Undergraduate Honors Thesis


Exploring the Efficacy of Baited Remote Underwater Video Systems When Assessing Fish Biodiversity, Composition, and Abundance in Coral Reefs Public Deposited

  • Coral reefs play a critical role in supporting marine fish biodiversity, yet increasing anthropogenic and natural disturbances threaten their diversity. Baited Remote Underwater Video systems (BRUVs) are becoming a non-invasive, time-efficient underwater tool for assessing reef community structure, fish biodiversity, and species-specific behaviors. To explore the efficacy of BRUV deployments, particularly when surveying reefs to collect species richness and abundance data, I conducted 12 BRUV deployments across seven coral reef sites in Cozumel, Mexico. Two separate student pairs processed the video footage from each deployment (five pairs of students in total), followed by a consensus analysis performed by the author. This study design allowed for an in-depth analysis of inter-rater reliability and potential factors that increase observation variation between analysts, such as species misidentification, miscounting fish abundance, or excessively recounting individuals. I compared my findings to existing literature in order to determine the most prominent strengths and limitations of BRUVs and propose ideal deployment methods for optimal results. In total, the BRUVs detected 63 fish species from 26 families and captured an average of 68.2% of estimated species richness. Based on comparisons between student teams and author data, inter-rater agreement of species composition within sites averaged 60.1%. Proposed improvements for optimal BRUV deployment include marking transects within the BRUV’s field of view and reducing fish identification requirements to family or genera rather than species. Taken together, results of this research emphasize the utility of BRUVs for assessing fish biodiversity in coral reefs, while highlighting limitations that should be considered in future study designs. These proposed improvements may reduce error in future BRUV studies and help make BRUVs a viable, accurate technique to be implemented by citizen scientists.
Date Awarded
  • 2019-01-01
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Granting Institution
Last Modified
  • 2020-01-06
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