Monitoring Sea Turtle Nesting with Drones: A Comprehensive Review

Introduction

Sea turtle conservation efforts worldwide rely heavily on monitoring nesting beaches to assess population trends, protect nests, and implement effective conservation strategies. For decades, these monitoring activities have primarily depended on labor-intensive human patrolling techniques, with volunteers and researchers walking long distances on beaches, often at night and in challenging conditions. These traditional methods, while valuable, present numerous limitations including safety concerns for patrollers, resource constraints, and potential gaps in detection accuracy.

In recent years, drone technology, also known as Unmanned Aerial Vehicles (UAVs), has emerged as a promising tool to enhance sea turtle monitoring efforts. This report provides a comprehensive review of the current state of drone technology applications in sea turtle nesting monitoring, examining both the technological advancements and conservation outcomes, as well as limitations and future directions.

Traditional Monitoring Methods and Their Limitations

Sea turtle projects monitor over 3,200 nesting beaches globally, with more than 60 beaches in Costa Rica alone dedicated to protecting and surveying sea turtle nesting (Fonseca et al., 2015; The State of the World's Sea Turtles [SWOT], 2022). These long-term, global efforts have enabled scientists to assess population trends, provided evidence of recovering populations, and identified ongoing challenges (Mazaris et al., 2017; Piacenza et al., 2019; Godley et al., 2020; Mortimer et al., 2020).

However, traditional monitoring methods face several significant limitations:

1. Labor Intensity: Traditional methods require extensive human resources, often relying on volunteers, students, or early-career field scientists to gather data through long hours of beach patrolling (Shanker et al., 2003; Chacón et al., 2007; Quesada-Rodríguez et al., 2021).

2. Safety Concerns: Patrollers face various dangers including encounters with poachers, drug traffickers, feral animals, and other hazards. In extreme cases, these dangers have led to beaches being left unmonitored. For example, Moín beach on the Caribbean coast of Costa Rica was left unprotected after conservationist Jairo Mora was murdered by poachers in 2013, resulting in nearly 100% of nests being poached (Fonseca et al., 2015; Smith, 2016).

3. Resource Sustainability: The COVID-19 pandemic highlighted the vulnerability of traditional monitoring approaches, stressing both personnel and finances, leading to interruptions in data collection, nest management, and anti-poaching surveillance (Gardner, 2020; Quesada-Rodríguez et al., 2021).

4. Detection Limitations: Human observers may miss nesting activities, particularly in remote areas or during challenging conditions, potentially leading to incomplete data collection.

   
   

Drone Technology in Sea Turtle Monitoring

Evolution of Drone Use in Wildlife Conservation

Drones have proven effective in wildlife conservation contexts, particularly for surveying in remote and dangerous habitats and combating poaching (Hodgson et al., 2013; Mulero-Pázmány et al., 2014; Butcher et al., 2021). In South Africa, drone surveillance has been instrumental in protecting rhinos from poaching, with one project conducting 3,000 flight hours over 20 months and effectively stopping poaching in study areas within 5-7 days (Snitch, 2015).

For wildlife population monitoring, drones have demonstrated superior accuracy compared to traditional survey techniques. Studies have shown that drones can be 43-96% more accurate than ground-based methods for counting birds in seabird colonies (Hodgson et al., 2018) and can detect 26% more Nile crocodiles in lake surveys than ground methods (Ezat et al., 2018).

RGB Drones for Diurnal Sea Turtle Monitoring

Initial applications of drones in sea turtle conservation primarily utilized red-green-blue (RGB) cameras for daytime monitoring. These applications include:

1. Population Surveys: Drones have revealed adult sex ratios at breeding sites, identified individuals marked with satellite tags, and differentiated between species by size (Schofield et al., 2017; Schofield et al., 2019).

2. Mass Nesting Events: During mass nesting events (arribadas), drones have detected more turtles than manual counts. Gray et al. (2019) detected 8% more turtles than manual counts during a mass nesting event on Ostional beach, Costa Rica.

3. Habitat Assessment: Drones have been used to model nesting beaches, assess habitat quality, and monitor changes over time.

However, RGB drones are primarily limited to daytime use, while most turtle nesting activity occurs at night (Miller, 2017).

Thermal Infrared (TIR) Drones for Nocturnal Monitoring

The integration of thermal infrared (TIR) sensors with drones has revolutionized nocturnal sea turtle monitoring. TIR sensors detect body heat or thermal differences between animals and their surrounding environment (Hambrecht et al., 2019; Kays et al., 2019; Fust and Loos, 2020).

