Estimating animal population density using passive acoustics

Abstract

Reliable estimation of the size or density of wild animal populations is very important for effective wildlife management, conservation and ecology. Currently, the most widely used methods for obtaining such estimates involve either sighting animals from transect lines or some form of capture‐recapture on marked or uniquely identifiable individuals. However, many species are difficult to sight, and cannot be easily marked or recaptured. Some of these species produce readily identifiable sounds, providing an opportunity to use passive acoustic data to estimate animal density. In addition, even for species for which other visually based methods are feasible, passive acoustic methods offer the potential for greater detection ranges in some environments (e.g. underwater or in dense forest), and hence potentially better precision. Automated data collection means that surveys can take place at times and in places where it would be too expensive or dangerous to send human observers. Here, we present an overview of animal density estimation using passive acoustic data, a relatively new and fast‐developing field. We review the types of data and methodological approaches currently available to researchers and we provide a framework for acoustics‐based density estimation, illustrated with examples from real‐world case studies. We mention moving sensor platforms (e.g. towed acoustics), but then focus on methods involving sensors at fixed locations, particularly hydrophones to survey marine mammals, as acoustic‐based density estimation research to date has been concentrated in this area. Primary among these are methods based on distance sampling and spatially explicit capture‐recapture. The methods are also applicable to other aquatic and terrestrial sound‐producing taxa. We conclude that, despite being in its infancy, density estimation based on passive acoustic data likely will become an important method for surveying a number of diverse taxa, such as sea mammals, fish, birds, amphibians, and insects, especially in situations where inferences are required over long periods of time. There is considerable work ahead, with several potentially fruitful research areas, including the development of ( i ) hardware and software for data acquisition, ( ii ) efficient, calibrated, automated detection and classification systems, and ( iii ) statistical approaches optimized for this application. Further, survey design will need to be developed, and research is needed on the acoustic behaviour of target species. Fundamental research on vocalization rates and group sizes, and the relation between these and other factors such as season or behaviour state, is critical. Evaluation of the methods under known density scenarios will be important for empirically validating the approaches presented here.

Shown for reading. Its publisher allows quoting it, not republishing it.

References 174

1. 1.

   Abbot TA, Premus VE, Abbot PA. A real-time method for autonomous passive acoustic detection-classification of humpback whales. The Journal of the Acoustical Society of America. 2010;127:2894-2903.

   Not in our library10.1121/1.3365255

2. 2.

   Acevedo MA, Corrada-Bravo CJ, Corrada-Bravo H, Villanueva-Rivera LJ, Aide TM. Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Ecological Informatics. 2009;4:206-214.

   Not in our library

3. 3.

   Acevedo MA, Villanueva-Rivera LJ. Using automated digital recording systems as effective tools for the monitoring of birds and amphibians. Wildlife Society Bulletin. 2006;34:211-214.

   Not in our library

4. 4.

   Adi K, Johnson MT, Osiejuk TS. Acoustic censusing using automatic vocalization classification and identity recognition. The Journal of the Acoustical Society of America. 2010;127:874-883.

   Not in our library10.1121/1.3273887

5. 5.

   Akamatsu T, Matsuda A, Suzuki S, Wang D, Wang K, Suzuki M, Muramoto H, Sugiyama N, Oota K. New stereo acoustic data logger for free-ranging dolphins and porpoises. Marine Technology Society Journal. 2005;39:3-9.

   Not in our library

6. 6.

   Akamatsu T, Wang D, Wang K, Li S, Dong S, Zhao X, Barlow J, Stewart BS, Richlen M. Estimation of the detection probability for Yangtze finless porpoises (Neophocaena phocaenoides asiaeorientalis) with a passive acoustic method. The Journal of the Acoustical Society of America. 2008;123:4403-4411.

   Not in our library10.1121/1.2912449

7. 7.

   Alldredge MW, Simons TR, Pollock KH. A field evaluation of distance measurement error in auditory avian point count surveys. Journal of Wildlife Management. 2007;71:2759-2766.

   Not in our library

8. 8.

   Amorim M, Simões J, Almada V, Fonseca P. Stereotypy and variation of the mating call in the Lusitanian toadfish, Halobatrachus didactylus. Behavioral Ecology and Sociobiology. 2011;65:707-716.

   Not in our library

9. 9.

   Amorim M, Vasconcelos RO. Variability in the mating calls of the Lusitanian toadfish Halobatrachus didactylus: potential cues for individual recognition. Journal of Fish Biology. 2008;72:1355-1368.

   Not in our library

10. 10.

    Amorim MCP, Vasconcelos RO, Marques JF, Almada F. Seasonal variation of sound production in the Lusitanian toadfish, Halobatrachus didactylus. Journal of Fish Biology. 2006;69:1892-1899.

    Not in our library

11. 11.

    Anderson DR, Burnham KP, Lubow BC, Thomas L, Corn PS, Medica PA, Marlow RW. Field trials of line transect methods applied to estimation of desert tortoise abundance. Journal of Wildlife Management. 2001;65:583-597.

    Not in our library

12. 12.

    Anderson KA, Rountree RA, Juanes F. Soniferous fishes in the Hudson river. Transactions of the American Fisheries Society. 2008;137:616-626.

    Not in our library

13. 13.

    André M, van der Schaar M, Zaugg S, Houégnigan L, Sánchez A, Castell J. Listening to the deep: live monitoring of ocean noise and cetacean acoustic signals. Marine Pollution Bulletin. 2011;63:18-26.

    Not in our library10.1016/j.marpolbul.2011.04.038

14. 14.

    Aubauer R, Lammers MO, Au WWL. One-hydrophone method of estimating distance and depth of phonating dolphins in shallow water. The Journal of the Acoustical Society of America. 2000;107:2744-2749.

