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<p>The spatiotemporal distribution of interactions with industrial fisheries remains poorly understood for many incidentally caught species, largely due to data limitations such as sparse observer coverage and biases associated with zero-inflation. Here, we analyze 18&#xa0;years (2002–2019) of Japanese longline observer data to identify seasonal high likelihood areas for turtle bycatch, using a zero-inflated negative binomial model based on stochastic partial differential equations (SPDE) combined with hotspot analysis. This framework allows us to extract meaningful patterns from the first and fourth quarters of each year to generate spatially explicit estimates of relative leatherback density and potential bycatch interactions. Our results reveal that bycatch hotspots occur predominantly near the African coast in the first quarter and extend from the African coast across the North Atlantic, reaching as far west as Central America in the fourth quarter. Seasonal differences in estimated densities were more pronounced than interannual fluctuations, aligning with known migratory behaviors of leatherbacks. These findings underscore the importance of season-specific conservation strategies such as time-area closures or dynamic bycatch avoidance measures, providing actionable spatial and seasonal hotspot maps that could inform the design and timing of mitigation measures. More broadly, although our approach was not able to generate statically robust results for situations of extreme data paucity (quarters 2 and 3 in the Japan data), it offers a practical solution and additional tool for robust analyses under most data-limited conditions and enhances evidence-based conservation planning in marine ecosystems.</p>

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Identifying seasonal bycatch hotspots using longline observer data: a case study of leatherback turtles in the Atlantic Ocean

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Makoto Nishimoto,
  • Shintaro Ueno,
  • Kei Okamoto,
  • Hirotaka Ijima,
  • Daisuke Ochi

摘要

The spatiotemporal distribution of interactions with industrial fisheries remains poorly understood for many incidentally caught species, largely due to data limitations such as sparse observer coverage and biases associated with zero-inflation. Here, we analyze 18 years (2002–2019) of Japanese longline observer data to identify seasonal high likelihood areas for turtle bycatch, using a zero-inflated negative binomial model based on stochastic partial differential equations (SPDE) combined with hotspot analysis. This framework allows us to extract meaningful patterns from the first and fourth quarters of each year to generate spatially explicit estimates of relative leatherback density and potential bycatch interactions. Our results reveal that bycatch hotspots occur predominantly near the African coast in the first quarter and extend from the African coast across the North Atlantic, reaching as far west as Central America in the fourth quarter. Seasonal differences in estimated densities were more pronounced than interannual fluctuations, aligning with known migratory behaviors of leatherbacks. These findings underscore the importance of season-specific conservation strategies such as time-area closures or dynamic bycatch avoidance measures, providing actionable spatial and seasonal hotspot maps that could inform the design and timing of mitigation measures. More broadly, although our approach was not able to generate statically robust results for situations of extreme data paucity (quarters 2 and 3 in the Japan data), it offers a practical solution and additional tool for robust analyses under most data-limited conditions and enhances evidence-based conservation planning in marine ecosystems.