A roadmap for biomonitoring in the 21st century: Merging methods into metrics via ecological networks

• Published in: Advances in ecological research Vol. 68; pp. 1 - 34
• Main Authors: Cuff, Jordan P., Deivarajan Suresh, Mukilan, Dopson, Matthew E.G., Hawthorne, Ben S.J., Howells, Thomas, Kitson, James J.N., Miller, Kyle A., Xin, Tong, Evans, Darren M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2023
• Subjects: Acoustic monitoring; Automation; Deep learning; Global change; Interaction diversity; Metabarcoding; Molecular analysis; Network ecology; Survey techniques; Visual analysis; Deep learning; Metabarcoding; Automation; Global change; Visual analysis; Molecular analysis; Interaction diversity; Network ecology; Survey techniques; Acoustic monitoring
• ISSN: 0065-2504
• DOI: 10.1016/bs.aecr.2023.09.002

The urgent need for highly resolved and broad biomonitoring has never been greater as challenges such as climate change, agricultural intensification and biodiversity loss increasingly threaten the integrity of ecosystems globally. There has been unprecedented progress in the operationalisation of such biomonitoring by leveraging technological advances in molecular, image-based and wider monitoring techniques, ultimately building toward automation at massive scales. Given these rapid developments, an updated roadmap is needed to signpost the future direction of biomonitoring that integrates technologies and maximises advances in network ecology. Here, we highlight recent progress, particularly through the examples of molecular techniques, image-based analyses and acoustic monitoring, and how these complementary approaches can be integrated to vastly enhance biomonitoring. We summarise recent progress in network inference and key methods that show promise for the future transition toward automation. We also discuss advances in data merging, the implications of this for network construction and analysis, and emerging best practices. This is presented within the context of capitalising on ecological networks to generate informative, meaningful and accessible metrics that can assess and guide biomonitoring. Through this roadmap, we ultimately highlight three distinct challenge areas in need of urgent progress: (1) unified methods for data collection, (2) reproducible and robust big data, and (3) accurately inferring and analysing networks. Addressing these challenges through an iterative process within a communicative knowledge network encompassing researchers, policymakers, end users and industry is crucial to ensure progress toward sustainable, effective and meaningful biomonitoring.