Data analysis:
C-POD data were processed using the Chelonia CPOD.exe software (V. 2044) and inbuilt KERNO classifier to detect harbour porpoise click trains. F-POD data were processed in a similar manner using the custom F-POD.exe software (V 1.1) and KERNO-F classifier (Chelonia Ltd., 2022). Click trains were classified as “NBHF” (narrowband high frequency) and all train quality classes were exported for further examination. Train quality filters are defined as “Hi” (high),” Mod” (moderate), and “Lo” (low). All detections were visually verified following guidelines from the manufacturer (Chelonia Ltd., 2022). Data were exported as different detection metrics; number of clicks ‘NClx’, detection positive days ‘DPD’, detection positive hours ‘DPH’ and detection positive minutes ‘DPM’.
Detection metrics were summarised for each deployment across three groupings of train quality filters, specifically HiModLo, HiMod, and Hi, reflecting commonly used groupings in the literature (Sarnochinska et al., 2016; Clausen et al., 2018). Kendall’s rank (non-parametric) correlation tests were carried out between the detections on the C-POD and F-POD at the scale of each temporal detection metric and for each train quality classification.
Both monthly and seasonal DPH were summarised for both the C-POD and the F-POD and compared using a detection ratio, expressed as: CF = Det_C/Det_F. This ratio was used to explore the comparability between the PODs across time and by what margin the F-POD detects more echolocation clicks than the C-POD.
Data on echolocation clicks were also exported and used to identify buzzes, assumed to be foraging behaviour (Verfuß et al., 2009), based on the duration of the inter-click interval (ICI). Gaussian mixture models were used to categorise echolocation clicks based on their ICI (Pirottaet al. , 2014). Buzzes were defined as echolocation clicks with an ICI of less than 10 ms (Carlström, 2005). Detections were then summarised as foraging buzzes per hour (BPH) for further analysis.