Mapping blue and fin whale density over large scales using acoustic data.

Speaker: Danielle Harris (CREEM)

Abstract

Using data from passive acoustic monitoring (PAM) to estimate animal density can provide a cost-effective alternative to traditional visual surveys.  One option when designing a PAM survey is to deploy sensors evenly over a large area of interest, thereby maximizing spatial coverage. However, a consequence of this design is that each vocalization cannot be heard across multiple sensors, restricting the choice of density estimation methods.  Density estimation approaches for sparsely distributed sensors have been developed, but may only apply to small ocean areas, and/or require unrealistic assumptions about local animal distribution.  Here, data from the Comprehensive Nuclear Test Ban Treaty Organization International Monitoring System (CTBTO IMS) have been utilized 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.  The method requires estimated bearings to calls and a signal to noise ratio (SNR) for each detected call.  Data about sound propagation, call source levels and ambient noise levels, and a detector characterization analysis that estimates the probability of detecting a call as a function of SNR are also required.  Combining this information with spatial modeling, results in an estimated density map around each sensor.  Results from a case study using fin whale detections on CTBTO IMS hydrophones at Wake Island in the Pacific Ocean will be presented. Using data from passive acoustic monitoring (PAM) to estimate animal density can provide a cost-effective alternative to traditional visual surveys.  One option when designing a PAM survey is to deploy sensors evenly over a large area of interest, thereby maximizing spatial coverage. However, a consequence of this design is that each vocalization cannot be heard across multiple sensors, restricting the choice of density estimation methods.  Density estimation approaches for sparsely distributed sensors have been developed, but may only apply to small ocean areas, and/or require unrealistic assumptions about local animal distribution.  Here, data from the Comprehensive Nuclear Test Ban Treaty Organization International Monitoring System (CTBTO IMS) have been utilized 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.  The method requires estimated bearings to calls and a signal to noise ratio (SNR) for each detected call.  Data about sound propagation, call source levels and ambient noise levels, and a detector characterization analysis that estimates the probability of detecting a call as a function of SNR are also required.  Combining this information with spatial modeling, results in an estimated density map around each sensor.  Results from a case study using fin whale detections on CTBTO IMS hydrophones at Wake Island in the Pacific Ocean will be presented.