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The consequences of temperature-resource interactions for the future of marine phytoplankton communities

Periodic Reporting for period 1 - TROPHY (The consequences of temperature-resource interactions for the future of marine phytoplankton communities)

Periodo di rendicontazione: 2018-04-01 al 2020-03-31

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.
As part of the core of this project, I have:

1) Developed machine learning techniques to infer how high-dimensional interactions shape phytoplankton growth from noisy time series.

2) Tested and validated this approach through simulations of known lower-dimension shapes.

3) Applied this approach to data from the field, especially Bodensee/Lake Constance in Germany/Switzerland, but also Narragansett in the US, and the L4 station off the UK.

4) As part of this analysis, I have been able to infer how >40 species in a complex community respond to changes in 6 environmental dimensions simultaneously.

5) I have additionally been able to rule out the existence (for this community) of most of the hypothesized trade-offs that are believed to shape patterns of phytoplankton co-existence, including the widely accepted gleaner-opportunist trade-off.

6) Furthermore, I am able to show that a complex trade-off does exist, but it exists in multiple dimensions simultaneously. In other words, coexistence and biodiversity patterns are shaped by several dimensions; species trade off performance in different ways.

As collaborative efforts connected to the primary theme of this project, I have participated in projects that:

7) examined how interactions between environmental factors shaped minimum nutrient requirements, an important determinant of the outcome of competition (Lewington-Pearce et al. 2019)

8) examined how resource requirements are shaped by evolution under different environmental conditions (Bernhardt et al. 2020)

9) studied niche separation in field zooplankton communities using machine learning approaches for inference (Lindegren et al. 2019)

10) shown that the commonly-assumed gleaner-opportunist trade-off does not exist, across a wide range of sizes and taxonomic groups (Kiørboe & Thomas, 2020)
As part of my work:

1) I have inferred how >40 species in a complex community respond to changes in 6 environmental dimensions simultaneously. This goes far beyond what is presently possible empirically both in terms of number of species (1-5 species is common in a study) and dimensions (1 is common, 2 is rarely done with sufficient resolution). My approach achieves orders of magnitude more complexity than is possible in a lab study, at the cost of the precision possible in a controlled lab study.

2) I have simultaneously addressed 6 major types of trade-offs that have been assumed to exist in theoretical models. Previously, only two have been addressed individually, with far smaller numbers of species and substantial error introduced by variation in experiment methods and biases in species selection. Importantly, my work shows essentially no support for any of these trade-offs. This alters our understanding of the drivers of coexistence and biodiversity. Especially since phytoplankton are a model system that is at the core of the development of these bodies of theory, this has implications for our understanding of coexistence and diversity-maintenance mechanisms more broadly.

3) Furthermore, I am able to show that though a trade-off does exist, it exists across multiple dimensions, and is therefore extremely difficult to detect using lower-dimensional approaches. This opens up new avenues for exploring how biodiversity is generated and maintained across space and time. It also has strong implications for understanding the degree to which environmental change will influence biodiversity around the world.

4) My general inferences can inform lake ecosystem models and Earth Systems Models. I am developing collaborations to evaluate the ecosystem-level implications of some of these findings.

5) The general approach I have developed is not specific to phytoplankton or observational time series; I am developing collaborations to extend this approach to more taxa and empirical time series as well, both for additional validation and to enable useful ecophysiological inferences in a wider range of taxa, helping to advance ecological research in general and model-data synthesis more specifically.
Growth rate of one species across 4 interacting dimensions, inferred using machine learning.