Stochastic flowering phenology in Dactylis glomerata populations described by Markov chain modelling

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.

Environmental DNA reveals diversity and abundance of Alternaria species in neighbouring heterogeneous landscapes in Worcester, UK

“…A. dactylidicola is a grass-inhabiting species that was first found to associate with the leaves, roots and dead stems of the grass D. glomerata in Italy and it is phylogenetically linked with Alternaria cesenica (Thambugala et al, 2017). Lolium, Holcus and Dactylis are the most abundant grass genera in Worcester (including Lakeside) and are harvested as feeds for livestock (Brennan et al, 2019;Frisk et al, 2021). Harvesting of grasses for hay is a common practice among livestock farmers and starts from June to September and peaks in July (Jefferson, 2005).…”

“…Apart from grass mowing for hay, Worcester urban green areas such as grasslands, pastureland, public parks, the racecourse and the cricket ground are potential sources and were previously associated with Alternaria spores in the city (Sadyś et al, 2016). In Worcester, the grass species, D. glomerata, flowers in June-August (Brennan et al, 2019;Frisk et al, 2021), which coincides with the period of high Alternaria spp. spore concentrations in Worcester (Apangu et al, 2020).…”

“…dactylidicola is a saprobe (Thambugala et al, 2017), suggesting that it is active in decomposition within unmanaged grasslands and nutrient recycling in terrestrial ecosystems. Whereas pollen from D. glomerata, the main host of A. dactylidicola, is highly allergenic (D'Amato et al, 1991;Frisk et al, 2021), it is unknown whether A. dactylidicola also has such allergenic attributes since it is a newly discovered species. Neither is there any study on the pathological properties of A. dactylidicola.…”

Atmospheric transport reveals grass pollen dispersion distances

“…Adams-Groom et al, 2020;Lo et al, 2019). This community is able to provide informed grass pollen warnings to the public by collating information from a wide range of sources, including but not limited to grass pollen monitoring datasets, grass pollen trend analyses (Emberlin et al, 1999;, local grass flowering observations (Frisk et al, 2021;León-Ruiz et al, 2011), grass maps (McInnes et al, 2017), current weather conditions and climatic trends. Previous studies are generally in agreement that temperature, precipitation, and local grass areas are important factors in this dynamic (García-Mozo et al, 2010Jung et al, 2021;Recio et al, 2010;Ščevková et al, 2020;Skjøth et al, 2013;Werchan et al, 2017).…”