We performed a global analysis with data from 149 countries to test whether temperature can explain the spatial variability of the spread rate and mortality of COVID-19 at the global scale. We performed partial correlation analysis and linear mixed effect modelling to evaluate the association of the spread rate and motility of COVID-19 with maximum, minimum, average temperatures and diurnal temperature variation (difference between daytime maximum and night-time minimum temperature) and other environmental and socioeconomic parameters. After controlling the effect of the duration since the first positive case, partial correlation analysis revealed that temperature was not related with the spatial variability of the spread rate of COVID-19 at the global scale. Mortality was negatively related with temperature in the countries with high-income economies. In contrast, diurnal temperature variation was significantly and positively correlated with mortality in the lowand middle-income countries. Taking the country heterogeneity into account, mixed effect modelling revealed that inclusion of temperature as a fixed factor in the model significantly improved model skill predicting mortality in the low-and middle-income countries. Our analysis suggests that warm climate may reduce the mortality rate in high-income economies, but in low-and middle-income countries, high diurnal temperature variation may increase the mortality risk.

The density and guard cell length of stomata regulate the physiological processes in plants. Yet, the variation of stomatal characteristics among different functional groups of trees is not been well understood. Particularly, a comprehensive understanding of stomatal behaviour in Bangladeshi moist forest trees is lacking. The study investigated how abaxial stomatal density (SD) and guard cell length (GCL) vary among tree functional types and leaf phenological groups in a moist tropical forest of Bangladesh. Cluster dendrogram revealed three groups of species based on SD and GCL. The independent sample t-test showed that there was a significant difference in SD between evergreen and deciduous tree species (t = 4.18, P < 0.001) but no significant difference in GCL between the two phenological groups. ANOVA revealed no significant difference in SD among the light demanding, intermediate shade tolerant and shade tolerant species (F = 0.76, P = 0.47). However, GCL significantly differed among the three functional groups (F = 3.3, P < 0.05). Maximum theoretical stomatal conductance (gmax) varied between evergreen and deciduous species but did not vary with species shade tolerance. In general, there was a significant trade-off between SD and GCL. However, the inverse relationship was stronger in deciduous and shade tolerant species than in evergreen and shade intolerant species. Leaf dry matter content was positively related with SD and negatively related with GCL. Specific leaf area and leaf thickness were not related to the stomatal traits. Our analyses suggest that leaf phenology and species shade tolerance need to be considered while estimating gas exchange through the stomata in tropical moist forests.

We performed a global analysis with data from 149 countries to test whether temperature can explain the spatial variability of the spread rate and mortality of COVID-19 at the global scale. We performed partial correlation analysis and linear mixed effect modelling to evaluate the association of the spread rate and motility of COVID-19 with maximum, minimum, average temperatures and temperature extreme (difference between maximum and minimum temperature) and other environmental and socioeconomic parameters. After controlling the effect of the duration after the first positive case, partial correlation analysis revealed that temperature was not related with the spatial variability of the spread rate of COVID-19. Mortality was negatively related with temperature in the countries with high-income economies. In contrast, temperature extreme was significantly and positively correlated with mortality in the low-and middle-income countries. Taking the country heterogeneity into account, mixed effect modelling revealed that inclusion of temperature as a fixed effect in the model significantly improved model skill predicting mortality in the low-and middle-income countries. Our analysis suggest that warm climate may reduce the mortality rate in high-income economies but in low and middle-income countries temperature extreme may increase the mortality risk.