TROPHY · The consequences of temperature-resource interactions for the future of marine phytoplankton communities
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2018-04-01 → 2020-03-31
- EU contribution
- €212,195
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
Lines connect the coordinator with its partners.
Results in brief
The consequences of temperature-resource interactions for the future of marine phytoplankton communities
Phytoplankton are responsible for nearly half of global primary production. In other words, they take nearly as much carbon out of the atmosphere as all land plants together. Consequently, understanding how they will respond to environmental change is an important piece of understanding how global carbon concentrations and temperatures will change in the coming decades and centuries. However, we presently have a weak understanding of how important environmental factors - temperature, light, nutrients - interact in complex ways to influence phytoplankton growth. A few experiments with one species have shown that these interactions could be extremely important in determining where different phytoplankton will be able to live and whether they will be able to grow. This will shape not just global carbon and temperature levels, but also the food available to aquatic food webs, and the probability and frequency of harmful algal blooms. The experiments needed to accurately quantify these interactions are large and not feasible at present. Therefore, my work proposed applying machine learning methods to existing time series datasets of phytoplankton community composition and environmental factors, as an alternative to reach the same understanding. In effect, we would use natural variation in multiple environmental factors to draw inferences and learn, instead of experimental manipulation in the lab. We use machine learning instead of standard statistical approaches to capture the high-dimensional interactions that we presently do not understand well enough to write as equations for statistical fitting. With this approach, our objectives are to (1) understand the shape of high-dimensional interactions and develop equations to describe them, that can then be used to improve Earth Systems Model predictions of environmental change, (2) quantify the traits of entire natural phytoplankton communities simultaneously, to enable the development of lake ecosystem models that can be parameterized accurately at a species level instead of the functional group or community level, (3) quantify the trade-offs experienced by phytoplankton species that govern patterns of population dynamics and coexistence, in order to better understand how changes in the environment will affect the composition of communities in the future.
Data: CORDIS, © European Union
Project objective
Temperature, nutrients and light drive the growth of phytoplankton, aquatic photosynthetic microbes responsible for nearly half of global primary production. Because phytoplankton influence global biogeochemical cycles, carbon sequestration and climate, accurately modelling their growth is vital to forecasting our future. However, the models we use for global ecosystem forecasts do not consider how these factors interact, even though the interactions lead to qualitative and quantitative differences in outcomes. My goal is to therefore build a mechanistic understanding of how temperature and resources interact to influence phytoplankton growth, productivity and biogeochemical cycles.This project has three objectives (i) develop statistical models describing how phytoplankton growth changes as a joint function of temperature, nutrients and light, (ii) develop mechanistic models characterising how temperature and resources influence cellular processes in phytoplankton, and ultimately their growth, and (iii) implement dynamic versions of the mechanistic model to forecast how marine phytoplankton communities will respond to future changes in temperature, resources and predation.My work will involve applying machine learning techniques to published laboratory and field datasets to understand complex interactions between the three factors. By combining this understanding with insights from ecological theory, I will generate an accurate mechanistic model of growth, and then test the power of this model to predict patterns in the ocean using independent field datasets. Finally, I will use the validated mechanistic model to forecast changes to global patterns in phytoplankton growth and primary productivity. This project will enable us to generate credible forecasts of phytoplankton productivity and biogeochemical cycles in a warming ocean, and improve our understanding of fundamental ecological processes by uniting major fields of ecological theory.
Original text from CORDIS.
Participants
- DANMARKS TEKNISKE UNIVERSITET · Kongens LyngbyCoordinatorDenmark
Links
Data: CORDIS, © European Union