Robust Mathematical Models of Ecotoxicological Dynamics Subject to Stoichiometric Constraints

Accurately assessing the risks of contaminants requires knowledge of complex ecological interactions, elemental cycling, and the interactive effects of natural stressors, such as resource limitations and contaminant stressors. The development of ecotoxicological models have contributed significantly to our understanding of how contaminants impact organisms and cycle through aquatic food webs. However current models do not incorporate dynamical interactive effects of contaminant stressors and elemental constraints, such as nutrient/light availability and food quality. Investigators will design and conduct laboratory experiments in conjunction with mathematical models that account for both nutritional value of diet and toxicant load. The results will generate knowledge and enhance understandings of the environment and global change. The project will shed light on nutrient and chemical contaminant cycling and ultimately improve toxicological risk assessment protocols, which will be beneficial to environmental managers and policy decision makers. User-friendly versions of the models will be disseminated on the project's webpage. Graduate students will receive interdisciplinary training in the fields of mathematics and ecotoxicology to gain a broad understanding of scientific problems, necessary to communicate effectively across disciplines. Investigators and graduate students will be involved in K-12 outreach initiatives organized by the STEM Center for Outreach, Research, and Education at Texas Tech University.<br/><br/>The theory of ecological stoichiometry emphasizes the balance of multiple chemical elements and the relative abundance of essential elements in organisms that shape ecological dynamics. Investigators will develop and analyze a series of empirically testable and robust mathematical models of population dynamics structured under the framework of ecological stoichiometry. In parallel to model development, investigators will integrate data from the literature and from new experiments designed in this research, in order to parameterize, test, and improve the models. Dynamical systems theory and tools will be used to interpret and analyze the models including analytical, computational and numerical, as well as bifurcation analysis. The synthesis of the models and experiments will result in the development of a robust theoretical framework appropriate for improved risk assessment applications in ecotoxicology that incorporate the effects of stoichiometric constraints on concurrent ecological and toxicological processes.