Improving measurements of the falling trajectory and terminal velocity of wind‐dispersed seeds

终端速度 坠落(事故) 弹道 风速 生物扩散 风洞 加速度 航程(航空) 气象学 球体 环境科学 终端(电信) 模拟 大气科学 统计 计算机科学 机械 数学 物理 工程类 电信 航空航天工程 社会学 人口学 天文 环境卫生 经典力学 医学 人口
作者
Jinlei Zhu,Carsten M. Buchmann,Frank M. Schurr
出处
期刊:Ecology and Evolution [Wiley]
卷期号:12 (8) 被引量:4
标识
DOI:10.1002/ece3.9183
摘要

Seed dispersal by wind is one of the most important dispersal mechanisms in plants. The key seed trait affecting seed dispersal by wind is the effective terminal velocity (hereafter "terminal velocity", Vt ), the maximum falling speed of a seed in still air. Accurate estimates of Vt are crucial for predicting intra- and interspecific variation in seed dispersal ability. However, existing methods produce biased estimates of Vt for slow- or fast-falling seeds, fragile seeds, and seeds with complex falling trajectories. We present a new video-based method that estimates the falling trajectory and Vt of wind-dispersed seeds. The design involves a mirror that enables a camera to simultaneously record a falling seed from two perspectives. Automated image analysis then determines three-dimensional seed trajectories at high temporal resolution. To these trajectories, we fit a physical model of free fall with air resistance to estimate Vt . We validated this method by comparing the estimated Vt of spheres of different diameters and materials to theoretical expectations and by comparing the estimated Vt of seeds to measurements in a vertical wind tunnel. Vt estimates closely match theoretical expectations for spheres and vertical wind tunnel measurements for seeds. However, our Vt estimates for fast-falling seeds are markedly higher than those in an existing trait database. This discrepancy seems to arise because previous estimates inadequately accounted for seed acceleration. The presented method yields accurate, efficient, and affordable estimates of the three-dimensional falling trajectory and terminal velocity for a wide range of seed types. The method should thus advance the understanding and prediction of wind-driven seed dispersal.
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