The Relationships Among Longitude, Latitude and Elevation of Occurrence of Parocneria Orienta (Lepidoptera: Lymantriidae) in China

The relationships among longitude, latitude and elevation of occurrence of Parocneria orienta (Lepidoptera: Lymantriidae) in China

Hua Yang Corresp., 1 , Jin Zhang 2 , Wei Yang 1 , Chun-Ping Yang 1 , Wei Zhou 3 , Tao Li 4 , Jia-Wen Wang 1 , Ru-Lin Wang 5

1 Key Laboratory of Ecological Forestry Engineering of Sichuan Province/College of Forestry, Sichuan Agricultural University, Chengdu, China 2 Provincial Key Laboratory of Agricultural Environmental Engineering, Sichuan Agricultural University, Chengdu, China 3 Science and Technology Department, Sichuan Agricultural University, Chengdu, China 4 Forestry Department of Sichuan Province, Chengdu, China 5 Sichuan Provincial Rural Economic Information Centre, Chengdu, China

Corresponding Author: Hua Yang Email address: [email protected]

Background Parocneria orienta is the primary defoliator of Cupressaceae plants in China, and its geographic range is expanding. Research is needed to identify the geographic distribution of P. orienta and its major areas of occurrence and to formulate measures for early warning, monitoring and control of this pest. Methods Data on the occurrence P. orienta were collected from 4,688 monitoring sites in Sichuan Province from 2012 to 2016. Analyses of the spatial distribution and model fit were carried out using ArcGIS and Matlab software. Results We found that the occurrence of P. orienta complied with a normal distribution law (α=1% confidence level) in terms of longitude and latitude and belonging to generalized extreme-value distribution (α=1% confidence level) in terms of elevation. According to the double factor variance analysis taking year-month as the time variance and longitude, latitude and elevation as the space variance, the hazard centroid shifted significantly by 6 minutes of longitude in March (105°46′37″E) and July (105°40′30″E) of the same year. The hazard centroid has not changed much over time in terms of latitude and elevation. Discussion The regions of greatest damage by P. orienta were in eastern and southeastern parts of Sichuan Province (105.7°E–31.1°N, elevation 400 m), an area that features plains and low mountains with lush vegetation and abundant Cupressus funebris. In March, there is little rainfall, which is beneficial for growth and breeding of larvae, and causes a rapid increase of population that then results in widespread damage to Cupressaceae plants.

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 1 The relationships among longitude, latitude and elevation of

2 occurrence of Parocneria orienta (Lepidoptera:

3 Lymantriidae) in China

4 Hua Yang1*, Jin Zhang2, Wei Yang1, Chun-Ping Yang1, Wei Zhou3, Tao Li4, Jia-Wen Wang1

5 and Ru-Lin Wang5

8 1. Key Laboratory of Ecological Forestry Engineering of Sichuan Province/College of Forestry, Sichuan

9 Agricultural University, Chengdu, Sichuan 611130, China.

10 2. Provincial Key Laboratory of Agricultural Environmental Engineering, Sichuan Agricultural

11 University, Chengdu, Sichuan 611130, China.

12 3. Science and Technology Department, Sichuan Agricultural University, Chengdu, Sichuan 611130,

13 China.

14 4. Forestry Department of Sichuan Province, Chengdu, Sichuan 610081, China.

15 5. Sichuan Provincial Rural Economic Information Centre, Chengdu 610072, China.

17 ORCID ID: 0000-0002-7523-5862

18 *Correspondence: Hua Yang, [email protected]

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 21 ABSTRACT

22 Background Parocneria orienta is the primary defoliator of Cupressaceae plants in China, and

23 its geographic range is expanding. Research is needed to identify the geographic distribution of P.

24 orienta and its major areas of occurrence and to formulate measures for early warning,

25 monitoring and control of this pest.

