Eco-evolutionary dynamics of disease under human-induced selection
More than ten thousand years ago, humans started breeding plants as food supply: they chose those varieties of nutritional interest, grew them, and kept the seeds of the best plants for the next season. These practices were the beginning of agriculture, a long-term evolutionary experiment where humans act as a selective force. Active breeding is not the only way in which humans modify evolutionary trajectories: they also change the environment where species live. For example, global trade creates novel species interactions, and the urbanisation of wild areas alters ecological niches. Another compelling case of human-induced selection – and the topic of interest in this thesis – is the control of pathogens. Pathogens are regarded as a threat for human species survival, either because they are causing diseases in humans or because they constitute a risk to food security. In consequence, humans have developed management practices which intend to reduce or eradicate the population of these pathogens by applying abiotic (e.g. drugs) or biotic (e.g. biocontrol with other species) pressures. These strategies, as they deal with populations of living organisms, involve ecological and evolutionary processes. Thus, to improve pathogen control, we need to apply the current knowledge and techniques of ecology and evolution. This thesis studies how pathogen populations are affected by the alternation of selective pressures to which they are exposed. Mainly, I study the dynamics of pathogen populations when host species are switched along time. The different reproductive rates of the pathogen in each host species can slow down the growth or diminish its population in the long-term. In agriculture, this can be achieved by using crop rotations in a field; in vector-borne diseases, the vector and the host are two different ecological niches for the pathogen, and the administration of drugs to the human host can be disadvantageous for pathogen reproduction in the vector. Using mathematical and computational models, I study host-pathogen interactions in infected crop fields and human populations affected by malaria. I simulate infections under multiple scenarios of selection in alternating host species and observe their progress or regression. The results are used to assess the optimality of human interventions for the control of the disease-causing pathogens. Overall, this thesis confirms that a better knowledge of eco-evolutionary principles in disease management can improve the design of strategies. This is especially true given the need for practices which are both efficient and sustainable across generations.
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