Machine Learning Enables Improved Runtime and Precision for Bio-loggers on Seabirds

Overview

Journal Commun Biol

Specialty Biology

Date 2020 Oct 31

PMID 33127951

Citations 16

Authors

Joseph Korpela

Hirokazu Suzuki

Sakiko Matsumoto

Yuichi Mizutani

Masaki Samejima

Takuya Maekawa

Junichi Nakai

Ken Yoda

Affiliations

Soon will be listed here.

Abstract

Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspects of animals' lives, including their behaviours, physiology, social interactions, and external environment. However, bio-loggers have short runtimes when collecting data from resource-intensive (high-cost) sensors. This study proposes using AI on board video-loggers in order to use low-cost sensors (e.g., accelerometers) to automatically detect and record complex target behaviours that are of interest, reserving their devices' limited resources for just those moments. We demonstrate our method on bio-loggers attached to seabirds including gulls and shearwaters, where it captured target videos with 15 times the precision of a baseline periodic-sampling method. Our work will provide motivation for more widespread adoption of AI in bio-loggers, helping us to shed light onto until now hidden aspects of animals' lives.

Citing Articles

A benchmark for computational analysis of animal behavior, using animal-borne tags.

Hoffman B, Cusimano M, Baglione V, Canestrari D, Chevallier D, DeSantis D Mov Ecol. 2024; 12(1):78.

PMID: 39695785 PMC: 11654173. DOI: 10.1186/s40462-024-00511-8.

Mapping spatial memory in teleosts: a new Frontier in neural logging techniques.

Takahashi S, Sawatani F, Ide K, Abe T, Kitagawa T, Makiguchi Y Front Physiol. 2024; 15:1499058.

PMID: 39568545 PMC: 11576370. DOI: 10.3389/fphys.2024.1499058.

Time synchronisation for millisecond-precision on bio-loggers.

Wild T, Wilbs G, Dechmann D, Kohles J, Linek N, Mattingly S Mov Ecol. 2024; 12(1):71.

PMID: 39468685 PMC: 11520525. DOI: 10.1186/s40462-024-00512-7.

Hidden rivals: the negative impacts of dolphinfish on seabird foraging behaviour.

Koyama S, Goto Y, Furukawa S, Maekawa T, Yoda K Biol Lett. 2024; 20(8):20240223.

PMID: 39106947 PMC: 11303019. DOI: 10.1098/rsbl.2024.0223.

Machine learning approaches for biomolecular, biophysical, and biomaterials research.

Rickert C, Lieleg O Biophys Rev (Melville). 2024; 3(2):021306.

PMID: 38505413 PMC: 10914139. DOI: 10.1063/5.0082179.

References

Hussey N, Kessel S, Aarestrup K, Cooke S, Cowley P, Fisk A . ECOLOGY. Aquatic animal telemetry: A panoramic window into the underwater world. Science. 2015; 348(6240):1255642. DOI: 10.1126/science.1255642. View

Watanabe Y, Takahashi A . Linking animal-borne video to accelerometers reveals prey capture variability. Proc Natl Acad Sci U S A. 2013; 110(6):2199-204. PMC: 3568313. DOI: 10.1073/pnas.1216244110. View

Cox S, Orgeret F, Gesta M, Rodde C, Heizer I, Weimerskirch H . Processing of acceleration and dive data on-board satellite relay tags to investigate diving and foraging behaviour in free-ranging marine predators. Methods Ecol Evol. 2018; 9(1):64-77. PMC: 5812097. DOI: 10.1111/2041-210X.12845. View

Williams H, Taylor L, Benhamou S, Bijleveld A, Clay T, de Grissac S . Optimizing the use of biologgers for movement ecology research. J Anim Ecol. 2019; 89(1):186-206. PMC: 7041970. DOI: 10.1111/1365-2656.13094. View

Kays R, Crofoot M, Jetz W, Wikelski M . ECOLOGY. Terrestrial animal tracking as an eye on life and planet. Science. 2015; 348(6240):aaa2478. DOI: 10.1126/science.aaa2478. View

Fehlmann G, King A . Bio-logging. Curr Biol. 2016; 26(18):R830-R831. DOI: 10.1016/j.cub.2016.05.033. View

Troscianko J, Rutz C . Activity profiles and hook-tool use of New Caledonian crows recorded by bird-borne video cameras. Biol Lett. 2015; 11(12):20150777. PMC: 4707697. DOI: 10.1098/rsbl.2015.0777. View

Volpov B, Rosen D, Hoskins A, Lourie H, Dorville N, Baylis A . Dive characteristics can predict foraging success in Australian fur seals (Arctocephalus pusillus doriferus) as validated by animal-borne video. Biol Open. 2016; 5(3):262-71. PMC: 4810750. DOI: 10.1242/bio.016659. View

Volpov B, Hoskins A, Battaile B, Viviant M, Wheatley K, Marshall G . Identification of Prey Captures in Australian Fur Seals (Arctocephalus pusillus doriferus) Using Head-Mounted Accelerometers: Field Validation with Animal-Borne Video Cameras. PLoS One. 2015; 10(6):e0128789. PMC: 4479472. DOI: 10.1371/journal.pone.0128789. View

10.

Goldbogen J, Cade D, Boersma A, Calambokidis J, Kahane-Rapport S, Segre P . Using Digital Tags With Integrated Video and Inertial Sensors to Study Moving Morphology and Associated Function in Large Aquatic Vertebrates. Anat Rec (Hoboken). 2017; 300(11):1935-1941. DOI: 10.1002/ar.23650. View

11.

Rutz C, Bluff L, Weir A, Kacelnik A . Video cameras on wild birds. Science. 2007; 318(5851):765. DOI: 10.1126/science.1146788. View

12.

Nishiumi N, Matsuo A, Kawabe R, Payne N, Huveneers C, Watanabe Y . A miniaturized threshold-triggered acceleration data-logger for recording burst movements of aquatic animals. J Exp Biol. 2018; 221(Pt 6). DOI: 10.1242/jeb.172346. View

13.

Yoda K, Naito Y, Sato K, Takahashi A, Nishikawa J, Ropert-Coudert Y . A new technique for monitoring the behaviour of free-ranging Adélie penguins. J Exp Biol. 2001; 204(Pt 4):685-90. DOI: 10.1242/jeb.204.4.685. View

14.

Yoshino K, Takahashi A, Adachi T, Costa D, Robinson P, Peterson S . Acceleration-triggered animal-borne videos show a dominance of fish in the diet of female northern elephant seals. J Exp Biol. 2020; 223(Pt 5). DOI: 10.1242/jeb.212936. View

15.

Yonehara Y, Goto Y, Yoda K, Watanuki Y, Young L, Weimerskirch H . Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction. Proc Natl Acad Sci U S A. 2016; 113(32):9039-44. PMC: 4987799. DOI: 10.1073/pnas.1523853113. View

16.

Le Maho Y, Whittington J, Hanuise N, Pereira L, Boureau M, Brucker M . Rovers minimize human disturbance in research on wild animals. Nat Methods. 2014; 11(12):1242-4. DOI: 10.1038/nmeth.3173. View

17.

ODonoghue P, Rutz C . Real-time anti-poaching tags could help prevent imminent species extinctions. J Appl Ecol. 2016; 53(1):5-10. PMC: 4949716. DOI: 10.1111/1365-2664.12452. View

18.

Cooke S, Hinch S, Wikelski M, Andrews R, Kuchel L, Wolcott T . Biotelemetry: a mechanistic approach to ecology. Trends Ecol Evol. 2006; 19(6):334-43. DOI: 10.1016/j.tree.2004.04.003. View