26 Methods Data on the occurrence P. orienta were collected from 4,688 monitoring sites in

27 Sichuan Province from 2012 to 2016. Analyses of the spatial distribution and model fit were

28 carried out using ArcGIS and Matlab software.

29 Results We found that the occurrence of P. orienta complied with a normal distribution law

30 (α=1% confidence level) in terms of longitude and latitude and belonging to generalized

31 extreme-value distribution (α=1% confidence level) in terms of elevation. According to the

32 double factor variance analysis taking year-month as the time variance and longitude, latitude

33 and elevation as the space variance, the hazard centroid shifted significantly by 6 minutes of

34 longitude in March (105°46′37″E) and July (105°40′30″E) of the same year. The hazard centroid

35 has not changed much over time in terms of latitude and elevation.

36 Discussion The regions of greatest damage by P. orienta were in eastern and southeastern parts

37 of Sichuan Province (105.7°E–31.1°N, elevation 400 m), an area that features plains and low

38 mountains with lush vegetation and abundant Cupressus funebris. In March, there is little rainfall,

39 which is beneficial for growth and breeding of larvae, and causes a rapid increase of population

40 that then results in widespread damage to Cupressaceae plants.

42 Keywords

43 Parocneria orienta, distribution model, geographic distribution, cypress

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 44 1. INTRODUCTION

45 Parocneria orienta Chao (Lepidoptera: Lymantriidae: Parocneria) is widely distributed in

46 the hills and mountain areas around the Sichuan Basin. A few occurrences of Parocneria orienta

47 are found also in Chongqing, Guizhou, Hunan and Fujian provinces (He 1998; Yang et al. 2015).

48 The main food species for P. orienta is Cupressus funebris, but where food supplies are short,

49 the larvae will also use Platycladus orientalis, Junperus chinensis, C. lusitanica and other

50 Cupressaceae plants (Wen et al. 2009). Two generations of Parocneria orienta can occur in

51 Sichuan Province in a single year. During the first 10 days in March, overwintering eggs start to

52 hatch and feed on varieties of Cupressaceae. During the first 10 days in July, the first generation

53 hatches and begins to feed (Zhang et al. 2012). Compared with the overwintering generation, the

54 first generation produces fewer larvae. Therefore, the damage caused is not as severe as that of

55 the overwintering generation. Recently, the losses caused by Parocneria orienta to Cupressus

56 funebris in Sichuan Province have become more severe. The damaged area covers 660,000 hm2

57 each year, causing significant economic losses and affecting the development of an ecological

58 barrier in the upper reaches of the Yangtze River in China (Feng et al. 2009). Parocneria orienta

59 has become the primary defoliator of Cupressaceae plants in China (Fig. 1).

60 The spatial distribution pattern is an important feature of insect populations, and is the result

61 of the interactions and coevolution of the biological characteristics of the insect population and

62 habitat conditions. Understanding the spatial distribution and dynamics of insect pests forms the

63 basis for their management. However, research on these topics still contains some gaps. These

64 are mainly reflected in two aspects. First, much research only focuses on time series instead of

65 focusing on the impact of spatial factors on population dynamics (Cheng & Yang 1992; Gong et

66 al. 1997; Huang et al. 1995). Second, as far as spatial dynamics of regional insect pests is

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 67 concerned, research has focused on Lymantria dispar (Hohn et al. 1993; Liebhold et al. 1998),

68 Nilaparvata lugens (Wang et al. 1998), Helicoverpa armigera (Ge et al. 2000; Lu & Liang 2002),

69 Chilo suppressalis (Wang et al. 2000), and Helicoverpa armigera (Wang et al. 2004). Due to

70 limited data on insect pests and a deficiency of effective analysis tools, research on these aspects

71 in China still only involves small scales and analysis of spatial structure. Large scale research on

72 the spatial distribution and dynamics of insect pests are seldom reported.

