Influence of Temperature on the Northern Distribution Limits Of

Journal of Thermal Biology 37 (2012) 130–137

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Journal of Thermal Biology

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Inﬂuence of temperature on the northern distribution limits of Scirpophaga incertulas Walker (Lepidoptera: Pyralidae) in China

Peijian Shi a,1, Bo Wang a, Matthew P. Ayres b,1, Feng Ge a,n, Ling Zhong c, Bai-Lian Li d a State Key Laboratory of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China b Department of Biological Sciences, Dartmouth College, Hanover, NH, USA c Plant Protection and Quarantine Bureau of Jiangxi Province, Nanchang, China d Ecological Complexity and Modeling Laboratory, University of California, Riverside, CA, USA article info abstract

Article history: We explored the inﬂuence of temperature on the northern distribution limits of Scirpophaga incertulas Received 28 September 2011 Walker, an important agricultural pest of rice in Asia. We analyzed Z48 years of records from 186 Accepted 5 December 2011 climate stations of Mainland China to estimate the annual probabilities of reaching the lower lethal Available online 9 December 2011 temperature for S. incertulas. The relevant climatic metric, minimum annual temperature, approxi- Keywords: mated a normal distribution. Consequently, the probability density function for any site could be Local regression method characterized with the mean and standard deviation of minimum annual temperatures. We used the Rice local regression method to map the mean and standard deviation of minimum annual temperatures Lower lethal temperature throughout Mainland China and then calculated isolines representing annual probabilities for reaching Sharpe-Schoolﬁeld-Ikemoto model or exceeding the lower lethal temperature of S. incertulas. In addition, we calculated and mapped the Sum of effective temperatures number of generations per year based on the annual accumulative degree days and the sum of effective temperatures required to complete one generation. The empirical northern distribution limits of S. incertulas were generally congruent with the theoretical limits based on winter survival, with exceptions within the Shandong and Sichuan provinces, which are apparently thermally suitable but where the host plant is not cultivated. The expected number of generations per year was 3–5 within most of the range of S. incertulas in China. In central China, the expected number of generations per year was about 3. A climate warming scenario of 4 1C in minimum and maximum daily temperatures predicted an increase in the expected number of generations per year in central China from about 3to4. & 2011 Elsevier Ltd. All rights reserved.

1. Introduction least of a closely related species in the family Pyralidae. It is always one of the main rice pest insects in southern China. For example, in Rice is the most important human food crop in the world, with 2005, the area infested by this pest insect in China reached 5 million China and India being the leading producers (Maclean et al., 2002). ha, causing a loss of 240 thousand tons of rice (Guo and Zhao, 2006). The history of rice cultivation in China dates from 8000 B.C.E. to Cai (1959) studied the northern distribution limits of the pest insect 6000 B.C.E. (Lawler, 2009). In 2009, the rice output of China was in China, but he only used the 14 1C annual isotherm of China as the about 195 million tons (National Bureau of Statistics of China, http:// possible theoretical northern distribution limits of the pest without www.stats.gov.cn/). Scirpophaga incertulas is an important pest of any clear explanations. Cai (1959) also reported that its real north- rice in Southern China. The distribution areas of S. incertulas includes ern distribution limits lie in 36 1N. Zhang and Zhao (1996) specu- China, Japan, the Philippines, Vietnam, Laos, Kampuchea, Burma, lated that the northern distribution limits in eastern China might be India, Thailand, Bangladesh, Pakistan, Sri Lanka, Malaysia, Indonesia, around 37 1N according to the previous reports. However, they did Afghanistan, Egypt, Papua New Guinea, Northern Australia, and not provide an explanation for the existing northern distribution some islands in the Paciﬁc Ocean (Zhang and Zhao, 1996). In China, limits. the pest insect is autochthonous and monophagous on rice. Terrestrial poikilotherms are largely affected by temperature ‘‘The Book of Songs’’ (Big ﬁeld), edited by Confucius (from 551 (Makarieva et al., 2005a, 2005b). S. incertulas is highly responsive B.C.E. to 479 B.C.E.), might be the earliest record of this pest or, at to environmental temperature (Zhang, 1992; Rahman and Khalequzzaman, 2004; Stevenson et al., 2005). The minimum annual temperature (MAT) has been considered to be a determin-

n ing factor of northern distribution limits for many insects Corresponding author. Tel.: þ86 10 6880 7123; fax: þ86 10 6480 7099. E-mail address: [email protected] (F. Ge). (Uvarov, 1931; Ungerer et al., 1999). When minimum annual 1 These two authors contributed equally to this work. temperatures approximate a normal distribution, and the lower

Table 1 Summary of abbreviations used.

