Introduction:
Passive acoustic monitoring (PAM) is a well-established method used to monitor acoustically active species and the habitat they reside in (Merchant et al. , 2015). Over the years PAM technology has greatly advanced the field of cetacean ecology, allowing a cost-effective alternative to extensive visual surveys that is not reliant on daylight hours, favourable weather conditions and availability of observers. Passive acoustic monitoring of cetaceans has resulted in high-resolution data providing us with insights of population size and abundance (Marques et al. , 2013; Amundinet al. , 2022), habitat use (Fleming et al. , 2018; Palmeret al. , 2019), and behaviour (Pirotta et al. , 2014; Malinka et al. , 2021; Todd et al., 2022) for many species. Such technology is also fundamental for long-term monitoring, particularly with the increase in coastal developments and potential disturbance from construction, marine renewable devices, shipping, and fisheries (e.g., Todd et al., 2020, 2022; Omeyer et al. , 2020; Ramesh et al. , 2021; Fernandez-Betelu, Graham and Thompson, 2022).
While there are many useful applications of PAM, fixed autonomous acoustic recording devices can increase deployment times and sampling frequencies (Sousa-Lima et al. , 2013). Data loggers or echolocation click detectors, such as the C-POD (Cetacean POrpoise Detector) (Chelonia Ltd., 2022) are a user-friendly, relatively inexpensive device which can be deployed for continuous monitoring periods of 3-6 months. C-PODs detect individual echolocation clicks between 20-160 kHz and have been a popular tool used to study odontocete ecology and behaviour worldwide (e.g. Carstensen, Henriksen and Teilmann, 2006; Simon et al., 2010; Nykänen, 2016; Jaramillo-Legorreta et al., 2017; Garagouni, 2019). Although no waveform data are stored by the devices, summary data on each click are preserved allowing post-deployment classification of detected sounds into sequences called click trains. Further data analysis is then performed where click trains are assigned to dolphin or porpoise origins based on frequency and bandwidth. While it is often not possible to differentiate between dolphin species (Robbins et al., 2015), based on in-field testing, Roberts and Read (2015) reported that C-PODs perform well with a relatively high accuracy in detecting cetacean echolocation. C-PODs have been used for over a decade and now form the basis of valuable long-term monitoring datasets. The F-PODs (Full waveform capture Pods) are the successor of the C-PODs, and the manufacturer is recommending a transition from C-PODs to F-PODs as availability and support for C-PODs will be limited in the coming years. This may have important implications for long-term monitoring programmes (and the associated archival data from such) as C-PODs are replaced due to equipment loss (often as a result of storms, theft etc.) or reach the end of their operational lifetimes. The F-PODs have been designed to improve and upgrade the data associated with C-PODs by recording more details of selected clicks including position of loudest cycle, frequency range and capture of full waveform (Chelonia Ltd., 2022). These new features enhance train detection, providing increased sensitivity with lower false positive rates compared to C-PODs (Chelonia Ltd., 2022).
One of the main advantages of PAM is its potential to be implemented in long-term monitoring programmes to study the change in species occurrence and behaviour over longer temporal scales. Many studies currently using C-PODs need to ensure the longevity of their data for monitoring purposes, particularly in the light of climate change and habitat alterations through coastal developments. However, to date there have been no studies reporting how the C-POD and its successor the F-POD compare in detection capacity and ability to identify trends in spatio-temporal drivers of detected cetaceans. In this study, we used data from a co-deployed C-POD and F-POD to compare the performance of the PODs in detecting habour porpoise (Phocoena phocoena) across various commonly used detection metrics. Additionally, Generalised additive models (GAMs) were used to explore how both PODs identifed spatiotemporal variation in harbour porpoise occurrence and foraging activity in relation to environmental variables.