We live in an era of global change, where emerging infectious diseases such as Ebola, Zika, bird flu, and white nose syndrome are affecting humans, wildlife, and domesticated species at an increasing rate. To understand and predict the dynamic spread of these infectious agents and other symbionts through host populations and communities, we need dynamic mathematical models that accurately portray host-symbiont transmission. But transmission is an inherently difficult process to measure or study, because it is actually a series of interacting processes influenced by abiotic and biotic factors at multiple scales, and thus empirical tests of the transmission function within epidemiological models are rare. Therefore, in this dissertation, I explore factors at the individual, population, and community-levels that influence host contact rates or symbiont transmission success in a common snail-symbiont system, providing a detailed description of the multi-faceted nature of symbiont transmission. From a review of the ecological literature, I found that most models assume that transmission is a linear function of host population density, whereas most empirical studies describe transmission as a nonlinear function of density. I then quantified the net nonlinear transmission-density relationship in a system where ectosymbiotic oligochaetes are directly transmitted among snail hosts, and I explored the ecological mechanisms underlying the nonlinear transmission-density relationship observed in the field via intraspecific transmission success and contact rate experiments in the laboratory. I found that the field results could be explained by heterogeneity in transmission success among snails with different characteristics and nonlinear contact-density relationships caused by non-instantaneous handling times. After I 'unpacked'population-level transmission dynamics into those individual-level mechanistic processes, I used this same approach to examine higher-level ecological organization by describing the mechanistic underpinnings of interspecific or community-level transmission in the same snail-symbiont system. I found that low interspecific transmission rates in the field were the product of opposing interactions between high population densities, high prevalences of infection, and very low interspecific transmission success caused by strong symbiont preferences for their current host species. Unpacking transmission in this way resulted in one of the most detailed empirical studies of transmission dynamics in a wildlife system, and yielded many surprising new insights in symbiont ecology that would not have been discovered with a purely phenomenological or holistic view of transmission. Though simple, linear, and holistic epidemiological models will always be important tools in disease ecology, 'unpacking'transmission rates and adding heterogeneity and nonlinearity to models, as I have done here, will become increasingly important as we work to maximize model prediction accuracy in this era of increased disease emergence. / Ph. D. / Parasites and pathogens can have important implications for wildlife conservation, the production of domesticated animals and crops, and human health, and thus ecologists and epidemiologists need effective tools for understanding, predicting, and managing the spread of these important pathogens. Mathematical models that represent the transmission of pathogens within single wildlife host species (e.g., Ebola transmission within bat populations) and between different host species (e.g., Ebola transmission between bats and humans) are one such tool, and these same models can be used to understand the spread of beneficial symbionts that actually help the host by being present. But despite being critical tools, these mathematical models are not yet perfect. In fact, in this dissertation work, I demonstrate that the most commonly used models are not well-supported by data from real host-parasite systems, and that the fundamental assumptions underlying these models are rarely tested, because measuring transmission among individuals is often difficult. Therefore, I developed experimental methods to test some of these fundamental assumptions in a system where tiny annelid worms live on aquatic snails, and are only transmitted from one snail to the next during direct contacts between snails. In particular, I first used field studies in a Virginia pond to describe how the rate of worm transmission within and between snail species depends on snail density. I then used laboratory experiments to understand how the rate of contacts between snails and worm preferences for particular snail characteristics (i.e., size, species) influence worm transmission rates. Taken together, this work represents one of the most detailed studies of transmission dynamics in a wildlife system, and yielded many important new insights regarding how to make epidemiological models more biologically realistic. Though the simplest epidemiological models that we have relied on for decades will continue to be useful, the more complicated, biologically-realistic models explored here will become increasingly important as we work towards improving our abilities to precisely and accurately predict and manage parasite transmission.