Berdahl, Andrew MOkasaki, Connie2023-04-172023-04-172023-04-172023-04-172023Okasaki_washington_0250E_25208.pdfhttp://hdl.handle.net/1773/49956Thesis (Ph.D.)--University of Washington, 2023Statistical models are often abstract in nature. However, in environmental contexts, data are often limited and important insight can be gained by applying knowledge of real-world mechanisms. In this dissertation, I present three mechanistic statistical models, applied to the environment. In my second chapter, I model the effect of sociality on the migration of adult Pacific salmon past large-scale dams in the Columbia River Basin, USA. I explicitly break down and model the process by which a salmon passes a dam. In all three steps of this process, I evaluate the effect of the density of conspecifics, to determine whether sociality plays a role in dam passage. In my third chapter, I present a method for inferring the source of a signal which has been deformed by well-understood linear dynamics. I use as an example the case of a pollutant which, upon entering the environment, is subject to advective-diffusive transport. I show how to incorporate a mechanistic linear partial differential equation (PDE) model into the classic stochastic PDE (SPDE) method from spatial statistics, and how to invert the transport dynamics within a statistical model. In my fourth chapter, I present a mixed integer linear program (MILP) model for constructing optimal sampling design under complex logistical or budgetary constraints. I use as an example the case of the US Forest Service (USFS) Forest Inventory and Analysis (FIA) program in Tanana, Alaska. I compare solutions of this model with three randomized, design-based benchmarks based on MSE and feasibility.application/pdfen-USCC BY-NC-SAStatisticsEnvironmental scienceQuantitative ecology and resource managementThesis