Search

Pesticide resistance is a major challenge to increasing the resilience and sustainability of current food production systems. Preserving the susceptibility of pest organisms to chemical products is a key factor to optimize a pesticide-based strategy. However, resistance management strategies (RMSs) must consider unique species biologies, multiple resistance mechanisms, environmental factors, and pest management practices, which can make their implementation complex. Here, we develop a method to help manage this complexity using a grid-based simulation framework for pesticide resistance evolution including population growth and dispersal dynamics. This framework was applied to the fall armyworm, Spodoptera frugiperda, for which resistance evolution is a major concern. We explored the sensitivity of 13 parameters dealing with landscape structure, dispersal rate, chemical treatment protocols, chemical degradation rate, dose-response curves and transition rates (i.e., flux between sub-population driven by the mutation rate). From the sensitivity analysis of simulations, we computed heat maps of the influence of each parameter on a set of variables (total pest population size, fully resistant population size, and resistance frequency). Assuming a large but realistic range for each parameter, Sobol's sensitivity index showed that resistant transition rate (from phenotypically susceptible to resistant sub-populations) and pesticide properties (in particular, degradation rate and dose-response curve) are more important in the outbreak of resistance compared with resistance ratio (i.e., the benefit of being resistant over susceptible in terms of fitness), chemical application intensity and landscape composition. In addition, using Pareto optimality, we assessed the performance of different pesticide application regimes according to total population size, population size of resistant individuals, the total amount of pesticide, and the total area of host plants suitable for

Biological phenomena occur across wide scales in space, time, and organisational complexity. Molecules, which are small, quickly transforming units, exhibit new emergent properties when they are arranged into ecosystems. These properties of ecosystems, such as species diversity, distribution, standing biomass, or rates of nutrient turnover involve large spatial and temporal scales, as well as many underlying processes that make their study inherently complex. Integration across disciplines and across levels of biological organisation is one of the grand challenges in biology. Towards this end, novel methods are required so that cross-disciplinary phenomena can be quantified using a common metric. Energy and mass are two universal currencies that are able to cut through the hierarchy of biology, which must be both conserved irrespective to the scale of inquiry. Dynamic Energy Budget (DEB) theory builds upon the laws of energy and mass conservation by identifying other universal constraints on the metabolic organisation of diverse species. While DEB theory is commonly perceived to be relevant at fine biological resolutions, particularly the individual level, it has received little recognition from population, community, and ecosystem biologists despite its application to many supra-individual topics. In this thesis, I bring principles of DEB theory to bear against several current problems in biology that each span multiple organisational levels. As pointed out by renowned mathematical ecologist, Richard Levins, different models may take a different emphasis on precision, generality, or realism and do so antagonistically (at the expense of the other qualities). In thesis I take an emphasis on generality by developing simple, parameter-sparse DEB-based models that are able to yield predictive synthesis on cross-disciplinary issues, demonstrating the parsimony of DEB approaches. This departs from previous DEB studies on macro-ecological patterns, which take more of an em