Pen-Yuan Hsing
Large-scale mammal monitoring: the potential of a citizen science camera-trapping project in the United Kingdom
Hsing, Pen-Yuan; Hill, Russell; Smith, Graham; Bradley, Steven; Green, Sian; Kent, Vivien; Mason, Samantha; Rees, Jonathan; Whittingham, Mark; Cokill, Jim; scientists, MammalWeb citizen; Stephens, Philip
Authors
Professor Russell Hill r.a.hill@durham.ac.uk
Professor
Graham Smith
Professor Steven Bradley s.p.bradley@durham.ac.uk
Professor
Sian Green sian.e.green@durham.ac.uk
PGR Student Doctor of Philosophy
Vivien Kent
Samantha Mason samantha.s.mason@durham.ac.uk
PGR Student Doctor of Philosophy
Jonathan Rees jonathan.p.rees@durham.ac.uk
PGR Student Doctor of Philosophy
Mark Whittingham
Jim Cokill
MammalWeb citizen scientists
Professor Philip Stephens philip.stephens@durham.ac.uk
Professor
Abstract
1. In light of global biodiversity loss, there is an increasing need for large-scale wildlife monitoring. This is difficult for mammals, since they can be elusive and nocturnal. In the United Kingdom (UK), there is a lack of systematic, widespread mammal monitoring, and a recognised deficiency of data. Innovative new approaches are required. 2. We developed MammalWeb, a portal to enable UK-wide camera-trapping by a network of citizen scientists and partner organisations. MammalWeb citizen scientists contribute to both the collection and classification of camera trap data. Following trials in 2013-17, MammalWeb has grown organically to increase its geographic reach (e.g. ~2,000 sites in Britain). It has so far provided the equivalent of over 340 camera trap-years of wild mammal monitoring, and produced nearly 440,000 classified image sequences and videos, of which, over 180,000 are mammal detections. 3. We describe MammalWeb, its background, its development and the novel approaches we have for participation. We consider the data collected by MammalWeb participants, especially in light of their relevance to the main goals of wildlife monitoring: to provide spatial data, abundance data, and temporal behavioural data. 4. MammalWeb can complement existing approaches to mammal monitoring. Explicit accounting for spatial and temporal patterns in animal activity enables accounting of bias relative to ad hoc observational data. Estimating abundance presents challenges, as for many camera trapping studies, but we discuss the potential of the data as they stand, and opportunities to advance their value for abundance estimation. 5. Challenges remain to MammalWeb’s central missions of enhancing engagement with and connection to nature, and delivering policy-relevant data on Britain's wild mammals. We discuss these challenges and the opportunities they provide for advances in respect of engagement, science and financial security. 6. Our approach reduces administrative burden and increases spatial coverage and, as such, MammalWeb provides a useful addition to existing case studies of citizen science camera trapping program design. We believe MammalWeb is an important step towards fulfilling calls for UK-wide mammal monitoring and our description of challenges identifies an agenda for fulfilling that purpose.
Citation
Hsing, P.-Y., Hill, R., Smith, G., Bradley, S., Green, S., Kent, V., Mason, S., Rees, J., Whittingham, M., Cokill, J., scientists, M. C., & Stephens, P. (2022). Large-scale mammal monitoring: the potential of a citizen science camera-trapping project in the United Kingdom. Ecological Solutions and Evidence, 3(4), Article e12180. https://doi.org/10.1002/2688-8319.12180
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 18, 2022 |
Online Publication Date | Oct 11, 2022 |
Publication Date | Oct 11, 2022 |
Deposit Date | Aug 18, 2022 |
Publicly Available Date | Oct 12, 2022 |
Journal | Ecological solutions and evidence. |
Print ISSN | 2688-8319 |
Publisher | Wiley Open Access |
Peer Reviewed | Peer Reviewed |
Volume | 3 |
Issue | 4 |
Article Number | e12180 |
DOI | https://doi.org/10.1002/2688-8319.12180 |
Public URL | https://durham-repository.worktribe.com/output/1196776 |
Files
Published Journal Article
(2 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2022 The Authors. Ecological Solutions and Evidence published by John Wiley & Sons Ltd on behalf of British Ecological Society.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
You might also like
Economical crowdsourcing for camera trap image classification
(2018)
Journal Article
ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference
(2021)
Book Chapter
Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models
(2020)
Book Chapter
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search