Spatially explicit capture–recapture methods to estimate minke whale density from data collected at bottom-mounted hydrophones
- 516 Downloads
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.
KeywordsMinke whale Passive acoustic monitoring Proximity detector Spatially explicit capture recapture (SECR) OpenBUGS
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.
- Borchers DL (this volume) A non-technical overview of spatially explicit capture-recapture models. J OrnitholGoogle Scholar
- Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2001) Introduction to distance sampling—estimating abundance of biological populations. Oxford University Press, OxfordGoogle Scholar
- Burnham KP, Buckland ST, Laake JL, Borchers DL, Marques TA, Bishop JRB, Thomas L (2004) Further topics in distance sampling. In: Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (eds) Advanced distance sampling. Oxford University Press, Oxford, pp 307–392Google Scholar
- Efford MG (2009) secr—spatially explicit capture-recapture in R, version 1.2.10. Department of Zoology, University of Otago, DunedinGoogle Scholar
- Efford MG, Borchers DL, Byrom AE (2008) Density estimation by spatially explicit capture-recapture: likelihood-based methods. In: Modeling demographic processes in marked populations. Environmental and Ecological Statistics. Springer, New York, pp 255–269Google Scholar
- Mobley Jr JR, Grotefendt RA, Forestell HP, Frankel AS (1999) Results of aerial surveys of marine mammals in the major Hawaiian Islands (1993–98). Tech. rep., Final Report to the Acoustic Thermometry of Ocean Climate Program (ATOC MMRP)Google Scholar
- R Development Core Team (2009) R: A language and environment for statistical computing http://www.R-project.org, ISBN 3-900051-07-0
- Thomas A, OHara B, Ligges U, Sturtz S (2006) Making BUGS open. R News 6:12–17Google Scholar