Key Findings from Empirical Studies

1. Detection Capabilities:

2. Improved Detection Rates:

3. Optimal Parameters for Detection:

   
   

• The optimal camera angle was 35–45 degrees downward from horizontal at a height of 50 meters above the ground for best identification of turtles and maximum beach coverage.

• At 50 meters altitude, the drone was inaudible due to wave sounds, though its positioning lights remained visible.

• Even at altitudes as low as 4 meters above a nesting turtle, the animals appeared unperturbed, though the automatic landing lights at such low altitudes could potentially deter more light-sensitive species.

• The "black hot" thermal visualization mode was judged better for detecting fine- scale features compared to "white hot" mode, though no significant difference was noted between the two.

4. Species Identification and Detailed Observation:

5. Anti-Poaching Applications:

6. Equipment Specifications:

• Autel Robotics drone (EVO II Dual 8K and Autel Explorer App) (Sellés-Ríos, 2023)

• DJI Mavic 2 Enterprise Dual Advanced with infrared camera capabilities (Crespo, 2023)

Novel Methodological Frameworks

Papazekou et al. (2024) presented a novel methodological framework for monitoring sea turtles using advanced technologies, including UAVs coupled with image and temperature sensors. Their approach focuses on collecting information for critical biological parameters concerning species reproduction and habitat use, following the complex life cycle of sea turtles. The framework encompasses:

1. Reproduction Potential Assessment: Characterizing nesting beaches in terms of their potential to sustain reproductive output.

2. Habitat Use and Pressure Monitoring: Identifying habitat use patterns and pressure drivers across different life and reproductive stages of marine turtle species.

3. Temporal Change Detection: Observing changes over time to provide early-warning recommendations for adaptive management.

This holistic and standardized approach using advanced technologies aims to foster conservation capacity, resolve difficulties previously addressed, and improve the collection of biological and environmental data in the frame of an adaptive management scheme.

Practical Applications and Case Studies

Puerto Rico: Community-Based Conservation

Luis Crespo of Friends of the Sea Turtles (ATMAR Inc.) in Maunabo, Puerto Rico, has implemented drone technology for sea turtle monitoring with significant success (Crespo, 2023). After 23 years of traditional monitoring methods, the organization adopted a DJI Mavic 2 Enterprise Dual Advanced drone with both visible and infrared cameras.

Key outcomes include:

1. Efficient Beach Monitoring: The drone allows monitoring of remote beaches without requiring complete beach walks, significantly reducing effort.

2. Enhanced Night Detection: The drone has proven "the most useful, efficient, and powerful tool" for night monitoring, allowing the team to identify nesting turtles without long night walks. Once a turtle is detected, volunteers can move directly to the location for tagging, measurements, and egg relocation if necessary.

3. Long-Distance Detection: The drone has detected leatherback turtles more than 100 meters away from the drone and about 2 kilometers away from the volunteer base.

4. Detailed Observation: Flying at low altitudes without disturbing turtles, the team has identified metal flipper tags, satellite transmitters, large scars, flipper damage, and even measured temperatures of sand, water, and turtles.

5. Poacher and Predator Detection: The drone enhances the ability to detect poachers and feral animals that threaten nests, though the team has not encountered poachers during drone operations yet.

6. Documentation: The drone has enabled exceptional night videos and photos, enhancing documentation capabilities.

Costa Rica: Scientific Validation

Research in Costa Rica has provided scientific validation of drone effectiveness for sea turtle monitoring. On Piro Beach (Osa Peninsula), thermal drone surveys began in 2021 and verified the effectiveness of drones for spotting sea turtles and tracks, differentiating between species, and detecting hatchlings and wildlife (Sellés-Ríos, 2023).

The comparative studies between foot and drone patrols demonstrated that thermal drones detected 20% more nesting activity than human patrollers and were better at spotting potential predators and humans, including potential poachers missed by foot patrols (Sellés-Ríos et al., 2022).

Challenges and Limitations

Despite the promising results, several challenges and limitations must be considered:

1. Identification Challenges: Some tracks and turtles can be difficult to identify and may be confused with marks made by wave action or other beach features like logs and debris (Sellés-Ríos, 2023).

2. Analysis Time: While drone flights typically last 21-25 minutes, video analysis may require an additional 45 minutes to prevent double-counting of tracks and other potential errors. This challenge could potentially be addressed through higher resolution TIR cameras and by georeferencing tracks with artificial intelligence

(Sellés-Ríos, 2023).

