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

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

The social determinants of adolescent smoking in Russia in 2004.

OBJECTIVES: To determine the prevalence of adolescent smoking in the Russian Federation and examine what factors are associated with it. METHODS: Data were drawn from Round 13 of the Russia Longitudinal Monitoring Survey (RLMS) carried out in 2004. The sample consists of 815 adolescents (430 boys, 385 girls) aged 14-17 years who answered questions about their health behaviours. RESULTS: Smoking was more prevalent among boys than girls (26.1 vs. 5.7%). Maternal smoking and adolescent alcohol use were associated with smoking among both sexes. The self-assessment of one's socioeconomic position as unfavourable was associated with girls' smoking, while living in a disrupted family, physical inactivity and having a low level of self-esteem were predictive of boys' smoking. CONCLUSIONS: The family environment appears to be an important determinant of adolescent smoking in Russia. In particular, boys and girls may be modelling the negative health behaviour lifestyles of their parents, with unhealthy behaviours clustering. Efforts to reduce adolescent smoking in Russia must address the negative effects emanating from the parental home whilst also addressing associated behaviours such as alcohol use