WIREDGOV

Pilot study on the use of underwater passive acoustic recordings to monitor changes in biodiversity on the East Coast of Scotland. The focus of this study was to assess fish’s and crustacean’s active and passive sounds.

Introduction

The importance of monitoring

Marine biodiversity is rapidly changing due to the direct and indirect impacts of climate change and ocean sprawl, which is the proliferation of human-made structures in the ocean and coastal areas (Duarte 2014).The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Service (IPBES) declared a global biodiversity emergency (IPBES, 2019), and it is therefore essential that effective long-term monitoring programmes are established to ensure sustainable development (Borja and Elliott 2021). This is particularly important in an offshore marine environment, which can be difficult and expensive to access and where impacts are not always clearly visible (Nathanael et al. 2024). Due to the dynamic nature of marine ecosystems, it can be challenging to obtain a clear signal to connect human activities (and the associated pressures) with an ecosystem response. On the other hand, some interventions, if done correctly, can potentially enhance biodiversity and improve ecosystem outcomes (Strain et al. 2018). Therefore, monitoring methods should be able to detect when biodiversity increases as well as potential decreases as a route to demonstrating environmental gains, which is especially important in light of recent discussions around Marine Net Gain (Hooper et al. 2021).

Traditional monitoring methods for monitoring the biodiversity associated with benthic habitats include grab sampling, box cores, benthic trawls and visual surveys (remotely operated vehicles, autonomous underwater vehicles and time-lapse images/videos) (Scottish Government report, 2023). Except for time-lapse cameras attached to benthic observation stations and landers (Kutti et al. 2014; Osterloff et al. 2016; De Clippele et al. 2023; Clark et al. 2024), these approaches only provide a snapshot of information at one point in time. Some of these tools can introduce sampling biases and cause damage to sensitive features (Beisiegel et al. 2017). There is a need, therefore, to improve monitoring regimes and investigate the effectiveness of non-invasive cost-effective tools, such as Passive Acoustic Monitoring (PAM).

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