73 Currently, research on Parocneria orienta mainly focuses on biological characteristics and

74 control methods. No research has been carried out into its large-scale spatial distribution. Here,

75 we used ArcGIS and Matlab software to investigate the relationship between the probability of

76 occurrence of Parocneria orienta in each monitoring site in Sichuan Province and longitude,

77 latitude and elevation, to fit a spatial distribution model, analyze the trends in the annual

78 occurrence of Parocneria orienta in Sichuan Province and provide an important reference and

79 theoretical basis for formulating reasonable prevention and control measures.

80 2. METHODS AND DATA

81 2.1 Data

82 The data for Parocneria orienta mainly comprised the survey area and occurrence rate

83 calculated for each monitoring site in Sichuan Province from 2012 to 2016. The data were taken

84 from the Forestry Pest Platform of Forest Diseases and Insect Pests Prevention and Control

85 Center of Sichuan Province. There were 4,688 monitoring sites in Sichuan Province, including

86 2,285 monitoring sites for Parocneria orienta (Fig. 2).

87 2.2 Distribution test

88 Previous studies have shown that the relationship between a species population and the area

89 of suitable habitat follows certain distribution laws. Based on our data, the numbers of P. orienta

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 90 by longitude and latitude followed a normal distribution law. Thus, the probability density

91 function of normal distribution was used to investigate the distribution of P. orienta in relation to

92 longitude and latitude, as follows:

( )2 1 2 93 f( ) = ‒ 푥 ‒ 푥 (1) 2휎 2휋휎 94 where f(x) is the probability density, x is the longitude푥 or latitude,푒 and the and δ are unknown

95 parameters that can be estimated by equation (1). 푥

96 The relationship with elevation followed the law of generalized extreme-value distribution.

97 The probability density function of generalized extreme-value distribution was used to

98 investigate the distribution of Parocneria orienta in relation to elevation as follows:

1 1 99 f(y) = (1 ky) 1 ( (1 ) ) (2) 푘 ‒ 푘 ‒ 푒푥푝 ‒ ‒ 푘푦 100 where f(y) is the probability density, y is the elevation, and k is the unknown parameter that can

101 be estimated by equation (2).

102 2.3 Variance analysis

103 The distribution of P. orienta is influenced by many factors, which leads to variation in its

104 occurrence in space and time. We used a two-factor covariance analysis to investigate the

105 difference in distribution in space and time. Longitude and latitude were chosen as the space

106 variables that influenced the distribution of P. orienta, and the month was chosen as the time

107 variable. The two-factor covariance analysis is shown in Table 1.

108 Here, SSR is the mean square error from the space variable, and MRc is the mean square

109 error from the time variable. MRe is the mean square error from the unexplained factors that

110 could not be described by space and time variables; k is the number of space variables, r is the

111 number of time variables. The SSR, SSC and SSE can be calculated as follows:

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 112 SSR = r 푘 ( . ) = 1 푥푖 ‒ 푥 푖 113 SSC = k 푟 ( ) ∑= 1 푥.푗 ‒ 푥 푗 114 SSE = 푘 푟 ( ) = 1 = 1∑ 푥푖,푗 ‒ 푥푖. ‒ 푥.푗 ‒ 푥 115 SST푖 =푗 SSR+SSC+SSE

116 where xi,j is the survey data, its units are ∑mu;∑ is the average value of survey data, its units are

117 mu; . is the average value of the ith space 푥 variable; is the average value of the jth time

푖 .푗 118 variable.푥 푥

119 3. RESULTS

120 3.1 Occurrence of P. orienta in Sichuan Province

121 According to the survey data for each monitoring site, Parocneria orienta occurred mainly

122 on the East Sichuan Plain (S1 Spreadsheet). Comparison of the occurrence areas in March and

123 July from 2012 to 2016 showed that the overwintering generation (March) did more harm than

124 the first generation. The occurrence region of the first generation (July) was smaller and the

125 occurrence area was reduced, which stemmed from the biological characteristics of Parocneria

126 orienta (Fig. 3).

