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.

在过去的 20 年里，创新导致了令人兴奋的新技术的发展和现有技术的新应用，共同增加了潮汐沼泽系统研究的规模、范围和质量。因此，与更普遍的生态研究一样，由于新工具和分析技术的出现，沼泽生态研究正在发生革命性的变化。这一观点强调了沼泽生态学的新型研究技术的当前和潜在应用。这些总结在几个主题下： (1.) 图像——安装在卫星、无人机和水下航行器上的复杂成像传感器；(2.) 动物跟踪 — 声学遥测、无源集成转发器 (PIT) 标签和卫星跟踪，以及 (3. ) 生物示踪剂——使用化学示踪剂（如化合物特异性稳定同位素、同位素添加实验、污染物分析和 eDNA）研究能量途径和食物网结构。虽然这些技术进步的采用极大地增强了我们研究潮汐沼泽生态学中当代问题的能力，但这些应用程序也对生成的大量数据的可访问性、处理和合成提出了重大挑战。开放科学实践的实施允许更多地访问数据。新可用的机器学习算法已被广泛应用于解决在海量环境数据集中检测模式的挑战。在多个数字平台上的潜在整合，