Summary of the results from a principal component analysis performed on the weather variables.

Two principal components, explaining 66.3% of the total variation, were retained from the PCA analysis. The first axis was used to represent favourable weather conditions. This axis was positively correlated with temperature and photoperiod and negatively with humidity and barometric pressure. This axis represents a gradient from hot dry days with a long period of sunshine typically observed during summer to cold, humid days with less sunshine characterising winter. The negative correlation with barometric pressure represents thermal lows present in arid environments during the summer. The factor coordinate correlations and eigenvalues of the variables are shown. Values in bold represent loading scores greater than 0.50.Summary of the results from a principal component analysis performed on the weather variables.