Spatially explicit capture–recapture methods to estimate minke whale density from data collected at bottom-mounted hydrophones

Abstract

Estimation of cetacean abundance or density using visual methods can be cost-ineffective under many scenarios. Methods based on acoustic data have recently been proposed as an alternative, and could potentially be more effective for visually elusive species that produce loud sounds. Motivated by a dataset of minke whale (Balaenoptera acutorostrata) “boing” sounds detected at multiple hydrophones at the U.S. Navy’s Pacific Missile Range Facility (PMRF), we present an approach to estimate density or abundance based on spatially explicit capture–recapture (SECR) methods. We implement the proposed methods in both a likelihood and a Bayesian framework. The point estimates for abundance and detection parameters from both implementation methods are very similar and agree well with current knowledge about the species. The two implementation approaches are compared in a small simulation study. While the Bayesian approach might be easier to generalize, the likelihood approach is faster to implement (at least in simple cases like the one presented here) and more readily amenable to model selection. SECR methods seem to be a strong candidate for estimating density from acoustic data where recaptures of sound at multiple acoustic sensors are available, and we anticipate further development of related methodologies.

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Acknowledgments

This research was undertaken as part of the DECAF project (Density Estimation for Cetaceans from passive Acoustic Fixed sensors), funded under the National Oceanographic Partnership Program jointly by the Joint Industry Programme and US National Marine Fisheries Service. We thank the other DECAF project members for their many contributions to this work. Murray Efford and Andy Royle provided prompt response to any issues that arose relating to implementation of the analysis. We are particularly grateful to David Borchers, who kindly stepped in to give the underlying EURING talk at short notice when T.A.M. had to cancel his attendance at the last moment. Two anonymous reviewers and the session chairs have provided many helpful comments which improved the quality of this work.

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Correspondence to Len Thomas.

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Communicated by M. Schaub.

Appendix: example openBUGS code

Appendix: example openBUGS code

This is the code used to run the application example in the Bayesian framework, a passive acoustic SECR with half-normal (HN) detection function. The user must input as data the following objects (object names in the code given inside parentheses): (1) the number of detected animals (n), (2) the boundaries of the region over which integration takes place (Xl, Xu and Yl, Yu), (3) the upper bound on the prior for sigma (maxSigma), (4) the number of added all 0’s capture histories required for data augmentation (nzeroes), (5) the traps locations (traps, the trap x and y coordinates need to be respectively in columns 1 and 2), (6) the area over which abundance is estimated (Area), and (7) the capture histories (Y, a matrix in which position ik is 1 if animal i was detected on trap k, and 0 otherwise). The random variables involved for which priors are required are (1) the inclusion probability (psi), (2) the HN detection function parameter (sigma), (3) a vector of latent indicator variables associated with each of M (= n + nzeroes) animals (z), and (4) the M animals location (respectively x and y coordinates (x1 and x2).

The model specification is:

figurea

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Marques, T.A., Thomas, L., Martin, S.W. et al. Spatially explicit capture–recapture methods to estimate minke whale density from data collected at bottom-mounted hydrophones. J Ornithol 152, 445–455 (2012). https://doi.org/10.1007/s10336-010-0535-7

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Keywords

  • Minke whale
  • Passive acoustic monitoring
  • Proximity detector
  • Spatially explicit capture recapture (SECR)
  • OpenBUGS