Estimating whale abundance using sparse hydrophone arrays

Abstract

Passive acoustic monitoring has been used to investigate many aspects of marine mammal ecology, although methods to estimate absolute abundance and density using acoustic data have only been developed in recent years. The instrument configuration in an acoustic survey determines which abundance estimation methods can be used. Sparsely distributed arrays of instruments are useful because wide geographic areas can be covered. However, instrument spacing in sparse arrays is such that the same vocalisation will not be detected on multiple instruments, excluding the use of some abundance estimation methods. The aim of this thesis was to explore cetacean abundance and density estimation using novel sparse array datasets, applying existing methods where possible, or developing new approaches.

The wealth of data collected by sparse arrays was demonstrated by analysing a 10-year dataset collected by the U.S. Navy’s Sound Surveillance System in the north-east Atlantic. Spatial and temporal patterns of blue (Balaenoptera musculus) and fin whale (Balaenoptera physalus) vocal activity were investigated using generalised additive models.

Distance sampling-based methods were applied to fin whale calls recorded by an array of Ocean Bottom Seismometers in the north-east Atlantic. Estimated call density was 993 calls/1000 km².hr⁻¹ (CV: 0.39). Animal density could not be estimated because the call rate was unknown. Further development of the call localisation method is required so the current density estimate may be biased. Furthermore, analysing a single day of data resulted in a high variance estimate.

Finally, a new simulation-based method developed to estimate density from single hydrophones was applied to blue whale calls recorded in the northern Indian Ocean. Estimated call density was 3 calls/1000 km².hr⁻¹ (CV: 0.17). Again, density of whales could not be estimated as the vocalisation rate was unknown. Lack of biological knowledge poses the greatest limitation to abundance and density estimation using acoustic data.