Wetland ecosystems are highly biodiverse, essential to human health and well‐being, and in decline, yet knowledge of the natural dynamics and distributions of wetland systems is lacking globally, hindering conservation efforts. We integrated data generated from novel remote‐sensing techniques and Bayesian hierarchical modeling to estimate the daily surface area of 149 wetlands (vernal pools) over a 20‐year period. We used these data to quantify the proportion of pools found suitable for local threatened and endangered species, and we discuss how to apply these methods to help answer fundamental ecological questions. Our modeling approaches, which can be applied to many kinds of wetlands and other freshwater systems, expand our ability to understand hydrologic dynamics across scales relevant to species, populations, and ecosystems, as well as to human needs, concerns, and decision making.

中文翻译：

---

通过卫星图像扩展湿地水文周期数据，以用于生态应用

湿地生态系统具有高度生物多样性，对人类健康和福祉至关重要，并且在下降中，但是全球缺乏对湿地系统自然动态和分布的知识，这阻碍了保护工作。我们整合了从新颖的遥感技术和贝叶斯层次模型生成的数据，以估算20年内149个湿地（春季池）的日表面积。我们使用这些数据来量化发现适合当地受威胁和濒危物种的池的比例，并且我们讨论如何应用这些方法来帮助回答基本的生态问题。我们的建模方法可以应用于多种湿地和其他淡水系统，从而扩展了我们了解与物种，种群和生态系统相关的尺度上的水文学动态的能力，