THIS GRANT IS A CONTINUATION OF N000141410394Large scale density estimation of blue and fin whales: Utilizing sparse array data to develop and implement a new method for estimating blue and fin whale

Abstract

Activities related to the proposed work include an initial pilot study using 3-6 months of data at a single CTBTO location in Year 1 to provide the CREEM researchers with a sample dataset to begin model development. CREEM will then develop a new density estimation theory based on bearings as opposed to distance sampling using the OBS dataset as a method of verifying and assessing the new model performance. Efforts in Year 2 include ARL PSU processing of CTBTO data to provide CREEM with species specific vocalization time series information, ambient sound level time series, and propagation information for use in density estimation models. CREEM will apply the density algorithms as this information becomes available. We will continue to communicate and work with tagging groups in Years 1-2 to obtain the most accurate source level and call rate parameters for model inputs. Year 3 will be devoted to completing the CTBTO processing and incorporating the data into the density estimation algorithms. A time series analysis of density estimates will be conducted in Year 3 to explore seasonal trends and patterns. Acoustic detection information for each species will ultimately be archived by ARL PSU at a publically accessible site such as OBIS-SEAMAP or one of the NOAA mapping databases currently being developed.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 12, 2016
Source ID
N000141612364

Entities

People

  • Len Thomas

Organizations

  • Office of Naval Research
  • United States Navy
  • University of St Andrews

Tags

Readers

  • Marine Mammal Biology
  • Neural Network Machine Learning.
  • Research Science/Academic Research