A Trait-Based Clustering for Phytoplankton Biomass Modeling and Prediction

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

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Official URL: http://dx.doi.org/10.3390/d12080295

Abstract/Summary

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-basedtaxaasalternativestofunctionaltypes. Hereweintroducesuchamethodanddemonstrate 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 under lying phytoplankton biomass dynamics and predicting the community response to environmental changes.

Item Type: Publication - Article
Additional Keywords: Bayesian inference; phytoplankton functional types; Gaussian mixture model; diatoms; dinoflagellates; root mean squared prediction error; soft clustering
Divisions: Plymouth Marine Laboratory > National Capability categories > Single Centre NC - CLASS
Plymouth Marine Laboratory > National Capability categories > Western Channel Observatory
Plymouth Marine Laboratory > Science Areas > Marine Ecology and Biodiversity
Depositing User: S Hawkins
Date made live: 03 Sep 2020 09:56
Last Modified: 03 Sep 2020 09:56
URI: http://plymsea.ac.uk/id/eprint/9031

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