My co-authors and I are pleased to announce the publication of our paper, Categorizing click trains to increase taxonomic precision in echolocation click loggers.

As this research is in its early stages, we would especially like to offer our categorisation model to any colleagues who have with visually validated C-POD data. Otherwise, anyone wishing to use or build on these methods may obtain the R model from this study via GitHub repository  https://github.com/JPalmerK/C-POD-encounter-classification

Warm Regards,

Kaitlin Palmer

http://asa.scitation.org/doi/10.1121/1.4996000

Passive acoustic monitoring is an efficient way to study acoustically active animals but species identification remains a major challenge. C-PODs are popular logging devices that automatically detect odontocete echolocation clicks. However, the accompanying analysis software does not distinguish between delphinid species. Click train features logged by C-PODs were compared to frequency spectra from adjacently deployed continuous recorders. A generalized additive model was then used to categorize C-POD click trains into three groups: broadband click trains, produced by bottlenose dolphin (Tursiops truncatus) or common dolphin (Delphinus delphis), frequency-banded click trains, produced by Risso's (Grampus griseus) or white beaked dolphins (Lagenorhynchus albirostris), and unknown click trains. Incorrect categorization rates for broadband and frequency banded clicks were 0.02 (SD 0.01), but only 30% of the click trains met the categorization threshold. To increase the proportion of categorized click trains, model predictions were pooled within acoustic encounters and a likelihood ratio threshold was used to categorize encounters. This increased the proportion of the click trains meeting either the broadband or frequency banded categorization threshold to 98%. Predicted species distribution at the 30 study sites matched well to visual sighting records from the region.