Robust, real‐time and autonomous monitoring of ecosystems with an open, low‐cost, networked device

Sethi, Sarab S.; Ewers, Robert M.; Jones, Nick; Orme, C. David L.; Picinali, Lorenzo

doi:10.1111/2041-210x.13089

Cited by 70 publications

(81 citation statements)

References 15 publications

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“…Looking forward, emerging open-source, microcomputer-based sensors are significantly cheaper than commercial alternatives (Sethi, Ewers, Jones, Orme, & Picinali, 2018;Whytock & Christie, 2017). For instance, the AudioMoth can be mass-produced to reduce unit cost to around US$30 (Hill et al, 2018), thereby drastically lowering the initial financial barriers to large multisensor surveys, although maintenance costs (e.g., regular replacement of batteries and SD cards) may substantially increase in larger projects.…”

Section: Passive Acoustic Sensor Hardwarementioning

confidence: 99%

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

et al. 2018

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High‐throughput environmental sensing technologies are increasingly central to global monitoring of the ecological impacts of human activities. In particular, the recent boom in passive acoustic sensors has provided efficient, noninvasive, and taxonomically broad means to study wildlife populations and communities, and monitor their responses to environmental change. However, until recently, technological costs and constraints have largely confined research in passive acoustic monitoring (PAM) to a handful of taxonomic groups (e.g., bats, cetaceans, birds), often in relatively small‐scale, proof‐of‐concept studies. The arrival of low‐cost, open‐source sensors is now rapidly expanding access to PAM technologies, making it vital to evaluate where these tools can contribute to broader efforts in ecology and biodiversity research. Here, we synthesise and critically assess the current emerging opportunities and challenges for PAM for ecological assessment and monitoring of both species populations and communities. We show that terrestrial and marine PAM applications are advancing rapidly, facilitated by emerging sensor hardware, the application of machine learning innovations to automated wildlife call identification, and work towards developing acoustic biodiversity indicators. However, the broader scope of PAM research remains constrained by limited availability of reference sound libraries and open‐source audio processing tools, especially for the tropics, and lack of clarity around the accuracy, transferability and limitations of many analytical methods. In order to improve possibilities for PAM globally, we emphasise the need for collaborative work to develop standardised survey and analysis protocols, publicly archived sound libraries, multiyear audio datasets, and a more robust theoretical and analytical framework for monitoring vocalising animal communities.

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Section: Passive Acoustic Sensor Hardwarementioning

confidence: 99%

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

et al. 2018

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“…While much research over the past few decades has increased our understanding of ecosystem processes in the Amazon, much work still remains to further our knowledge regarding development plans for the region that integrates forest conservation with the needs of Amazonian residents. This can be undertaken with the development of new and more advanced sensors that can be built and deployed (often at low-cost) to measure the environment across broad scales and remotely [62,63], as well as through use of new computational advances such as artificial intelligence and machine learning to analyze these enormous datasets for further insights that may otherwise not be detected from current statistical approaches [64].…”

Section: Tradeoffs and Public Policiesmentioning

confidence: 99%

Agricultural Impacts on Hydrobiogeochemical Cycling in the Amazon: Is There Any Solution?

Figueiredo

Cak

Markewitz

2020

Water

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Expansion of agriculture in the Brazilian Amazon has been driven not just by demands from traditional, rural producers, but also large agriculture and cattle producers, both of whom have put considerable pressure on remaining forests and their watersheds. Monitoring of these watersheds has been a focus of intensive study for the past 20 years and although this work has greatly increased our understanding, considerable gaps still remain in our ability to provide adequate recommendations for land management and associated public policies. In this study we present a summary of findings from these previous results. For small properties, the use of fire to prepare land for cultivation remains controversial, while in large properties, forest conversion to pasture and/or crop production has had a meaningful and adverse effect on water quality. Riparian forest conservation can make a significant difference in reducing impacts of land-use change. Secondary vegetation can also play an important role in mitigating these impacts. New types of sustainable agricultural production systems, together with incentives such as payments for ecosystem service can also contribute. Continued monitoring of these changes, together with robust sustainable development plans, can help to preserve forest while still addressing the social and economic needs of Amazonian riverine inhabitants.

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“…While here we have tackled the task of ecological monitoring, the same approach can easily be generalised to other fields employing acoustic analysis, for example in healthcare 1 , construction 2 , surveillance 3 or manufacturing 4 . Pairing these new computational methods with networked acoustic recording platforms 43 offers promise as a general framework on which to base larger efforts at standardised, autonomous system monitoring.…”

Section: The Future Of Automated Environmental Monitoringmentioning

confidence: 99%

Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

Sethi¹,

Jones²,

Fulcher³

et al. 2019

Preprint

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Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labour-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we developed a generalisable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed ecosystem soundscapes from a wide variety of biomes 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, paving the way for real-time detection of irregular environmental behaviour including illegal activity. Our highly generalisable approach, and the common set of features, will enable scientists to unlock previously hidden insights from eco-acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.

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Robust, real‐time and autonomous monitoring of ecosystems with an open, low‐cost, networked device

Cited by 70 publications

References 15 publications

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

Agricultural Impacts on Hydrobiogeochemical Cycling in the Amazon: Is There Any Solution?

Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

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