MAMMALNET – Citizen Science Data Collection from a One Health Perspective

Smith, Graham; Roy, David; Stephens, Philip; Casaer, Jim; Jansen, Patrick; Blanco-Aguiar, Jose Antonio

Abstract

The ambition of One Health (OH) is to focus on people, animals and the ecosystem equally (Tripartite and UNEP support OHHLEP’s definition of “One Health” (who.int)). This requires adequate data on wildlife. MAMMALNET is a European consortium set up to collect wildlife occurrence data, with the specific aim of improving our understanding and prediction of disease spread.
MAMMALMET encourages citizens and professionals to report mammal sightings on an ad hoc basis (iMammalia app) or through surveys using remote camera traps (MammalWeb or Agouti). This combines data from different sources, increases our understanding of mammal distribution and aids in monitoring the spread of invasive species. MAMMALNET participants can see their records and maintain a list of species sightings. These data are vital to our understanding of the ecosystem and how this may change over time, providing background data for monitoring species.
These data complement and contribute to reinforcing wildlife health reports, such as recording dead wild boar in outbreak areas of African Swine Fever. Such records are followed up for disease sampling to monitor the spread of disease. The data can also be used to predict the distribution and abundance of wild species, provide the denominator data for disease reports and predict the potential for disease spread and control. MAMMALNET is committed to open science since OH requires not only an interdisciplinary approach but practical collaboration and sharing of standardized data.
These outputs can help predict the potential spread and control of zoonotic diseases, such as rabies, with benefits for human health.

Citation

Smith, G., Roy, D., Stephens, P., Casaer, J., Jansen, P., & Blanco-Aguiar, J. A. (2023). MAMMALNET – Citizen Science Data Collection from a One Health Perspective. One Health Cases, 2023, https://doi.org/10.1079/onehealthcases.2023.0021

• Journal Article Type: Article
• Acceptance Date: Jul 1, 2023
• Online Publication Date: Jul 6, 2023
• Publication Date: Jul 6, 2023
• Deposit Date: Jul 31, 2023
• Publicly Available Date: Jan 7, 2024
• Journal: One Health Cases
• Peer Reviewed: Peer Reviewed
• Volume: 2023
• DOI: https://doi.org/10.1079/onehealthcases.2023.0021
• Public URL: https://durham-repository.worktribe.com/output/1708394