Zhu Jinlei, Buchmann Carsten M, Schurr Frank M
Institute of Landscape and Plant Ecology University of Hohenheim Stuttgart Germany.
Ecol Evol. 2022 Aug 4;12(8):e9183. doi: 10.1002/ece3.9183. eCollection 2022 Aug.
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", ), the maximum falling speed of a seed in still air. Accurate estimates of are crucial for predicting intra- and interspecific variation in seed dispersal ability. However, existing methods produce biased estimates of 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 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 . We validated this method by comparing the estimated of spheres of different diameters and materials to theoretical expectations and by comparing the estimated of seeds to measurements in a vertical wind tunnel. estimates closely match theoretical expectations for spheres and vertical wind tunnel measurements for seeds. However, our 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.
风力传播种子是植物最重要的传播机制之一。影响种子风力传播的关键种子特性是有效终端速度（以下简称“终端速度”， ），即种子在静止空气中的最大下落速度。准确估计 对于预测种子传播能力的种内和种间变异至关重要。然而，现有方法对于下落速度慢或快的种子、易碎种子以及下落轨迹复杂的种子会产生有偏差的 估计值。我们提出了一种基于视频的新方法，用于估计风力传播种子的下落轨迹和 。该设计包括一面镜子，使相机能够从两个角度同时记录下落的种子。然后，自动图像分析以高时间分辨率确定种子的三维轨迹。对于这些轨迹，我们拟合了一个考虑空气阻力的自由落体物理模型来估计 。我们通过将不同直径和材料的球体的估计 与理论预期进行比较，以及将种子的估计 与垂直风洞中的测量值进行比较，对该方法进行了验证。 估计值与球体的理论预期以及种子的垂直风洞测量值紧密匹配。然而，我们对快速下落种子的 估计值明显高于现有性状数据库中的值。这种差异似乎是因为先前的估计没有充分考虑种子的加速。所提出的方法能够对广泛的种子类型的三维下落轨迹和终端速度进行准确、高效且经济实惠的估计。因此，该方法应能推动对风力驱动种子传播的理解和预测。
