Browsing by Author "Beck, Suzanne"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A review of new and existing non-extractive techniques for monitoring marine protected areas
    (Frontiers Media, 2023-07-19) McGeady, Ryan; Runya, Robert M.; Dooley, James S. G.; Howe, John A.; Fox, Clive J.; Wheeler, Andrew J.; Summers, Gerard; Callaway, Alexander; Beck, Suzanne; Brown, Louise S.; Dooly, Gerard; McGonigle, Chris
    Ocean biodiversity loss is being driven by several anthropogenic threats and significant efforts are required to halt losses and promote healthy marine ecosystems. The establishment of a network of Marine Protected Areas (MPAs) can help restrict damaging activities and have been recognised as a potential solution to aid marine conservation. When managed correctly they can deliver both ecological and socio-economic benefits. In recent times, MPA designations have increased rapidly while many countries have set future MPA targets for the decades ahead. An integral element of MPA management is adequate monitoring that collects data to assess if conservation objectives are being achieved. Data acquired by monitoring can vary widely as can the techniques employed to collect such data. Ideally, non-destructive and non-invasive methods are preferred to prevent damage to habitats and species, though this may rule out a number of traditional extractive sampling approaches such as dredges and trawls. Moreover, advances in ocean observation technologies enable the collection of large amounts of data at high resolutions, while automated data processing is beginning to make analyses more logistically feasible and less time-consuming. Therefore, developments to existing marine monitoring techniques and new emerging technologies have led to a diverse array of options when choosing to implement an MPA monitoring programme. Here, we present a review of new and existing non-extractive techniques which can be applied to MPA monitoring. We summarise their capabilities, applications, advantages, limitations and possible future developments. The review is intended to aid MPA managers and researchers in determining the suitability of available monitoring techniques based on data requirements and site conditions.
  • Thumbnail Image
    Item
    More than a whistle: Automated detection of marine sound sources with a convolutional neural network
    (Frontiers Media, 2022-10-04) White, Ellen L.; White, Paul R.; Bull, Jonathan M.; Risch, Denise; Beck, Suzanne; Edwards, Ewan W.J.
    The effective analysis of Passive Acoustic Monitoring (PAM) data has the potential to determine spatial and temporal variations in ecosystem health and species presence if automated detection and classification algorithms are capable of discrimination between marine species and the presence of anthropogenic and environmental noise. Extracting more than a single sound source or call type will enrich our understanding of the interaction between biological, anthropogenic and geophonic soundscape components in the marine environment. Advances in extracting ecologically valuable cues from the marine environment, embedded within the soundscape, are limited by the time required for manual analyses and the accuracy of existing algorithms when applied to large PAM datasets. In this work, a deep learning model is trained for multi-class marine sound source detection using cloud computing to explore its utility for extracting sound sources for use in marine mammal conservation and ecosystem monitoring. A training set is developed comprising existing datasets amalgamated across geographic, temporal and spatial scales, collected across a range of acoustic platforms. Transfer learning is used to fine-tune an open-source state-of-the-art ‘small-scale’ convolutional neural network (CNN) to detect odontocete tonal and broadband call types and vessel noise (from 0 to 48 kHz). The developed CNN architecture uses a custom image input to exploit the differences in temporal and frequency characteristics between each sound source. Each sound source is identified with high accuracy across various test conditions, including variable signal-to-noise-ratio. We evaluate the effect of ambient noise on detector performance, outlining the importance of understanding the variability of the regional soundscape for which it will be deployed. Our work provides a computationally low-cost, efficient framework for mining big marine acoustic data, for information on temporal scales relevant to the management of marine protected areas and the conservation of vulnerable species.