Over the last 20 years, innovations have led to the development of exciting new technologies and novel applications of established technologies, collectively increasing the scale, scope, and quality of research possible in tidal marsh systems. Thus, ecological research on marshes is being revolutionized, in the same way as ecological research more generally, by the availability of new tools and analytical techniques. This perspective highlights current and potential applications of novel research technologies for marsh ecology. These are summarized under several themes: (1.) imagery — sophisticated imaging sensors mounted on satellites, drones, and underwater vehicles; (2.) animal tracking — acoustic telemetry, passive integrated transponder (PIT) tags, and satellite tracking, and (3.) biotracers — investigation of energy pathways and food web structure using chemical tracers such as compound-specific stable isotopes, isotope addition experiments, contaminant analysis, and eDNA. While the adoption of these technological advances has greatly enhanced our ability to examine contemporary questions in tidal marsh ecology, these applications also create significant challenges with the accessibility, processing, and synthesis of the large amounts of data generated. Implementation of open science practices has allowed for greater access to data. Newly available machine learning algorithms have been widely applied to resolve the challenge of detecting patterns in massive environmental datasets. The potential integration on digital platforms of multiple, large data streams measuring physical and biological components of tidal marsh ecosystems is an opportunity to advance science support for management responses needed in a rapidly changing coastal landscape.
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Conflict of Interest
This perspective arose from a meeting held at the Dauphin Island Sea Lab (DISL) 1–3 November 2019 as part of the Coastal and Estuarine Research Federation (CERF) 2019 Conference Workshop Program. The meeting was funded by grants to R. Baker from the University of South Alabama and DISL, and sponsorship from Mississippi-Alabama-, Georgia-, and Washington-Sea Grants, the Grand Bay National Estuarine Research Reserve, the DISL Foundation, and CERF. RMC is supported by the Global Wetlands Project. NJW is funded by Australian Government National Environmental Science Program Tropical Water Quality Hub. TAW was supported by an anonymous grant to The Nature Conservancy.
Communicated by Kenneth L. Heck
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Kimball, M.E., Connolly, R.M., Alford, S.B. et al. Novel Applications of Technology for Advancing Tidal Marsh Ecology. Estuaries and Coasts 44, 1568–1578 (2021). https://doi.org/10.1007/s12237-021-00939-w
- Animal tracking
- Machine learning
- Open science