Author

C. D. L. Orme

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 4

Signature Assays, Organisms & Methods

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Research Focus:ecology & biodiversity· 3 Approach:experimental assay· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Assay:field survey / ecological data· 1 Organism:plant (other)· 1

Frequent Co-Authors

R. M. Ewers· 4 shared L. Picinali· 3 shared S. S. Sethi· 3 shared N. S. Jones· 2 shared A. Chung· 1 shared A. Faruk· 1 shared A. Signorelli· 1 shared B. D. Fulcher· 1 shared

Activity by Year

2019-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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4 shared papersEcology

R. M. Ewers

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Author profile →

4 papers

Recent·Most cited

Variable responses of individual species to tropical forest degradation

R. M. Ewers, W. D. Pearse, C. D. L. Orme, P. Amarasekare, T. De Lorm, N. Granville, R. Adzhar, D. C. Aldridge, M. Ancrenaz, G. Atton, H. Barclay, M. L. Barclay, H. Bernard, J. E. Bicknell, T. R. Bishop, , F

The study examines how individual species respond to tropical forest degradation, finding significant variation in responses across populations and surveys.

bioRxiv · Feb 9, 2024 · PreprintEcology

MAARU: Multichannel Acoustic Autonomous Recording Unit for spatial ecosystem monitoring.

B. E. Heath, N. P. Le Penru, J. Skinner, C. D. L. Orme, S. S. Sethi, R. M. Ewers, L. Picinali

The paper introduces MAARU, a low-cost, open-source multichannel acoustic recorder for spatial ecosystem monitoring, demonstrating its accuracy in localizing sounds and improving species identification.

bioRxiv · Jan 23, 2024 · PreprintEcology

SAFE Acoustics: an open-source, real-time eco-acoustic monitoring network in the tropical rainforests of Borneo

S. S. Sethi, R. M. Ewers, N. S. Jones, A. Signorelli, L. Picinali, C. D. L. Orme

The paper presents an open-source, real-time eco-acoustic monitoring network in Borneo's tropical rainforests, providing code, design, and infrastructure for data collection and analysis.

bioRxiv · Feb 27, 2020Ecology

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

S. S. Sethi, N. S. Jones, B. D. Fulcher, L. Picinali, D. J. Clink, H. Klinck, C. D. L. Orme, P. H. Wrege, R. M. Ewers

This paper develops a machine learning method using eco-acoustic data to monitor ecosystem changes and detect anomalies efficiently.

bioRxiv · Dec 5, 2019 · PreprintEcology