Toward a Standardized Method for Quantifying Ecosystem Hot Spots and Hot Moments
Toward a Standardized Method for Quantifying Ecosystem Hot Spots and Hot Moments
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DOI：
10.1007/s10021-023-00839-z
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发表时间：
2023-04
期刊：
影响因子：
3.7
通讯作者：
J. Walter;Robert A. Johnson;J. Atkins;D. Ortiz;G. Wilkinson
中科院分区：
文献类型：
--
作者：
J. Walter;Robert A. Johnson;J. Atkins;D. Ortiz;G. Wilkinson
生态系统“热点”和“热点时刻”--分别指生物地球化学活动异常强烈的地点和时间--是生态系统科学中一个重要且经常被提及的概念。尽管这一概念很受欢迎，但没有标准的方法来量化热点和热点时刻，阻碍了对这一现象的理解。例如，缺乏标准的量化方法阻碍了跨数据集和可能代表不同过程、生态系统类型、地点和时间的研究的综合所产生的进展。我们提出了一种基于数据分布的偏度和峰度来量化热点和热点时刻的方法。我们的方法明确地测试热点和/或热点时刻的存在，以及识别被视为热点和/或热点时刻的观测。我们将我们的方法应用于代表不同生态系统背景和焦点变量的三个案例研究：湿润温带分水岭的土壤孔隙CO2浓度；高度富营养化的浅水湖的溶解氧饱和度；以及热带岛屿海岸的海草新陈代谢。基于稀疏性的灵敏度分析表明，使用我们的方法检测HSHM可以对空间和时间采样制度的变化敏感，这取决于系统的行为。为了便于采用我们的方法，我们提供了R包“hotspoments”来实现该方法。对热点和热点时刻采用标准的量化方法将促进生态系统科学的发展。
Ecosystem “hot spots” and “hot moments”—respectively, places and times of disproportionately high biogeochemical activity—are an important and often invoked concept in ecosystem science. Despite the popularity of the concept, there is no standard approach to quantifying hot spots and hot moments, hindering progress in understanding the phenomenon. For example, lack of a standard quantitative approach hinders advances arising from synthesis across datasets and studies potentially representing different processes, ecosystem types, places, and times. We present an approach to quantifying hot spots and hot moments based on the skewness and kurtosis of data distributions. Our approach explicitly tests for the presence of hot spots and/or hot moments, as well as identifies observations regarded as hot spots and/or hot moments. We apply our method to three case studies representing different ecosystem contexts and focal variables: soil pore space CO2concentrations in a humid temperate watershed; dissolved oxygen saturation in a hypereutrophic, shallow lake; and seagrass metabolism on the coast of a tropical island. A rarefaction-based sensitivity analysis showed how detection of HSHM using our methods can be sensitive to changes in spatial and temporal sampling regimes, depending on the behavior of the system. To facilitate adoption of our approach, we provide the R package “hotspomoments” to implement the method. Adoption of a standard quantitative approach to hot spots and hot moments would advance ecosystem science.