Large Scale Density Estimation of Blue and Fin Whales (LSD)

• Main Authors: Thomas, Len, Harris, Danielle, Miksis-Olds, Jennifer L
• Other Authors: SAINT ANDREWS UNIV (UNITED KINGDOM)
• Format: Text
• Language: English
• Published: 2014
• Subjects: Biological Oceanography; Acoustics; *ACOUSTIC DATA; *WHALES; ACOUSTIC ARRAYS; DENSITY; ESTIMATES; OCEAN BOTTOM; PACIFIC OCEAN; SEISMOMETERS; SPATIAL DISTRIBUTION; OBS(OCEAN BOTTOM SEISMOMETERS); SPARSE ARRAYS; BLUR WHALES; FIN WHALES; LSD(LARGE SCALE DENSITY); CTBTO IMS(COMPREHENSIVE NUCLEAR TEST BAN TREATY ORGANIZATION INTERNATIONAL MONITORING SYSTEM); DETECTION PROBABILITY; VOCALIZATIONS; Pacific; Fin whale
• Online Access: http://www.dtic.mil/docs/citations/ADA618038; http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA618038

Effective management and mitigation of marine mammals in response to potentially negative interactions with human activity requires knowledge of how many animals are present in an area during a specific time period. Many marine mammal species are relatively hard to sight, making standard visual methods of density estimation difficult and expensive to implement; however many of these same species produce vocalizations that are relatively easy to hear, making density estimation from passive acoustic monitoring data an attractive, cost-effective alternative. A particularly efficient passive acoustic monitoring design is a sparse array , where sensors are distributed evenly over a large area of interest however a consequence of this design is that each vocalization cannot be heard at multiple sensor locations, restricting the choice of methods that can be used to estimate density. Nevertheless, sparse array methods have been developed and demonstrated (Marques et al., 2011, K sel et al., 2011; Harris, 2012; Harris et al., 2013). While these studies represent an important step forward in making the methods more generally applicable at reasonable cost, they have some drawbacks: they either are only applicable to small local ocean areas, or they require unrealistic assumptions about animal distribution around the sensors, or both. The goal of this research is to develop and implement a new method for estimating blue and fin whale density that is effective over large spatial scales and is designed to cope with spatial variation in animal density utilizing sparse array data from the Comprehensive Nuclear Test Ban Treaty Organization International Monitoring System (CTBTO IMS) and Ocean Bottom Seismometers (OBSs). Prepared in collaboration with the Applied Research Laboratory, Pennsylvania State University, State College.