Comparing Coral Colony Surveys From In-Water Observations and Structure-From-Motion Imagery Shows Low Methodological Bias

As the threats to coral reefs mount, scientists and managers are looking for innovative ways to increase the scope, scale, and efficiency of coral reef monitoring. Monitoring changes in coral communities and demographic features provides key information about ecosystem function and resilience of reefs. While most monitoring programs continue to rely on in-water visual survey methods, scientists are exploring 3D imaging technologies such as photogrammetry, also known as Structure-from-Motion (SfM), to enhance precision of monitoring, increase logistical efficiency in the field, and generate a permanent record of the reef. Here, we quantitatively compare data generated from in-water surveys to SfM-derived metrics for assessing coral demography, bleaching, and diversity in the main Hawaiian Islands as part of NOAA's National Coral Reef Monitoring Program. Our objectives were to compare between-method error to within method error, test for bias between methods, and identify strengths and weaknesses of both methods. Colony density, average colony diameter, average partial mortality, prevalence of bleaching, species richness, and species diversity were recorded using both methods within the same survey areas. For all metrics, the magnitude of between method error was comparable to the within-method error for the in-water method and between method error was significantly higher than within-method error for SfM for one of the seven metrics. Our results also reveal that a majority of the metrics do not vary significantly between methods, nor did we observe a significant interaction between method and habitat type or method and depth. Exceptions include estimates of partial mortality, bleaching prevalence, and Porites juvenile density-though differences between methods are generally small. Our study also highlights that SfM offers a unique opportunity to more rigorously quantify and mitigate inter-observer error by providing observers unlimited "bottom time" and the opportunity to work together to resolve difficult annotations. However, the necessary investment in equipment and expertise does present substantial up-front costs, and the time associated with curating imagery, photogrammetric modeling, and manual image annotation can reduce the timeliness of data reporting. SfM provides a powerful tool to reimagine how we study and manage