Modelling Phytophthora disease risk in Austrocedrus chilensis forests of Patagonia

Abstract

Austrocedrus chilensis forests suffer from a disease caused by Phytophthora austrocedrae, which is found often in wet soils. We applied three widely used modelling techniques, with different data requirements, to model disease potential distribution under current environmental conditions: Mahalanobis distance, Maxent and Logistic regression. Each model was built using field data of health condition and landscape layers of environmental conditions (distance to streams, slope, aspect, elevation, mean annual precipitation and soil pH NaF). We compared model predictions by area under the receiver operating characteristic curve and Kappa statistics. A reasonable ability to predict observed disease distribution was found for each of the three modelling techniques. However, Maxent and Logistic regression presented the best predictive performance, with significant differences with respect to the Mahalanobis distance model. Our results suggested that if good absence data are available, Logistic regression should be used in order to better discriminate sites with high risk of disease. On the other hand, if absence data are not available or doubtful, Maxent could be a very good option. The three models predicted that around 50% (49-56%) of the currently asymptomatic forests are located on sites at risk of disease according to abiotic factors. Most of these asymptomatic forests surround the current diseased patches, at distances lower than 100 m from diseased patches. Management considerations and the scope of future studies were discussed in this article.

Evaluating Abiotic Factors Related to Forest Diseases: Tool for Sustainable Forest Management

“…The performance of the three models described in this chapter (i.e. Mahalanobis distance, Maxent and Logistic Regression) was compared for modeling a forest disease in Patagonia (La Manna et al, 2012). Results showed that all the models were consistent in their prediction; however, Maxent and Logistic regression presented a better performance, with greater values of AUC and Kappa statistics; and logistic regression allowed the best discrimination of high risk sites.…”

“…Among the available modeling techniques, three are described in this chapter on the basis of their requirements on disease presence or disease presence/absence data: Mahalanobis distance (requires only presence data), Maxent (requires only presence data and generates pseudo-absences) and Logistic regression (based on presence/absence data). These methods are inherently flexible, being applicable to a wide range of ecological questions, taxonomic units, and sampling protocols and they produced useful predictions in other studies (DeVries, 2005;Elith et al, 2006;Hellgren et al, 2007;La Manna et al, 2008b, 2012Marsden & Fielding, 1999;Pearson et al, 2006;Schadt et al, 2002b).…”

“…The mathematical definition of Maxent and the detailed algorithms are described by Phillips et al (2006), Phillips & Dudík (2008) and Elith et al (2011). Maxent has been applied to modeling species distributions and disease risk with good performance (La Manna et al, 2012;Pearson et al, 2007;Phillips & Dudík, 2008;Phillips et al, 2006).…”

“…geo-located forests patches whose health condition and abiotic factors are known. Training sites can be selected from field checking (La Manna et al, 2012) or from the map of species distribution and health condition (La Manna et al, 2008b). The patches should have an homogeneous health conditions; and training sites should include diseased and healthy patches or just diseased ones, depending on model requirements.…”

“…In some cases, absence data are doubtful; for example for forest diseases that are manifested earlier in the lower stem and latter in the crown, delaying detection by remote sensing. In these cases, health condition of training sites should be obtained from the field (La Manna et al, 2012), since failure to detect absences results in false negatives, which change mathematical functions describing habitats. Among the available modeling techniques, three are described in this chapter on the basis of their requirements on disease presence or disease presence/absence data: Mahalanobis distance (requires only presence data), Maxent (requires only presence data and generates pseudo-absences) and Logistic regression (based on presence/absence data).…”