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

• Published in: Journal of Ornithology
• Main Authors: Marques, Tiago A., Thomas, Len, Martin, Stephen W., Mellinger, David K., Jarvis, Susan, Morrissey, Ronald P., Ciminello, Carroll-Anne, DiMarzio, Nancy
• Other Authors: Centre for Research into Ecological and Environmental Modelling, University of St Andrews Scotland, Space and Naval Warfare Systems Center Pacific (NIWC Pacific), Cooperative Institute for Marine Resources Studies, Oregon State University (OSU), Naval Undersea Warfare Center Division
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2010
• Subjects: Minke whale; Passive acoustic monitoring; Proximity detector; Spatially explicit capture recapture (SECR); OpenBUGS; Pacific; minke whale
• Online Access: https://hal.archives-ouvertes.fr/hal-00618203; https://hal.archives-ouvertes.fr/hal-00618203/document; https://hal.archives-ouvertes.fr/hal-00618203/file/PEER_author_10.1007%252Fs10336-010-0535-7.pdf; https://doi.org/10.1007/s10336-010-0535-7

International audience 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 () "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.