3. Environmental Factors: The effectiveness of thermal detection depends on significant temperature differences between sand and sea turtles. Further testing on beaches with varying sand color and heat retention is needed to understand where thermal drone work can be most effective (Sellés-Ríos, 2023).

4. Lighting Conditions: Moonlight or artificial lights may reduce animal detection using TIR drones, requiring consideration of lunar cycles and light pollution in monitoring plans (Sellés-Ríos, 2023).

5. Cost Barriers: The cost of thermal drone technology may be prohibitive for some conservation projects. The Autel Robotics drone used in the Costa Rica study cost approximately US$10,000, though other models like the DJI Mavic 3 Enterprise Thermal camera may offer more cost-effective alternatives (Sellés-Ríos, 2023).

6. Regulatory Compliance: Drone operations must comply with local aviation regulations, which vary by country and may require permits, certified operators, or other compliance measures.

7. Weather Limitations: Drone operations may be restricted during adverse weather conditions such as high winds or heavy rainfall, potentially creating gaps in monitoring coverage.

Recommendations for Implementation

Based on the reviewed literature, several recommendations emerge for implementing drone technology in sea turtle nesting monitoring:

1. Equipment Testing: Test various drone models for their cost-effectiveness and potential light and sound effects on nesting sea turtles before embarking on a long- term thermal drone monitoring effort (Sellés-Ríos, 2023).

2. Complementary Approach: Use TIR drones, human beach patrols, and effective local law enforcement as a complementary set of tools to combat threats on sea turtle beaches while supporting monitoring and research goals (Sellés-Ríos, 2023).

3. Standardized Protocols: Develop standardized protocols for drone monitoring to ensure coherent and systematic data collection across scales and regions (Papazekou et al., 2024).

4. Operator Training: Ensure drone operators receive proper training in both drone operation and sea turtle identification to maximize the effectiveness of monitoring efforts.

5. Data Management Systems: Implement robust data management systems to handle the large volumes of imagery and video generated by drone monitoring.

6. Community Involvement: Where possible, involve local communities in drone monitoring programs, as demonstrated in Puerto Rico, to build capacity and enhance conservation outcomes (Crespo, 2023).

7. Technological Integration: Explore the integration of artificial intelligence and machine learning for automated detection and identification of sea turtles and their tracks to reduce analysis time and improve accuracy.

Future Directions

The future of drone technology in sea turtle nesting monitoring holds significant promise, with several emerging areas for development:

1. rtificial Intelligence Integration: Developing AI algorithms for automated detection and identification of sea turtles, tracks, nests, and potential threats could significantly enhance the efficiency and accuracy of drone monitoring.

2. Multispectral Imaging: Exploring the use of multispectral imaging beyond thermal infrared could provide additional insights into sea turtle behavior and habitat use.

3. Extended Flight Capabilities: Advances in battery technology and drone design could extend flight times, allowing for more comprehensive monitoring coverage.

4. Miniaturization and Cost Reduction: Continued miniaturization of thermal sensors and overall cost reduction would make this technology more accessible to conservation projects with limited resources.

5. Integrated Monitoring Systems: Developing integrated systems that combine drone monitoring with other technologies such as satellite tracking, environmental sensors, and beach cameras could provide a more comprehensive understanding of sea turtle ecology and conservation needs.

6. Standardized Global Protocols: Establishing standardized global protocols for drone monitoring of sea turtles would facilitate data sharing and comparative analyses across different regions and projects.

Conclusion

Drone technology, particularly when equipped with thermal infrared sensors, represents a significant advancement in sea turtle nesting monitoring. Empirical studies have demonstrated that drones can detect more nesting activity than traditional human patrolling, enhance safety by reducing the need for dangerous night patrols, and provide detailed observations that would be difficult or impossible to obtain through ground-based methods.

While challenges remain, including identification difficulties, analysis time, environmental factors, and cost barriers, the potential benefits of drone technology for sea turtle conservation are substantial. By addressing these challenges and following the recommendations outlined in this report, conservation practitioners can effectively integrate drone technology into their monitoring programs, enhancing their ability to protect these endangered species and their critical nesting habitats.

As technology continues to advance and costs decrease, drone monitoring is likely to become an increasingly important tool in the global effort to conserve sea turtles, complementing traditional methods and providing new insights into sea turtle ecology and conservation.

References

1. Butcher, P. A., Colefax, A. P., Gorkin, R. A., Kajiura, S. M., López, N. A., Mourier, J., ... & Raoult, V. (2021). The drone revolution of shark science: a review. Drones, 5(1), 8.

