Novel Applications of Technology for Advancing Tidal Marsh Ecology
Autor: | Nathan J. Waltham, Matthew D. Kenworthy, Scott B. Alford, Rod M. Connolly, Gregory S. Norris, Thomas A. Worthington, W. Ryan James, Eric L. Sparks, Sarah Ramsden, Denise D. Colombano, Jeff Ollerhead, Matthew D. Taylor, Jennifer S. Rehage, Matthew E. Kimball |
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Rok vydání: | 2021 |
Předmět: |
0106 biological sciences
geography geography.geographical_feature_category Marsh 010504 meteorology & atmospheric sciences Ecology Scope (project management) Computer science Data stream mining Emerging technologies 010604 marine biology & hydrobiology Scale (chemistry) Ecology (disciplines) Aquatic Science 01 natural sciences Data access Salt marsh Ecology Evolution Behavior and Systematics 0105 earth and related environmental sciences |
Zdroj: | Estuaries and Coasts. 44:1568-1578 |
ISSN: | 1559-2731 1559-2723 |
Popis: | 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. |
Databáze: | OpenAIRE |
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