Listening forward: approaching marine biodiversity assessments using acoustic methods

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mooney, T. A., Di Iorio, L., Lammers, M., Lin, T., Nedelec, S. L., Parsons, M., Radford, C., Urban, E., & Stanley, J. Listening forward: approaching marine biodiversity assessments using acoustic methods. Royal Society Open Science, 7(8), (2020): 201287, doi:10.1098/rsos.201287.Ecosystems and the communities they support are changing at alarmingly rapid rates. Tracking species diversity is vital to managing these stressed habitats. Yet, quantifying and monitoring biodiversity is often challenging, especially in ocean habitats. Given that many animals make sounds, these cues travel efficiently under water, and emerging technologies are increasingly cost-effective, passive acoustics (a long-standing ocean observation method) is now a potential means of quantifying and monitoring marine biodiversity. Properly applying acoustics for biodiversity assessments is vital. Our goal here is to provide a timely consideration of emerging methods using passive acoustics to measure marine biodiversity. We provide a summary of the brief history of using passive acoustics to assess marine biodiversity and community structure, a critical assessment of the challenges faced, and outline recommended practices and considerations for acoustic biodiversity measurements. We focused on temperate and tropical seas, where much of the acoustic biodiversity work has been conducted. Overall, we suggest a cautious approach to applying current acoustic indices to assess marine biodiversity. Key needs are preliminary data and sampling sufficiently to capture the patterns and variability of a habitat. Yet with new analytical tools including source separation and supervised machine learning, there is substantial promise in marine acoustic diversity assessment methods.Funding for development of this article was provided by the collaboration of the Urban Coast Institute (Monmouth University, NJ, USA), the Program for the Human Environment (The Rockefeller University, New York, USA) and the Scientific Committee on Oceanic Research. Partial support was provided to T.A.M. from the National Science Foundation grant OCE-1536782

Sounding out ecoacoustic metrics: avian species richness is predicted by acoustic indices in temperate but not tropical habitats

Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the de- velopment of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures are lacking. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithol- ogists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (Root-mean-square, Spectral centroid and Zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses demonstrated that each habitat has a very distinct soundscape and that AIs track observed differences in habitat-dependent community composition. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors are significant contributors to avian diversity prediction in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking subtler habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks

Impacts of coffee fragmented landscapes on biodiversity and microclimate with emerging monitoring technologies

Habitat fragmentation and loss are causing biodiversity declines across the globe. As biodiversity is unevenly distributed, with many hotspots located in the tropics, conserving and protecting these areas is important to preserve as many species as possible. Chapter 2 presents an overview of the Ecology of the Atlantic Forest, a highly fragmented biodiversity hotspot. A major driver of habitat fragmentation is agriculture, and in the tropics coffee is major cash crop. Developing methods to monitor biodiversity effectively without labour intensive surveys can help us understand how communities are using fragmented landscapes and better inform management practices that promote biodiversity. Acoustic monitoring offers a promising set of tools to remotely monitor biodiversity. Developments in machine learning offer automatic species detection and classification in certain taxa. Chapters 3 and 4 use acoustic monitoring surveys conducted on fragmented landscapes in the Atlantic Forest to quantify bird and bat communities in forest and coffee matrix, respectively. Chapter 3 shows that acoustic composition can reflect local avian communities. Chapter 4 applies a convolutional neural network (CNN) optimised on UK bat calls to a Brazilian bat dataset to estimate bat diversity and show how bats preferentially use coffee habitats. In addition to monitoring biodiversity, monitoring microclimate forms a key part of climate smart agriculture for climate change mitigation. Coffee agriculture is limited to the tropics, overlapping with biodiverse regions, but is threatened by climate change. This presents a challenge to countries strongly reliant on coffee exports such as Brazil and Nicaragua. Chapter 5 uses data from microclimate weather stations in Nicaragua to demonstrate that sun-coffee management is vulnerable to supraoptimal temperature exposure regardless of local forest cover or elevation.Open Acces

Passive acoustic monitoring in terrestrial vertebrates: a review

Passive acoustic monitoring (PAM) has become increasingly popular in ecological studies, but its efficacy for assessing overall terrestrial vertebrate biodiversity is unclear. To quantify this, its performance for species detection must be directly compared to that obtained using traditional observer-based monitoring (OBM). Here, we review such comparisons across all major terrestrial vertebrate classes and identify factors impacting PAM performance. From 41 studies, we found that while PAM-OBM comparisons have been made for all major terrestrial vertebrate classes, most comparisons have focused on birds (65%) in North America (52%). PAM performed equally well or better (61%) compared to OBM in general. We found no statistical difference between the methods for total number of species detected across all vertebrate classes (excluding reptiles); however, recording period and region of study influenced the relative performance of PAM, while acoustic analysis method and which method sampled for longer overall showed no impact. Further studies comparing PAM performance in non-avian vertebrates using standardised methods are needed to investigate in more detail the factors that may influence PAM performance. While PAM is a valuable tool for vertebrate surveys, a combined approach with targeted OBM for non-vocal species should achieve the most comprehensive assessment of terrestrial vertebrate communities

Using acoustic indices in ecology : guidance on study design, analyses and interpretation