    Not in our library10.1121/1.428660

15. 15.

    Baird RW, Webster DL, Schorr GS, Mcsweeney DJ, Barlow J. Diel variation in beaked whale diving behaviour. Marine Mammal Science. 2008;24:630-642.

    Not in our library

16. 16.

    Baggenstoss PM. An algorithm for the localization of multiple interfering sperm whales using multi-sensor time difference of arrival. The Journal of the Acoustical Society of America. 2011;130:102-112.

    Not in our library10.1121/1.3598454

17. 17.

    Bardeli R, Wolff D, Kurth F, Koch M, Tauchert K-H, Frommolt K-H. Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognition Letters. 2010;31:1524-1534.

    Not in our library

18. 18.

    Barlow J, Taylor B. Estimates of sperm whale abundance in the northeastern temperate Pacific from a combined acoustic and visual survey. Marine Mammal Science. 2005;21:429-445.

    Not in our library

19. 19.

    Barnes C, Best M, Johnson F, Phibbs P, Pirenne B. Transforming the ocean sciences through cabled observatories. Marine Technology Reporter. 2008;51:30-36.

    Not in our library

20. 20.

    Baumgartner MF, Fratantoni DM. Diel periodicity in both sei whale vocalization rates and the vertical migration of their copepod prey observed from ocean gliders. Limnology and Oceanography. 2008;53:2197-2209.

    Not in our library

21. 21.

    Baumgartner MF, Van Parijs SM, Wenzel FW, Tremblay CJ, Esch HC, Warde AM. Low frequency vocalizations attributed to sei whales (Balaenoptera borealis. The Journal of the Acoustical Society of America. 2008;124:1339-1349.

    Not in our library10.1121/1.2945155

22. 22.

    Bee M, Kozich C, Blackwell K, Gerhardt HC. Individual variation in advertisement calls of territorial male green frogs, Rana clamitans: implications for individual discrimination. Ethology. 2001;107:65-84.

    Not in our library

23. 23.

    von Benda-Beckmann A, Lam F, Moretti D, Fulkerson K, Ainslie M, van IJsselmuide S, Theriault J, Beerens S. Detection of Blainville's beaked whales with towed arrays. Applied Acoustics. 2010;71:1027-1035.

    Not in our library

24. 24.

    Bingham G. Status and Applications of Acoustic Mitigation and Monitoring Systems for Marine Mammals. 2011.

    Not in our library

25. 25.

    Blumstein DT, Mennill DJ, Clemins P, Girod L, Yao K, Patricelli G, Deppe JL, Krakauer AH, Clark C, Cortopassi KA, Hanser SF, McCowan B, Ali AM, Kirschel ANG. Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus. Journal of Applied Ecology. 2011;48:758-767.

    Not in our library

26. 26.

    Borchers DL. A non-technical overview of spatially explicit capture-recapture models. Journal of Ornithology. 2012;152:435-444.

    Not in our library

27. 27.

    Borchers DL, Buckland ST, Zucchini W. Estimating Animal Abundance. 2002.

    Not in our library

28. 28.

    Borchers DL, Burnham KP, Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L. General formulation for distance sampling. Advanced Distance Sampling. 2004;307-392.

    Not in our library

29. 29.

    Borchers DL, Efford M. Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics. 2008;64:377-385.

    Not in our library10.1111/j.1541-0420.2007.00927.x

30. 30.

    Borchers DL, Marques TA, Gunnlaugsson T, Jupp PE. Estimating distance sampling detection functions when distances are measured with errors. Journal of Agricultural, Biological, and Environmental Statistics. 2010;15:346-361.

    Not in our library

31. 31.

    Bower JL, Clark CW. A field test of the accuracy of a passive acoustic location system. Bioacoustics. 2005;15:1-14.

    Not in our library

32. 32.

    Brandes TS, Naskrecki P, Figueroa HK. Using image processing to detect and classify narrow-band cricket and frog calls. The Journal of the Acoustical Society of America. 2006;120:2950-2957.

    Not in our library10.1121/1.2355479

33. 33.

    Bridges AS, Dorcas ME. Temporal variation in anuran calling behavior: implications for surveys and monitoring programs. Copeia. 2000;2000:587-592.

    Not in our library

34. 34.

    Bucci M, Petryszyn Y, Krausman PR. Occurrence and activity of bats at three national monuments in central Arizona. The Southwestern Naturalist. 2010;55:207-216.

    Not in our library

35. 35.

    Buckland ST. Point transect surveys for songbirds: robust methodologies. The Auk. 2006;123:345-357.

    Not in our library

36. 36.

    Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L. Introduction to Distance Sampling – Estimating Abundance of Biological Populations. 2001.

    Not in our library

37. 37.

    Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers D, Thomas L. Advanced Distance Sampling. 2004.

    Not in our library

38. 38.

    Burgess WC. The Acousonde: a miniature autonomous wideband recorder. The Journal of the Acoustical Society of America. 2009;125:2588.

    Not in our library

39. 39.

    Burgess WC, Tyack PL, LeBoeuf BJ, Costa DP. A programmable acoustic recording tag and first results from free-ranging northern elephant seals. Deep Sea Research II. 1998;45:1327-1351.

    Not in our library

40. 40.

    Cato DH. Simple methods of estimating source levels and locations of marine animal sounds. The Journal of the Acoustical Society of America. 1998;104:1667-1678.

    Not in our library10.1121/1.424379

Cited by papers we hold 4

• soundClass: An automatic sound classification tool for biodiversity monitoring using machine learning · 2022
  Not checked
• Computational bioacoustics with deep learning: a review and roadmap · 2022
  No notice
• Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations · 2021
  Not checked
• Temporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species · 2019
  Not checked

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.