Please use this identifier to cite or link to this item: https://doi.org/10.1038/srep39930
Title: Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species
Authors: Adams, M.P
Collier, C.J
Uthicke, S
Ow, Y.X 
Langlois, L
O'Brien, K.R
Keywords: model
nonhuman
photosynthetic rate
seagrass
species
Alismatales
biological model
photosynthesis
physiology
reproducibility
statistical analysis
temperature
tropic climate
Alismatales
Data Interpretation, Statistical
Models, Biological
Photosynthesis
Reproducibility of Results
Temperature
Tropical Climate
Issue Date: 2017
Citation: Adams, M.P, Collier, C.J, Uthicke, S, Ow, Y.X, Langlois, L, O'Brien, K.R (2017). Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species. Scientific Reports 7 : 39930. ScholarBank@NUS Repository. https://doi.org/10.1038/srep39930
Rights: Attribution 4.0 International
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 (T opt) for maximum photosynthetic rate (P max). 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. © The Author(s) 2017.
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/178714
ISSN: 20452322
DOI: 10.1038/srep39930
Rights: Attribution 4.0 International
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