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Article

A Trait-Based Clustering for Phytoplankton Biomass Modeling and Prediction

1
Department of Mathematics and Statistics, Dalhousie University, Halifax, NS B3H 4R2, Canada
2
Department of Oceanography, Dalhousie University, Halifax, NS B3H 4R2, Canada
3
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
*
Author to whom correspondence should be addressed.
Diversity 2020, 12(8), 295; https://doi.org/10.3390/d12080295
Received: 8 June 2020 / Revised: 20 July 2020 / Accepted: 21 July 2020 / Published: 28 July 2020
When designing models for predicting phytoplankton biomass or characterizing traits, it is useful to aggregate the myriad of species into a few biologically meaningful groups and focus on group-level attributes, the common practice being to combine phytoplankton species by functional types. However, biogeochemists and plankton ecologists debate the most applicable grouping for describing phytoplankton biomass patterns and predicting future community structure. Although trait-based approaches are increasingly being advocated, methods are missing for the generation of trait-based taxa as alternatives to functional types. Here we introduce such a method and demonstrate the usefulness of the resulting clustering with field data. We parameterize a Bayesian model of biomass dynamics and analyze long-term phytoplankton data collected at Station L4 in the Western English Channel between April 2003 and December 2009. We examine the tradeoffs encountered regarding trait characterization and biomass prediction when aggregating biomass by (1) functional types, (2) the trait-based clusters generated by our method, and (3) total biomass. The model conveniently extracted trait values under the trait-based clustering, but required well-constrained priors under the functional type categorization. It also more accurately predicted total biomass under the trait-based clustering and the total biomass aggregation with comparable root mean squared prediction errors, which were roughly five-fold lower than under the functional type grouping. Although the total biomass grouping ignores taxonomic differences in phytoplankton traits, it predicts total biomass change as well as the trait-based clustering. Our results corroborate the value of trait-based approaches in investigating the mechanisms underlying phytoplankton biomass dynamics and predicting the community response to environmental changes. View Full-Text
Keywords: Bayesian inference; phytoplankton functional types; Gaussian mixture model; diatoms; dinoflagellates; root mean squared prediction error; soft clustering Bayesian inference; phytoplankton functional types; Gaussian mixture model; diatoms; dinoflagellates; root mean squared prediction error; soft clustering
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MDPI and ACS Style

Mutshinda, C.M.; Finkel, Z.V.; Widdicombe, C.E.; Irwin, A.J. A Trait-Based Clustering for Phytoplankton Biomass Modeling and Prediction. Diversity 2020, 12, 295. https://doi.org/10.3390/d12080295

AMA Style

Mutshinda CM, Finkel ZV, Widdicombe CE, Irwin AJ. A Trait-Based Clustering for Phytoplankton Biomass Modeling and Prediction. Diversity. 2020; 12(8):295. https://doi.org/10.3390/d12080295

Chicago/Turabian Style

Mutshinda, Crispin M., Zoe V. Finkel, Claire E. Widdicombe, and Andrew J. Irwin 2020. "A Trait-Based Clustering for Phytoplankton Biomass Modeling and Prediction" Diversity 12, no. 8: 295. https://doi.org/10.3390/d12080295

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