Texas A&M University – Corpus Christi
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Analysis of Seasonal Changes in Community Composition of Seagrass Epiphytes in Aransas Pass, Texas
No full textCollege of Science, Biology, Organismal Science (Plant); Faculty Mentor: Dr. Kirk CammarataGenerally, the type, and number of epiphytes found on seagrasses varies based on the time of year and the nutrient load of water, including man-made nutrient sources such as wastewater effluent. This study will examine the changes in community composition of epiphytes found on T. testudinum as a function of porewater nutrients and other environmental factors. Epiphytes will be removed from seagrasses collected seasonally from the ICW RV Park in Aransas Pass, Texas. Sites within the study area were chosen based on their proximity to the release point of wastewater effluent from the Aransas Pass Wastewater Treatment Plant. Comparative analysis of fluorescence was done using four wavelengths: 415nm and 680nm were used to determine the amount of green pigments in the sample associated with green algae and 530nm and 576nm were used to determine the amount of red pigments in the sample associated with red algae. These measurements were then compared to determine the ratio of red to green pigments within the samples. Samples will also be run through a full pigment analysis. This will be done using acetone to extract the color from the samples and then analyzing the solution produced in a spectrophotometer. Preliminary results show that both summer and winter samples had higher levels of red algae than green algae when comparing fluorescence analysis. However, individual sites differ, with some showing decreases in red pigment domination and others showing increases from summer to winter. However, further analysis and sampling needs to be done before any final patterns can be suggested. This project will allow for a better picture of the seasonal changes in epiphyte composition on T. testudinum and provide some comparisons between sites with differing nutrient inputs, especially those influenced by wastewater effluent
Socio-Economic drivers of surface water quality impairment
Get PDFA Thesis Submitted In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in Environmental Science from Texas A&M University-Corpus Christi.Water quality is a key factor in ecosystem health. While physical and ecological models of pollution have been widely used to determine water quality, there is a significant gap in the use of socioeconomic metrics in these models. The purpose of this study was to identify and visualize social, cultural, behavioral, and economic drivers of surface water quality impairment. Standardized socio-economic data with links to water quality data were mapped across two study sites: San Antonio Bay and Baffin Bay. A binomial logistic regression model was utilized to identify connections between socio-economic metrics and water quality impairment status. In the San Antonio Bay study site, important predictors of surface water quality impairment were housing type and transportation vulnerability, percentage of 5% or more impervious land cover within 30m if shoreline, amount of developed area, and forest and woody wetland cover. In the Baffin Bay study site, important predictors were minority status and language vulnerability, percentage of 5% or more impervious land cover within 30m of shoreline, forest and woody wetland cover, and amount of cropland. Understanding the interactions between communities and local water quality will allow for more thorough and effective management of water resources.Environmental ScienceCollege of Scienc
Topic 13: Design patterns
No full textObjectives of this topic:
Understand the concept of design patterns,
Evaluate various design patterns by category,
Analyze the advantages of the design patterns in question,
Understand how some significant patterns are use
Freeze-Disturbance effects on biomass allocation in expansion of Black Mangrove (Avicennia Germinans) along a latitudinal gradient in Texas
No full textIn response to warming minimum temperatures, Avicennia germinans is encroaching poleward on the Texas Gulf Coast (TGC) into saline marshes dominated by Spartina alterniflora and Batis maritima. Increased Avicennia cover provides greater protection from soil subsidence and shoreline retreat. However, intense freeze disturbances cause widespread mangrove mortality reversing succession, and increasing the risk of soil subsidence and shoreline retreat due to the loss of below-ground biomass. We conducted a “natural experiment of opportunity” to measure below- and above-ground biomass allocation in Avicennia recovering from catastrophic disturbance caused by the 2021 Winter Storm Uri at sites along a freeze-disturbance gradient across the South and Central TGC. Port O’Connor (28.46°N) was the most severely affected site, Cohn Preserve on Mustang Island (27.71°N) was moderately affected, and Laguna Atascosa National