ausblenden:
Schlagwörter:
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Zusammenfassung:
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.
