Abstract
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.
Highlights
Grass ecology is of considerable importance to human society and culture, mainly through cereal cultivation, animal fodder and forestry and through grass pollen allergy
Studies have shown that there is a strong relationship between airborne pollen concentrations and the flowering phenology of grasses (Ghitarrini et al 2017; Kmenta et al 2016; RomeroMorte et al 2020)
This study demonstrates that the flowering progression of Dactylis glomerata populations follows a nonnormal distribution, with a skewness towards the beginning of the season
Summary
Grass ecology is of considerable importance to human society and culture, mainly through cereal cultivation, animal fodder and forestry and through grass pollen allergy. Spatial and temporal observations of flowering of multiple populations are often used to understand demographic elements in the study of grasses in agriculture (Rossignol et al 2014; Smith 1944), ecology (Eagles 1972; Lindner and Garcia 1997) and aerobiology (Rojo et al 2017; Romero-Morte et al 2018). Combining this approach with a stochastic Markov chain modelling approach (Balzter 2000) and a model grass species may provide deeper multifaceted understanding with respect to underlying flowering processes. Markov chain approaches have previously been used to accurately model demographic and stochastic elements in grass populations (Nakaoka 1996) (e.g. Canales et al 1994; Silva et al 1991)
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