Inferring climatic controls of rice stem borers’ spatial distributions using maximum entropy modelling

Abstract

Accurate assessment of pest potential distributions is needed to identify their establishment risks that play a key role in pest management in agricultural ecosystems. We used a correlative niche modelling method (Maxent) to predict and map the spatial distributions of two important rice stem borers, Chilo suppressalis and Sesamia cretica, in paddy fields of Iran. In total, 195 presence occurrence records (101 records for C. suppressalis and 94 records for S. cretica) were compiled. A set of environmental and topographic variables, with the highest effects on the species distributions and the lowest correlations among themselves, were used. The results showed that mainly the northern parts of Iran were the most suitable areas for C. suppressalis, and north, north‐east and south‐west of Iran as the most suitable areas for S. cretica. Both models performed well, with an area under the receiver operating characteristic curve (AUC) of 0.983 and 0.786 for C. suppressalis and S. cretica, respectively. The Maxent models showed higher accuracy for predicting the distribution of the specialist pest with the small range sizes compared to the generalist species. Assessing the importance of environmental variables, which were derived from the jackknife test, showed the precipitation as the variable with the highest contribution (66%) in explaining the spatial distribution of C. suppressalis compared to the other variables. The distribution of S. cretica was influenced by a set of variables derived from both the precipitation and temperature. The Maxent predictions were useful to map the geographical distributions of the risk for both rice stem borers that is needed to develop effective management strategies.

Distribution and invasion risk assessment of Oryctes rhinoceros (L.) in China under changing climate

“…Therefore, insufficient distribution information may lead to errors in the model predictions. This work considered only the influence of climatic factors and did not consider the many other aspects, such as topography, intraspecific and interspecific relationships, and even human factors, which will affect the accuracy of the model (Alaniz et al., 2018; Jalaeian et al., 2018). In addition, the environmental variables selected for the forecast were based on data only from 1970 to 2000.…”

Climate change effects on the global distribution and range shifts of citrus longhorned beetle Anoplophora chinensis

“…As the use of correlated and redundant variables brings the risk of multicollinearity and overfitting to the model (Merow et al, 2013), we selected the primary 8 variables from the 19 bioclimatic variables based on their independence and contribution to the environment envelope, also considering the biological and ecological characteristics of CLB. The process was conducted by performing Pearson's correlations of bioclimate data at CLB occurrence locations and using principal component analysis to identify the first 4 principal components (PCs) that explained over 90% of the total variation in bioclimatic variables and obtain the most contributed bioclimatic variables with the ‘psych’ R package (Jalaeian et al, 2018). First, bio1, bio2, bio3, bio4, bio5, bio8, bio12, bio15, bio18 and bio19 were selected as candidate variables not only following the variable contribution orders in PCA but also ensuring that they were not related to each other ( r < 0.8).…”

“…For probability, the grids with survival probability <0.04 were regarded as unsuitable for CLB based on Jenks’ natural breaks under historical conditions, while the rest were classified into 4 gradients (i.e. 0.04–0.16, 0.16–0.36, 0.36–0.60 and more than 0.60) (Jalaeian et al, 2018). The functions of ‘inverse distance‐weighted (IDW)’ and ‘Reclassify’ of ArcGIS 10.6 were used to process the model outputs for both historical and future climate conditions.…”

Predicting the potential distribution of four endangered holoparasites and their primary hosts in China under climate change

“…The AUC, ranging from 0.5 (random prediction) to 1.0 (perfect prediction), is threshold independent ( Manel et al, 2001 ; Chen et al, 2020 ). Generally, the AUC was classified into five groups: (1) excellent: 0.90–1.00; (2) good: 0.80–0.90; (3) fair: 0.70–0.80; (4) poor: 0.60–0.70; (5) failing: 0.50–0.60 ( Phillips and Dudík, 2008 ; Jalaeian et al, 2018 ). The TSS value is threshold dependent and calculated as: TSS = Sensitivity + Specificity – 1.…”