Functional richness outperforms taxonomic richness in predicting ecosystem functioning in natural phytoplankton communities

Abstract

Abstract Recent studies clearly support a positive biodiversity–ecosystem functioning (BEF) relationship in phytoplankton. As taxon richness does not quantify functional diversity, functional approaches have been developed to link community functioning to diversity. Compared to terrestrial plant communities, only a few studies have validated phytoplankton functional approaches in BEF relationships. Furthermore, the ability of functional and taxonomic richness measures in predicting ecosystem functioning of natural phytoplankton communities has not been compared yet. Here, we analysed the BEF relationship using taxonomic and functional (trait categories and response groups sensu Reynolds) approaches in a broad‐scale phytoplankton dataset from Fennoscandia. First, we analysed how taxonomic and functional compositions were related to local environmental predictors. We then compared how taxonomic and functional richness performed in predicting resource‐use efficiency (the yield in phytoplankton biomass standardised by total phosphorus) as an ecosystem functioning measure. Finally, we tested whether the relationship between ecosystem functioning and taxonomic richness is further enhanced once each of the functional richness measure is also considered. Among the approaches, phytoplankton functional trait categories as community matrix showed the best correspondence with the local environment. The richness of phytoplankton response groups predicted ecosystem functioning significantly better than the taxonomic and the functional trait category richness—both in the full dataset and in almost all Fennoscandian countries. On top of taxonomic richness, only the residual variation in response group richness predicted ecosystem functioning positively in the entire dataset and in all individual countries. Applying functional approaches, reduced complexity of data should come along with reduced ecological information. We showed, however, that both functional approaches represented some functional redundancy among taxa in a meaningful way, and enhanced our ability in predicting community composition from environmental predictors. Moreover, phytoplankton response group richness sensu Reynolds summarises information on ecosystem functioning contained in the taxonomic data in a way that represents functional diversity better than the richness of functional trait categories. Interestingly, the response group approach, which is exclusively derived from field observations rather than from quantified phytoplankton traits, outperforms taxonomic richness and trait category richness in predicting ecosystem functioning in our dataset. This may highlight that our ability to quantify phytoplankton traits is still limited. Existing phytoplankton functional approaches, however, can translate the taxonomic information into a reduced but reliable functional matrix already and predict ecosystem functioning better than taxonomic data.