| Summary: | As the earth’s climate rapidly changes, it remains unclear how populations will respond to novelsystems and anthropogenic inputs to said systems. The development of new applied conservationtools will be critical to the implementation of solutions to novel problems. My dissertation focuseson developing and implementing novel quantitative models to directly estimate demographic pa-rameters, and inform our understanding of ecological and evolutionary processes. I use a 29-year(1986–2014) dataset on black brant geese (Branta bernicla nigricans, hereafter brant) collected onthe Yukon-Kuskokwim River Delta in western Alaska.In Chapter 2 (Riecke et al., 2018), I use simulated and real data to develop a novel fully con-ditional robust design likelihood which we implement in a Bayesian framework. I test my novellikelihood using simulated data, demonstrating its efficacy, and then use this model structure toexamine the effects of environmental conditions during growth on future lifetime fitness in brant.My results clearly indicate that more structurally developed goslings experienced increased breed-ing probability as an adult (β = 0.14; f = 0.94) with no effect on adult survival (β = 0.01;f = 0.62). I also provide evidence for long-term declines in apparent survival of breeding adultfemales (β = −0.01; f = 0.90). Thus, long-term declines in gosling growth rates (Lohman et al.,2019) may lead to reduced future reproductive potential at the population level. Further, temporalvariation in gosling growth rates may lead to substantial differences in reproductive potential amongcohorts.In my third chapter (Riecke et al., 2019a), I use simulated data to test the impacts of the inverseWishart prior for the covariance matrix of a multivariate normal distribution, and propose a novelalternative distribution. My simulations and analyses demonstrate the inverse Wishart prior distri-bution substantially affects estimates of covariances and variances of demographic parameters, andthat our proposed alternative distribution is ...
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