Matching Items (7)
Filtering by
- All Subjects: conservation
- Creators: Senko, Jesse
- Status: Published
Description
The Arizona Board of Education decides the science curricula for students K-6. The standards lack an in depth knowledge of marine life, marine science, ocean conservation, and more related topics. Through interviews with teachers, faculty, and research on ocean literacy and coral reefs, My Coral Reef Booklet assembles various learning activities to cater to students from a variety of education, financial and impairment backgrounds. My Coral Reef Booklet addresses coral reef basics and how students can play their part in coral reef conservation despite their location.
ContributorsHynds, Janna (Author) / Hedges, Craig (Thesis director) / Senko, Jesse (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsHynds, Janna (Author) / Hedges, Craig (Thesis director) / Senko, Jesse (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsHynds, Janna (Author) / Hedges, Craig (Thesis director) / Senko, Jesse (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Life Sciences (Contributor)
Created2023-05
Description
The Gulf of Mexico (or “Gulf”) is of critical significance to the oil and gas industries’ offshore production, but the potential for accidental petrochemical influx into the Gulf due to such processes is high; two of the largest marine oil spills in history, Pemex's Ixtoc I spill (1979) and British Petroleum's (BP) Deepwater Horizon (2010), have occurred in the region. However, the Gulf is also of critical significance to thousands of unique species, many of which may be irreparably harmed by accidental petrochemical exposure. To better manage the conservation and recovery of marine species in the Gulf ecosystem, a Petrochemical Vulnerability Index was developed to determine the potential impact of a petrochemical influx on Gulf marine fishes, therein providing an objective framework with which to determine the best immediate and long term management strategies for resource managers and decision-makers. The resulting Petrochemical Vulnerability Index (PVI) was developed and applied to all bony fishes and shark/ray species in the Gulf of Mexico (1,670 spp), based on a theoretical petrochemical vulnerability framework developed by peer review. The PVI for fishes embodies three key facets of species vulnerability: likelihood of exposure, individual sensitivity, and population resilience, and comprised of 11 total metrics (Distribution, Longevity, Mobility, Habitat, Pre-Adult Stage Length, Pre-Adult Exposure; Increased Adult Sensitivity Due to UV Light, Increased Pre-Adult Sensitivity Due to UV Light; and Abundance, Reproductive Turnover Rate, Diet/Habitat Specialization). The resulting PVI can be used to guide attention to the species potentially most in need of immediate attention in the event of an oil spill or other petrochemical influx, as well as those species that may require intensive long-term recovery. The scored relative vulnerability rankings can also provide information on species that ought to be the focus of future toxicological research, by indicating which species lack toxicological data, and may potentially experience significant impacts.
ContributorsWoodyard, Megan (Author) / Polidoro, Beth (Thesis advisor) / Saul, Steven (Thesis advisor) / Matson, Cole (Committee member) / Arizona State University (Publisher)
Created2020
Description
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.
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
Description
As human populations continue to expand, interactions with wildlife are expected to increase due to destruction of land and global climate change threatening native habitats. Established areas of protection are becoming essential to species survival and biodiversity protection. National Parks (NP) are a globally ubiquitous method employed to protect wildlife and habitats. Often NPs are mosaics of relatively small protected areas in a “sea” of human-dominated landscapes, and these remaining habitat “islands” are becoming essential to preventing species extinction. However, the establishment of a NP can lead to increased human-wildlife conflicts (HWC) and disenfranchisement of local communities, particularly along their borders. We conducted semi-structured interviews in six different countries to better understand the nature of HWCs at the borders of major NPs: (1) Khao Yai NP, Thailand; (2) Myall Lakes NP, Australia; (3) Chitwan NP, Nepal; (4) Kruger NP, South Africa; (5) Chingaza NP, Colombia, and (6) Yellowstone NP, United States. We evaluated affinity to wildlife, perception of conflicts, management success, and potential solutions at each park to better understand the global nature of HWCs.We also evaluated these data in relationship to the Human Development Index (HDI) to determine if there was a correlation between development and conflict issues. We found the intrinsic value of wildlife to not markedly differ between countries. Conflict was perceived as higher in the United States and Australia but was known to be of greater intensity in Nepal and South Africa. Management of NPs was well-regarded with a slight decrease from less-developed countries to more-developed countries, with solutions that were creative and unique to each region. Results appeared to be related to shifting baselines between countries and also to equivalency in a cross-cultural assessment. When these theories are taken into account, the complexity of HWCs globally is better understood. As our world continues to expand and NPs become some of the only contiguous native habitat and refuges for wildlife, it is important to understand the complex relationships occurring at the interface between natural and human communities and to explore effective solutions to these problems.
ContributorsRagan, Kinley Ann (Author) / Schoon, Michael (Thesis director) / Schipper, Jan (Committee member) / Senko, Jesse (Committee member) / School of Life Sciences (Contributor, Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
With human populations continuing to expand, encounters with wildlife become more frequent, and more people have the ability to go hunting. When it comes to top level predators, there have often been questions raised about the effects hunting has on their populations. Some speculate there are compensatory measures as a result, others that hunting has an additive mortality effect. The purpose of this project was to do a literature review over the topic of hunting mountain lions in order to definitively determine what the effects of hunting are on their populations. It was concluded that hunting has a negative impact on mountain lion populations, having an additive effect on their mortality rate, reducing their genetic biodiversity, reducing their rate of emigration/immigration, and changing their population demographics.
ContributorsDaily, Austin (Author) / Senko, Jesse (Thesis director) / Bennett, Ira (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Sustainability (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2022-05