Nocturnal flight calling behaviour of thrushes in relation to artificial light at night

Gillings, Simon; Scott, Chris

doi:10.1111/ibi.12955

Cited by 14 publications

(14 citation statements)

References 32 publications

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“…Paumen et al 2022). When compared to other avian studies (Ruff et al 2020, Gillings and Scott 2021, Nolan et al 2022, our trained multilabel CNN achieved similar or even higher levels of classification accuracy ( ≥ 99%) and proved to be highly capable of distinguishing between the 5 different avian species calls herein. The CNN also successfully identified portions of recordings that were devoid of bird calls (i.e., noise clips), resulting in a false positive error rate of zero.…”

Section: T a B L Ementioning

confidence: 51%

An efficient acoustic classifier for high‐priority avian species in the southern Great Plains using convolutional neural networks

Wolfe,

Proctor,

Nolan

et al. 2023

Wildlife Society Bulletin

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Passive acoustic monitoring is a valuable ecological and conservation tool that allows researchers to collect data from vocal species across large geographic areas and temporal spans. Grassland bird populations, many of which are indicators of ecosystem health, have experienced precipitous declines over the past several decades. Acoustic monitoring of grassland bird populations provides opportunities to monitor declines and focus conservation practices, yet the ability to identify species efficiently and accurately from acoustic data is challenging. Therefore, development of automated classifiers such as convolutional neural networks (CNNs) are at the forefront of streamlining detection and identification of individual species. Here, we present a CNN classifier for 5 key grassland bird species across southcentral Oklahoma, a part of the southern Great Plains: northern bobwhite (Colinus virginianus), painted bunting (Passerina ciris), dickcissel (Spiza americana), eastern meadowlark (Sturnella magna), and Bell's vireo (Vireo bellii). We compiled a high‐quality training dataset consisting of 6,933 calls, built semiautonomously using template matching that can be expanded easily to any bird species of interest. Our trained multilabel CNN achieved a high level of classification accuracy (≥98%) for the 5 species using the library of test calls and field recordings played using a programmable game caller. The ability to conduct acoustic wildlife surveys across large spatial extents will allow for more efficient monitoring of wildlife to determine key population parameters and trends and effects of biotic and abiotic factors (e.g., vegetation, disturbance, weather) on these key species.

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Section: T a B L Ementioning

confidence: 51%

An efficient acoustic classifier for high‐priority avian species in the southern Great Plains using convolutional neural networks

Wolfe,

Proctor,

Nolan

et al. 2023

Wildlife Society Bulletin

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“…Our enhanced recogniser that included seasonal variation (R2) still performed poorly compared to many other machine learning PAM studies (Jahn et al 2017; Bravo et al 2017; Knight and Bayne 2019; Knight et al 2020; Balantic and Donovan 2020; Gillings and Scott 2021). However, our results are comparable to similar studies in tropical environments (Swiston and Mennill 2009; Heinicke et al 2015).…”

Section: Discussionmentioning

confidence: 77%

Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: a case study of two sympatric macaw species

Lewis

Vargas

Beckerman

et al. 2022

Preprint

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Passive acoustic monitoring (PAM) - the use of autonomous recording units to record ambient sound - offers the potential to dramatically increase the scale and robustness of species monitoring in rainforest ecosystems. PAM generates large volumes of data that require automated methods of target species detection. Species-specific recognisers, which often use supervised machine learning, can achieve this goal. However, they require a large training dataset of both target and non-target signals, which is time-consuming and challenging to create. Unfortunately, very little information about creating training datasets for supervised machine learning recognisers is available, especially for tropical ecosystems. Here we show an iterative approach to creating a training dataset that improved recogniser precision from 0.12 to 0.55. By sampling background noise using an initial small recogniser, we can address one of the significant challenges of training dataset creation in acoustically diverse environments. Our work demonstrates that recognisers will likely fail in real-world settings unless the training dataset size is large enough and sufficiently representative of the ambient soundscape. We outline a simple workflow that can provide users with an accessible way to create a species-specific PAM recogniser that addresses these issues for tropical rainforest environments. Our work provides important lessons for PAM practitioners wanting to develop species-specific recognisers for acoustically diverse ecosystems.

