Detecting and mapping the habitat suitability of the Cossid Moth, (Coryphodema tristis) on Eucalyptus nitens in Mpumalanga, South Africa.

Abstract

Cossid moth (Coryphodema tristis) is an indigenous wood-boring insect that presents serious environmental, ecological and economic problems globally. An extensive analysis of the current spatial distribution of Coryphodema tristis is therefore essential for providing applicable management approaches at both local and regional scales. This aim of the study was to assess GIS and remote sensing applications combined with species distribution models (Maxent) to monitor habitat suitability of the Coryphodema tristis in Mpumalanga, South Africa. The first objective of the study focused on comparing the robustness of species distribution models using Maxent (presence-data only) and Logistic regression (presence-absence data) in characterizing the habitat suitability of the Coryphodema tristis. The second objective of the study evaluated the effectiveness of the freely available Sentinel 2 multispectral imagery in detecting and mapping the habitat suitability of the C. tristis. The models sought to identify the factors that can be used to predict habitat suitability for the C. tristis using environmental and climatic variables. Presence and absence records were collected through systematic surveys of forest plantations. The models were applied on Eucalyptus nitens plantations of the study area for habitat preferences. The overall accuracies indicated that Maxent (AUC = 0.84 and 0.810) was more robust than the Logistic regression model (AUC= 0.745 and 0.677) using training and testing datasets, respectively. In Maxent, the jackknife indicated that mean temperature for October, aspect, age, mean temperature for February, June, December and elevation as the most influential predictor variables. Meanwhile, age was the only significant variable in the Logistic regression model. Therefore, results concluded that temperature, aspect, age and elevation were optimal in modelling habitat suitability for the Coryphodema tristis. For the second objective, model performance was evaluated using the Receiver Operating Characteristics (ROC) curve showing the Area Under the Curve (AUC) and True Skill Statistic (TSS), while the performance of predictors was displayed in the jackknife. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under curve (AUC) of 0.89. The Photosynthetic vigor ratio,

ii Red edge (705 nm), Red (665 nm), Green NDVI hyper, Green (560 nm) and Shortwave infrared (SWIR) (2190 nm) were identified as the most influential predictor variables. Results of this study suggests that remotely sensed derived vegetation indices from cost effective platforms could play a crucial role in supporting forest pest management strategies and infestation control. Overall, these results improve the assessment of temporal changes in habitat suitability of Coryphodema tristis, which is crucial in the management and control of these pests.