Categorizing click trains to increase taxonomic precision in echolocation click loggers

L.R. and K.J.P. were supported by Marine Scotland Science and the Marine Alliance for Science and Technology for Scotland (MASTS) pooling initiative and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.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.PostprintPeer reviewe

Using technology to improve the management of development impacts on biodiversity

Funder: The research was funded through a long‐term collaboration between Conservational International and Chevron.Abstract: The mitigation hierarchy (MH) is a prominent tool to help businesses achieve no net loss or net gain outcomes for biodiversity. Technological innovations offer benefits for business biodiversity management, yet the range and continued evolution of technologies creates a complex landscape that can be difficult to navigate. Using literature review, online surveys, and semi‐structured interviews, we assess technologies that can improve application of the MH. We identify six categories (mobile survey, fixed survey, remote sensing, blockchain, data analysis, and enabling technologies) with high feasibility and/or relevance to (i) aid direct implementation of mitigation measures and (ii) enhance biodiversity surveys and monitoring, which feed into the design of interventions including avoidance and minimization measures. At the interface between development and biodiversity impacts, opportunities lie in businesses investing in technologies, capitalizing on synergies between technology groups, collaborating with conservation organizations to enhance institutional capacity, and developing practical solutions suited for widespread use