Van Staden et al. (2005) used a bioclimatic species distribution model to find the broad habitat distribution and potential distribution of two fungal pathogens of commercially important tree species, pines and eucalyptus, in South Africa under varying climate change scenarios. The distribution and infectivity of both pathogens are affected by certain climatic parameters (e.g. hail damage, high rainfall, and humidity) and climate change impacts these variables. Fungal incidence data for the study consisted of 87 confirmed reports of S. sapinea and 17 reports of C. cubensis and climate data for the area were obtained from existing literature and a digital elevation model for South Africa. Climate data included five variables: altitude, average rainfall of driest and wettest month, and average temperature of hottest and coldest month. The bioclimatic model incorporated these five variables, created a multidimensional scatter plot using for each variable for each grid cell in South Africa (11,800 total), generated matrix of covariates for each cell, and then transformed that matrix into a probability of occurrence for each fungus for each cell. Consequently, they were able to identify core-risk regions for both fungi, and found that those regions included major commercial forestry plantations. They report this as the first study to utilize a bioclimatic model to predict the distribution of economically relevant pathogens for eventual use in decision support systems for forestry management. This study could be improved by increased data on the fungus (more than 100 counts of each) and potentially exploring the variation in predictions generated by the model. It would be interesting to explore different combinations of variables or data points and how the predications would change based on each combination.
van Staden, V. et al., 2004. Modelling the spatial distribution of two important South African plantation forestry pathogens. Forest Ecology and Management, 187(1), pp.61–73.