbial nutrient uptake, and to predict outcome of competition. This modeling paradigm involves observed half saturation constants intrepreted as a species specific trait. This interpretation is invalid, however, as this "constant" depends on environmental characteristics as well as several flexible biological traits. A challenge is to resolve these traits and how they interact with the environmental variables
Why mechanistic modeling: Models that are derived from explicit hypotheses about causality have two main advantages over ad hoc models. First, they include interpretable parameters with physical units. Second, they provide a priori expectations that can be confronted with observations.
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