2. Chacón, D., Dick, B., Harrison, E., Sarti, L., & Solano, M. (2007). Manual sobre técnicas de manejo y conservación de las tortugas marinas en playas de anidación de Centroamérica. Secretaría Pro Tempore de la Convención Interamericana para la Protección y Conservación de las Tortugas Marinas (CIT), San José, Costa Rica.

3. Crespo, L. (2023). Use of an Unmanned Aerial Vehicle (UAV-drone) to Monitor Sea Turtles on Nesting Beaches in Southeast Puerto Rico. New England Aquarium. https://www.neaq.org/use-of-an-unmanned-aerial-vehicle-uav-drone-to-monitor-sea-turtles-on-nesting-beaches-in-southeast-puerto-rico/

4. Dunstan, A. J., Robertson, K., Fitzpatrick, R., Pickford, J., & Meager, J. (2020). Use of unmanned aerial vehicles (UAVs) for mark-resight nesting population estimation of adult female green sea turtles at Raine Island. PloS one, 15(6), e0228524.

5. Ezat, M. A., Fritsch, C. J., & Downs, C. T. (2018). Use of an unmanned aerial vehicle (drone) to survey Nile crocodile populations: A case study at Lake Nyamithi, Ndumo game reserve, South Africa. Biological Conservation, 223, 76-81.

6. Fonseca, L. G., Murillo, G. A., Guadamúz, L., Spínola, R. M., & Valverde, R. A. (2015). Downward but stable trend in the abundance of arribada olive ridley sea turtles (Lepidochelys olivacea) at Nancite Beach, Costa Rica (1971–2007). Chelonian Conservation and Biology, 14(1), 11-20.

7. Fust, P., & Loos, J. (2020). Development perspectives for the application of autonomous, unmanned aerial systems (UASs) in wildlife conservation. Biological Conservation, 241, 108380.

8. Gardner, C. J. (2020). Not teaching in vain: how environmental education can help to address the biodiversity crisis. Environmental Conservation, 47(2), 99-102.

9. Godley, B. J., Broderick, A. C., Colman, L. P., Formia, A., Godfrey, M. H., Hamann, M., ... & Witt, M. J. (2020). Reflections on sea turtle conservation. Oryx, 54(3), 287-289.

10. Gonzalez, L. F., Montes, G. A., Puig, E., Johnson, S., Mengersen, K., & Gaston, K. J. (2016). Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors, 16(1), 97.

11. Gray, P. C., Fleishman, A. B., Klein, D. J., McKown, M. W., Bézy, V. S., Lohmann, K. J., & Johnston, D. W. (2019). A convolutional neural network for detecting sea turtles in drone imagery. Methods in Ecology and Evolution, 10(3), 345-355.

12. Hambrecht, L., Brown, R. P., Piel, A. K., & Wich, S. A. (2019). Detecting 'poachers' with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania. Biological Conservation, 233, 109-117.

13. Hodgson, J. C., Baylis, S. M., Mott, R., Herrod, A., & Clarke, R. H. (2016). Precision wildlife monitoring using unmanned aerial vehicles. Scientific reports, 6(1), 1-7.

14. Hodgson, J. C., Mott, R., Baylis, S. M., Pham, T. T., Wotherspoon, S., Kilpatrick, A. D., ... & Koh, L. P. (2018). Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution, 9(5), 1160-1167.

15. Kays, R., Sheppard, J., Mclean, K., Welch, C., Paunescu, C., Wang, V., ... & Crofoot, M. (2019). Hot monkey, cold reality: surveying rainforest canopy mammals using drone-mounted thermal infrared sensors. International Journal of Remote Sensing, 40(2), 407-419.

16. Linchant, J., Lisein, J., Semeki, J., Lejeune, P., & Vermeulen, C. (2015). Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Review, 45(4), 239-252.

17. Mazaris, A. D., Schofield, G., Gkazinou, C., Almpanidou, V., & Hays, G. C. (2017). Global sea turtle conservation successes. Science Advances, 3(9), e1600730.

18. Meletis, Z. A., & Harrison, E. C. (2010). Tourists and turtles: Searching for a balance in Tortuguero, Costa Rica. Conservation and Society, 8(1), 26-43.

19. Miller, J. D. (2017). Reproduction in sea turtles. In The biology of sea turtles (pp. 51-81). CRC Press.

20. Mortimer, J. A., Esteban, N., Guzman, A. N., & Hays, G. C. (2020). Estimates of marine turtle nesting populations in the south-west Indian Ocean indicate the importance of the Chagos Archipelago. Oryx, 54(3), 332-343.