Abbreviation Meaning

MAT Minimum annual temperature, i.e., the coldest temperature of the coldest night in winter AMAT Average minimum annual temperature SDMAT Standard deviation of minimum annual temperatures SCP Supercooling point; temperature at which sudden crystallization occurs (frequently the lower lethal temperature for insects, as in S. incertulas) LLT Lower lethal temperature, i.e., temperature below which insects cannot survive (even with brief exposure when LLT¼SCP, as in S. incertulas) PLLT Probability of reaching the lower lethal temperature during one winter NGEN Average number of generations per year LOESS Local regression (a non-parametric ﬁtting method)

lethal temperature (LLT) for an insect species is known, the probability of reaching or exceeding the lower lethal temperature (PLLT) can be regarded as the definite integral of the probability density function of MAT from minus infinity to LLT (Ungerer et al., Fig. 1. Climate stations used in the study. The provinces in blue font represent the 1999). places where the northern distribution limits of S. incertulas were observed In the current study, we used the local regression models according to the previous report of Zhang and Zhao (1996). (LOESS) (Cleveland, 1979) to predict the average minimum annual temperature and the standard deviation of minimum annual temperature based on 186 climate stations in China. From this we could map the probabilities of reaching the lower lethal temperature for S. incertulas across China. The number of generations per year of S. incertulas varies in different areas of China due to different climate conditions (e.g., Xu, 1991; Chen, 1996; Li, 2000; Zhong et al., 2000; Zhao and Xue, 2001; Zhang, 2002; Wang and Zhong, 2003; Song et al., 2005; Kong et al., 2006). The number of generations per year descends with latitude decreasing from seven generations in Hainan province (Xu, 1991) to three generations in the more northern Jiangsu province (Kong et al., 2006). There would be value in understanding the influence of climate change on the number of generations because more generations per year tend to yield greater damage to rice crops. Therefore, we used the local regression models to predict the average number of generations based on the law of accumulative effective temperatures. We developed and evaluated the first process-based model of how geographically structured climatic variation influences the distribution limits and annual reproductive potential (generations per year) of this important rice pest species. This permitted defensible Fig. 2. Probability of reaching the lower lethal temperature (PLLT) at Deqin station predictions of future pestilence under a changing climate. (28.481N, 98.921E) of Yunnan province. The dark curve represents the empirical Table 1 lists the meanings of all abbreviations used in the accumulative distribution; the red curve represents the normal accumulative distribution function; the gray point represents the probability of reaching the lower lethal present study. temperature (¼0.38) when minimum annual temperatures are less than 11 1C. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 2. Methods the lower lethal temperature (LLT) for this species. Following 2.1. Site selection Zhang, we estimated the LLT for S. incertulas to be 11 1C based on Table 2. At any specified climate station, the annual probability We selected 186 climate stations in China (Fig. 1). All stations of reaching the lower lethal temperature (PLLT) is equal to the provided at least 48-year records of minimum annual tempera- proportion of years with MATtr 11 1C. PLLT approximates to a ture (MAT). Daily maximum and minimum temperature records definite integral of the probability density function with a mean of these sites were also available. The data came from China AMAT and a standard deviation SDMAT from minus infinity to Meteorological Data Sharing Service System (http://cdc.cma.gov. lower lethal temperature. That is, cn/). We used the Shapiro-Wilk W statistic (e.g., Xue and Chen, Z 2007) to test the normality of the MAT for each site. LLT PLLT ¼ f ðTÞdT, 1 2.2. Simulating the spatial patterns in PLLT of S. incertulas where "# 1 1 TAMAT 2 Zhang (1990) measured the supercooling points of larvae of f ðTÞ¼ pffiffiffiffiffiffi exp ð1Þ 2 USDMAT 2 SDMAT S. incertulas (Table 2) and showed that the supercooling point is p 132 P. Shi et al. / Journal of Thermal Biology 37 (2012) 130–137

