02431nas a2200205 4500008004100000245009800041210006900139520172100208653002101929653001401950653002601964653003301990653003202023653002202055100002002077700002402097700002502121700001802146856006102164 2019 eng d00aUsing acoustic metrics to characterize underwater acoustic biodiversity in the Southern Ocean0 aUsing acoustic metrics to characterize underwater acoustic biodi3 a

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.

10aAcoustic metrics10aAntarctic10acommunity composition10amarine acoustic environments10apassive acoustic monitoring10aspecies diversity1 aRoca, Irene, T.1 aVan Opzeeland, Ilse1 aPettorelli, Nathalie1 aQuick, Nicola uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.12902435nas a2200205 4500008004100000245009800041210006900139520172500208653002101933653001401954653002601968653003301994653003202027653002202059100002002081700002402101700002502125700001802150856006102168 2019 eng d00aUsing acoustic metrics to characterize underwater acoustic biodiversity in the Southern Ocean0 aUsing acoustic metrics to characterize underwater acoustic biodi3 a

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 bio- diversity 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 differ- ent AM to develop a supervised machine learning approach to discriminate between acoustic environments. AM performance was similar to the full spec- trum, 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 diver- sity, acoustic communities and underwater habitat quality.

10aAcoustic metrics10aAntarctic10acommunity composition10amarine acoustic environments10apassive acoustic monitoring10aspecies diversity1 aRoca, Irene, T.1 aVan Opzeeland, Ilse1 aPettorelli, Nathalie1 aQuick, Nicola uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.12902134nas a2200193 4500008004100000022001400041245013800055210006900193260001600262300001400278490000800292520147900300100002601779700002201805700002101827700001701848700002401865856005101889 2018 eng d a0001-496600aOn the reliability of acoustic annotations and automatic detections of Antarctic blue whale calls under different acoustic conditions0 areliability of acoustic annotations and automatic detections of  cJan-08-2018 a740 - 7540 v1443 a

Evaluation of the performance of computer-based algorithms to automatically detect mammalian vocalizations often relies on comparisons between detector outputs and a reference data set, generally obtained by manual annotation of acoustic recordings. To explore the reproducibility of these annotations, inter- and intra-analyst variability in manually annotated Antarctic blue whale (ABW) Z-calls are investigated by two analysts in acoustic data from two ocean basins representing different scenarios in terms of call abundance and background noise. Manual annotations exhibit strong inter- and intra-analyst variability, with less than 50% agreement between analysts. This variability is mainly caused by the difficulty of reliably and reproducibly distinguishing single calls in an ABW chorus made of overlaying distant calls. Furthermore, the performance of two automated detectors, based on spectrogram correlation or subspace-detection strategy, is evaluated by comparing detector output to a “conservative” manually annotated reference data set, which comprises only analysts' matching events. This study highlights the need for a standardized approach for human annotations and automatic detections, including a quantitative description of their performance, to improve the comparability of acoustic data, which is particularly relevant in the context of collaborative approaches in collecting and analyzing large passive acoustic data sets.

1 aLeroy, Emmanuelle, C.1 aThomisch, Karolin1 aRoyer, Jean-Yves1 aBoebel, Olaf1 aVan Opzeeland, Ilse uhttp://asa.scitation.org/doi/10.1121/1.5049803