Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, 80523, USA.
National Socio-Environmental Synthesis Center, Annapolis, MD, 21401, USA.
Glob Chang Biol. 2017 May;23(5):1774-1782. doi: 10.1111/gcb.13504. Epub 2016 Nov 1.
Intensification of the global hydrological cycle, ranging from larger individual precipitation events to more extreme multiyear droughts, has the potential to cause widespread alterations in ecosystem structure and function. With evidence that the incidence of extreme precipitation years (defined statistically from historical precipitation records) is increasing, there is a clear need to identify ecosystems that are most vulnerable to these changes and understand why some ecosystems are more sensitive to extremes than others. To date, opportunistic studies of naturally occurring extreme precipitation years, combined with results from a relatively small number of experiments, have provided limited mechanistic understanding of differences in ecosystem sensitivity, suggesting that new approaches are needed. Coordinated distributed experiments (CDEs) arrayed across multiple ecosystem types and focused on water can enhance our understanding of differential ecosystem sensitivity to precipitation extremes, but there are many design challenges to overcome (e.g., cost, comparability, standardization). Here, we evaluate contemporary experimental approaches for manipulating precipitation under field conditions to inform the design of 'Drought-Net', a relatively low-cost CDE that simulates extreme precipitation years. A common method for imposing both dry and wet years is to alter each ambient precipitation event. We endorse this approach for imposing extreme precipitation years because it simultaneously alters other precipitation characteristics (i.e., event size) consistent with natural precipitation patterns. However, we do not advocate applying identical treatment levels at all sites - a common approach to standardization in CDEs. This is because precipitation variability varies >fivefold globally resulting in a wide range of ecosystem-specific thresholds for defining extreme precipitation years. For CDEs focused on precipitation extremes, treatments should be based on each site's past climatic characteristics. This approach, though not often used by ecologists, allows ecological responses to be directly compared across disparate ecosystems and climates, facilitating process-level understanding of ecosystem sensitivity to precipitation extremes.
全球水文循环的强化,从较大的单个降水事件到更极端的多年干旱,有可能导致生态系统结构和功能的广泛改变。有证据表明,极端降水年份(根据历史降水记录统计定义)的发生率正在增加,因此显然需要确定最容易受到这些变化影响的生态系统,并了解为什么一些生态系统比其他生态系统对极端情况更敏感。迄今为止,对自然发生的极端降水年份的机会主义研究,以及相对较少的实验结果,为生态系统敏感性的差异提供了有限的机制理解,这表明需要新的方法。跨多个生态系统类型排列并专注于水的协调分布式实验(CDE)可以增强我们对降水极端差异生态系统敏感性的理解,但仍有许多设计挑战需要克服(例如,成本、可比性、标准化)。在这里,我们评估了在野外条件下操纵降水的当代实验方法,为“干旱网络”(Drought-Net)的设计提供信息,这是一种相对低成本的 CDE,可模拟极端降水年份。一种常见的施加干湿年份的方法是改变每个环境降水事件。我们支持这种方法来施加极端降水年份,因为它同时改变了其他降水特征(即事件大小),与自然降水模式一致。然而,我们并不主张在所有站点应用相同的处理水平——这是 CDE 标准化的常见方法。这是因为降水变率在全球范围内变化超过五倍,导致定义极端降水年份的生态系统特定阈值范围很广。对于专注于降水极端的 CDE,处理应该基于每个站点的过去气候特征。这种方法虽然不常被生态学家使用,但允许直接在不同的生态系统和气候之间比较生态响应,促进对降水极端情况下生态系统敏感性的过程水平理解。