Anthropogenic land conversion has dramatically altered the environment experienced by wild populations, often resulting in small and fragmented populations. It is urgent that we develop approaches to understand the ecological and evolutionary dynamics of such populations to craft effective conservation management. In this dissertation, I use genomics, life history information, and long-term demographic data to explore the evolutionary dynamics of small populations. Using simulations, I test how, and under what conditions, comparisons between the population genetics of different age groups can be used to identify population demographic declines (Chapter 2). I discover that older individuals retain higher genetic diversity representative of past population size after a decline, and that comparison to the genetic diversity in younger individuals can increase power to detect a decline when generation time is long, and a decline has been severe. With a focus on the eastern massasauga (Sistrurus catenatus), I next explore how anthropogenic land conversion has impacted already small and fragmented populations of this threatened rattlesnake. First, I document population structure and demographic history across the range of eastern massasaugas and test if anthropogenic disturbance is a predictor of inbreeding and genetic drift, with particular attention to populations at their range center (Chapter 3). I find significant structure among populations that follows a pattern of isolation by distance, as well as evidence for recent inbreeding that is associated with increased history of disturbance. Second, using a rich dataset of over ten years of capture-recapture data, I test if inbreeding in eastern massasaugas impacts two major components of fitness: survival and reproduction (Chapter 4). I find that snakes with increased inbreeding have lower apparent annual survival and lower probability of having offspring, direct evidence of inbreeding depression. Together, this body of research highlights the power of genomic and life history data to understand the hidden dynamics of small and isolated populations.
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