Detection efficiency of acoustic biotelemetry sensors on Wave Gliders

Animal Biotelemetry - Tập 6 - Trang 1-14 - 2018
Megan Cimino1,2, Michael Cassen1, Sophia Merrifield1, Eric Terrill1
1Scripps Institution of Oceanography, University of California at San Diego, La Jolla, USA
2Institute of Marine Science, University of California Santa Cruz, Santa Cruz, USA

Tóm tắt

Detecting tagged animals in coastal environments is often limited to stationary arrays of acoustic receivers that can decode transmissions from tags on animals. However, mobile autonomous platforms are becoming important tools that support the science of understanding biophysical relationships because they can concurrently detect tagged individuals and measure properties of their ocean habitat. To assess the effectiveness of these emerging platforms, proper validation and range detection studies are necessary. Here, we report on the deployment of a wave powered unmanned surface vessel, the Liquid Robotics Wave Glider SV3, equipped with a forward- and backward-facing acoustic receiver (VR2W) and transceiver (VR2Tx) at 4 m depth. Surveys were conducted around two stationary moorings equipped with receivers, transceivers or tags emitting signals with different power outputs. During our study, the sea state was mild with low wind speeds (< 10 kts) and small wave heights (< 0.8 m). We determined the influence of environmental and vehicle factors on the detection range of transmitters with various power outputs. Overall, the highest detection efficiencies (~ 50–95%) were at distances < 0.1 km, lower detection efficiencies (0.1–50%) were > 0.5 km and the maximum range was 0.5–1.2 km. The forward-facing receiver had almost half the detection efficiency of the backward-facing transceiver, suggesting a backward configuration is optimal to reduce the influence of the moving platform. The higher power output transmitters had a 20% detection efficiency to ranges of ~ 0.5 km (153 dB) and ~ 0.8 km (160 dB). Distance between the receiver and transmitter was the main factor affecting detection probability, with background noise, receiver heading, angle between transmitter and receiver and wave height also being important. Wind speed, water temperature, mooring line tilt angle and vehicle dynamics were found not to be as important over the limited range of conditions over which our study was conducted. Wave Gliders equipped with receivers can provide useful data and can be an effective biotelemetry asset that could supplement stationary arrays of acoustic receivers or act as an exploratory technology to search for biologically important areas.