Wildlife Refuge (26.35°N) was minimally affected (min. temp. °C ~ -9.0, ~ -7.4, ~ -5.5 respectively). A second freeze event occurred in December 2022 that severely affected Port O’Connor and moderately affected Cohn Preserve (min. temp. °C ~ -6.6, ~ -5.8 respectively). In an additional methods experiment, we quantified differences in root productivity in in-growth cores containing either peat moss or local substrate at each site. Multiple root ingrowth cores were inserted near the canopy edge of isolated Avicennia shrubs (n=6; 5 at Port O’Connor) at the three sites and collected at 4-month intervals (total cores = 102). Root productivity (g * (m-2 day1)) assessed in a one-way ANOVA and Tukey multiple comparisons, increased with increasing freeze-disturbance effects (F2,28 = 6.386, p<0.01, Port O’Connor: mean = 0.192, sd = 0.188, Cohn Preserve: mean = 0.065, sd = 0.081, Laguna Atascosa: mean = 0.047, sd = 0.089). Using the below-and above-ground relative growth rates to assess the root:shoot biomass allocation ratio, we found an increasing ratio (greater roots to shoots) with increased freeze disturbance (one-way ANOVA and Tukey HSD tests, F2,12 = 6.049, p<0.05, Port O’Connor: mean = 0.191, sd = 0.118, Cohn Preserve: mean = 0.061, sd = 0.066, Laguna Atascosa: mean = 0.015, sd = 0.01). Further, we found no clear trend in quantity or variability in root productivity between native and peat moss substrate types in root ingrowth cores for root biomass at any site (F2, 53 = 0.021, p=0.8). However, peat moss ingrowth cores did consistently have less root necromass after the Dec. 2022 freeze suggesting better survival or lower turnover. Peat moss ingrowth cores contained lower quantities of live root biomass indicating higher rates of root mortality or a stunting effect on root productivity post-second freeze. This finding suggests that cumulative impacts of two freezes occurring less than two years apart are greater than the effects of individual freezes. Increasing root productivity with greater freeze disturbance suggests that recovering standing root biomass may be important for the recovery of above-ground biomass in freeze affected Avicennia. Rapid recovery of below-ground biomass will also contribute to ameliorating rates of soil subsidence and shoreline retreat. Lastly, we found that peat moss is a viable substrate type for future root ingrowth studies if the focus of the study is on total root biomass. However, if a second freeze event occurs during the root ingrowth study, there may be unequal effects between peat moss and local substrate.Life SciencesCollege of Scienc
Quarter von Mises distribution
No full textA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in MathematicsThe world today is increasingly relying on data science and statistics to analyze various types of directional data, such as text data, health studies, image processing, wireless sensor networks, environmental monitoring, robotics, and materials science. In many cases, these data exhibit positive orientation and require probability distributions that are confined to positive regions, such as the positive quarter of the unit circle. These facts highlight the main objective of this thesis, which is to propose a new transformation of the von Mises distribution specifically tailored for the positive quarter of the unit circle. Currently, no such distribution exists. The newly introduced distribution, referred to as the Quarter von Mises Distribution, has been thoroughly investigated in this work. The research includes characterizing the distribution through moments and developing its main properties. Additionally, methods for estimating the distribution parameters using maximum likelihood estimation are presented, along with a hypothesis testing approach using the likelihood ratio test. Furthermore, practical data applications are demonstrated to showcase the effectiveness of these methods. Overall, this thesis contributes to the field of data science and statistics by providing a novel distribution that can accurately model directional data restricted to the positive quarter of the unit circle.Mathematics & StatisticsCollege of Scienc
Breast health and preventative screening
No full textBreast health and its importance in every stage of a woman’s life is discussed from adolescence, across the life span, and addressing the aging process. The importance of self-breast exams, clinical breast exam, and screening mammograms play an integral part of ensuring early detection of breast cancer. The risk factors, genetic mutations, and how breast cancer is triggered is discussed. The advancements in screening recommendations, genetic testing, and treatments is explored and what innovative approaches are being taken to prevent, treat, and cure breast cancer
Microbial dynamics of a hypersaline creek: Community response to disturbance and connectivity to wildlife
