Author

H. Klinck

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

Biology Disciplines

Click to filter author papers

Ecology· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:ecology & biodiversity· 3 Assay:imaging· 2 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

R. M. Ewers· 2 shared S. S. Sethi· 2 shared A. Bick· 1 shared B. D. Fulcher· 1 shared C. D. L. Orme· 1 shared C. J. Sayers· 1 shared D. A. Coomes· 1 shared D. J. Clink· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersEcology

R. M. Ewers

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Is there an accurate and generalisable way to use soundscapes to monitor biodiversity?

S. S. Sethi, A. Bick, R. M. Ewers, H. Klinck, V. Ramesh, M.-N. Tuanmu, D. A. Coomes

This study investigates the generalisability of soundscape analysis for biodiversity monitoring and finds that acoustic features and machine learning models are inconsistent across datasets.

bioRxiv · Dec 19, 2022 · PreprintEcology

Simultaneous passive acoustic monitoring uncovers evidence of potentially overlooked temporal variation in an Amazonian bird community

W. A. Hopping, C. J. Sayers, H. Klinck, N. R. Huaraca-Charca

This study uses passive acoustic monitoring to reveal significant temporal variation in Amazonian bird vocal activity and species richness, highlighting the need for simultaneous data collection to avoid temporal bias in surveys.

bioRxiv · Sep 25, 2021Ecology

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