How to measure mast seeding?

How to measure mast seeding?
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如何测量肥大播种量?

DOI:
10.1111/nph.18984
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发表时间:
2023
期刊:
影响因子:
9.4
通讯作者:
Bogdziewicz M
Bogdziewicz M
中科院分区:
生物学1区
文献类型:
--
作者:
Bogdziewicz M

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大种子作物的周期性生产,或masting,是多年生植物中普遍存在的现象。这种行为可以提高植物的繁殖效率,从而增加适应性,并对食物网产生涟漪反应。虽然每年的变化是桅杆的一个定义特征,但用于量化这种变化的方法存在很大争议。常用的变异系数缺乏解释主数据中序列依赖性的能力,并且可能受到零的影响,使其不太适合基于个体水平观测的各种应用,例如表型选择,遗传力和气候变化研究,这些研究依赖于通常包含许多零的个体植物水平数据集。为了解决这些局限性,我们提出了三个案例研究,并介绍了波动性和周期性,这占在频域的方差强调的意义,在桅杆长间隔。通过利用例子ofSorbus aucuparia,松树,栎robur,栎,和山毛榉,我们演示了如何波动捕获方差的影响,在高和低的频率,即使在零的存在下,导致改善生态解释的结果。长期的个体植物数据集的日益可用性有望在该领域取得重大进展,但需要新指标提供的适当分析工具。
The periodic production of large seed crops, or masting, is a widespread phenomenon in perennial plants. This behavior can enhance the reproductive efficiency of plants, leading to increased fitness, and produce ripple effects on food webs. While variability from year to year is a defining characteristic of masting, the methods used to quantify this variability are highly debated. The commonly used coefficient of variation lacks the ability to account for the serial dependence in mast data and can be influenced by zeros, making it a less suitable choice for various applications based on individual‐level observations, such as phenotypic selection, heritability, and climate change studies, which rely on individual‐plant‐level datasets that often contain numerous zeros. To address these limitations, we present three case studies and introduce volatility and periodicity, which account for the variance in the frequency domain by emphasizing the significance of long intervals in masting. By utilizing examples ofSorbus aucuparia,Pinus pinea,Quercus robur,Quercus pubescens, andFagus sylvatica, we demonstrate how volatility captures the effects of variance at both high and low frequencies, even in the presence of zeros, leading to improved ecological interpretations of the results. The growing availability of long‐term, individual‐plant datasets promises significant advancements in the field, but requires appropriate tools for analysis, which the new metrics provide.
预测多年生植物的种子产量:识别挑战并规划前进的道路。
DOI: --
发表时间: 2023
期刊: New Phytologist
影响因子: 9.4
作者:
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DOI: 10.1034/j.1600-0706.2000.900306.x
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