Smith, S. Lan, Pahlow, Markus, Merico, Agostino ORCID: https://orcid.org/0000-0001-8095-8056 and Wirtz, Kai W. (2011) Optimality-based modeling of planktonic organisms. Limnology and Oceanography, 56 (6). pp. 2080-2094. DOI https://doi.org/10.4319/lo.2011.56.6.2080.

Full text not available from this repository.

Abstract

On the basis of the assumption that natural selection should tend to produce organisms optimally adapted to their environments, we consider optimality as a guiding concept for abstracting the behavior of aquatic micro‐organisms (plankton) to develop models to study and predict the behavior of planktonic organisms and communities. This is closely related to trait‐based ecology, which considers that traits and functionality can be understood as the result of the optimization inherent in natural selection, subject to constraints imposed by fundamental processes necessary for life. This approach is particularly well suited to plankton because of their long evolutionary history and the ease with which they can be manipulated in experiments. We review recent quantitative modeling studies of planktonic organisms that have been based on the assumption that adaptation of species and acclimation of organisms maximize growth rate. Compared with mechanistic models not formulated in terms of optimality, this approach has in some cases yielded simpler models, and in others models of greater generality. The evolutionary success of any given species must depend on its interactions with both the physical environment and other organisms, which depend on the evolving traits of all organisms concerned. The concept of an evolutionarily stable strategy (ESS) can, at least in principle, constrain the choice of goal functions to be optimized in models. However, the major challenge remains of how to construct models at the level of organisms that can resolve short‐term dynamics, e.g., of phytoplankton blooms, in a way consistent with ESS theory, which is formulated in terms of a steady state.

Document Type:	Article
Programme Area:	UNSPECIFIED
Research affiliation:	Integrated Modelling > Systems Ecology
Refereed:	Yes
Open Access Journal?:	No
DOI:	https://doi.org/10.4319/lo.2011.56.6.2080
ISSN:	00243590
Date Deposited:	27 Sep 2019 18:08
Last Modified:	26 Mar 2024 13:29
URI:	http://cris.leibniz-zmt.de/id/eprint/2922

Actions (login required)

View Item