Mobility as a Species Identifier of Hooked Fishes: A Study of Geospatial Time Series

The use of smart fishing devices (SFDs) is expanding for applications in marine science and fisheries management. These devices signal a capture event through low-power wide-area networks (LPWAN) or non-terrestrial networks (NTN), providing real-time data. A critical challenge in these applications is ensuring the healthy release of non-target species, as post-release stress and survival are strongly species-specific. Consequently, identifying the species that has triggered the device is paramount. This study investigates whether the analysis of an SFD’s GPS periodic position is sufficient to infer the captured species. We examined a dataset comprising 11,905 GPS positions from 983 distinct capture events. Our findings indicate that GPS data is not sufficient for accurate species classification. However, the data can effectively be used to detect self-release events, achieving 72% accuracy from a sequence of 3 GPS positions and 80% accuracy from 12 positions. This finding demonstrates that SFD positional data is a promising tool for remotely monitoring gear availability. Furthermore, the inability to classify species with this data highlights the need to incorporate more granular sensors, such as accelerometers or higher-frequency GPS, in future work.