Model fit versus biological relevance: evaluating photosynthesis-temperature models for three tropical seagrass species

Adams, Matthew P., Collier, Catherine J., Uthicke, Sven, Ow, Yan X., Langlois, Lucas, and O'brien, Katherine R. (2017) Model fit versus biological relevance: evaluating photosynthesis-temperature models for three tropical seagrass species. Scientific Reports, 7. pp. 1-12.

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Abstract

When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

Item ID: 47004
Item Type: Article (Refereed Research - C1)
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This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

ISSN: 2045-2322
Funders: Great Barrier Reef Foundation (GBRF), University of Queensland (UQ), National Environmental Science Programme (NESP)
Projects and Grants: GBRF Project name: Seagrass growth and diversity: attributes of a resilient GBR, UQ Engineering, Architecture and Information Technology strategic fellowship
Date Deposited: 03 Feb 2017 03:52
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0501 Ecological Applications > 050101 Ecological Impacts of Climate Change @ 30%
06 BIOLOGICAL SCIENCES > 0607 Plant Biology > 060701 Phycology (incl Marine Grasses) @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080202 Applied Discrete Mathematics @ 20%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960507 Ecosystem Assessment and Management of Marine Environments @ 30%
96 ENVIRONMENT > 9608 Flora, Fauna and Biodiversity > 960808 Marine Flora, Fauna and Biodiversity @ 40%
96 ENVIRONMENT > 9603 Climate and Climate Change > 960305 Ecosystem Adaptation to Climate Change @ 30%
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