Development of a Web-Responsive Analysis Tool for Tracking Sea Turtle Behavior and Habitat

  The environmental issues caused by marine debris and the problem of habitat pollution for marine organisms are pervasive worldwide. Both floating debris and sunken debris contaminate various habitats, including coastlines, coral reefs, and seaweed beds. Various marine organisms exposed to such marine debris ultimately suffer from entanglement and ingestion, with sea turtles, in particular, accounting for 66% of reported cases of harm among all marine mammals. In Korea, various cases of mortality due to entanglement and ingestion in sea turtles have been widely reported. To comprehend the correlation between the behavior, habitats, and marine debris associated with sea turtles, ecological research is being conducted through location tracking. it is essential to conduct habitat degradation research for sea turtles by analyzing their spatial behavior using location-based methods and understanding feeding patterns using various environmental information. To address these issues, it is crucial to accurately understand the movement routes and activity patterns of marine organisms. In the field of wildlife research, various studies are being conducted using geographic information systems to utilize diverse analytical methods.  In this study, we aimed to develop a web-responsive analysis tool for continuous tracking of sea turtle behavior and habitat foraging. The analysis module comprises three parts: the preprocessing module, spatial analysis module, and exploratory analysis module. The preprocessing module functions to extract necessary data from Argos satellite-received location information and refine it into clean data. It extracts latitude, longitude, sea surface temperature, and depth information from multiple files, organizes them into a single table, and saves them in a analyzable file format. The analysis module includes functions for deriving sea turtle activity ranges and overlapping analyses of habitat within activity zones. The activity range analysis utilizes Kernel Density Estimation (KDE) based on sea turtle location point data. Bandwidth, defined automatically based on the distribution of accumulation and points, allows for efficient analysis. The habitat overlapping analysis integrates various biological occurrence information such as coral, algae, and jellyfish within the sea turtle's activity zone. This enables exploration of the sea turtle's habitat environment within dense areas. The exploratory analysis module offers visualization features for location information, received depth, and sea surface temperature derived from data received by Argos satellites. Depth and sea surface temperature details are presented alongside location information, utilizing color coding for enhanced comprehension.  The analysis module and the platform it is implemented on were developed in the form of a responsive web application using the open-source R-shiny. The responsive web application allows researchers to input and analyze sea turtle location data directly from a web page in any internet-enabled environment. It is fast and efficient as the results can be promptly visualized on a map. The sea turtle behavioral analysis tool developed in this study enables researchers to obtain standardized information related to behavior and habitat using location-based sea turtle data received from various satellites. It establishes a systematic approach for researchers to easily utilize this information through the web.