Understanding the relationship between flowering patterns and pollen dispersal is important in climate change modelling, pollen forecasting, forestry and agriculture. Enhanced understanding of this connection can be gained through detailed spatial and temporal flowering observations on a population level, combined with modelling simulating the dynamics. Species with large distribution ranges, long flowering seasons, high pollen production and naturally large populations can be used to illustrate these dynamics. Revealing and simulating species-specific demographic and stochastic elements in the flowering process will likely be important in determining when pollen release is likely to happen in flowering plants. Spatial and temporal dynamics of eight populations of Dactylis glomerata were collected over the course of two years to determine high-resolution demographic elements. Stochastic elements were accounted for using Markov chain approaches in order to evaluate tiller-specific contribution to overall population dynamics. Tiller-specific developmental dynamics were evaluated using three different RV matrix correlation coefficients. We found that the demographic patterns in population development were the same for all populations with key phenological events differing only by a few days over the course of the seasons. Many tillers transitioned very quickly from non-flowering to full flowering, a process that can be replicated with Markov chain modelling. Our novel approach demonstrates the identification and quantification of stochastic elements in the flowering process of D. glomerata, an element likely to be found in many flowering plants. The stochastic modelling approach can be used to develop detailed pollen release models for Dactylis, other grass species and probably other flowering plants.

了解开花方式与花粉散布之间的关系对于气候变化建模，花粉预测，林业和农业至关重要。通过在种群水平上进行详细的时空开花观察以及模拟动态的建模，可以增强对这种联系的理解。分布范围广，花期长，花粉产量高和自然种群众多的物种可以用来说明这些动态。在确定开花植物何时可能发生花粉释放时，揭示和模拟开花过程中特定物种的人口统计学和随机因素可能很重要。毛小球藻8个种群的时空动态在两年的过程中收集了这些数据，以确定高分辨率的人口统计要素。为了评估分chain特定因素对总体种群动态的贡献，使用马尔可夫链方法解释了随机因素。使用三种不同的RV矩阵相关系数评估分iller特定的发育动力学。我们发现，所有人口的人口发展模式都是相同的，关键的物候事件在整个季节中仅相差几天。许多分till从未开花到完全开花都非常迅速地转变，这一过程可以用马尔可夫链模型进行复制。我们的新颖方法证明了D. glomerata开花过程中随机元素的鉴定和定量，可能在许多开花植物中发现。随机建模方法可用于为仙人掌，其他草种以及可能的其他开花植物开发详细的花粉释放模型。