Revealing coccolithophore ecology via trait-based statistics.

Student thesis: Master's ThesisMaster of Science by Research (MScR)

Abstract

Coccolithophore’s significant contribution to marine primary production alongside their considerable diversity has made this phytoplankton group an important focus of ocean research. Globally,
marine science has made great strides regarding coccolithophore morphology and, more recently,
the role they play in both the organic and inorganic carbon pumps. Due to the complex nature of
coccolithophore diversity, we have limited understanding of coccolithophore functional groups.
Despite the fact that the key traits such as cell size, morphology, life cycle stage and PIC:POC
have been collated for the most abundant species. Moreover, the intersection of statistical methods
and trait-based ecology is a recent development in the field of oceanography. The applications
of such methods to coccolithophores is hence a novel subdivision of ocean science. I present the
application of well-established clustering algorithms to determine coccolithophore functional
groups. This method relies on compiling coccolithophore traits for each species, the subsequent
creation of a Gower distance matrix and clustering to produce statistically grouped species with
similar traits. From these newly established coccolithophore functional groups, I determine the
role of trait variability in coccolithophore ecology and make inferences about their contribution to
ocean ecosystem services such as blue carbon.
Date of Award5 Dec 2023
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorFanny M Monteiro (Supervisor) & Levi J Wolf (Supervisor)

Cite this

'