Identifying Potentially Climatic Suitability Areas for Arma custos (Hemiptera: Pentatomidae) in China under Climate Change

Abstract

Spodoptera frugiperda is a notorious pest that feeds on more than 80 crops, and has spread over 100 countries. Many biological agents have been employed to regulate it, such as Arma custos. A. custos is a polyphagous predatory heteropteran, which can effectively suppress several agricultural and forest pests. Thus, in order to understand where A. custos can survive and where can be released, MaxEnt was used to predict the potentially suitable areas for A. custos in China under climate change conditions. The results show that the annual mean temperature (bio1) and annual precipitation (bio12) are the major factors influencing the distribution of A. custos. The optimal range of the two are 7.5 to 15 °C, 750 to 1200 mm, respectively. The current climate is highly suitable for A. custos in Hebei, Henan, Shandong, Anhui, Hubei, Jiangsu, and Zhejiang Provinces. Considering the currently suitable distribution area of S. frugiperda, artificially reared A. custos is suitable for release in Fujian, Zhejiang, Jiangxi, Hunan, and southeastern Sichuan Provinces. Under the future climatic scenarios, the suitable area will decrease and shift towards the north. Overall, this result can provide a reference framework for future application of A. custos for biological control.

Modeling of the potential geographical distribution of naked oat under climate change

“…5 According to the relevant literature, the organic carbon content (t_oc) of the upper soil attribute in the soil variable and the organic carbon content (s_oc) and exchangeable sodium salt (s_esp) of the lower soil attribute were selected ( Huang et al., 2022 ; Shao et al., 2022 ). In order to avoid overfitting of the model, principal component analysis was used to screen the environmental variables with low correlation but high significance ( Fan et al., 2020 ). In this study, SPSS was used for Pearson correlation analysis of environmental factors, from which 9 environmental variables: bio2,bio4, bio13, bio14, bio15,Altitude, Slope, Subsoil Organic Carbon(s_oc), Subsoil Sand Fraction (s_esp) were selected.…”

“…We used the "Checkerboard2" method to calculate the Akaike information standard factor (AICc) and selected the lowest incremental AICc to run the final Maxent model (Wei et al, 2020). In this study, the best parameter setting for FC was 0.5 for LQ and RM (Figure 2) (Fan et al, 2020). The other parameters were set as follows: 25% of the distribution points were selected as the test set, 75% of the distribution points were used as the training set (Constandinou et al, 2018;Wang et al, 2021), thhe crossvalidation method was used, the default setting of the maximum number of iterations was 500, the maximum number of background points was 10,000, and the rest of the default settings were selected, and the final output ASCII result file was the average of 10 iterations.…”

The King’s Lace Bug Recaredus rex Distant, 1909 (Hemiptera: Heteroptera: Tingidae): Systematic Position, First Palaearctic and Afrotropical Records, and Ecological Niche Modelling

“…In true bugs (Hemiptera: Heteroptera), ENM has also been used, but not so often. This method was applied several times to invasive alien species [ 17 , 18 , 19 , 20 , 21 ] and agricultural pests [ 22 , 23 ] and incorporated in ecological studies of various types [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Study on the Potential Distribution of Leptinotarsa decemlineata and Its Natural Enemy Picromerus bidens Under Climate Change

“…Principal component analysis using IBM SPSS Statistics 24 was employed to screen the environmental variables with a low correlation but a high significance to avoid overfitting of the model (Fan et al, 2020). Seven environmental variables, including Mean temperature of coldest quarter (bio11), Precipitation of dnest quarter (bio17), Precipitation of Warmest Quarter (bio18), elve, slope, T_SAND, and T_OC, were selected from 28 environmental factors for CPB modeling (Table 1).…”