Thesis
The role of phytoplankton diversity in driving productivity in light of environmental control
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2025
- Thesis identifier
- T17327
- Person Identifier (Local)
- 202177175
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Accounting for about half the global photosynthetic activity and at least half of the oxygen production, phytoplankton are an incredibly diverse group of unicellular organisms with an important role in aquatic environments maintaining food webs and mediating global biogeochemical cycles. Fast-paced loss of biodiversity poses a threat to ecosystem functioning and its ability to provide services. Understanding the mechanisms driving biodiversity and ecosystem functioning (BEF) relationships is crucial for predicting ecosystem responses to environmental change. BEF theory predicts a positive linear relationship between diversity and productivity, with increasing diversity leading to higher ecosystem function via two main mechanisms: complementarity and selection effects. The extent to which these mechanisms drive phytoplankton productivity in natural ecosystems, however, is still under-explored. Using a combination of mechanistic and statistical modelling approaches, this thesis investigates the role of phytoplankton diversity, particularly taxonomic and size structured diversity, in shaping ecosystem productivity. Going beyond species richness, we investigate the effects of phytoplankton diversity, as well as, which mechanisms are responsible for driving the relationship between biodiversity and ecosystem function. We used a long-term dataset from the San Francisco Bay, to evaluate how phytoplankton diversity, size structure, and environmental control influence several productivity proxies (e.g. biomass accumulation, resource use efficiency, Chl a). These findings challenge the widely accepted positive effect of richness on ecosystem function. In the San Francisco Bay system, diversity and productivity often exhibited a negative relationship, with species richness having a weak effect on ecosystem function. Instead, trait diversity, particularly related to size, emerged as a strong driver of productivity. Environmental control acted on productivity by modulating community composition and size structure. To further investigate the underlying mechanisms, nutrient–phytoplankton (NP) and a nutrient-phytoplankton-zooplankton (NPZ) model including multiple size classes were developed to separate the diversity effect from nutrient enrichment on productivity. Results from these models suggest that nutrient input was consistently the main driver of productivity, particularly at low diversity levels. Species richness plays a secondary role through its interaction with environmental conditions, i.e. nutrient levels. Notably, trophic interactions shift dominant biodiversity effects, which emphasizes the role of predator–prey dynamics in shaping productivity patterns. Finally, we developed a structural equation model (SEM) using both in situ and simulation results to quantify causal direct and indirect effects of the environment and diversity on productivity. Unlike traditional bivariate statistical approaches, SEM allows for the simultaneous estimation of multiple causal pathways. Here too, richness had a weak effect on phytoplankton productivity. Instead, environment and community size structure were the main drivers, jointly affecting levels of productivity, with richness playing a secondary role mainly through its indirect effects on size diversity and evenness. Overall,we demonstrate that phytoplankton diversity effects on productivity are strongly mediated by the environmental context and functional traits, rather than species richness alone. By integrating observational and modelling approaches, this work furthers our understanding of BEF dynamics in marine ecosystems and provides new insights into the ecological mechanisms that drive phytoplankton productivity under changing environmental conditions.
- Advisor / supervisor
- Chen, Bingzhang
- Resource Type
- DOI
- Funder
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PDF of thesis T17327 | 2025-07-02 | Public | Download |