127 3.2 Effects of longitude on the distribution of P. orienta

128 Based on the monitoring data, Parocneria orienta was distributed from longitude

129 103.202°E to longitude 108.103°E, crossing five longitude zones. The change trend between

130 March and July from 2012 to 2016 showed that the occurrence probability of Parocneria orienta

131 was relatively small at the western longitude range. The probability then increased with the

132 increments of longitude. After reaching 105.5°–106.0°E, the probability of occurrence reduced

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 133 with the increments of longitude. Therefore, Parocneria orienta followed a normal distribution

134 along the longitude zone (Fig. 4). The normal distribution model verified that the distribution of

135 Parocneria orienta complied with the normal distribution rule in terms of longitude in March

136 and July from 2012 to 2016 at the α=1% confidence level (Table 2).

137 3.3 Effects of latitude on the distribution of P. orienta

138 Parocneria orienta was distributed from latitude 32.445°N to latitude 27.877°N, crossing

139 five latitude zones. According to the change trend between March and July from 2012 to 2016,

140 the occurrence probability of Parocneria orienta was relatively small at the start of its known

141 latitudinal range. The probability then increased with the increments of latitude. After reaching

142 31.0°–31.5°N, the probability of occurrence reduced again with the increments of latitude.

143 Therefore, we inferred that Parocneria orienta followed a normal distribution in terms of

144 distribution along the latitude zone (Fig. 5). Based on verification through the normal

145 distribution model, the distribution of Parocneria orienta complied with the normal distribution

146 rule in terms of latitude in March and July from 2012 to 2016 at a α=1% confidence level (Table

147 2).

148 3.4 Effects of elevation on the distribution of P. orienta

149 The elevation where Parocneria orienta was distributed ranged from dozens of meters to

150 over two thousand meters. Most individuals were found in elevations below 1000m and they

151 were concentrated in the areas with elevations of about 400m. According to the trends between

152 March and July from 2012 to 2016, the occurrence probability of Parocneria orienta was

153 relatively small at low elevations. The probability increased rapidly with altitude up to around

154 400m, when the occurrence probability of Parocneria orienta began to decrease slowly with

155 greater elevations. Therefore, we can infer that the distribution of Parocneria orienta reflects a

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 156 generalized extreme-value distribution with elevation (Fig. 6). According to verification through

157 the generalized extreme-value distribution model, the distribution of Parocneria orienta

158 complied with the generalized extreme-value distribution rule in terms of elevation in March and

159 July from 2012 to 2016 at a α=1% confidence level (Table 2).

160 3.5 Comparison of occurrence in different months and years

161 The difference in the spatial and temporal distribution of Parocneria orienta was

162 investigated using double factor variance analysis taking year-month as the time variable and

163 longitude, latitude and elevation as the space variables. Like the distribution of Parocneria

164 orienta, in terms of latitude and elevation the hazard centroid did not show a significant

165 difference over time. However, the hazard centroid shifted by about 6 minutes of longitude in

166 March (105°46′37″E) and July (105°40′30″E), which was significantly different (Table 3-5).

167 4. DISCUSSION

168 The number of insects showed a gradual change with changes of latitude or longitude. Other

169 research suggests that this change is a survival strategy used by insects to adapt to the

170 surrounding environment (Sun et al. 2007). The photoperiod, which controls factors such as the

171 growth and diapause of insects, will change with longitude and latitude. This could explain why

172 the longitude and latitude gradient affect the quantity of insects (Amy & Nicholas 1999). The

173 distribution of insects is also affected by many factors, and changes in environmental factors

174 may cause changes in distribution (Zhang 1978). Parocneria orienta has a specialist feeding

175 habit. The Eastern Sichuan Plain is the area in Sichuan Province with major Cupressus funebris

176 plantations. This abundance of food explains why the hazard zone of Parocneria orienta is

177 centered around 105.5°–106.0°E, 31.0°–31.5°N.

178 Elevation is an important non-biological ecological factor affecting the soil, vegetation and

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 179 microclimate of mountain forests, the growth and distribution of plants, as well as the growth,

180 development, survival, reproduction and distribution of insects and thereby affecting the

181 composition, structure and functions of the insect population (Carpenter 2005; Sánchez-

182 González & López-Mata 2005). Elevational differences affect the quantity of insects through

183 extreme, rather than average, temperature and humidity (Chang et al. 2008; Gao et al. 2011). The

184 occurrence of Parocneria orienta in Sichuan Province complied with a generalized extreme-

185 value distribution in terms of elevation. The occurrence probability of Parocneria orienta was

186 the highest when elevation was 400 m above sea level. Parocneria orienta can adapt favorably to

187 temperature, and can grow normally between 5°C and 35°C. However, the optimum humidity for

188 the growth of larva is 75%. This could explain why the occurrence probability of Parocneria

189 orienta rose rapidly and fell slowly around elevations of 400m.

190 The occurrence area of the overwintering generation (March) of Parocneria orienta greatly

191 exceeded that of the first generation (July), mainly caused by characteristics of the species,

192 artificial interference and weather conditions. If food is adequate, overwintering generation

193 female adults can produce 264 eggs on average. However, the first generation of female adults

194 can only produce 124 eggs on average. The eggs produced by the first generation of female

195 adults are few in quantity but high in quality, capable of resisting the impacts of adverse

196 environmental factors (He 1998). The local governments tend to pay great attention to the

197 outbreak of the overwintering generation and adopt different prevention and control measures.

198 Due to the small quantity and amount of damage, local governments always ignore the first

199 generation. Sichuan Province features a subtropical humid climate, with rain and heat occurring

200 in the same period. There is heavy rainfall in July. However, Parocneria orienta prefers high

201 temperatures and dry weather. Too much rainfall will harm its larva greatly. However, in our

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 202 comparison of different years and different months, the hazard centroid was only located in the

203 longitude zone. There was a significant difference between the centroid position in March and

204 July, which might be related to rainfall, temperature and humidity.

205 5. CONCLUSION

206 The area worst-hit by Parocneria orienta was in the east and southeast parts of Sichuan

207 Province (105.7°E–31.1°N, elevation: 400 m). These areas mainly feature plains and low

208 mountains, with abundant vegetation and Cupressus funebris. There is little rainfall in March,

209 which is beneficial for the growth and breeding of larva (He 1998). The abundance of host plants

210 provide sufficient food and shelter for Parocneria orienta larva. These conditions may cause a

211 rapid increase of populations and thereby disastrous levels of insect damage.

212 Supplementary Materials

213 Spreadsheet S1: The data of each monitoring sites of Parocneria orienta.

214 Acknowledgments

215 We thank the anonymous reviewers for valuable comments on the manuscript. We thank

216 Leonie Seabrook, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for

217 editing the English text of a draft of this manuscript.

218 Figure Legends

219 Fig. 1 Parocneria orienta adult. ♂ male; ♀ female 220 Fig. 2 Distribution of the monitoring stations in Sichuan Province.

221 Fig. 3 Occurrence area of Parocneria orienta (2012–2016).

222 Fig. 4 Distribution of Parocneria orienta in relation to longitude. a March 2012; b July 2012;

223 c March 2014; d July 2014; e March 2016; f July 2016.

224 Fig. 5 Distribution of Parocneria orienta in relation to latitude. a March 2012; b July 2012;

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 225 c March 2014; d July 2014; e March 2016; f July 2016.

226 Fig. 6 Distribution of Parocneria orienta in relation to elevation. a March 2012; b July 2012;

227 c March 2014; d July 2014; e March 2016; f July 2016.

228

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282

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 Table 1(on next page)

The structure of double factor variance analysis

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 1 Table 1 The structure of double factor variance analysis Source Degree of Mean square error F value F critical-value freedom Space k-1 MRs = Fr = 1 푆푆푅 푀푅푠 time r-1 MRc = 푘 ‒ Fc = 푀푅푒 1 푆푆퐶 푀푅푐 Errors (k-1)(r-1) MRe = 푟 ‒ 푀푅푒 1) 푆푆퐸 2 (푘 ‒ 1)(푟 ‒ 3 4 5 6

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 Table 2(on next page)

Parameter estimation

(*denotes significance at the 1% level)

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 1 Table 2 Parameter estimation longitude latitude elevation mean 105.788±0.0006* 31.086±0.0004* 411.041±0.0724* March 2012 sigma 0.819±0.0004 0.610±0.0003 87.229±0.0551 mean 105.677±0.0006* 31.146±0.0005* 415.700±0.0817* July 2012 sigma 0.736±0.0004 0.581±0.0003 85.915±0.0625 mean 105.794±0.0004* 31.130±0.0003* 418.792±0.0504* March 2014 sigma 0.844±0.0003 0.619±0.0002 87.445±0.0383 mean 105.707±0.0006* 31.086±0.0005* 398.556±0.0823* July 2014 sigma 0.691±0.0004 0.600±0.0004 83.0223±0.0645 mean 105.748±0.0004* 31.146±0.0003* 421.530±0.0525* March 2016 sigma 0.810±0.0003 0.625±0.0002 87.219±0.0400 mean 105.642±0.0006* 31.271±0.0006* 448.464±0.0891* July 2016 sigma 0.708±0.0004 0.662±0.0004 89.383±0.0659 2 (*denotes significance at the 1% level) 3

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 Table 3(on next page)

Double factor variance analysis (longitude)

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 1 Table 3 Double factor variance analysis (longitude) quadratic sum degree of freedom mean square F value F critical-value * SSR=0.0154 1 MRS=0.0154 Fr=192.1331 F0.05(1, 2)=98 SSC=0.0032 2 MRC=0.0016 FC=20.0021 F0.05(1, 2)=99 SSE=0.0002 2 MRe=0.0001 SST=0.0188 1 2 (*denotes significance at the 5% level) 3

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 Table 4(on next page)

Double factor variance analysis (latitude)

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 1 Table 4 Double factor variance analysis (latitude) quadratic sum degree of freedom mean square F value F critical-value

SSR=0.0034 1 MRs=0.0034 Fr=0.9306 F0.05(1, 2)=98 SSC=0.0125 2 MRC=0.0063 FC=1.7097 F0.05(1, 2)=99 SSE=0.0073 2 MRe=0.0037 SST=0.0231 1 2

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 Table 5(on next page)

Double factor variance analysis (elevation)

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 1 Table 5 Double factor variance analysis (altitude) quadratic sum degree of freedom mean square F value F critical-value

SSR=21.4969 1 MRS=21.4969 Fr=0.0772 F0.05(1, 2)=98 SSC=788.4420 2 MRC=394.2210 FC=1.4159 F0.05(1, 2)=99 SSE=556.8240 2 MRe=278.4120 SST=1366.7630 1 2 3

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27051v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018 Figure 1(on next page)

Parocneria orienta adult

♂ male; ♀ female

Distribution of the monitoring station in Sichuan Province

Occurrence area of Parocneria orienta (2012–2016)

Distribution of Parocneria orienta in relation to longitude a March 2012; b July 2012; c March 2014; d July 2014; e March 2016; f July 2016

Distribution of Parocneria orienta in relation to latitude a March 2012; b July 2012; c March 2014; d July 2014; e March 2016; f July 2016

Distribution of Parocneria orienta in relation to elevation. a March 2012; b July 2012; c March 2014; d July 2014; e March 2016; f July 2016