Novel Applications of Technology for Advancing Tidal Marsh Ecology

• Published in: Estuaries and coasts 2021-09, Vol.44 (6), p.1568-1578
• Main Authors: Kimball, Matthew E., Connolly, Rod M., Alford, Scott B., Colombano, Denise D., James, W. Ryan, Kenworthy, Matthew D., Norris, Gregory S., Ollerhead, Jeff, Ramsden, Sarah, Rehage, Jennifer S., Sparks, Eric L., Waltham, Nathan J., Worthington, Thomas A., Taylor, Matthew D.
• Format: Article
• Language: English
• Subjects: Acoustic telemetry; Acoustic tracking; Algorithms; Analytical methods; Coastal Sciences; Contaminants; Data transmission; Drone vehicles; Earth and Environmental Science; Ecological research; Ecology; Environment; Environmental DNA; Environmental Management; Food chains; Food webs; Freshwater & Marine Ecology; Imagery; Isotopes; Learning algorithms; Machine learning; New technology; Satellite imagery; Satellite tracking; Satellites; Special Issue: Concepts and Controversies in Tidal Marsh Ecology Revisited; Stable isotopes; Strategic management; Telemetry; Tidal marshes; Tracers; Transponders; Underwater acoustics; Underwater vehicles; Water and Health; Wetlands; Work platforms
• ISSN: 1559-2723; 1559-2731
• DOI: 10.1007/s12237-021-00939-w

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.