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Corynorhinus townsendii, a bat species residing in north-central Arizona, has historically been observed hibernating in highly ventilated areas within caves and abandoned mines, but there is little to no specific data regarding this tendency. Understanding how air movement may influence hibernacula selection is critical in bettering conservation efforts for Arizona

Corynorhinus townsendii, a bat species residing in north-central Arizona, has historically been observed hibernating in highly ventilated areas within caves and abandoned mines, but there is little to no specific data regarding this tendency. Understanding how air movement may influence hibernacula selection is critical in bettering conservation efforts for Arizona bats, especially with white-nose syndrome continuing to devastate bat species populations throughout the United States. My study aimed to begin filling in this knowledge gap. I measured wind speed in three known Arizona hibernacula during the winter hibernation season and combined this data with the locations of bats observed throughout each of the three survey locations. I modeled our findings using a generalized linear model, which confirmed that wind speed is indeed a predictor of C. townsendii roost selection.

ContributorsKitchel, Heidi (Author) / Moore, Marianne (Thesis director) / Saul, Steven (Committee member) / Barrett, The Honors College (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2022-05
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Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were

Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were proposed to overcome the challenges in practice. There are three major parts in the dissertation.

In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.

In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.

A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
ContributorsLu, Xuetao (Author) / McCulloch, Robert (Thesis advisor) / Hahn, Paul (Committee member) / Lan, Shiwei (Committee member) / Zhou, Shuang (Committee member) / Saul, Steven (Committee member) / Arizona State University (Publisher)
Created2020