Microbial ecosystems, both on land and in the oceans, are the staging ground for the biogeochemical activity that sustains habitable conditions on Earth. Ocean microbes are of particular importance; the primary producers that drive biogeochemical ocean processes are almost entirely microbial, and are collectively responsible for about half of global net primary production. These ecosystems are extraordinarily complex, by virtue of being driven by very large numbers of living individuals, constantly interacting with each other and their highly dynamic physical environment. Much uncertainty remains about how these dynamics, from micro- to macro-scales, ultimately impact key ecosystem properties such as spatial dynamics and growth rates of populations and communities. In this project I use individual-based modelling (IBM) across a range of spatial and temporal scales, leveraging large, high-resolution physical and biological datasets along with advancements in modelling tools to shed light on spatial and temporal dynamics of microbial populations in inherently fluctuating environments. In doing so I clarify and quantify hitherto unresolved ecological questions relating to interactions between microbes and turbulence, inaccuracies in conventional modelling approaches, and the balance of competition and coexistence between microbes in the marine environment.
In the Introductory Chapter, I review our current understanding of microbial ecology in the oceans, and illustrate how complex ecological behaviour emerges from the constant interaction of microbial individuals with each other and with their environment.
In the Second Chapter, I begin at the smallest scales directly relevant to ocean microbes, investigating the impact of turbulence on microbial spatial dynamics and patchiness. I adopt an existing mathematical framework for modelling microbes capable of gyrotactic locomotion, with an IBM to reproduce their motion within a fully-resolved 3D simulation of convective turbulence. This work clarifies and extends to more realistic flow regimes the existing theory connecting micro-scale microbe patchiness to a coupling of turbulence and individual motility. Interpreting my results in the context of varying turbulent conditions from the surface to the bottom of the mixed layer, I propose that this turbulence-driven patchiness is ephemeral, non-ubiquitous, and depth-dependent.
In the Third Chapter, I transition to larger spatial and temporal scales, and develop an IBM on top of the NEMO-MEDUSA oceanographic model and the global Biotraits database. I use this model to quantify, for the first time, to what degree fluctuating environmental conditions can influence estimates of a microbe's growth rate, due to nonlinear averaging effects similar to the phenomenon known as Jensen's Inequality. In a microbial growth context, such effects predict that growth rate estimates based on mean environmental conditions will differ from realised growth rates in a dynamic environment. I substantiate this prediction by simulating populations of marine phytoplankton following ocean currents, and demonstrating that realised growth differs substantially from mean-environment growth estimates for a clear majority of these simulated populations. I quantify the relative contributions of temperature and nutrient fluctuations to this microbial ``growth gap'' -- the magnitude of the difference between realised and mean-environment growth rates, and discuss the implications of my findings under a warming climate.
In the Fourth Chapter, I apply my NEMO-MEDUSA-Biotraits IBM to investigate the ‘Paradox of the Plankton’ -- the puzzling absence of competitive exclusion among ocean microbes. I simulate populations of distinct species with thermal histories which significantly overlap in both space and time within the IBM, which I treat as competitors. I then examine whether the distinct thermal adaptations of these competitors can cause competitive advantage to shift back and forth over time as environmental conditions fluctuate, thus preventing any individual species from permanently outcompeting others.
In the Final Chapter, I link my findings to each other and to the bigger picture of microbial ocean ecology, emphasizing how a maturing body of mathematical ecological theory, increasingly large and detailed datasets, and modern computational tools, allow us to shed light on long-standing questions by closely examining interactions between individuals and a dynamic environment.Open Acces
