The Cougar Repository
This repository, hosted by the College of Charleston Libraries, holds a variety of scholarship produced by students from the Graduate School and the Honors College.
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Estimating the Tag-Reporting Rate and Length-Based Selectivity of Red Drum (Sciaenops ocellatus) in South Carolina Using a Long-Term Tag-Recapture Study
Troha, Lukas
Tag-recapture studies are often utilized to generate precise, externally derived, estimates of stock assessment parameters such as tag-reporting rate and selectivity. These estimates can be used to increase the accuracy of recent stock assessments for red drum (Sciaenops ocellatus), which have exhibited significant uncertainty and largely leave the population status in question. Using more than forty years of red drum tag-recapture data available from the South Carolina Department of Natural Resources (SCDNR) including a high-reward tagging study, we estimated the tag-reporting rate of red drum, as well as the length-based selectivity of fishery-independent sampling gears (trammel net, electrofishing, stop net, and longline) and recreational hook-and-line. Tag-reporting rate in South Carolina is high overall, approaching maximal reporting (100%) in St. Helena Sound, Charleston Harbor, and Winyah Bay, while Port Royal Sound displayed 58.9% reporting rate. The shape of fishery-independent selectivity curves depended on gear type, with each gear selecting for a different size class. A dome-shaped pattern of recreational hook-and-line selectivity was observed for harvested and released fish in nearly all management periods, though the size of maximum selectivity in South Carolina recreational fisheries varied based on fate of the fish after capture. The results of this study provide essential information to be used in future red drum stock assessments and will subsequently influence management of the species.
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Self-reporting Sustainability Frameworks and Stakeholder Buy-in: A Case Study of AASHE STARS at the College of Charleston
Jones, Oliver
Higher Education Institutions (HEIs) not only educate their students, but also influence and define common standards in the broader social sphere. This influence extends into the field of sustainability, particularly within assessment and reporting efforts. Sustainability reporting (SR) often utilizes guiding structures like sustainability reporting frameworks to establish standard reporting measures. This study evaluates institutional sustainability at the College of Charleston through two lenses: the externally projected image of sustainability at the institution through STARS reporting, and the internal perceptions of sustainability from academic & non-academic staff participating in the STARS reporting process. To analyze externalized sustainability metrics, the College of Charleston’s recent 2023 STARS report was compared to the 2020 submission. To identify internal perspectives, surveys pre- and post-reporting were administered, with the interpretation of these results aided by qualitative data obtained from semi-structured in-person meetings. The results of this study indicate a general shift from academically-focused sustainability efforts in the 2020 reporting period (2017 - 2020) to operations-focused sustainability in the 2023 reporting period (2020 - 2023). This shift may be attributed to changes in CofC organizational structure, dependence on CSD data collection, and employee turnover. In addition, analysis of internal perspectives indicates a potential disconnect between preference for sustainability and perceived progress towards sustainability at the College for some employees.
Item
Estimating the Tag-Reporting Rate and Length-Based Selectivity of Red Drum (Sciaenops ocellatus) in South Carolina Using a Long-Term Tag-Recapture Study
Troha, Lukas
Tag-recapture studies are often utilized to generate precise, externally derived, estimates of stock assessment parameters such as tag-reporting rate and selectivity. These estimates can be used to increase the accuracy of recent stock assessments for red drum (Sciaenops ocellatus), which have exhibited significant uncertainty and largely leave the population status in question. Using more than forty years of red drum tag-recapture data available from the South Carolina Department of Natural Resources (SCDNR) including a high-reward tagging study, we estimated the tag-reporting rate of red drum, as well as the length-based selectivity of fishery-independent sampling gears (trammel net, electrofishing, stop net, and longline) and recreational hook-and-line. Tag-reporting rate in South Carolina is high overall, approaching maximal reporting (100%) in St. Helena Sound, Charleston Harbor, and Winyah Bay, while Port Royal Sound displayed 58.9% reporting rate. The shape of fishery-independent selectivity curves depended on gear type, with each gear selecting for a different size class. A dome-shaped pattern of recreational hook-and-line selectivity was observed for harvested and released fish in nearly all management periods, though the size of maximum selectivity in South Carolina recreational fisheries varied based on fate of the fish after capture. The results of this study provide essential information to be used in future red drum stock assessments and will subsequently influence management of the species.
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PASSIVE ACOUSTIC MONITORING DETECTS DIFFERENT FREQUENCIES AND DIVERSITY OF SONGBIRDS THAN A POINT COUNT METHOD: A CASE STUDY OF EPHEMERAL WETLANDS
Williamson, Ansley Vaughan
There is a growing interest in using artificial intelligence (AI) for the passive acoustic monitoring (PAM) of avian biodiversity due to its potential to provide cost-effective and non-invasive methods for processing large amounts of data. Traditional point count survey methods provide essential data regarding the diversity of many songbird communities; however, they are often costly and cumbersome compared to PAM. The purpose of our study was to compare species occurrences and community diversity detected by PAM and point count detection of bird biodiversity in ephemeral wetlands while varying the PAM confidence threshold used to score a species detection and the spatial scale. Our PAM system used Solo audio recorders and the BirdNET AI algorithm to identify species. We sampled 17 ephemeral wetlands and 5 uplands for 3 days each between May 15, 2022 - June 15, 2022.
The degree of difference between PAM and point counts depended strongly on the confidence threshold and spatial scale. Specifically, the median species occupancy detected between methods was significantly different for several species. Lower confidence thresholds (e.g., 0.1) in BirdNET resulted in greater species occupancy estimates than point counts, while higher thresholds (e.g., 0.9) yielded lower estimates. BirdNET estimated lower species occupancy than point counts. The confidence threshold also had a strong effect on community-level estimates of diversity, but this effect changed across the sample, site, and study spatial scales. In general, when the confidence threshold was low PAM detected higher diversity than point counts and this effect was stronger at larger spatial scales. Our results demonstrate that the confidence threshold and spatial scale will strongly influence comparisons between PAM and the point count method. PAM and point counts can be used complementarily, but the differences in monitoring shown in this analysis should be considered when designing a study that uses PAM.
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Categorizing 8-K Filings: A Comparison of Machine Learning Models
Montgomery, Meg
In the United States, public companies are required to submit form 8-K filings to the SEC within four days of any material event occurring. The timely nature of these announcements makes them important resources for investors, industry professionals, and researchers alike. As part of an 8-K filing, one Item, Item 5.02, is of particular interest as it announces the arrival and departure of key executive officers and board members. Machine learning and classification algorithms have been applied most commonly to 10-K and 10-Q SEC filings, but 8-K filings have only received little research attention. Studying the contents of 8-K filings by applying and comparing advanced classification algorithms is therefore of outmost importance. This study uses a big dataset of 8-K textual filings and applies 11 classification models via two different experiments: (a) identifying the Item based on 8-K textual inputs, and (b) categorizing the type of executive departure event described in Item 5.02. The study compares four traditional classification algorithms combined with two feature extraction techniques, a recurrent neural network model (LSTM), and two transformer models (SmallBERT and GPT-2).