Leaving misleading legacies behind in plankton ecosystem modelling
S. Lan Smith
Agostino Merico
Kai W. Wirtz
Markus Pahlow
Abstract
The value of mechanistic ecosystem modelling has long been appreciated, and in connection with trait-based approaches it has recently stimulated a more process-based understanding of adaptive capacities and trade-offs. Notwithstanding recent advances, even sophisticated state-of-the-art models of plankton ecosystems, some of which include hundreds of idealized species, do not accurately represent the great biodiversity of plankton, or the associated flexible adaptive response of plankton communities. We build on previous reviews to suggest that it may be necessary to discard some common assumptions and try new approaches in order to construct models that can make new and testable predictions about the “adaptive capacity” of plankton ecosystems. Major challenges remain unresolved for modelling interacting communities of producers and consumers. Rather than the common approach of mixing and matching existing model components, each laden with its own legacy assumptions, we suggest that a judicious combination of innovative, mechanistic approaches that combine traits and trade-offs will likely better address such challenges.
Smith S.L., Merico A., Wirtz K.W. and Pahlow M. (2014) Leaving misleading legacies behind in plankton ecosystem modelling. Journal of Plankton Research 36(3): 613-620. 10.1093/plankt/fbu011
@article{Smith2014,
Title = {Leaving misleading legacies behind in plankton ecosystem modelling},
Author = {Smith, S. Lan and Merico, Agostino and Wirtz, Kai W. and Pahlow, Markus},
Editor = {},
Journal = {Journal of Plankton Research},
Year = {2014},
Pages = {613-620},
Volume = {36},
Doi = {10.1093/plankt/fbu011},
Abstract = {The value of mechanistic ecosystem modelling has long been appreciated, and in connection with trait-based approaches it has recently stimulated a more process-based understanding of adaptive capacities and trade-offs. Notwithstanding recent advances, even sophisticated state-of-the-art models of plankton ecosystems, some of which include hundreds of idealized species, do not accurately represent the great biodiversity of plankton, or the associated flexible adaptive response of plankton communities. We build on previous reviews to suggest that it may be necessary to discard some common assumptions and try new approaches in order to construct models that can make new and testable predictions about the “adaptive capacity” of plankton ecosystems. Major challenges remain unresolved for modelling interacting communities of producers and consumers. Rather than the common approach of mixing and matching existing model components, each laden with its own legacy assumptions, we suggest that a judicious combination of innovative, mechanistic approaches that combine traits and trade-offs will likely better address such challenges.},
}
TY - JOUR
AU - Smith, S. Lan
AU - Merico, Agostino
AU - Wirtz, Kai W.
AU - Pahlow, Markus
TI - Leaving misleading legacies behind in plankton ecosystem modelling
T2 - Journal of Plankton Research
PY - 2014
SP - 613-620
VL - 36
DO - 10.1093/plankt/fbu011
AB - The value of mechanistic ecosystem modelling has long been appreciated, and in connection with trait-based approaches it has recently stimulated a more process-based understanding of adaptive capacities and trade-offs. Notwithstanding recent advances, even sophisticated state-of-the-art models of plankton ecosystems, some of which include hundreds of idealized species, do not accurately represent the great biodiversity of plankton, or the associated flexible adaptive response of plankton communities. We build on previous reviews to suggest that it may be necessary to discard some common assumptions and try new approaches in order to construct models that can make new and testable predictions about the “adaptive capacity” of plankton ecosystems. Major challenges remain unresolved for modelling interacting communities of producers and consumers. Rather than the common approach of mixing and matching existing model components, each laden with its own legacy assumptions, we suggest that a judicious combination of innovative, mechanistic approaches that combine traits and trade-offs will likely better address such challenges.
ER -
