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Most directly transmitted infections require some form of close contact between infectious and susceptible hosts to spread. Often disease models assume contacts are equal and use mean field estimates of transmission probability for all interactions with infectious hosts. Such methods may inaccurately describe transmission when interactions differ substantially in their ability to cause infection. Understanding this variation in transmission risk may be critical to properly model and manage some infectious diseases. In this study, we investigate how varying exposure and transmission may be key to understanding disease dynamics in the threatened desert tortoise Gopherus agassizii. We created heterogeneity in Mycoplasma agassizii exposure (the putative bacterial agent of a respiratory disease) by varying the duration of interactions between naturally infected and uninfected captive desert tortoises. Using qPCR, we identified new infections and compared models of transmission probability as a function of contact duration and pathogen load. We then examined the contact patterns of a wild tortoise population using proximity loggers to identify heterogeneity in contact duration. The top-ranked model predicting M.agassizii transmission included a dose term defined as the product of the number of days in proximity to an infected host and the infection level of that host. Models predicted low transmission probability for short interactions, unless the infectious host had a high load of M.agassizii: such hosts were predicted to transmit infection at higher rates with any amount of contact. We observed predominantly short-lived interactions in a free-ranging tortoise population and thus, expect transmission patterns in this population to vary considerably with the frequency and duration of high infection levels. Mean field models may misrepresent natural transmission patterns in this and other populations depending on the distribution of high-risk contact and shedding events. Rapid outbreaks in generally solitary species may result from changes to their naturally low-risk contact patterns or due to increases in the frequency of severe infections or super-shedding events - population characteristics that should be further investigated to develop effective management strategies.