Browsing by Author "Sedinger, James S."
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- Bayesian mark-recapture-resight-recovery models: increasing user flexibility in the BUGS languageRiecke, Thomas, V; Gibson, Dan; Leach, Alan G.; Lindberg, Mark S.; Schaub, Michael; Sedinger, James S. (Wiley, 2021-12)Estimating demographic parameters of interest is a critical component of applied conservation biology and evolutionary ecology, where demographic models and demographic data have become increasingly complex over the last several decades. These advances have been spurred by the development and use of information theoretic approaches, programs such as MARK and SURGE, and Bayesian inference. The use of Bayesian analyses has also become increasingly popular, where WinBUGS, JAGS, Stan, and NIMBLE provide increased user flexibility. Despite recent advances in Bayesian demographic modeling, some capture-recapture models that have been implemented in Program MARK remain unavailable to quantitative ecologists that wish to use Bayesian modeling approaches. We provide novel parameterizations of capture-mark-recapture-resight-recovery models implemented in Program MARK that have not yet been implemented in the BUGS language. Simulations show that the models described herein provide accurate parameter estimates. Our parameterizations of these models can easily be extended to estimate additional parameters such as entry probability, additional live states, or cause-specific mortality rates. Additionally, implementing these models in a Bayesian framework allows users to readily estimate parameters as mixtures, incorporate random individual or temporal variation, and use informative priors to assist with parameter estimation.
- Fitness landscapes and life-table response experiments predict the importance of local areas to population dynamicsKane, Kristin; Sedinger, James S.; Gibson, Daniel; Blomberg, Erik; Atamian, Michael (Ecological Society of America, 2017-07)Animal resource requirements differ among life-history stages, and thus, habitat is most appropriately thought of as specific to a particular life stage. Accordingly, different habitats may vary in their significance as functions of (1) the sensitivity of population growth to the life stage for which the habitat is most important, (2) spatial association of each habitat to other habitats, and (3) the abundance of the habitat in question. We used an analogy to a life-table response experiment to develop spatial models linking key habitats to rates of population increase in Greater Sage-grouse. We parameterized models linking demographic rates to vegetation and physical attributes of habitats, including spatial association of some habitats to others, using a decade-long study of Greater Sage-grouse in central Nevada. We modeled the contribution of each pixel in the landscape to regional k (finite rate of population increase) using functional relationships between demographic rates and the attributes of that pixel, and the sensitivity of k to each demographic rate. We incorporated the following demographic rates into our model: female nesting success, survival of chicks from hatching to 45 d, and adult female annual survival. We also incorporated the probability a site was used for nesting. Chick survival (62%) and nest site selection (21%) explained most of the variance in lambda. We found that only similar to 8% of all habitat for Greater Sage-grouse contributed to lambda > 1. Habitat supporting population growth occurred in mid-high elevation areas with moderate slopes, and a high percent cover of sagebrush, and in nesting areas close to late-brood habitat. Our models indicate that a relatively small proportion of habitat available to Greater Sage-grouse in central Nevada is responsible for maintenance of the population in our study system. We suggest that the general approach we describe here can be used to improve understanding of habitats most likely to regulate populations in other systems, providing an important tool in ecology and conservation.