Matching Items (6)
Filtering by
- All Subjects: conservation
- Creators: Shrestha, Milan
- Status: Published
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
Invasive plants harm the ecological properties of natural systems, human health,
and local economies. However, the negative impacts of invasive species are not always
immediately visible and may be disregarded by local communities if social benefits of
control efforts are not clear. In this dissertation, I use a mixed-methods approach to
investigate the drivers of invasive plant distribution, potential financially feasible
management techniques to control invasion, and community forest user perceptions of
those techniques. In this work, I aim to incorporate the diverse perspectives of local
people and increase the long-term success of invasive species control activities in socio
economically vulnerable populations.
Integrating a spatially and temporally diverse data set, I explore the social and
ecological drivers of invasive plant abundance across 21 buffer zone community forests
in the Western Chitwan Valley of Nepal. I evaluate to what extent forest user and
collective manager activities, the legacies of historic activities, and ecological properties
influence present-day invasive plant abundance. I built upon this study to identify areas
with critically high levels of invasion then initiated a three-year, community-based
management intervention to evaluate traditional and adaptive land management
approaches to control invasive plants. I found that both approaches reduced invasive
plant abundance relative to the surrounding, untreated forest. I then interviewed focus
groups to investigate their perceived efficacy of the various treatment types and found
that almost all forest users and managers preferred the adaptive approach over the
traditional management approach. Notably, forest users cited the importance of the
availability of forest resources and lack of harmful plants in the plots that had undergone
this method. Understanding how forest users relate to and experience invasive plants has
been relatively understudied but can influence forest user engagement in different
management approaches. For this reason, I performed in-depth ethnoecological
interviews to explore how forest users perceive, how they utilize, and to what extent they
value invasive plants. This mixed-methods approach contributes to a more holistic
understanding of the role that local people play in invasive plant management and
restoration activities.
and local economies. However, the negative impacts of invasive species are not always
immediately visible and may be disregarded by local communities if social benefits of
control efforts are not clear. In this dissertation, I use a mixed-methods approach to
investigate the drivers of invasive plant distribution, potential financially feasible
management techniques to control invasion, and community forest user perceptions of
those techniques. In this work, I aim to incorporate the diverse perspectives of local
people and increase the long-term success of invasive species control activities in socio
economically vulnerable populations.
Integrating a spatially and temporally diverse data set, I explore the social and
ecological drivers of invasive plant abundance across 21 buffer zone community forests
in the Western Chitwan Valley of Nepal. I evaluate to what extent forest user and
collective manager activities, the legacies of historic activities, and ecological properties
influence present-day invasive plant abundance. I built upon this study to identify areas
with critically high levels of invasion then initiated a three-year, community-based
management intervention to evaluate traditional and adaptive land management
approaches to control invasive plants. I found that both approaches reduced invasive
plant abundance relative to the surrounding, untreated forest. I then interviewed focus
groups to investigate their perceived efficacy of the various treatment types and found
that almost all forest users and managers preferred the adaptive approach over the
traditional management approach. Notably, forest users cited the importance of the
availability of forest resources and lack of harmful plants in the plots that had undergone
this method. Understanding how forest users relate to and experience invasive plants has
been relatively understudied but can influence forest user engagement in different
management approaches. For this reason, I performed in-depth ethnoecological
interviews to explore how forest users perceive, how they utilize, and to what extent they
value invasive plants. This mixed-methods approach contributes to a more holistic
understanding of the role that local people play in invasive plant management and
restoration activities.
ContributorsClark, Michele Diane (Author) / Hall, Sharon J. (Thesis advisor) / Shrestha, Milan (Committee member) / Yabiku, Scott (Committee member) / Budruk, Megha (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
ContributorsMorgan, Marshall (Author, Co-author) / Shrestha, Milan (Thesis director) / Boyce-Jacino, Katherine (Committee member) / Barrett, The Honors College (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor)
Created2021-12
ContributorsMorgan, Marshall (Author, Co-author) / Shrestha, Milan (Thesis director) / Boyce-Jacino, Katherine (Committee member) / Barrett, The Honors College (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor)
Created2021-12
Description
Located directly south of Pima County, Arizona, there is a culturally rich community known as Santa Cruz County, Arizona. Santa Cruz County is a smaller community being home to an estimated 47,000 people. Santa Cruz County has a rich history given its proximity to the United States-Mexico border. One example of this rich history can be found at the Tumacacori National Historical Park (NHP) with the attractive and unique ruins. The Tumacacori NHP was once an active Spanish mission that oversaw the religious expansion and communal resources. While the Tumacacori NHP no longer operates in this manner, it does serve an important role in providing visitors with a diverse and immersive look into the history of the region and the Tumacacori mission. While the Tumacacori NHP is meant to be a place for all people to visit, most visitors are not from the local community. The reason for this stems from a lack of educational and engagement opportunities between the Tumacacori NHP and the community youth, mainly due to community members being unaware of the park’s existence or the programs that are offered. This project analyzes and uncovers this disconnect as well as the barriers between the Tumacacori NHP and the Santa Cruz County youth through a research study and the use of children’s literature.
ContributorsMorgan, Marshall (Author) / Shrestha, Milan (Thesis director) / Boyce-Jacino, Katherine (Committee member) / Barrett, The Honors College (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Community Resources and Development (Contributor) / School of Sustainability (Contributor)
Created2021-12