Lagged and dormant season climate better predict plant vital rates than climate during the growing season
Lagged and dormant season climate better predict plant vital rates than climate during the growing season
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DOI：
10.1111/gcb.15519
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发表时间：
2021-02-15
影响因子：
11.6
通讯作者：
Compagnoni, Aldo
中科院分区：
文献类型：
--
作者：
Evers, Sanne M.;Knight, Tiffany M.;Compagnoni, Aldo
了解气候对生命率的影响（例如，自然种群的生存、发展、繁殖）和动态是生态学的一项长期探索，在气候变化面前日益重要。然而，将气候驱动因素与人口过程联系起来需要确定气候影响生命率的适当时间窗口。研究人员通常无法获得测试大量窗户所需的长期数据，因此被迫做出先验选择。在这项研究中，我们首先综合文献，以评估目前的先验选择在104种植物物种的研究，气候驱动因素与人口的反应。其次，我们使用滑动窗口的方法来调查气候驱动因素和时间窗口的组合有最好的预测能力的四个多年生植物物种，每个有超过十年的人口统计数据（Helianthella quinquenervis，Frasera speciosa，Apendriopuntia imbricata，和Cryptantha flava）的生命率。我们的文献回顾表明，大多数研究考虑的时间窗口，只有一年前的重要率（S）的测量，并专注于年度或生长季节的时间尺度。相比之下，我们的滑动窗口分析显示，在13个生命率中，只有4个选定的气候驱动因素的时间窗口与生长季节一致或相似。对于许多生命率，在测量生命率之前，最佳窗口滞后超过1年，最多4年。我们的研究结果表明，对于这四个物种的生命率，滞后或生长季节之外的气候驱动因素是常态。我们的研究表明，考虑最近生长季节以外的气候预测因子将提高我们对气候如何影响种群动态的理解。
Understanding the effects of climate on the vital rates (e.g., survival, development, reproduction) and dynamics of natural populations is a long-standing quest in ecology, with ever-increasing relevance in the face of climate change. However, linking climate drivers to demographic processes requires identifying the appropriate time windows during which climate influences vital rates. Researchers often do not have access to the long-term data required to test a large number of windows, and are thus forced to make a priori choices. In this study, we first synthesize the literature to assess current a priori choices employed in studies performed on 104 plant species that link climate drivers with demographic responses. Second, we use a sliding-window approach to investigate which combination of climate drivers and temporal window have the best predictive ability for vital rates of four perennial plant species that each have over a decade of demographic data (Helianthella quinquenervis, Frasera speciosa, Cylindriopuntia imbricata, and Cryptantha flava). Our literature review shows that most studies consider time windows in only the year preceding the measurement of the vital rate(s) of interest, and focus on annual or growing season temporal scales. In contrast, our sliding-window analysis shows that in only four out of 13 vital rates the selected climate drivers have time windows that align with, or are similar to, the growing season. For many vital rates, the best window lagged more than 1 year and up to 4 years before the measurement of the vital rate. Our results demonstrate that for the vital rates of these four species, climate drivers that are lagged or outside of the growing season are the norm. Our study suggests that considering climatic predictors that fall outside of the most recent growing season will improve our understanding of how climate affects population dynamics.