21. Mulero-Pázmány, M., Stolper, R., Van Essen, L. D., Negro, J. J., & Sassen, T. (2014). Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PloS one, 9(1), e83873.

22. Papazekou, M., Kyprioti, A., Chatzimentor, A., Dimitriadis, C., Vallianos, N., & Mazaris, A. D. (2024). Advancing Sea Turtle Monitoring at Nesting and Near Shore Habitats with UAVs, Data Loggers, and State of the Art Technologies. Diversity, 16(3), 153. https://www.mdpi.com/1424-2818/16/3/153

23. Pheasey, H., Glen, F., Allison, N. L., Chacón-Chaverri, D., Iñíguez, M. L., Sánchez, F. A., ... & Williams, J. L. (2021). Quantifying illegal take and its impacts on immature green turtles in a major foraging ground. Global Ecology and Conservation, 28, e01644.

24. Piacenza, S. E., Richards, P. M., & Heppell, S. S. (2019). Fathoming sea turtles: monitoring strategy evaluation to improve conservation status assessments. Ecological Applications, 29(3), e01873.

25. Quesada-Rodríguez, C., Orientale, C., Diaz, R., & Sellés-Ríos, B. (2021). Impact of 2020 COVID-19 mobility restrictions in sea turtle conservation: a case study in Tortuguero, Costa Rica. Frontiers in Marine Science, 8, 694433.

26. Rees, A. F., Avens, L., Ballorain, K., Bevan, E., Broderick, A. C., Carthy, R. R., ... & Godley, B. J. (2018). The potential of unmanned aerial systems for sea turtle research and conservation: a review and future directions. Endangered Species Research, 35, 81-100.

27. Santidrian-Tomillo, P., Saba, V. S., Piedra, R., Paladino, F. V., & Spotila, J. R. (2008). Effects of illegal harvest of eggs on the population decline of leatherback turtles in Las Baulas Marine National Park, Costa Rica. Conservation Biology, 22(5), 1216-1224.

28. Schofield, G., Esteban, N., Katselidis, K. A., & Hays, G. C. (2019). Drones for research on sea turtles and other marine vertebrates–A review. Biological Conservation, 238, 108214.

29. Schofield, G., Katselidis, K. A., Lilley, M. K., Reina, R. D., & Hays, G. C. (2017). Detecting elusive aspects of wildlife ecology using drones: new insights on the mating dynamics and operational sex ratios of sea turtles. Functional Ecology, 31(12), 2310-2319.

30. Sellés-Ríos, B. (2023). Heat-Sensing Drones Open New Frontiers in Sea Turtle Research. The State of the World's Sea Turtles (SWOT). https://www.seaturtlestatus.org/articles/heat-sensing-drones-open-new-frontiers

31. Sellés-Ríos, B., Ortiz-García, J., García-Colomo, J., Latour, O., Flatt, E., & Whitworth, A. (2022). Warm beach, warmer turtles: Using drone-mounted thermal infrared sensors to monitor sea turtle nesting activity. Frontiers in Conservation Science, 3, 954791. https://www.frontiersin.org/journals/conservation-science/articles/10.3389/fcosc.2022.954791/full

32. Shanker, K., Pandav, B., & Choudhury, B. C. (2003). An assessment of the olive ridley turtle (Lepidochelys olivacea) nesting population in Orissa, India. Biological Conservation, 115(1), 149-160.

33. Smith, B. (2016). The murder that shook the jungle. Harpers Magazine, 332, 39-52.

34. Snitch, T. (2015). Satellites, mathematics and drones take down poachers in Africa. The Conversation, 27.

35. Stander, J., Scheele, B. C., Kark, S., & Auerbach, N. A. (2021). Thermal drone detects nests of the threatened Australasian bittern (Botaurus poiciloptilus). Ecological Management & Restoration, 22(3), 267-270.

36. The State of the World's Sea Turtles [SWOT]. (2022). The State of the World's Sea Turtles Report, vol. XVII.

37. Wall, T. (2014). Unmanned aerial vehicles & the future of conservation. Conservation Careers. https://www.conservation-careers.com/conservation-jobs-careers-advice/unmanned-aerial-vehicles-future-conservation/

38. Witt, M. J., Barandun, G., Beaumont, E. S., Bradbury, A. C., Parsons, D. R., & Lovell, P. (2020). Using drones to detect and monitor koalas in complex environments. Scientific Reports, 10(1), 1-9.