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Biogeochemical Impacts Of Mixotrophy and Ecological Stoichiometry

Final Report Summary - BIOMES (Biogeochemical Impacts Of Mixotrophy and Ecological Stoichiometry)

Research objectives:

1. Elucidate the ecological mechanisms and biogeochemical influence of mixotrophy. Aquatic plankton are typically modelled as belonging to one of two discrete trophic categories: autotrophic phytoplankton or heterotrophic zooplankton. This dichotomy is challenged by the existence of mixotrophic organisms, that combine both autotrophic and heterotrophic nutrition.

I have previously used an equilibrium model (Ward et al. 2011) to examine the survival of mixotrophs in relatively stable marine environments, such as the low-seasonality subtropical oceans. I will extend this work to look at the more seasonal temperate oceans, where Mixotrophy may provide the flexibility needed to endure large environmental. Using a large-scale modelling approach, I will be able to make a first assessment of the potentially large biogeochemical impact of mixotrophy in the Atlantic Ocean.

Hypothesis 1: Dinoflagellates, a large group of flagellated protists, are prevalent in seasonal and fluctuating environments because they use mixotrophy to hedge their bets between inorganic and organic resources. This generalist strategy means they are less competitive for any one given resource, but they are also exposed to less risk, because they are not wholly reliant on any single resource. Despite their apparent importance, mixotrophs have not yet been resolved in any large scale ocean models. The proposed research will improve our understanding of mixotroph ecology, and will provide the first quantitative estimate of their large-scale biogeochemical impact.

2. Explore the links among physiology, ecology and environmental stoichiometry.

I propose to investigate an “optimal stoichiometry” hypothesis that is based on the modification of cellular structure as phytoplankton adapt and acclimate to different niches. For example, to achieve higher growth rates, phytoplankton may invest in phosphorus rich growth machinery, such as DNA and ribosomes. On the other hand, to make the most of scarce resources, they may invest in nitrogen rich nutrient-uptake proteins, or light-capture machinery. Such changes in investment lead to variation of cellular N:P ratios, and may therefore affect the overall stoichiometry of marine communities. The optimal stoichiometry hypothesis has not yet been tested in a coupled ocean-circulation biogeochemistry model, but it has the potential to explain large-scale variation in marine N:P stoichiometry.

Hypothesis 2a: Phytoplankton stoichiometry is driven by adaptation and acclimation to different environments, and changes in community structure will therefore cause marine stoichiometry to change predictably across broad environmental gradients.
Resolution of diverse grazer communities would allow for much higher coexistence of different functional types, with community composition changing gradually over environmental gradients. With greater coexistence of growth and uptake adapted species, we should expect to see, on average, smoother and more realistic changes in stoichiometry between ocean biomes.

Hypothesis 2b: C:N:P ratios in marine systems are observed to be fairly constant at around 106:16:1, not because the C:N:P ratios of phytoplankton are constrained to a uniform value, but rather because complex trophic structure maintains the coexistence of organisms with individually much more variable stoichiometry. Uniting approaches from the fields of biogeochemistry and theoretical ecology, the proposed research has the potential to make important advances in the field of marine stoichiometry, and should improve our ability to predict the biogeochemical response of marine ecosystems to environmental change.

Work progress and achievements during the period Proposed and realised time lines

Hypothesis 1: Mixotroph biogeography
1. June 2012: AMT, CPR and Marine Productivity data gathered and collated. - achieved
2. September2012: Temporal and spatial maps of size and trophic strategy in N. Atlantic. – achieved (see Barton et al. 2013)
3. December 2012: Mixotrophy enabled in 1D and 3D version of size-structured model. - achieved
4. March 2013: Quantitative estimates of mixotroph biogeography and biogeochemistry. - achieved
5. June 2013: First draft of mixotroph biogeography paper.
6. June 2014: Major restructuring of model approach undertaken, and links with metagenomics group instigated for model validation.

Hypothesis 2a: Idealised emergent stoichiometry
1. December 2012: Data analysis yields constraints for stoichiometric model. – enabled by recent publication (Edwards et al. 2012)
2. June 2013: Zero-dimensional adaptive dynamic modelling complete. – this work has been incorporated into strand 1.
3. September 2013: First draft of idealised emergent stoichiometry paper. – this work has been incorporated into strand 1

Hypothesis 2b: Global emergent stoichiometry
1. June 2013: Global N:P stoichiometry data collated and mapped. – this work has been incorporated into strand 1.
2. September 2013: Two-way trade-off enabled in 3D version of size-structured model. - this work has been incorporated into strand 1.
3. December 2013: Global comparison of observed and modelled stoichiometry. - this work has been incorporated into strand 1.
4. March 2014: First draft of global emergent stoichiometry paper - ideas incorporated into hypothesis 1

New results demonstrated that Hypotheses 1 and 2 are more closely related than previously thought. As a consequence, the work plan has been adapted to examine the interconnection of organismal stoichiometry and trophic flexibility. This work is progressing well, and model shows string links between mixotrophy and export stoichiometry. New goal is to resubmit paper in October.

Work performed

I have previously developed a model (Ward et al. 2012) that includes both complex food-web structure, and resolves organismal nutrient stores, or 'quotas'. The model is therefore well-suited to addressing the scientific questions raised in Strands 1 and 2. This model was formally published in November 2012. A detailed analysis of the model behaviour across different temporal and spatial scales was accepted for publication this summer (Ward et al. 2013a).

Over the last two years the model has been developed to allow representation of mixotrophy. Plankton may now occupy any position on a continuous spectrum from phytoplankton to zooplankton. The development work was done in a one-dimensional water-column model, and was then extended to a full global ocean model.

A primary goal of Strand 2 is to understand changes in plankton elemental ratios across large spatial scales. The proposed work aims to look at the role of food-web structure in observed patterns, but it was noted in the proposal that nitrogen-fixation is an important determinant of global N:P ratios. To improve understanding in this area I have developed an idealised model of competition between marine nitrogen fixers and other phytoplankton, which was accepted for publication (Ward et al. 2013b).

Main results

Theoretical models and observations of marine planktonic food-webs suggest that small, nutrient-replete, phytoplankton are often able to coexist with larger, nutrient-limited, groups. Different stressors (i.e. nutrient limitation vs. grazing mortality) lead to stoichiometric imbalances across the food-web: smaller plankton are enriched in nutrient elements N, P and Fe, while larger groups are more C rich. I have found that mixotrophs tend to thrive in regions where stoichiometric imbalances are large, because trophic flexibility allows them to take advantage of resource pools that were previously thought of as mutually exclusive. This mechanism may have important implications, because the model results suggest Mixotrophy allows much more efficient transfer of biomass to higher levels in the food chain.

This work was presented during an invited talk at the International Conference of Protistology in Vancouver. I am currently working on adding a representation of the marine phosphorus cycle to the model.