Editorial: Modeling the Plankton–Enhancing the Integration of Biological Knowledge and Mechanistic Understanding
Editorial on the Research Topic Modeling the Plankton–Enhancing the Integration of Biological Knowledge and Mechanistic Understanding In marine science numerical models, and especially ecosystem models, have developed into an important tool for policy advice and environmental management applications...
Gespeichert in:
Veröffentlicht in: | Frontiers in Marine Science 2017-11, Vol.4 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Editorial on the Research Topic Modeling the Plankton–Enhancing the Integration of Biological Knowledge and Mechanistic Understanding In marine science numerical models, and especially ecosystem models, have developed into an important tool for policy advice and environmental management applications (Rose et al., 2010; Holt et al., 2014; Robson, 2014; Lynam et al., 2016). In recent years, new knowledge generated regarding organism physiology; ecosystem functioning; new data types and increased resolution of data acquisition, particularly those collected by satellites, autonomous platforms and through genetic analyses; as well as new approaches to model marine systems have emerged, altering the way we think about modeling the plankton. Using bulk nutrient uptake observations in combination with allometric scaling predictions, Atkins et al. suggest that net nitrogen dynamics can be quantified at an assemblage scale using size dependencies of Michaelis-Menten uptake parameters and that their method can be applied to particle size distributions that have been routinely measured in eutrophic systems. The contributions compiled here take important steps forward in demonstrating how modeling plankton yields important insights. [...]this compilation hopefully inspires others to integrate their empirical and analytical approaches with modeling, for equally fruitful outcomes. |
---|---|
ISSN: | 2296-7745 2296-7745 |
DOI: | 10.3389/fmars.2017.00358 |