No full textA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Marine Biology.Estuarine ecosystems can experience extended periods of salinity and temperature stress. In the South Texas region of the northwest Gulf of Mexico (GoM), climate models and current trends support increased temperatures, prolonged droughts, and increased storm severity. It is therefore imperative to assess how climate stress will impact South Texas estuarine ecosystems. Coastal lagoons are particularly vulnerable to disturbance, and the hypersaline Baffin Bay and Upper Laguna Madre Complex is a ‘hotspot’ of environmental change. In this dissertation, factors contributing to hypersaline microbial community dynamics in a hypersaline creek were assessed in three stand-alone research projects: 1) a short-term 2-month study of microbial community dynamics following a flood event, 2) a long-term 18-month study of microbial community dynamics that included flood and freeze events, and 3) a targeted study of wildlife connectivity (Mexican free-tailed bats) to microbial community dynamics and eutrophication. The short-term study revealed that flood events are disturbance events that cause pronounced shifts in microbial community structure. The long-term study revealed the hypersaline community was resilient to flood and freeze events. Additionally, whole genome sequencing of halophilic bacteria uncovered mechanisms of osmoregulation and heavy metal resistance. The targeted study revealed that bat guano is a source of dissolved organic carbon and potentially pathogenic bacteria. Severe heat coupled with severe flooding is anticipated to alter salinity regimes, increase osmotic stress, adversely impact ecosystem stability, and potentially restructure natural communities between drought and flood events. Climate stress will also affect the quality of riparian buffers and the wildlife inhabiting those buffers. A better understanding of microbial drought and flood resilience is critical to predicting how hypersaline coastal ecosystems will adapt and evolve under future climate scenarios.Physical and Environmental SciencesCollege of Scienc
Rafael Galvan, Juan Galvan, and Robert Lick
No full textRafael Galvan, Juan Galvan, Robert Lick posing for a pictur
Gendered online discussions surrounding climate change, capitalism, and marxism
No full textInspiration: Personal interest in ecocriticism, Passionate about online ecological activism, Combine linguistics with my (potential) thesis area of inquir
Overcoming data limitation challenges in predicting tropical storm surge with interpretable machine learning methods
No full textA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer ScienceThe impacts of climate change have increased the risk of storm surge flooding in coastal areas. Tropical islands are especially vulnerable to the effects of sea level rise and the increase in frequency and intensity of tropical cyclones (TCs). Typically, storm surge prediction is performed using a combination of numerical forecasting models, synoptic forecasting, and statistical methods. Machine learning techniques, particularly convolutional neural networks (CNNs), have shown promise in accurately predicting storm surge levels in the short term. However, deep learning methods are computationally expensive and require large amounts of data to train their models. Often researchers must train neural network models on synthetic data generated by numerical models. The goal of this work is to study the effectiveness of simpler, interpretable models, including random forest (RF) regression, multiple linear regression (MLR), and support vector machine regression (SVR), to predict storm surge in San Juan Bay, Puerto Rico using limited local meteorological and tidal data and hurricane reanalysis data from actual storm events over the last few decades. These algorithms were used to predict surge at five different lead times from one hour to 24 hours and were trained on three different feature sets with two different types of training data windows. Models were trained using a leave-one-out cross-validation (LOOCV) approach, in which data for one TC was separated out for each model as a validation dataset. The performance of the models and different training methods was compared in terms of root mean square error (RMSE), normalized RMSE, and error at peak surge. It was found that an RF model trained on data from only eight TCs was able to predict the peak surge of Hurricane Irma to within about 0.03 m and predicted time of peak surge within three hours at lead times up to 12 hours as long as one extreme TC event, in this case Hurricane Maria, was included in the training data. However, all models failed to accurately predict surge for Hurricane Maria, even when including other high-surge storms in the training data. Other training methods achieved lower RMSE when validated against a peak surge window from the 12 hours prior to 12 hours after peak surge, but could not approach the accuracy of the RF model at predicting the time of peak surge.Computer ScienceCollege of Engineering and Computer Scienc