TBL was supported by Leverhulme Trust, research grant number RPG-2020-160; the Lorentz Centre, Leiden, The Netherlands; and UKAN+. AE and OM were supported by UKAN+.The rise of passive acoustic monitoring and the rapid growth in large audio datasets is driving the development of analysis methods that allow ecological inferences to be drawn from acoustic data. Acoustic indices are currently one of the most widely applied tools in ecoacoustics. These numerical summaries of the sound energy contained in digital audio recordings are relatively straightforward and fast to calculate but can be challenging to interpret. Misapplication and misinterpretation have produced conflicting results and led some to question their value. To encourage better use of acoustic indices, we provide nine points of guidance to support good study design, analysis and interpretation. We offer practical recommendations for the use of acoustic indices in the study of both whole soundscapes and individual taxa and species, and point to emerging trends in ecoacoustic analysis. In particular, we highlight the critical importance of understanding the links between soundscape patterns and acoustic indices. Acoustic indices can offer insights into the state of organisms, populations, and ecosystems, complementing other ecological research techniques. Judicious selection, appropriate application and thorough interpretation of existing indices is vital to bolster robust developments in ecoacoustics for biodiversity monitoring, conservation and future research.Publisher PDFPeer reviewe

CIRN/GREC 2014 Campaign report

A cetacean survey campaign was performed around São Miguel Island (Azores) by Anacaona Sailing boat (GREC) from May 8th to May 17th and from July 4th to August 1st of 2014. The team conducted a total of 18 days of effort around the island. A total of 9 species were sighted during the survey: common dolphin (Delphinus delphis), atlantic spotted dolphin (Stenella frontalis), bottlenose dolphin (Tursiops trucnatus), stripped dolphin (Stenella coeruleoalba), risso’s dolphin (Grampus griseus), sperm whale (Physter macrocephalus), fin whale (Balaenoptera physalus), false killer whale (Pseudorca crassidens) and Blainville beaked whale (Mesoplodon densirostris). The common dolphin together with the Atlantic spotted dolphin were the most sighted species. Some differences in the distribution between the species can be appreciated in the corrected effort maps. Acoustic data is currently under study.info:eu-repo/semantics/publishedVersio

The Tallgrass Prairie Soundscape; Employing an Ecoacoustic Approach to Understand Grassland Response to Prescribed Burns and the Spatial and Temporal Patterns of Nechrophilous Invertebrate Communities

Tallgrass prairies are rapidly vanishing biodiversity hotspots for native and endemic species, yet little is known regarding how spatial and temporal variation of prairie soundscapes relates to seasonal changes, disturbance patterns and biological communities. Ecoacoustics, the study of environmental sounds using passive acoustics as a non-invasive tool for investigating ecological complexity, allows for long-term data to be captured without disrupting biological communities. Two studies were carried out by employing ecoacoustic methodology to study grassland carrion food webs and to capture the phenology of a grassland soundscape following a prescribed burn. Both studies were conducted at the Nature Conservancy’s Tallgrass Prairie Preserve (3650’N, 9625’W) and used six acoustic indices to quantify the ratio of technophony to biophony, acoustic complexity, diversity, evenness, entropy, and biological acoustic diversity from over 70,000 sound recordings. Acoustic index values were used to determine the relationship between Nicrophorus burying beetle species composition and the prairie soundscape (Chapter 1) and to determine if prescribed burning changes the composition of the soundscape over time (Chapter 2). In Chapter 1, I found that associations between Nicrophorus burying beetles and the soundscape were unique to particular species, acoustic indices and times of day. For example, N. americanus trap rates showed a positive correlation to areas of increased acoustic complexity specifically at dawn. In addition to positive associations with the soundscape, we found that N. marginatus was consistently negatively correlated to higher levels of biophony, while N. tomentosus was consistently positively correlated to places with higher levels of biophony. Although reproduction of all species examined is dependent upon securing small carrion for reproduction, I found that known habitat and activity segregation of five Nicrophorus beetle species may be reflective of the soundscape. Finally, I show that favorable habitat for a critically endangered necrophilous insect, the American burying beetle (Nicrophorus americanus) can be identified by the acoustic signature extracted from a short temporal window of its grassland ecosystem soundscape. Using the same suite of acoustic indices from Chapter 1, in Chapter 2 I examined acoustic recordings at a much larger time scale to determine distinctive acoustic events driven by biophony and geophony across a 23-week period. In addition to examining acoustic changes over time, I examined differences between 11 burned and unburned pastures. Results from this study indicate that prescribed burning does alter the soundscape, especially early in the post-burn period, but the effects are ameliorated by a significant increase in biophony as the growing and breeding season progressed into the warmer summer months. Both studies demonstrate that passive acoustic recording is a reliable method to assess relationships to acoustic communities over space and time

Classification and ranking of environmental recordings to facilitate efficient bird surveys

This thesis contributes novel computer-assisted techniques to facilitating bird species surveys from a large number of environmental audio recordings. These techniques are applicable to both manual and automated recognition of bird species by removing irrelevant audio data and prioritising those relevant data for efficient bird species detection. This work also represents a significant step towards using automated techniques to support experts and the general public to explore and gain a better understanding of vocal species

Characterising soundscapes across diverse ecosystems using a universal acoustic feature set

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts