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Potential distribution of Spodoptera frugiperda (J.E. Smith) in China and the major factors influencing distribution
Journal Pre-proof Potential distribution of Spodoptera frugiperda (J.E. Smith) in China and the major factors influencing distribution Rulin Wang, Chunxian Jiang, Xiang Guo, Dongdong Chen, Chaoyou, Yue Zhang, Mingtian Wang, Qing Li PII:
S2351-9894(19)30484-6
DOI:
https://doi.org/10.1016/j.gecco.2019.e00865
Reference:
GECCO 865
To appear in:
Global Ecology and Conservation
Received Date: 14 August 2019 Revised Date:
21 November 2019
Accepted Date: 30 November 2019
Please cite this article as: Wang, R., Jiang, C., Guo, X., Chen, D., Chaoyou, , Zhang, Y., Wang, M., Li, Q., Potential distribution of Spodoptera frugiperda (J.E. Smith) in China and the major factors influencing distribution, Global Ecology and Conservation (2020), doi: https://doi.org/10.1016/j.gecco.2019.e00865. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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Potential distribution of Spodoptera frugiperda (J.E. Smith) in China
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and the major factors influencing distribution
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Rulin Wanga, b, Chunxian Jianga, Xiang Guoc, Dongdong Chenc, Chaoyouc, Yue Zhanga, Mingtian Wangd, e, Qing Lia* a College of Agronomy, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China b Sichuan Provincial Rural Economic Information Center, Chengdu, 610072, Sichuan, China c Sichuan Province Agro-meteorological Center, Chengdu, 610072, Sichuan, China d Sichuan Meteorological Observatory, Chengdu, 610072, Sichuan, China e Water-Saving Agriculture in Southern Hill Area Key Laboratory of Sichuan Province, Chengdu, 610066, Sichuan, China *
Corresponding author, Qing Li. College of Agronomy, Sichuan Agricultural University, 211 Huimin Road, Chengdu, 611130, Sichuan, China. Tel: +86 13608022868 Email: [email protected]
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Potential distribution of Spodoptera frugiperda (J.E. Smith) in China
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and the major factors influencing distribution
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Abstract: Fall armyworm, Spodoptera frugiperda (J.E. Smith) is one of the most important agricultural pests in the world. At the end of 2018, S. frugiperda was first found in Yunnan, China, and then it had spread rapidly to 19 provinces and posed a serious threat to China's agricultural production. Based on the current distribution data of S. frugiperda and enviromental variables, this paper constructed a potential species distribution model (MaxEnt), and predicted the potential distribution area of S. frugiperda in China, and identified the dominant climatic factors that control its distribution. The results showed that: (1) The average AUC value of the model was 0.906, which demonstrated that the accuracy of the model was excellent．(2) Mean Temperature of Coldest Quarter (bio11), Min Temperature of Coldest Month (bio6), Mean Temperature of Warmest Quarter (bio10), Max Temperature of Warmest Month (bio5), were dominant climatic factors which affected and controlled the potential distribution of S. frugiperda, and the appropriate range were -14.77-22.86℃, -13.33-24.35℃, 19.15-29.73℃, and 24.55-36.83℃. (3) The most suitable area of S. frugiperda are mainly distributed in Guangxi, Jiangxi, Guangdong, Hunan, Fujian, Zhejiang, Yunnan, Hubei, Anhui, Sichuan, Chongqing, Hainan, Jiangsu, Henan, Taiwan, Guizhou, Shaanxi, Shanghai and Hongkong, and account for 12.09% of China's total area. According to the results of this study, it was known that the pest was a major threat to maize and rice in China, and it was urgently necessary to strengthen monitoring and management in its suitable range to prevent the invasion of S. frugiperda which would ensure the safety of agriculture. Key words: Spodoptera frugiperda (J.E. Smith); MaxEnt model; potential geographical distribution; environmental variables.
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1. Introduction
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Fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), native to tropical and subtropical America, is one of the most important agricultural pests in the world. It is a polyphagous pest that feeds on up to 353 species of plants, but mainly damages maize, rice, sorghum, cotton and sugarcane (Montezano et al., 2018). Spodoptera frugiperda is a typical long-distance migratory pest, but prior to 2015, there were no reports of the distribution of S. frugiperda outside the Americas. In 2016, S. frugiperda was confirmed to have invaded Africa and quickly spread to almost the entire African continent south of the Sahara, causing about 20% to 50% corn production losses (Day et al., 2017; Feldmann et al., 2019). In May 2018, S. frugiperda was discovered in India and subsequently discovered in Myanmar, Thailand, Yemen, and Sri Lanka (CABI, 2019; FAO, 2019; Kalleshwaraswamy et al., 2018). At the end of 2018, S. frugiperda was first found in Yunnan Province in China (Guo et al., 2019a). By July 2019, it had spread to 19 provinces in China (Guo et al.,
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2019b; Liao et al., 2019; Liu et al., 2019a; Liu et al., 2019b; Wu et al., 2019a; Xu et al., 2019a; Yan et al., 2019; Zhang et al., 2019). Therefore, it is particularly important to determine the potential geographical distribution of S. frugiperda in China for its monitoring, early warning and control (Wang et al., 2019). MaxEnt (maximum entropy) is one of the commonly used niche models and a tool for quantifying species distribution. It had been widely used in predicting potential distribution state of various species, such as birds, insects, aquatic animals, etc., especially in providing guidance for suitability assessment and risk zoning of invasive species (Moya et al., 2017; Wan et al., 2017; Wei et al., 2018). MaxEnt is a density estimation and species distribution prediction model based on the principle of maximum entropy (Elith et al., 2017). In the simulation of the model, the known distribution area of species was first determined, then the restricting factors that affect the distribution of species were analyzed to establish the constraint set and to obtain the relationship between them. The distribution with the largest entropy was selected as the optimal distribution (Warren et al., 2011; Phillips et al., 2017). The advantages of the MaxEnt model were: had higher prediction with small sample size, used only existing distribution points, was easy to interpret prediction results, measured the importance of environmental variables by knife cutting method, and predicted habitats of species under future climate change (Yi et al., 2011; Zhang et al., 2016; Urbani et al., 2017; Zhang et al., 2018a). Although it had not been present for a long time, but had been widely used in the prediction of potential geographical distribution of invasive organisms and became one of the most popular predictive models (Frid et al., 2013). More than 1,000 research results were obtained using this model, and more than 750 papers were published by Chinese scholars using this tool (Kong et al., 2019). In this paper, the MaxEnt niche model was used to predict the potential geographical distribution of S. frugiperda in China, and the suitable environmental factors affecting the distribution of the insect were further analyzed, in order to attract the attention of relevant departments in the region and to appropriate quarantine and inspection measures to prevent the invasion of S. frugiperda. At the same time, it also provide theoretical basis for the relevant departments to formulate corresponding quarantine measures and ensure the safety of grain production in China.
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2. Materials and Methods
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2.1. Species Occurrence record We obtained global geographical distribution records of S. frugiperda by visiting the Global biodiversity information facility (GBIF, 2019), and the Centre Agriculture Bioscience International (CABI, 2019). Other data were obtained by consulting journal articles published at home and abroad (Goergen et al., 2016; Early et al., 2018; Qi et al., 2019; Tang et al., 2019; Wu et al., 2019b; Wu et al., 2019c). Finally, 1019 valid records were used to build the predictive model (Fig. 1).
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Fig.1. Global distribution of S. frugiperda 2.2. Environmental Data for MaxEnt Climate data: Climate data were downloaded from the official website of Global Climate Data (Worldclim), including 19 bioclimatic variables with a spatial resolution of 30 sec (~1 km). The version of the data set was 2.0, and the time range was 1970-2000. In order to avoid the collinearity between variables affecting the simulation results, the modeling variables were filtered according to the following steps (Zhang and Liu, 2017). First, the MaxEnt model was run using the distribution data of S. frugiperda and 19 bioclimatic variables to obtain the percent contribution of each variable to the preliminary prediction results. Then we imported all distribution points in Arc-GIS and extracted the attribute values of the 19 variables. Pearson correlation analysis was performed on the extracted variables using SPSS 22 to avoid variable spatial autocorrelation. Referring to the biological characteristics of S. frugiperda (Kalleshwaraswamy et al., 2018) and comparing the percent contribution of each variable in the initial model (Table S1), the variables with correlation coefficient > |0.8| (very significant correlation) were screened. After screening procedures (Table S2), 10 bioclimatic variables were selected to establish the potential geographic distribution prediction of S. frugiperda, which was Mean Diurnal Range (bio2), Max Temperature of Warmest Month (bio5), Min Temperature of Coldest Month (bio6), Mean Temperature of Wettest Quarter (bio8), Mean Temperature of Warmest Quarter (bio10), Mean Temperature of Coldest Quarter (bio11), Precipitation of Driest Month (bio14), Precipitation Seasonality (bio15), Precipitation of Warmest Quarter (bio18), Precipitation of Coldest Quarter (bio19). Table 1 List of enviromental variables Variables
Abbreviation
Unites
Annual Mean Temperature Mean Diurnal Range (Mean of monthly (max temp - min temp)) Isothermality (BIO2/BIO7) (* 100) Temperature Seasonality (standard deviation *100) Max Temperature of Warmest Month Min Temperature of Coldest Month Temperature Annual Range (BIO5-BIO6)
bio1 bio2 bio3 bio4 bio5 bio6 bio7
℃ ℃ － ℃ ℃ ℃ ℃
Mean Temperature of Wettest Quarter Mean Temperature of Driest Quarter Mean Temperature of Warmest Quarter Mean Temperature of Coldest Quarter Annual Precipitation Precipitation of Wettest Month Precipitation of Driest Month Precipitation Seasonality (Coefficient of Variation) Precipitation of Wettest Quarter Precipitation of Driest Quarter Precipitation of Warmest Quarter Precipitation of Coldest Quarter 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165
bio8 bio9 bio10 bio11 bio12 bio13 bio14 bio15 bio16 bio17 bio18 bio19
℃ ℃ ℃ ℃ mm mm mm － mm mm mm mm
2.3. Distribution Modeling Using MaxEnt Occurrence data of S. frugiperda and 10 environmental variables were imported into the MaxEnt model, and the ‘Create response curves’ and ‘Do jackknife to measure variable importance’ options were selected. The format of the model output file was selected as ‘Logistic’. 25% percent of the 1109 distribution points were randomly selected as ‘test data’, and the model replicates were set to 10 times, and the remaining parameters were selected as the default parameters of the software. The area under the receiver operating characteristics (ROC) curve was chosen as an index to evaluate the accuracy of the model. AUC values greater than 0.6 indicated that the simulated results were 'fair', while AUC values greater than 0.9 indicate that the predicted results were 'excellent'. The model with the highest AUC value was selected to analyze the suitability of S. frugiperda in China (Phillips et al., 2006; Zhang et al., 2018a; Zhang et al., 2018b; Xu et al., 2019b). 2.4. Distribution modeling We imported the ASC II format file output by MaxEnt into ArcGIS 10.2, and converted it into raster format file by using the conversion tool in ArcToolbox, which was used to classify and visualize the distribution area (Montemayor et al., 2015; López-Martínez et al., 2016; Lantschner et al., 2017). The map of China was used as the base map to extract the potential suitable distribution of S. frugiperda in China. Jenks'natural breaks were used to reclassify the suitability and classify the suitability into five categories: unsuitalbe area (P < 0.08), low suitable area (0.08 < P < 0.25), moderate suitable area (0.25 < P < 0.47), good suitable area (P < 0.47), most suitable area (Shiravand et al., 2018).
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3. Results
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3.1. Result validation The MaxEnt model can draw the ROC curve by itself and calculate the AUC value of the model, which can be directly used as the criterion for model prediction. The ROC curve of the predicted results of this study was shown in Fig. 2. The curve
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analysis results showed that the average AUC value of the model was 0.906, which was much higher than the AUC value of the random prediction model (0.5), indicating that the prediction results had higher accuracy. That was, the model-predicted distribution area had a good fit to the actual distribution area of the species.
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Fig. 2. AUC result of MaxEnt modeling (10 runs)_ 3.2. Potential distribution area of S. frugiperda in China The prediction results of MaxEnt model indicated that the suitable area of S frugiperda in China was very wide, and the unsuitable areas were mainly concentrated in the Qinghai-Tibet Plateau, Xinjiang, Gansu, Inner Mongolia, Heilongjiang, Jilin, and western Sichuan (Fig. 3). The areas of unsuitable area, low suitable area, moderately suitable area, good suitable area and most suitable area accounted for 65.96%, 9.19%, 7.69%, 5.07%, and 12.09% of China's total area, respectively (Table 2). In this paper, the most suitable area were more concentrated, including Guangxi, Jiangxi, Guangdong, Hunan, Fujian, Zhejiang, Yunnan, Hubei, Anhui, Sichuan, Chongqing, Hainan, Jiangsu, Henan, Taiwan, Guizhou, Shaanxi, Shanghai and Hongkong. The good suitable areas were mainly the outer extension of the most suitable areas, mainly distributed in Yunan, Henan, Sichuan, Hubei, Anhui, Hunan, Guizou, Guangxi, Jiangsu and Chongqing. The moderate suitable areas were mainly distributed in Guizhou. Guizhou, Yunan, Henan, Sichuan, Shaanxi, Shandong, Jangsu, Hube, Shanxi, Chongqing, Gansu, Hebei, Anhui and Hunan.
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Fig. 3. Potential geographical distribution of S. frugiperda in China Table 2 The suitable area of in China under current climate condition Province Guangxi Jiangxi Guangdong Hunan Fujian Zhejiang Yunnan Hubei Anhui Sichuan Chongqing Hainan Jiangsu Henan Taiwan Guizhou Shaanxi Shanghai Hong Kong Heilongjiang Inner Mongolia Xinjiang Jilin
Predicted area (×104 km2) Unsuitable
Low suitable
Moderate suitable
Good suitable
Most suitable
0.00 0.00 0.00 0.00 0.00 0.00 3.92 0.07 0.00 23.00 0.11 0.00 0.00 0.02 0.56 0.01 0.73 0.00 0.00 54.44 124.66 174.22 21.19
0.03 0.01 0.00 0.04 0.00 0.00 6.05 0.86 0.01 5.11 0.54 0.00 0.00 0.37 0.74 1.43 10.73 0.00 0.00 0.00 4.45 1.12 0.10
0.56 0.04 0.04 1.13 0.01 0.06 9.70 3.98 1.17 8.38 1.91 0.03 5.94 8.50 0.47 9.94 7.36 0.00 0.00 0.00 0.00 0.28 0.00
3.37 0.46 0.81 4.06 0.14 0.81 7.07 5.28 5.00 5.48 2.31 0.35 2.37 6.00 0.30 3.68 0.98 0.02 0.02 0.00 0.00 0.00 0.00
16.98 14.76 14.61 14.16 10.69 8.35 7.54 7.37 7.18 3.56 2.87 2.47 1.26 1.23 1.07 0.90 0.59 0.52 0.05 0.00 0.00 0.00 0.00
Liaoning Gansu Hebei Beijing Shanxi Tianjin Ningxia Qinghai Shandong Tibet Total 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
8.44 29.98 6.96 0.29 3.84 0.01 1.76 70.67 0.01 108.66 633.55
7.15 9.75 10.89 1.43 8.63 1.21 3.47 0.67 8.02 5.50 88.31
0.00 1.80 1.76 0.00 3.33 0.00 0.03 0.00 7.27 0.16 73.86
3.3. Analysis of the importance of environmental variables The statistical results of the contribution rate of each environmental variable showed that (Fig. 4), four environmental variables (bio11, bio18, bio6 and bio5) contributed more than 10% to the model. They were the main environmental variables to simulate the suitable habitat of S. frugiperda and could reflect most of the information of its optimal habitat distribution. All the selected variables had different contribution rates in the model, and the contribution rate of the least contributing variable was 0.4%, indicating that there was no unrelated variable participating in the analysis of the MaxEnt model. It also showed that the method of selecting variables used in this paper was very effective for constructing the MaxEnt species distribution model. Jackknife test results of contribution rate of single variable (Fig. 5) showed that the AUC values of five environmental variables (bio11, bio6, bio10, and bio5) were all > 0.8, which indicated that they were the main factors affecting the potential distribution area of S. frugiperda. The AUC values of bio11 and bio3 all exceeded 0.83, indicating that the extreme temperature factor was the most important variable affecting the geographical distribution of S. frugiperda. 40
Percent contribution
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34 27.6
25 20 15
17.5 13
10 5
2.6
1.6
1.3
1.1
0.8
0.4
0 bio11 bio6 bio10 bio5 bio14 bio19 bio18 bio8 bio15 bio2
Environmental variables
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0.00 0.00 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.00 48.67
Fig. 4. Analysis of the importance of environmental variables
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 116.16
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Fig. 5. Jackknife test of vairable importance for train data 3.4. Relationship between environmental variables and probability of presence of S. frugiperda Combined with the potential distribution threshold determined by the method part, the response curve of the MaxEnt model output in this study reflected the suitability threshold of the dominant environmental variable controlling the potential distribution of S. frugiperda. The distribution probability of S. frugiperda increased with the increase of the value of each environmental variable within a certain range, and decreased with the increase of the variable after a certain peak value. The results showed that the appropriate range ranges of bio 11, bio6, bio10, bio5, bio8, and bio18 were -14.77-22.86 , -13.33-24.35 , 19.15-29.73 , 24.55-36.83 , 17.59-28.67 , and 184.01-951.8mm, respectively (Fig. 6).
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Fig. 6. Response curves between the probability of presence and environmental variables
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4. Discussion
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Due to its strong ability to migrate long distances and often cause sudden damage, S. frugiperda were widely concerned by agricultural workers around the world. In 2016, S. frugiperda invaded Africa and settled, causing potential economic losses of 2.481 billion to 6.187 billion dollars per year (Day et al., 2017). At the end of 2018, S. frugiperda was first reported in Yunnan, China. By July 2019, the insect had spread to 19 provinces. Studies had shown that with the strengthening of the southwest summer monsoon, Yunnan and Guangxi had become the main immigrants of S. frugiperda in Myanmar (Wu et al., 2019c). Because the niche of invasive species sometimes would drift, only using the distribution data of origin to predict the potential distribution of invasive species in the invasive area might lead to deviation, making it difficult to accurately predict the suitable distribution area (Qi et al., 2012). In this work, the selected distribution points include the origin and invasion areas of S. frugiperda all over the world, as well as the known distribution sites in China, which can make up for the error of only using the origin ecological environment to simulate its potential distribution in the invasion area, and improve the reliability of prediction. Wang et al. (2019) combined with the biological characteristics of S. frugiperda and the climatic characteristics of China, and speculated that the perennial occurrence of S. frugiperda in China was in the south of the 10 °C isotherm in January, mainly including southern Yunnan and central and southern Guangxi, Hainan, Fujian, and Taiwan. The most suitable area for the study was predicted to be more northward. The reason for this difference might be that Wang et al. only analyzed the limiting effect of temperature on S. frugiperda, and our study more scientifically analyzed the combined effects of various climate variables on their distribution. At present, S. frugiperda could be divided into two haplotypes, corn-strain and rice-straiin. Corn-strain S. frugiperda mainly feed on corn, cotton and sorghum, while rice-strain feed on rice and various pastures (Dumas et al., 2015). Duan and Zhou (2011) analyzed the potential distribution of rice in China, and concluded that the suitable growth area was located in the northeast plain and south of the Yangtze River, including Jiangsu, Anhui, Hubei, Chongqing, Sichuan, Zhejiang, Jiangxi, Hunan, Guizhou, Yunnan, Fujian, Guangdong, Guangxi, Hainan and Taiwan, which had a high degree of coincidence with the most suitable area of S. frugiperda. Maize was one of the main food crops in China. It was widely planted and distributed, mainly in the spring maize area of East and North China, summer maize area of Huanghuaihai Plain, irrigation maize area of Northwest China, mountain maize area of Southwest China, hilly maize area of South China and maize area of Qinghai-Tibet Plateau (He and Zhou, 2011; He and Zhou, 2012). The southwest mountain maize region and the southern hilly maize region were both located in the most suitable area for S. frugiperda. Since the first discovery of S. frugiperda in Pu'er City, Yunnan Province
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on January 11, 2019, as of June 14, 2019, the pest had been found in 921 counties in 19 provinces, with an area of 256700 hm2. In view of the rapid spread rate of the insect, the relevant departments should pay enough attention to it and take effective measures to eliminate it immediately to prevent its further spread (Wang et al., 2019). At the same time, the insect should be monitored within its suitable range. Once it was found, it would be removed immediately, otherwise it would pose a serious threat to the safe production of food crops in China. Temperature is the main environmental factor affecting the growth, reproduction and distribution of S. frugiperda. He et al. (2019) found that the temperature adaptability of S. frugiperda was very strong, and the suitable temperature range for population growth and development was 20-30 . This study found that when Mean Temperature of Warmest Quarter was 19.15-29.73 , the existence probability of S. frugiperda was higher, which was consistent with the result found by He et al. (2019). At 28 , S. frugiperda developed fastest, a generation could be completed in about 30 days during the warm summer months (Kebede, 2018), and the similar rule was found by Garcia et al. (2018). In this paper, the results showed that the probability of the presence of reached highest when Mean Temperature of Warmest Quarter was about 28.6 . This was in agreement with the previous studies, indicating that 28 was the optimum temperature for the development of S. frugiperda. Spodoptera frugiperda doesn’t diapause and therefore can not past below freezing conditions in any stage. In South China, the annual average temperature was above 20 , the coldest monthly average temperature was above 10 , the winter temperature was higher and the duration of low temperature was short, which provided good climatic conditions for the persistence of S. frugiperda. Therefore, it was speculated that S. frugiperda could reproduce all year round in South China, and may become an important initial source of S. frugiperda in the Yangtze River Basin, North China, Northwest and even Northeast China. In a suitable temperature range, the growth rate of insects increased with the increase of external temperature, but when the temperature rose to a certain extent, the development rate would decrease. Valdez-Torres et al. (2012) showed that under laboratory conditions, the lowest and highest temperature thresholds for the development of S. frugiperda were 8.7 and 39.8 , respectively. According to the response curve of bio5, the probability of S. frugiperda decreased rapidly when the temperature exceeded 36.7 . This suggested that high temperature in summer may be an important factor limiting the distribution of S. frugiperda. The habitat predicted by MaxEnt model was similar to that of the known occurrence point, but there was still a certain deviation from the actual occurrence area of pests. Because the distribution patterns and environmental requirements of different species were different, the predictive effects of different species and environmental variables would also be different (Neven et al., 2018). In this study, 19 bioclimatic variables related to temperature and humidity were selected as initial predictors. However, there were many non-climatic factors affecting the distribution of insect suitability, such as host, natural enemy, management level and human activities (Hill et al., 2012). Although S. frugiperda has a strong ability of
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long-distance transmission, experts analyzed that the reason for its transmission between continents was the movement of infested plant material. Cock et al. (2017) speculated that S. frugiperda entered Africa from American as a stowaway on a passenger flight, while Faulkner et al. (2017) inferred that it spread rapidly throughout Africa was possibly aided by intra-continental transportation links. Kalleshwaraswamy's (2018a) analysis suggested that it was introduced from Africa to India through the import of agricultural commodities.Therefore, the impact of human activities on its geographical distribution can not be ignored. Hosts of S. frugiperda were very extensive, and the main hosts were widely distributed around the world (Kalleshwaraswamy et al., 2018b), so the host might not be the main factor limiting their distribution. However, considering its strong migration ability, airflow might be an important factor affecting its distribution, which had not been considered in this study. Whether it would had a significant impact on the accuracy of the prediction results was still the next step to be studied.
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CABI., 2019. Centre Agriculture Bioscience International. Available at https://www.cabi.org/isc/fallarmyworm (Accessed on 2019). Cock, M.J., Beseh, P.K., Buddie, A.G., Cafá, G., Crozier, J., 2017. Molecular methods to detect Spodoptera frugiperda in Ghana, and implications for monitoring the spread of invasive species in developing countries. Scientific reports, 7(1), 4103. Day, R., Abrahams, P., Bateman, M., Beale, T., Clottey, V., Cock, M., Colmenarez,
Funding This work was supported by the Science and Technology Program of Sichuan, China (2019YFN0180), the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (2019QZKK068), and the Technological Development of Meteorological Administration/Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province (Key Laboratory of Sichuan Province-2018-Key-05-11). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions Qing Li planned and supervised the project. Rulin Wang, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools. Chunxian Jiang conceived and designed the experiments, performed the experiments, analyzed the data. Xiang Guo, Dongdong Chen, and Chaoyou analyzed the data and performed simulations. Yue Zhang analyzed the data. Mingtian Wang conceived and designed the experiments. Competing interests The authors declare no competing interests.
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503-513. Wu, Q.L., Jiang, Y.Y., Wu, K.M., 2019c. Analysis of migration routes of the fall armyworm Spodoptera frugiperda (J.E. Smith) from Myanmar to China. Plant Protection, 45(2), 1-6. Wang, L., Chen, K.W., Zhong, G.H., Xian, J.D., He, X.F., Lu, Y.Y., 2019. Progress for occurrence and management and the strategy of the fall armyworm Spodoptera frugiperda (Smith). Journal of Environmental Entomology, 41(3), 479-487. Xu, L.N., Hu, B.J., Su, X.Y., Qi, R.D., Su, W.H., Qiu, K., Zhou, Z.Y., Zheng, Z.Y., Zhang, Q.Y., Hu, F., Wang, Z.Y., 2019a. Genetic analysis of the fall armyworm Spodoptera frugiperda invaded in Anhui province. Plant Protection, 45(5), 47-53. Xu, D.P., Wang, R.L., Ye, M., Pu, B., 2019b. Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling. Global Ecology and Conservation, 19, e00691. Yan, W.J., Yang, S., Wang, Y.Q., Zheng, Q., Zhang, Z.X., Xu, H.H., 2019. Comparison of the effectiveness of chemical and biological agents for the emergency control of Spodoptera frugiperda in the field. Chinese Journal of Applied Entomology, 56(4), 788-792. Yi, Y.J., Cheng, X., Yang, Z.F., Zhang, S.H., 2016. Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecological Engineering, 92, 260-269. Zhang, K.L., Yao, L.J., Meng, J.S., Tao, J., 2018a. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the Total Environment, 634, 1326-1334. Zhang, H.T., Luo, D., Mu, X.D., Xu, M., Wei, H., Luo, J.R., Zhang, J.E., Hu, Y.C., 2016. Predicting the potential suitable distribution area of the apple snail Pomacea canaliculata in China based on multiple ecological niche models. Chinese Journal of Applied Ecology, 27(4), 1277-1284. Zhang, T.J., Liu, G., 2017. Study of methods to improve the temporal transfer ability of niche model. Journal of China Agricultural University, 22(2), 98-105. Zhang, X., Li, G., Sheng, D., 2018b. Simulating the potential distribution of Elaeagnus angustifolia L. based on climatic constraints in China. Ecological Engineering, 113, 27-34. Zhang, L., Liu, B., Jiang, Y.Y., Liu, J., Wu, K.M., Xiao, Y.T., 2019. Molecular characterization analysis of fall armyworm populations in China. Plant Protection, 45(4), 20-27.
The authors declared that they have no conflicts of interest to this work.
S2351-9894(19)30484-6
DOI:
https://doi.org/10.1016/j.gecco.2019.e00865
Reference:
GECCO 865
To appear in:
Global Ecology and Conservation
Received Date: 14 August 2019 Revised Date:
21 November 2019
Accepted Date: 30 November 2019
Please cite this article as: Wang, R., Jiang, C., Guo, X., Chen, D., Chaoyou, , Zhang, Y., Wang, M., Li, Q., Potential distribution of Spodoptera frugiperda (J.E. Smith) in China and the major factors influencing distribution, Global Ecology and Conservation (2020), doi: https://doi.org/10.1016/j.gecco.2019.e00865. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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Potential distribution of Spodoptera frugiperda (J.E. Smith) in China
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and the major factors influencing distribution
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Rulin Wanga, b, Chunxian Jianga, Xiang Guoc, Dongdong Chenc, Chaoyouc, Yue Zhanga, Mingtian Wangd, e, Qing Lia* a College of Agronomy, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China b Sichuan Provincial Rural Economic Information Center, Chengdu, 610072, Sichuan, China c Sichuan Province Agro-meteorological Center, Chengdu, 610072, Sichuan, China d Sichuan Meteorological Observatory, Chengdu, 610072, Sichuan, China e Water-Saving Agriculture in Southern Hill Area Key Laboratory of Sichuan Province, Chengdu, 610066, Sichuan, China *
Corresponding author, Qing Li. College of Agronomy, Sichuan Agricultural University, 211 Huimin Road, Chengdu, 611130, Sichuan, China. Tel: +86 13608022868 Email: [email protected]
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Potential distribution of Spodoptera frugiperda (J.E. Smith) in China
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and the major factors influencing distribution
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Abstract: Fall armyworm, Spodoptera frugiperda (J.E. Smith) is one of the most important agricultural pests in the world. At the end of 2018, S. frugiperda was first found in Yunnan, China, and then it had spread rapidly to 19 provinces and posed a serious threat to China's agricultural production. Based on the current distribution data of S. frugiperda and enviromental variables, this paper constructed a potential species distribution model (MaxEnt), and predicted the potential distribution area of S. frugiperda in China, and identified the dominant climatic factors that control its distribution. The results showed that: (1) The average AUC value of the model was 0.906, which demonstrated that the accuracy of the model was excellent．(2) Mean Temperature of Coldest Quarter (bio11), Min Temperature of Coldest Month (bio6), Mean Temperature of Warmest Quarter (bio10), Max Temperature of Warmest Month (bio5), were dominant climatic factors which affected and controlled the potential distribution of S. frugiperda, and the appropriate range were -14.77-22.86℃, -13.33-24.35℃, 19.15-29.73℃, and 24.55-36.83℃. (3) The most suitable area of S. frugiperda are mainly distributed in Guangxi, Jiangxi, Guangdong, Hunan, Fujian, Zhejiang, Yunnan, Hubei, Anhui, Sichuan, Chongqing, Hainan, Jiangsu, Henan, Taiwan, Guizhou, Shaanxi, Shanghai and Hongkong, and account for 12.09% of China's total area. According to the results of this study, it was known that the pest was a major threat to maize and rice in China, and it was urgently necessary to strengthen monitoring and management in its suitable range to prevent the invasion of S. frugiperda which would ensure the safety of agriculture. Key words: Spodoptera frugiperda (J.E. Smith); MaxEnt model; potential geographical distribution; environmental variables.
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1. Introduction
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Fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), native to tropical and subtropical America, is one of the most important agricultural pests in the world. It is a polyphagous pest that feeds on up to 353 species of plants, but mainly damages maize, rice, sorghum, cotton and sugarcane (Montezano et al., 2018). Spodoptera frugiperda is a typical long-distance migratory pest, but prior to 2015, there were no reports of the distribution of S. frugiperda outside the Americas. In 2016, S. frugiperda was confirmed to have invaded Africa and quickly spread to almost the entire African continent south of the Sahara, causing about 20% to 50% corn production losses (Day et al., 2017; Feldmann et al., 2019). In May 2018, S. frugiperda was discovered in India and subsequently discovered in Myanmar, Thailand, Yemen, and Sri Lanka (CABI, 2019; FAO, 2019; Kalleshwaraswamy et al., 2018). At the end of 2018, S. frugiperda was first found in Yunnan Province in China (Guo et al., 2019a). By July 2019, it had spread to 19 provinces in China (Guo et al.,
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2019b; Liao et al., 2019; Liu et al., 2019a; Liu et al., 2019b; Wu et al., 2019a; Xu et al., 2019a; Yan et al., 2019; Zhang et al., 2019). Therefore, it is particularly important to determine the potential geographical distribution of S. frugiperda in China for its monitoring, early warning and control (Wang et al., 2019). MaxEnt (maximum entropy) is one of the commonly used niche models and a tool for quantifying species distribution. It had been widely used in predicting potential distribution state of various species, such as birds, insects, aquatic animals, etc., especially in providing guidance for suitability assessment and risk zoning of invasive species (Moya et al., 2017; Wan et al., 2017; Wei et al., 2018). MaxEnt is a density estimation and species distribution prediction model based on the principle of maximum entropy (Elith et al., 2017). In the simulation of the model, the known distribution area of species was first determined, then the restricting factors that affect the distribution of species were analyzed to establish the constraint set and to obtain the relationship between them. The distribution with the largest entropy was selected as the optimal distribution (Warren et al., 2011; Phillips et al., 2017). The advantages of the MaxEnt model were: had higher prediction with small sample size, used only existing distribution points, was easy to interpret prediction results, measured the importance of environmental variables by knife cutting method, and predicted habitats of species under future climate change (Yi et al., 2011; Zhang et al., 2016; Urbani et al., 2017; Zhang et al., 2018a). Although it had not been present for a long time, but had been widely used in the prediction of potential geographical distribution of invasive organisms and became one of the most popular predictive models (Frid et al., 2013). More than 1,000 research results were obtained using this model, and more than 750 papers were published by Chinese scholars using this tool (Kong et al., 2019). In this paper, the MaxEnt niche model was used to predict the potential geographical distribution of S. frugiperda in China, and the suitable environmental factors affecting the distribution of the insect were further analyzed, in order to attract the attention of relevant departments in the region and to appropriate quarantine and inspection measures to prevent the invasion of S. frugiperda. At the same time, it also provide theoretical basis for the relevant departments to formulate corresponding quarantine measures and ensure the safety of grain production in China.
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2. Materials and Methods
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2.1. Species Occurrence record We obtained global geographical distribution records of S. frugiperda by visiting the Global biodiversity information facility (GBIF, 2019), and the Centre Agriculture Bioscience International (CABI, 2019). Other data were obtained by consulting journal articles published at home and abroad (Goergen et al., 2016; Early et al., 2018; Qi et al., 2019; Tang et al., 2019; Wu et al., 2019b; Wu et al., 2019c). Finally, 1019 valid records were used to build the predictive model (Fig. 1).
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Fig.1. Global distribution of S. frugiperda 2.2. Environmental Data for MaxEnt Climate data: Climate data were downloaded from the official website of Global Climate Data (Worldclim), including 19 bioclimatic variables with a spatial resolution of 30 sec (~1 km). The version of the data set was 2.0, and the time range was 1970-2000. In order to avoid the collinearity between variables affecting the simulation results, the modeling variables were filtered according to the following steps (Zhang and Liu, 2017). First, the MaxEnt model was run using the distribution data of S. frugiperda and 19 bioclimatic variables to obtain the percent contribution of each variable to the preliminary prediction results. Then we imported all distribution points in Arc-GIS and extracted the attribute values of the 19 variables. Pearson correlation analysis was performed on the extracted variables using SPSS 22 to avoid variable spatial autocorrelation. Referring to the biological characteristics of S. frugiperda (Kalleshwaraswamy et al., 2018) and comparing the percent contribution of each variable in the initial model (Table S1), the variables with correlation coefficient > |0.8| (very significant correlation) were screened. After screening procedures (Table S2), 10 bioclimatic variables were selected to establish the potential geographic distribution prediction of S. frugiperda, which was Mean Diurnal Range (bio2), Max Temperature of Warmest Month (bio5), Min Temperature of Coldest Month (bio6), Mean Temperature of Wettest Quarter (bio8), Mean Temperature of Warmest Quarter (bio10), Mean Temperature of Coldest Quarter (bio11), Precipitation of Driest Month (bio14), Precipitation Seasonality (bio15), Precipitation of Warmest Quarter (bio18), Precipitation of Coldest Quarter (bio19). Table 1 List of enviromental variables Variables
Abbreviation
Unites
Annual Mean Temperature Mean Diurnal Range (Mean of monthly (max temp - min temp)) Isothermality (BIO2/BIO7) (* 100) Temperature Seasonality (standard deviation *100) Max Temperature of Warmest Month Min Temperature of Coldest Month Temperature Annual Range (BIO5-BIO6)
bio1 bio2 bio3 bio4 bio5 bio6 bio7
℃ ℃ － ℃ ℃ ℃ ℃
Mean Temperature of Wettest Quarter Mean Temperature of Driest Quarter Mean Temperature of Warmest Quarter Mean Temperature of Coldest Quarter Annual Precipitation Precipitation of Wettest Month Precipitation of Driest Month Precipitation Seasonality (Coefficient of Variation) Precipitation of Wettest Quarter Precipitation of Driest Quarter Precipitation of Warmest Quarter Precipitation of Coldest Quarter 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165
bio8 bio9 bio10 bio11 bio12 bio13 bio14 bio15 bio16 bio17 bio18 bio19
℃ ℃ ℃ ℃ mm mm mm － mm mm mm mm
2.3. Distribution Modeling Using MaxEnt Occurrence data of S. frugiperda and 10 environmental variables were imported into the MaxEnt model, and the ‘Create response curves’ and ‘Do jackknife to measure variable importance’ options were selected. The format of the model output file was selected as ‘Logistic’. 25% percent of the 1109 distribution points were randomly selected as ‘test data’, and the model replicates were set to 10 times, and the remaining parameters were selected as the default parameters of the software. The area under the receiver operating characteristics (ROC) curve was chosen as an index to evaluate the accuracy of the model. AUC values greater than 0.6 indicated that the simulated results were 'fair', while AUC values greater than 0.9 indicate that the predicted results were 'excellent'. The model with the highest AUC value was selected to analyze the suitability of S. frugiperda in China (Phillips et al., 2006; Zhang et al., 2018a; Zhang et al., 2018b; Xu et al., 2019b). 2.4. Distribution modeling We imported the ASC II format file output by MaxEnt into ArcGIS 10.2, and converted it into raster format file by using the conversion tool in ArcToolbox, which was used to classify and visualize the distribution area (Montemayor et al., 2015; López-Martínez et al., 2016; Lantschner et al., 2017). The map of China was used as the base map to extract the potential suitable distribution of S. frugiperda in China. Jenks'natural breaks were used to reclassify the suitability and classify the suitability into five categories: unsuitalbe area (P < 0.08), low suitable area (0.08 < P < 0.25), moderate suitable area (0.25 < P < 0.47), good suitable area (P < 0.47), most suitable area (Shiravand et al., 2018).
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3. Results
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3.1. Result validation The MaxEnt model can draw the ROC curve by itself and calculate the AUC value of the model, which can be directly used as the criterion for model prediction. The ROC curve of the predicted results of this study was shown in Fig. 2. The curve
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analysis results showed that the average AUC value of the model was 0.906, which was much higher than the AUC value of the random prediction model (0.5), indicating that the prediction results had higher accuracy. That was, the model-predicted distribution area had a good fit to the actual distribution area of the species.
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Fig. 2. AUC result of MaxEnt modeling (10 runs)_ 3.2. Potential distribution area of S. frugiperda in China The prediction results of MaxEnt model indicated that the suitable area of S frugiperda in China was very wide, and the unsuitable areas were mainly concentrated in the Qinghai-Tibet Plateau, Xinjiang, Gansu, Inner Mongolia, Heilongjiang, Jilin, and western Sichuan (Fig. 3). The areas of unsuitable area, low suitable area, moderately suitable area, good suitable area and most suitable area accounted for 65.96%, 9.19%, 7.69%, 5.07%, and 12.09% of China's total area, respectively (Table 2). In this paper, the most suitable area were more concentrated, including Guangxi, Jiangxi, Guangdong, Hunan, Fujian, Zhejiang, Yunnan, Hubei, Anhui, Sichuan, Chongqing, Hainan, Jiangsu, Henan, Taiwan, Guizhou, Shaanxi, Shanghai and Hongkong. The good suitable areas were mainly the outer extension of the most suitable areas, mainly distributed in Yunan, Henan, Sichuan, Hubei, Anhui, Hunan, Guizou, Guangxi, Jiangsu and Chongqing. The moderate suitable areas were mainly distributed in Guizhou. Guizhou, Yunan, Henan, Sichuan, Shaanxi, Shandong, Jangsu, Hube, Shanxi, Chongqing, Gansu, Hebei, Anhui and Hunan.
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Fig. 3. Potential geographical distribution of S. frugiperda in China Table 2 The suitable area of in China under current climate condition Province Guangxi Jiangxi Guangdong Hunan Fujian Zhejiang Yunnan Hubei Anhui Sichuan Chongqing Hainan Jiangsu Henan Taiwan Guizhou Shaanxi Shanghai Hong Kong Heilongjiang Inner Mongolia Xinjiang Jilin
Predicted area (×104 km2) Unsuitable
Low suitable
Moderate suitable
Good suitable
Most suitable
0.00 0.00 0.00 0.00 0.00 0.00 3.92 0.07 0.00 23.00 0.11 0.00 0.00 0.02 0.56 0.01 0.73 0.00 0.00 54.44 124.66 174.22 21.19
0.03 0.01 0.00 0.04 0.00 0.00 6.05 0.86 0.01 5.11 0.54 0.00 0.00 0.37 0.74 1.43 10.73 0.00 0.00 0.00 4.45 1.12 0.10
0.56 0.04 0.04 1.13 0.01 0.06 9.70 3.98 1.17 8.38 1.91 0.03 5.94 8.50 0.47 9.94 7.36 0.00 0.00 0.00 0.00 0.28 0.00
3.37 0.46 0.81 4.06 0.14 0.81 7.07 5.28 5.00 5.48 2.31 0.35 2.37 6.00 0.30 3.68 0.98 0.02 0.02 0.00 0.00 0.00 0.00
16.98 14.76 14.61 14.16 10.69 8.35 7.54 7.37 7.18 3.56 2.87 2.47 1.26 1.23 1.07 0.90 0.59 0.52 0.05 0.00 0.00 0.00 0.00
Liaoning Gansu Hebei Beijing Shanxi Tianjin Ningxia Qinghai Shandong Tibet Total 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
8.44 29.98 6.96 0.29 3.84 0.01 1.76 70.67 0.01 108.66 633.55
7.15 9.75 10.89 1.43 8.63 1.21 3.47 0.67 8.02 5.50 88.31
0.00 1.80 1.76 0.00 3.33 0.00 0.03 0.00 7.27 0.16 73.86
3.3. Analysis of the importance of environmental variables The statistical results of the contribution rate of each environmental variable showed that (Fig. 4), four environmental variables (bio11, bio18, bio6 and bio5) contributed more than 10% to the model. They were the main environmental variables to simulate the suitable habitat of S. frugiperda and could reflect most of the information of its optimal habitat distribution. All the selected variables had different contribution rates in the model, and the contribution rate of the least contributing variable was 0.4%, indicating that there was no unrelated variable participating in the analysis of the MaxEnt model. It also showed that the method of selecting variables used in this paper was very effective for constructing the MaxEnt species distribution model. Jackknife test results of contribution rate of single variable (Fig. 5) showed that the AUC values of five environmental variables (bio11, bio6, bio10, and bio5) were all > 0.8, which indicated that they were the main factors affecting the potential distribution area of S. frugiperda. The AUC values of bio11 and bio3 all exceeded 0.83, indicating that the extreme temperature factor was the most important variable affecting the geographical distribution of S. frugiperda. 40
Percent contribution
35 30
34 27.6
25 20 15
17.5 13
10 5
2.6
1.6
1.3
1.1
0.8
0.4
0 bio11 bio6 bio10 bio5 bio14 bio19 bio18 bio8 bio15 bio2
Environmental variables
216 217
0.00 0.00 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.00 48.67
Fig. 4. Analysis of the importance of environmental variables
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 116.16
218 219 220 221 222 223 224 225 226 227 228 229 230 231 232
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Fig. 5. Jackknife test of vairable importance for train data 3.4. Relationship between environmental variables and probability of presence of S. frugiperda Combined with the potential distribution threshold determined by the method part, the response curve of the MaxEnt model output in this study reflected the suitability threshold of the dominant environmental variable controlling the potential distribution of S. frugiperda. The distribution probability of S. frugiperda increased with the increase of the value of each environmental variable within a certain range, and decreased with the increase of the variable after a certain peak value. The results showed that the appropriate range ranges of bio 11, bio6, bio10, bio5, bio8, and bio18 were -14.77-22.86 , -13.33-24.35 , 19.15-29.73 , 24.55-36.83 , 17.59-28.67 , and 184.01-951.8mm, respectively (Fig. 6).
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Fig. 6. Response curves between the probability of presence and environmental variables
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4. Discussion
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Due to its strong ability to migrate long distances and often cause sudden damage, S. frugiperda were widely concerned by agricultural workers around the world. In 2016, S. frugiperda invaded Africa and settled, causing potential economic losses of 2.481 billion to 6.187 billion dollars per year (Day et al., 2017). At the end of 2018, S. frugiperda was first reported in Yunnan, China. By July 2019, the insect had spread to 19 provinces. Studies had shown that with the strengthening of the southwest summer monsoon, Yunnan and Guangxi had become the main immigrants of S. frugiperda in Myanmar (Wu et al., 2019c). Because the niche of invasive species sometimes would drift, only using the distribution data of origin to predict the potential distribution of invasive species in the invasive area might lead to deviation, making it difficult to accurately predict the suitable distribution area (Qi et al., 2012). In this work, the selected distribution points include the origin and invasion areas of S. frugiperda all over the world, as well as the known distribution sites in China, which can make up for the error of only using the origin ecological environment to simulate its potential distribution in the invasion area, and improve the reliability of prediction. Wang et al. (2019) combined with the biological characteristics of S. frugiperda and the climatic characteristics of China, and speculated that the perennial occurrence of S. frugiperda in China was in the south of the 10 °C isotherm in January, mainly including southern Yunnan and central and southern Guangxi, Hainan, Fujian, and Taiwan. The most suitable area for the study was predicted to be more northward. The reason for this difference might be that Wang et al. only analyzed the limiting effect of temperature on S. frugiperda, and our study more scientifically analyzed the combined effects of various climate variables on their distribution. At present, S. frugiperda could be divided into two haplotypes, corn-strain and rice-straiin. Corn-strain S. frugiperda mainly feed on corn, cotton and sorghum, while rice-strain feed on rice and various pastures (Dumas et al., 2015). Duan and Zhou (2011) analyzed the potential distribution of rice in China, and concluded that the suitable growth area was located in the northeast plain and south of the Yangtze River, including Jiangsu, Anhui, Hubei, Chongqing, Sichuan, Zhejiang, Jiangxi, Hunan, Guizhou, Yunnan, Fujian, Guangdong, Guangxi, Hainan and Taiwan, which had a high degree of coincidence with the most suitable area of S. frugiperda. Maize was one of the main food crops in China. It was widely planted and distributed, mainly in the spring maize area of East and North China, summer maize area of Huanghuaihai Plain, irrigation maize area of Northwest China, mountain maize area of Southwest China, hilly maize area of South China and maize area of Qinghai-Tibet Plateau (He and Zhou, 2011; He and Zhou, 2012). The southwest mountain maize region and the southern hilly maize region were both located in the most suitable area for S. frugiperda. Since the first discovery of S. frugiperda in Pu'er City, Yunnan Province
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on January 11, 2019, as of June 14, 2019, the pest had been found in 921 counties in 19 provinces, with an area of 256700 hm2. In view of the rapid spread rate of the insect, the relevant departments should pay enough attention to it and take effective measures to eliminate it immediately to prevent its further spread (Wang et al., 2019). At the same time, the insect should be monitored within its suitable range. Once it was found, it would be removed immediately, otherwise it would pose a serious threat to the safe production of food crops in China. Temperature is the main environmental factor affecting the growth, reproduction and distribution of S. frugiperda. He et al. (2019) found that the temperature adaptability of S. frugiperda was very strong, and the suitable temperature range for population growth and development was 20-30 . This study found that when Mean Temperature of Warmest Quarter was 19.15-29.73 , the existence probability of S. frugiperda was higher, which was consistent with the result found by He et al. (2019). At 28 , S. frugiperda developed fastest, a generation could be completed in about 30 days during the warm summer months (Kebede, 2018), and the similar rule was found by Garcia et al. (2018). In this paper, the results showed that the probability of the presence of reached highest when Mean Temperature of Warmest Quarter was about 28.6 . This was in agreement with the previous studies, indicating that 28 was the optimum temperature for the development of S. frugiperda. Spodoptera frugiperda doesn’t diapause and therefore can not past below freezing conditions in any stage. In South China, the annual average temperature was above 20 , the coldest monthly average temperature was above 10 , the winter temperature was higher and the duration of low temperature was short, which provided good climatic conditions for the persistence of S. frugiperda. Therefore, it was speculated that S. frugiperda could reproduce all year round in South China, and may become an important initial source of S. frugiperda in the Yangtze River Basin, North China, Northwest and even Northeast China. In a suitable temperature range, the growth rate of insects increased with the increase of external temperature, but when the temperature rose to a certain extent, the development rate would decrease. Valdez-Torres et al. (2012) showed that under laboratory conditions, the lowest and highest temperature thresholds for the development of S. frugiperda were 8.7 and 39.8 , respectively. According to the response curve of bio5, the probability of S. frugiperda decreased rapidly when the temperature exceeded 36.7 . This suggested that high temperature in summer may be an important factor limiting the distribution of S. frugiperda. The habitat predicted by MaxEnt model was similar to that of the known occurrence point, but there was still a certain deviation from the actual occurrence area of pests. Because the distribution patterns and environmental requirements of different species were different, the predictive effects of different species and environmental variables would also be different (Neven et al., 2018). In this study, 19 bioclimatic variables related to temperature and humidity were selected as initial predictors. However, there were many non-climatic factors affecting the distribution of insect suitability, such as host, natural enemy, management level and human activities (Hill et al., 2012). Although S. frugiperda has a strong ability of
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long-distance transmission, experts analyzed that the reason for its transmission between continents was the movement of infested plant material. Cock et al. (2017) speculated that S. frugiperda entered Africa from American as a stowaway on a passenger flight, while Faulkner et al. (2017) inferred that it spread rapidly throughout Africa was possibly aided by intra-continental transportation links. Kalleshwaraswamy's (2018a) analysis suggested that it was introduced from Africa to India through the import of agricultural commodities.Therefore, the impact of human activities on its geographical distribution can not be ignored. Hosts of S. frugiperda were very extensive, and the main hosts were widely distributed around the world (Kalleshwaraswamy et al., 2018b), so the host might not be the main factor limiting their distribution. However, considering its strong migration ability, airflow might be an important factor affecting its distribution, which had not been considered in this study. Whether it would had a significant impact on the accuracy of the prediction results was still the next step to be studied.
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Funding This work was supported by the Science and Technology Program of Sichuan, China (2019YFN0180), the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (2019QZKK068), and the Technological Development of Meteorological Administration/Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province (Key Laboratory of Sichuan Province-2018-Key-05-11). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions Qing Li planned and supervised the project. Rulin Wang, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools. Chunxian Jiang conceived and designed the experiments, performed the experiments, analyzed the data. Xiang Guo, Dongdong Chen, and Chaoyou analyzed the data and performed simulations. Yue Zhang analyzed the data. Mingtian Wang conceived and designed the experiments. Competing interests The authors declare no competing interests.
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The authors declared that they have no conflicts of interest to this work.