Summary
Climate change has the potential to greatly expand dry, fire-prone forests and grasslands to more moist and wet environments, and to extend and create more temporal variability in fire seasons. We built a machine learning random forest classifier to analyze the relationship between climatic, socio-economic, and fire history data with fire occurrence and extent for the years 2003–2011 in Puerto Rico, nearly 35,000 fires. Using classifiers based on climate measurements alone, we found that the climate space is a reliable associate, if not a predictor, of fire occurrence and extent in this environment. We found a strong relationship between occurrence and a change from average weather conditions, and between extent and severity of weather conditions. The probability that the random forest classifiers will rank a positive example higher than a negative example is 0.8–0.89 in the classifiers for deciding if a fire occurs, and 0.64–0.69 in the classifiers for deciding if the fire is greater than 5 ha. Future climate projections of extreme seasons indicate increased potential for fire occurrence with larger extents
