Original ArticlesAcoustic Complexity Index to assess benthic biodiversity of a partially protected area in the southwest of the UK
Introduction
Biodiversity provides a useful measure to assess ecosystem health (Worm et al., 2006), and is increasingly being used for conservation and monitoring purposes, with an observed decrease used as a proxy for a degraded or negatively impacted ecosystem (Wabnitz et al., 2018). To quantify and compare these changes in diversity, many univariate indices have been produced, which simplify an assemblage of taxa into a single value. The most commonly used indices involve integrating the number of species present with measures of how the species are distributed within the assemblages, such as Number of Species (Kaplan et al., 2015, Pieretti and Farina, 2013, Sheehan et al., 2013b), Shannon-Wiener’s diversity index (De-La-Ossa-Carretero et al., 2012), Simpson’s diversity index (Miralles et al., 2016, Rombouts et al., 2019) and taxonomic distinctness (Clarke and Warwick, 2001, Leonard et al., 2006), which also involves phylogenetic distance.
Historic methods for assessing marine biodiversity have often used destructive practices (Francour, 1994, Lipej et al., 2003), such as poisoning (Diamant et al., 1986) or trawling (Cappo et al., 2004). However, for the study of recovering and fragile benthic systems, such as those in Marine Protected Areas (MPAs), non-invasive, non-extractive methods such as Underwater Visual Census (UVC) or Underwater Video Survey (UVS) are considered more appropriate (Sheehan et al., 2013a, Sheehan et al., 2010). Visual methods will always have the drawback that there is no physical sample taken, although image libraries give a permanent record, and thus those species that are harder to identify visually will always be under-sampled; yet this lack of physical sample means the populations being researched are almost or completely unaffected by the survey taken. A potential addition to supplement visual survey would be the assessment of the marine soundscape (Staaterman et al., 2017). This method for sampling the marine environment is similarly non-extractive and non-invasive, while sampling components of the ecosystem potentially under-represented by visual methods alone.
The marine soundscape comprises both natural and anthropogenic elements. Assessment of the biological element (biophony) of the marine soundscape has been used to describe overall biodiversity (Bertucci et al., 2016), reproductive behaviour (de Jong et al., 2018), habitat selection (Vermeij et al., 2010), spawning (Casaretto et al., 2014, Hawkins and Amorim, 2000) and predator–prey interactions (Bernasconi et al., 2011, Giorli et al., 2016). Biophony is produced by a wide range of taxa ranging from large cetaceans producing low frequency (~20 Hz) calls or songs (Samaran et al., 2013), that can be detected up to thousands of kilometres away (Rivers, 1997), to crustaceans creating loud (190 dB re 1 µPa), broadband (2 kHz up to 300 kHz) ‘snaps’ and ‘pops’ (Picciulin et al., 2013).
Acoustic indices have been developed and utilised in marine (Gordon et al., 2018, Harris et al., 2016, Nedelec et al., 2015, Pieretti et al., 2017, Trenkel et al., 2011) and terrestrial (Farina and Pieretti, 2014, Merchant et al., 2015, Pieretti et al., 2015, Pieretti et al., 2011, Pijanowski et al., 2011, Sueur et al., 2008b) environments to assess whole ecosystem biodiversity. The use of these acoustic indices is perceived to allow hidden or shy species, overlooked by other survey methods, to be accounted for (Staaterman et al., 2017). The ACI as set out in Pieretti et al. (2011) quantifies the relative change in sound intensity across all frequencies of a soundscape, while being minimally affected by constant anthropogenic noise. The ACI was developed on the assumption that with increased diversity of species, there would be an increase in the complexity of biological sound produced. So far, most analyses of ACI have shown a positive correlation with a variety of biodiversity indices (Bertucci et al., 2016, Harris et al., 2016, Meyer et al., 2018, Pieretti et al., 2015, Pieretti et al., 2011).
The two survey methods, visual and acoustic, are thought to complement each other by overlapping, as well as covering differing spatial scales and taxonomic groups (Staaterman et al., 2017). However, the majority of studies to date regarding this interaction have been based either in areas of very high biodiversity, such as coral reef systems (Bertucci et al., 2016, Kaplan et al., 2015), or only focused on fish diversity (Harris et al., 2016). As such, the transferability to other habitats and ecosystems is limited.
This study assessed the suitability of the ACI index derived from using acoustic recording as a monitoring method and to explore its relationship with seabed biodiversity. As such, a 5 year study within a recovering temperate reef seabed ecosystem was undertaken, in which were protected areas and those open to bottom fishing.
It was expected that the ACI and two visual biodiversity indices, Number of Species and Shannon’s Diversity Index, derived from Baited Remote Underwater Video systems (BRUVs) data (‘visual biodiversity indices’ from now on), would increase over time in the MPA relative to the areas that continue to be fished. As a recovering system it would be predicted that the interaction of time and treatment would be significant. Therefore, the following hypotheses were assessed for inside vs outside the MPA:
- 1.
The ACI would increase over time,
- 2.
The visual biodiversity indices would increase over time,
- 3.
The visual biodiversity indices and the ACI would correlate with each other over time,
- 4.
Changes in the mobile benthic assemblage composition would result in similar changes to the ACI.
Section snippets
Study location
Lyme Bay (Fig. 1), is located on the south coast of England, and contains areas of rocky reef habitat known to include nationally important fragile reef building species (Hiscock and Breckels, 2007). A Statutory Instrument (SI), a type of MPA, was established in 2008 in Lyme Bay. The SI excluded all towed demersal fishing equipment (scallop dredging and trawling) from a 206 km2 area of the bay.
Experimental site selection was based on similar biotope classifications to negate any confounding
Acoustic Complexity Index
The interaction between year and treatment was significant for the ACI (Table 1: Pseudo-F = 2.6766, p = 0.0351). This significant interaction shows that there is a combined effect of year and treatment. The MPA was more acoustically complex than Open Controls (OC) in 2014 and 2018 (Table 1; 2014: p = 0.009; 2018: p = 0.0288), whereas the OC group was more complex in 2016 (Fig. 3A, Table 1; 2016: p = 0.0218). Overall across all years, mean ACI was lower inside the MPA (1.4% lower than outside:
Discussion
After high storm activity impacted the coastal systems of Lyme Bay and beyond (Masselink et al., 2016), acoustic and BRUV monitoring was implemented. It was hypothesised that the Acoustic Complexity Index would increase over time as the biodiversity of the area increased. Furthermore, the ACI was expected to be greater inside the protected area in comparison to the surrounding fished areas. Finally it was hypothesised that the ACI would change in a similar pattern to that of the mobile benthic
Authors’ contributions
EVS and MJA conceived the ideas and monitoring design; MJW provided technical advice regarding acoustic analytical methods; EVS, LH, AR and BFRD collected data; BFRD and LH organized and analysed data; BFRD, EVS and LH led the writing of the manuscript. All authors contributed critically to drafts and gave final approval for publication.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
To carry out fieldwork thanks are given to Lyme Bay Fishers John Walker, Robert King and Keiran Perree and University of Plymouth staff and student volunteers especially Amy Cartwright for her fieldwork support and logistics. Also, thank you to Marti Anderson for advice regarding statistical analysis. Funding: This work was supported by Natural England and The European Commission [EMFF RETURN ENG1388].
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