Tài liệu tham khảo

Hussey NE, Kessel ST, Aarestrup K, et al. Aquatic animal telemetry: a panoramic window into the underwater world. Science. 2015;348:1255642. Kessel ST, Cooke SJ, Heupel MR, et al. A review of detection range testing in aquatic passive acoustic telemetry studies. Rev Fish Biol Fish. 2014;24:199–218. Oliver MJ, Breece MW, Fox DA, et al. Shrinking the haystack: using an AUV in an integrated ocean observatory to map Atlantic Sturgeon in the coastal ocean. Fisheries. 2013;38:210–6. Breece MW, Fox DA, Dunton KJ, et al. Dynamic seascapes predict the marine occurrence of an endangered species: Atlantic Sturgeon Acipenser oxyrinchus oxyrinchus. Methods Ecol Evol. 2016;7:725–33. Haulsee DE, Breece MW, Miller DC, Wetherbee BM, Fox DA, Oliver MJ. Habitat selection of a coastal shark species estimated from an autonomous underwater vehicle. Mar Ecol Prog Ser. 2015;528:277–88. Clark CM, Forney C, Manii E, et al. Tracking and following a tagged leopard shark with an autonomous underwater vehicle. J Field Robot. 2013;30:309–22. Lin Y, Hsiung J, Piersall R, White C, Lowe CG, Clark CM. A multi-autonomous underwater vehicle system for autonomous tracking of marine life. J Field Robot. 2017;34:757–74. Grothus TM, Dobarro J, Ladd J, et al. Use of a multi-sensored AUV to telemeter tagged Atlantic sturgeon and map their spawning habitat in the Hudson River, USA. In: Autonomous underwater vehicles, 2008. AUV 2008. IEEE/OES; 2008; IEEE; 2008. https://doi.org/10.1109/AUV.2008.5347597. Grothues TM, Dobarro J, Eiler J. Collecting, interpreting, and merging fish telemetry data from an AUV: remote sensing from an already remote platform. Auton Underw Veh (AUV). 2010;2010:1–9. Carlon R. Tracking tagged fish using a wave glider. In: OCEANS’15 MTS/IEEE Washington; 2015; IEEE; 2015. https://doi.org/10.23919/OCEANS.2015.7404617. How JR, de Lestang S. Acoustic tracking: issues affecting design, analysis and interpretation of data from movement studies. Mar Freshw Res. 2012;63:312–24. Baker LL, Jonsen ID, Flemming JEM, et al. Probability of detecting marine predator-prey and species interactions using novel hybrid acoustic transmitter-receiver tags. PLoS ONE. 2014;9:e98117. Mathies NH, Ogburn MB, McFall G, Fangman S. Environmental interference factors affecting detection range in acoustic telemetry studies using fixed receiver arrays. Mar Ecol Prog Ser. 2014;495:27–38. Melnychuk M. Detection efficiency in telemetry studies: definitions and evaluation methods. Telemetry techniques: a user guide for fisheries research American Fisheries Society, Bethesda, Maryland. 2012339-357. Cote D, Nicolas J-M, Whoriskey FG, et al. Characterizing snow crab (Chionoecetes opilio) movements in the Sydney Bight (Nova Scotia, Canada): a collaborative approach using multi-scale acoustic telemetry. Can J Fish Aquat Sci. 2018;999:1–13 Oliver MJ, Breece MW, Haulsee DE, et al. Factors affecting detection efficiency of mobile telemetry Slocum gliders. Anim Biotelem. 2017;5:14. Reubens J, Verhelst P, van der Knaap I, Deneudt K, Moens T, Hernandez F. Environmental factors influence the detection probability in acoustic telemetry in a marine environment: results from a new setup. Hydrobiologia. 2018;. https://doi.org/10.1007/s10750-017-3478-7. Topping DT, Szedlmayer ST. Home range and movement patterns of red snapper (Lutjanus campechanus) on artificial reefs. Fish Res. 2011;112:77–84. How JR, de Lestang S. Acoustic tracking: issues affecting design, analysis and interpretation of data from movement studies. Mar Freshw Res. 2012;63:312–24. Welsh JQ, Fox RJ, Webber DM, Bellwood DR. Performance of remote acoustic receivers within a coral reef habitat: implications for array design. Coral Reefs. 2012;31:693–702. Heupel MR, Semmens JM, Hobday AJ. Automated acoustic tracking of aquatic animals: scales, design and deployment of listening station arrays. Mar Freshw Res. 2006;57:1–13. Singh L, Downey NJ, Roberts MJ, et al. Design and calibration of an acoustic telemetry system subject to upwelling events. Afr J Mar Sci. 2009;31:355–64. Gjelland KØ, Hedger RD. Environmental influence on transmitter detection probability in biotelemetry: developing a general model of acoustic transmission. Methods Ecol Evol. 2013;4:665–74. Everest FA, Young RW, Johnson MW. Acoustical characteristics of noise produced by snapping shrimp. J Acoust Soc Am. 1948;20:137–42. Kessel ST, Hussey NE, Webber DM, et al. Close proximity detection interference with acoustic telemetry: the importance of considering tag power output in low ambient noise environments. Anim Biotelem. 2015;3:5. TinHan TC, Mohan JA, Dumesnil M, DeAngelis BM, Wells RJD. Linking habitat use and trophic ecology of spotted seatrout (Cynoscion nebulosus) on a restored oyster reef in a subtropical estuary. Estuaries Coast. 2018;41:1–13. Eiler JH, Grothues TM, Dobarro JA, Masuda MM. Comparing autonomous underwater vehicle (AUV) and vessel-based tracking performance for locating acoustically tagged fish. Mar Fish Rev. 2013;75:27–42. Voegeli FA, Smale MJ, Webber DM, Andrade Y, O’dor RK. Ultrasonic telemetry, tracking and automated monitoring technology for sharks. Environ Biol Fishes. 2001;60:267–82. Welch DW, Boehlert GW, Ward BR. POST—the Pacific Ocean salmon tracking project. Oceanol Acta. 2002;25:243–53. Holland K, Brill R, Ferguson S, Chang R, Yost R. A small vessel technique for tracking pelagic fish. Mar Fish Rev. 1985;47:26–32. Ng CL, Able KW, Grothues TM. Habitat use, site fidelity, and movement of adult striped bass in a southern New Jersey estuary based on mobile acoustic telemetry. Trans Am Fish Soc. 2007;136:1344–55. Clements S, Jepsen D, Karnowski M, Schreck CB. Optimization of an acoustic telemetry array for detecting transmitter-implanted fish. N Am J Fish Manag. 2005;25:429–36. De’Ath G. Boosted trees for ecological modeling and prediction. Ecology. 2007;88:243–51. Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. J Anim Ecol. 2008;77:802–13. Buston PM, Elith J. Determinants of reproductive success in dominant pairs of clownfish: a boosted regression tree analysis. J Anim Ecol. 2011;80:528–38. Oppel S, Meirinho A, Ramírez I, et al. Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biol Cons. 2012;156:94–104. Leathwick JR, Elith J, Francis MP, Hastie T, Taylor P. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Mar Ecol Prog Ser. 2006;321:267–81. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189–232. Scales KL, Hazen EL, Maxwell SM, et al. Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time. Ecol Appl. 2017;27:2313–29. Soykan CU, Eguchi T, Kohin S, Dewar H. Prediction of fishing effort distributions using boosted regression trees. Ecol Appl. 2014;24:71–83. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. New York: Springer; 2001. Hosmer DW, Lemeshow S. Applied logistic regression. 2nd edn:Wiley;2000.