Correlations between climate variables and mass flowering
Table 3 shows correlation values (given in Spearman’s rank coefficient (rho)) for the correlation between yearly averaged climate and SOI variables and flowering intensity for three regions: Borneo, Malaysia and the Philippines. The p-values shown in the table are not adjusted for multiple inferences. With adjustment, there is no significance between any of the climate variables and flowering in all three regions. However, stronger negative correlation can be seen between SOI, precipitation and flowering in Borneo. In Malaysia there is a stronger negative correlation between cloud cover, precipitation and flowering. Negative relationship suggests that when the values for rainfall, SOI, and cloud cover are low, flowering tends to occur. Dry and clear conditions may play a role in inducing flowering in Borneo and Malaysia. However the correlation is not strong to be significant in the long term suggesting that other factors perhaps play a more significant role in flower induction.
Table 3: Spearman's correlation coefficients (rho) between climate variables and mass flowering
Next, I wanted to see whether there was a relationship between weather in a particular month and the occurrence of flowering. Relationship between monthly weather, SOI variables and flowering in the three regions was explored. Graph 9 shows monthly Spearman’s rank correlation value for the weather variables, SOI and flowering. In Borneo, February precipitation, November and December SOI, and July maximum temperature show the strongest correlation with flowering. In Malaysia, January and February precipitation and April cloud cover have the highest correlations with flowering. In the Philippines, November minimum temperature, March maximum temperature, and September precipitation show strongest correlation with flowering.
Graph 9: Correlation coefficients (rho) between monthly climate variables and flowering