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“…Nevertheless, flight calls have been used to study how migrants interact with topography and respond to weather (Gesicki et al, 2016;Smith et al, 2014) and light pollution (Gillings & Scott, 2021;Watson et al, 2016;Winger et al, 2019). To our knowledge, no studies have used flight calls to track species-specific phenology (but see e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have found relationships between acoustic activity and migration intensity measured by radar, thermal cameras or mist nets, but acoustics typically explain little variation in migration intensity (Farnsworth et al, 2004; Horton et al, 2015; Larkin et al, 2002; Sanders & Mennill, 2014). Nevertheless, flight calls have been used to study how migrants interact with topography and respond to weather (Gesicki et al, 2016; Smith et al, 2014) and light pollution (Gillings & Scott, 2021; Watson et al, 2016; Winger et al, 2019). To our knowledge, no studies have used flight calls to track species‐specific phenology (but see e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Light pollution and light‐induced collisions are particular concerns for migratory birds (Loss et al, 2015; Van Doren et al, 2017). Lights‐out initiatives are a focus of current policy actions, and increasing evidence suggests that acoustic communication during nocturnal migration is an important factor in birds' susceptibility to artificial light pollution (Gillings & Scott, 2021; Watson et al, 2016; Winger et al, 2019). In addition, understanding the impacts of climate change on organisms is of conservation and policy concern, and additional tools that can monitor their behavioural responses and population are important resources for decision‐making.…”

Section: Introductionmentioning

confidence: 99%

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Automated acoustic monitoring captures timing and intensity of bird migration

Doren

Lostanlen

Cramer

et al. 2022

Journal of Applied Ecology

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Monitoring small, mobile organisms is crucial for science and conservation, but is technically challenging. Migratory birds are prime examples, often undertaking nocturnal movements of thousands of kilometres over inaccessible and inhospitable geography. Acoustic technology could facilitate widespread monitoring of nocturnal bird migration with minimal human effort. Acoustics complements existing monitoring methods by providing information about individual behaviour and species identities, something generally not possible with tools such as radar. However, the need for expert humans to review audio and identify vocalizations is a challenge to application and development of acoustic technologies. Here, we describe an automated acoustic monitoring pipeline that combines acoustic sensors with machine listening software (BirdVoxDetect). We monitor 4 months of autumn migration in the northeastern United States with five acoustic sensors, extracting nightly estimates of nocturnal calling activity of 14 migratory species with distinctive flight calls. We examine the ability of acoustics to inform two important facets of bird migration: (1) the quantity of migrating birds aloft and (2) the migration timing of individual species. We validate these data with contemporaneous observations from Doppler radars and a large community of citizen scientists, from which we derive independent measures of migration passage and timing. Together, acoustic and weather data produced accurate estimates of the number of actively migrating birds detected with radar. A model combining acoustic data, weather and seasonal timing explained 75% of variation in radar‐derived migration intensity. This model outperformed models that lacked acoustic data. Including acoustics in the model decreased prediction error by 33%. A model with only acoustic information outperformed a model comprising weather and date (57% vs. 48% variation explained, respectively). Acoustics also successfully measured migration phenology: species‐specific timing estimated by acoustic sensors explained 71% of variation in timing derived from citizen science observations. Synthesis and applications. Our results demonstrate that cost‐effective acoustic sensors can monitor bird migration at species resolution at the landscape scale and should be an integral part of management toolkits. Acoustic monitoring presents distinct advantages over radar and human observation, especially in inaccessible and inhospitable locations, and requires significantly less expense. Managers should consider using acoustic tools for monitoring avian movements and identifying and understanding dangerous situations for birds. These recommendations apply to a variety of conservation and policy applications, including mitigating the impacts of light pollution, siting energy infrastructure (e.g. wind turbines) and reducing collisions with structures and aircraft.

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Nocturnal flight calling behaviour of thrushes in relation to artificial light at night

Cited by 14 publications

References 32 publications

An efficient acoustic classifier for high‐priority avian species in the southern Great Plains using convolutional neural networks

An efficient acoustic classifier for high‐priority avian species in the southern Great Plains using convolutional neural networks

Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: a case study of two sympatric macaw species

Automated acoustic monitoring captures timing and intensity of bird migration

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