Using acoustic metrics to characterize underwater acoustic biodiversity in the Southern Ocean

Abstract

Acoustic metrics (AM) assist our interpretation of acoustic environments by aggregating a complex signal into a unique number. Numerous AM have been developed for terrestrial ecosystems, with applications ranging from rapid biodiversity assessments to characterizing habitat quality. However, there has been comparatively little research aimed at understanding how these metrics perform to characterize the acoustic features of marine habitats and their relation with ecosystem biodiversity. Our objectives were to 1) assess whether AM are able to capture the spectral and temporal differences between two distinct Antarctic marine acoustic environment types (i.e., pelagic vs. on-shelf), 2) evaluate the performance of a combination of AM compared to the signal full frequency spectrum to characterize marine mammals acoustic assemblages (i.e., species richness-SR-and species identity) and 3) estimate the contribution of SR to the local marine acoustic heterogeneity measured by single AM. We used 23 different AM to develop a supervised machine learning approach to discriminate between acoustic environments. AM performance was similar to the full spectrum, achieving correct classifications for SR levels of 58% and 92% for pelagic and on-shelf sites respectively and > 88% for species identities. Our analyses show that a combination of AM is a promising approach to characterize marine acoustic communities. It allows an intuitive ecological interpretation of passive acoustic data, which in the light of ongoing environmental changes, supports the holistic approach needed to detect and understand trends in species diversity, acoustic communities and underwater habitat quality.

Year-round passive acoustic data reveal spatio-temporal patterns in marine mammal community composition in the Weddell Sea, Antarctica

“…Parks et al 2014, Desjonquères et al 2015, Buxton et al 2018. Application of these metrics to aquatic passive acoustic data requires re-evaluation of existing metrics and potentially developing new approaches before they can be reliably used for underwater biodiversity assessments (see Risch & Parks 2017 for a review, Roca & Van Opzeeland 2019). Here, we applied biodiversity metrics normally used for count data to hand- browsed audio data to explore spatio-temporal patterns in species composition of marine mammals.…”

“…Autonomously operating acoustic recording devices are deployed year-round throughout the world's oceans and, provided sufficient recording bandwidth, have the potential to provide a wealth of information relating to acoustic communities and local species compositions. To date, however, most marine passive acoustic studies have taken a single-species perspective (most often marine mammals), only rarely consolidating contextual acoustic data in analyses and interpretation providing information on acoustic environments, biodiversity or species association patterns (for a review, see Risch & Parks 2017, also see Roca & Van Opzeeland 2019).…”

Acoustic indexes for marine biodiversity trends and ecosystem health

“…Investigations found ACI to be a reliable metric for describing biological diversity and habit quality in reef fish communities [61] or to be correlated with key ecosystem functions [62]. It was suggested as the metric that better performed to discriminate species richness in the Southern Ocean [63] and was directly correlated with fish and snapping shrimp sound production in a temperate rocky reef or inshore waters [60,64]. Conversely, other studies could not find clear relationships with fish assemblages [65,66], tonal signals of fishes [60] and proper identification of the peak period of dense fish chorusing [39,58].…”

Ecoacoustics: acoustic sensing for biodiversity monitoring at scale

“…One paradigm measures the acoustic diversity of a soundscape through the computation of acoustic indices: algorithmically straightforward and highly scalable, these indices yield evidence of biodiversity that is implicit, but holistic across many taxa. Sánchez‐Giraldo et al (2020) and Roca and Van Opzeeland (2019) conduct large‐scale studies in very different ecological contexts – respectively in forests in the Columbian Andes, and underwater in the Southern Ocean – and quantify the reliability of such indices. Sánchez‐Giraldo et al (2020) tackle the widely encountered issue of the effect of rain noise on index computation, while Roca and Van Opzeeland (2019) reveal acoustic significant differences between distinct Antarctic marine habitats using a set of indices.…”

“…Sánchez‐Giraldo et al (2020) and Roca and Van Opzeeland (2019) conduct large‐scale studies in very different ecological contexts – respectively in forests in the Columbian Andes, and underwater in the Southern Ocean – and quantify the reliability of such indices. Sánchez‐Giraldo et al (2020) tackle the widely encountered issue of the effect of rain noise on index computation, while Roca and Van Opzeeland (2019) reveal acoustic significant differences between distinct Antarctic marine habitats using a set of indices. Campos‐Cerqueira et al (2019) develop another type of index by extracting compressed data from a long‐term spectrogram representation.…”