Assessing Herbicide Resistance : Mathematical Models of Weed Management and Resistance Evolution
Since the dawn of agriculture, crop production has been challenged by pests, among them weeds, causing the highest potential yield losses. Ever since, farmers have been searching for strategies to protect their crops, such as hand weeding and later mechanical control measures. The commercialisation of selective herbicides after World War II has revolutionised crop production, providing an easy and highly effective solution to weed management. Today, widespread resistance threatens the sustainability of herbicides and cropping systems. Herbicide resistance is a highly replicated example of rapid adaptation to human-mediated selection, extensively studied for more than 60 years, primarily in terms of mitigation practices. Yet, the precise determinants of the rate and nature of herbicide resistance evolution and how management practices alter the underlying eco-evolutionary processes remain poorly understood – especially in perennial species. In this thesis, I employ population dynamic models to identify critical factors defining the probability, timing, and source of resistance adaptation in a perennial weed and evaluate strategies to limit herbicide resistance evolution. The approaches developed in the individual chapters are tailored to address specific aspects of herbicide resistance evolution and the management of seed- and rhizome-propagated perennials. In essence, I analyse how specific life history traits and details of the resistance mechanism affect the adaptation and assess the sustainability of management interventions. Capturing population-level dynamics in the active weed population and the dormant seed bank, the models allow me to study the role of a seed bank in herbicide resistance evolution. This work aims to provide mechanistic insights into the eco-evolutionary processes driving herbicide resistance adaptation, highlighting the value of weed population dynamic models for gaining a holistic understanding of these dynamics.
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