Novel applications of technology for advancing tidal marsh ecology

Kimball, Matthew, 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., and Taylor, Matthew D. (2021) Novel applications of technology for advancing tidal marsh ecology. Estuaries and Coasts, 44. pp. 1568-1578.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website:


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.

Item ID: 68737
Item Type: Article (Research - C1)
ISSN: 1559-2731
Keywords: Animal tracking, Biotracers, Imagery, Machine learning, Open science
Copyright Information: (C) Coastal and Estuarine Research Federation 2021
Date Deposited: 17 Aug 2021 01:50
FoR Codes: 41 ENVIRONMENTAL SCIENCES > 4102 Ecological applications > 410203 Ecosystem function @ 30%
41 ENVIRONMENTAL SCIENCES > 4101 Climate change impacts and adaptation > 410102 Ecological impacts of climate change and ecological adaptation @ 30%
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management @ 40%
SEO Codes: 18 ENVIRONMENTAL MANAGEMENT > 1802 Coastal and estuarine systems and management > 180203 Coastal or estuarine biodiversity @ 80%
18 ENVIRONMENTAL MANAGEMENT > 1802 Coastal and estuarine systems and management > 180206 Rehabilitation or conservation of coastal or estuarine environments @ 20%
Downloads: Total: 1
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page