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Accommodating False Positives Within Acoustic Spatial Capture–Recapture, with Variable Source Levels, Noisy Bearings and an Inhomogeneous Spatial Density

Author

Listed:
  • Felix T. Petersma

    (University of St Andrews)

  • Len Thomas

    (University of St Andrews)

  • Aaron M. Thode

    (University of California San Diego)

  • Danielle Harris

    (University of St Andrews)

  • Tiago A. Marques

    (University of St Andrews
    Faculdade de Ciências da Universidade de Lisboa)

  • Gisela V. Cheoo

    (Faculdade de Ciências da Universidade de Lisboa)

  • Katherine H. Kim

    (Greeneridge Sciences, Inc.)

Abstract

Passive acoustic monitoring is a promising method for surveying wildlife populations that are easier to detect acoustically than visually. When animal vocalisations can be uniquely identified on an array of sensors, the potential exists to estimate population density through acoustic spatial capture–recapture (ASCR). However, sound classification is imperfect, and in some situations, a high proportion of sounds detected on just a single sensor (‘singletons’) are not from the target species. We present a case study of bowhead whale calls (Baleana mysticetus) collected in the Beaufort Sea in 2010 containing such false positives. We propose a novel extension of ASCR that is robust to false positives by truncating singletons and conditioning on calls being detected by at least two sensors. We allow for individual-level detection heterogeneity through modelling a variable sound source level, model inhomogeneous call spatial density, and include bearings with varying measurement error. We show via simulation that the method produces near-unbiased estimates when correctly specified. Ignoring source-level variation resulted in a strong negative bias, while ignoring inhomogeneous density resulted in severe positive bias. The case study analysis indicated a band of higher call density approximately 30 km from shore; 59.8% of singletons were estimated to have been false positives.

Suggested Citation

  • Felix T. Petersma & Len Thomas & Aaron M. Thode & Danielle Harris & Tiago A. Marques & Gisela V. Cheoo & Katherine H. Kim, 2024. "Accommodating False Positives Within Acoustic Spatial Capture–Recapture, with Variable Source Levels, Noisy Bearings and an Inhomogeneous Spatial Density," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 471-490, September.
    • Handle: RePEc:spr:jagbes:v:29:y:2024:i:3:d:10.1007_s13253-023-00563-0
    DOI: 10.1007/s13253-023-00563-0
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    References listed on IDEAS

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    1. D. L. Borchers & B. C. Stevenson & D. Kidney & L. Thomas & T. A. Marques, 2015. "A Unifying Model for Capture-Recapture and Distance Sampling Surveys of Wildlife Populations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 195-204, March.
    2. D. L. Borchers & M. G. Efford, 2008. "Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies," Biometrics, The International Biometric Society, vol. 64(2), pages 377-385, June.
    3. Ben C. Stevenson & David L. Borchers & Rachel M. Fewster, 2019. "Cluster capture‐recapture to account for identification uncertainty on aerial surveys of animal populations," Biometrics, The International Biometric Society, vol. 75(1), pages 326-336, March.
    4. Darren Kidney & Benjamin M Rawson & David L Borchers & Ben C Stevenson & Tiago A Marques & Len Thomas, 2016. "An Efficient Acoustic Density Estimation Method with Human Detectors Applied to Gibbons in Cambodia," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-16, May.
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