LOESS, short for local regression, is a non-parametric method Here, r represents development rate (days1), T represents absolute for estimating regression surfaces (Cleveland, 1979; Cleveland temperature (K), T^ represents the intrinsic optimum temperature and Grosse, 1991; Cleveland et al., 1992). We also evaluated a at which the probability of enzyme being in the active state is competing method based on generalized additive models (Hastie maximal, r^ represents development rate at T^, R represents the gas 1 1 and Tibshirani, 1990). We found little difference in the ﬁtted constant (¼1.987 cal deg mol ), DHA represents the enthalpy of spatial patterns of AMAT or SDMAT using these two methods, so activation of the reaction that is catalyzed by the enzyme 1 we chose to only use the former. We implemented LOESS using (cal mol ), DHL represents the change in enthalpy associated with 1 the R software package (http://www.r-project.org/) using 0.3 as low temperature inactivation of the enzyme (cal mol ), DHH the smoothing parameter for the LOESS ﬁtting. represents the change in enthalpy associated with high temperature 1 The following steps were taken to simulate the spatial patterns inactivation of the enzyme (cal mol ), TL represents the tempera- in PLLT of S. incertulas: ture at which the enzyme is 1/2 active and 1/2 inactive due to low

Step 1: Collect the MAT data for 186 climate sites with known temperature (K), and TH represents the temperature at which the latitude and longitude and calculate AMAT and SDMAT for enzyme is 1/2 active and 1/2 inactive due to high temperature (K). each site. Shi et al. (2011) provided an R procedure for quickly estimating the Step 2: Let AMAT or SDMAT be the dependent variable, and let parameters of the Sharpe–Schoolfield–Ikemoto model and further the geographical coordinates be independent variables. Use the demonstrated that over the mid-temperature range the tangent of LOESS procedure in R to estimate the response surface of AMAT or the Sharpe-Schoolfield-Ikemoto model at T^ approximates to Eq. (2). SDMAT at any specified location. Zhang (1992) studied the effect of temperature on the devel- Step 3: Calculate PLLT according to Eq. (1) or the cumulative opment of S. incertulas. We reanalyzed his data using Eqs. (2) and distribution function at any specified location. (3) (fitted results in Table 3 and Fig. 3). Table 3 shows that the sum of effective temperatures (k) of the whole preimaginal period 2.3. Simulating the spatial patterns in NGEN of S. incertulas of S. incertulas estimated by the Ikemoto–Takai model is 530 degree days; it was almost identical (529 degree days) when To estimate the number of generations per year, we calculated estimated using the tangent of the Sharpe–Schoolfield–Ikemoto the sum of effective temperatures for S. incertulas to complete one model at the intrinsic optimum temperature (T^). According to generation. Ikemoto and Takai (2000) suggested a linear model for the study of van der Have (2002), the range of [TL, TH] can estimating the lower developmental threshold and sum of effective approximate thermal tolerance of development in ectotherms. temperatures: Thus, we use [14.46, 32.40] (Table 3) as the suitable range of temperatures for S. incertulas to develop. DT ¼ kþtD ð2Þ To calculate NGEN of S. incertulas at any specified site, we Here, D represents developmental duration, T represents constant needed to know the total accumulative degree days per year at temperature, k represents the sum of effective temperatures, and t that site. The following steps were taken to calculate NGEN: presents the lower developmental threshold. They also suggested Step 1: Collect the daily maximum and minimum temperature using the reduced major axis for estimating k and t.Wenotethat data of 186 climate sites. Use the sine function to reflect the Eq. (2) holds only over the mid-temperature range, where it can be temperature change of one day: assumed that the physiological response is approximately linear. If ÀÁ some extreme low or high temperatures are involved, it is more 1þsin 2pt p T ¼ T þðT T Þ 2 ð4Þ effective to use a non-linear model to describe the temperature- min max min 2 dependent development rates. Ikemoto (2005, 2008) also proposed a non-linear model, which we refer to as the Sharpe–Schoolfield– Here, T represents variable temperature that is a function of time t Ikemoto model, for describing the effect of temperature on devel- (days), Tmin represents the recorded minimum temperature of one opment rate based on the noted Sharpe–Schoolfield model (Sharpe day, and Tmax represents the recorded maximum temperature of one and DeMichele, 1977; Schoolfield et al., 1981). day. The temperature at any time within one day can be estimated by hi the above equation. r T exp DHA 1 1 Step 2: Set the lower and upper limits of effective temperatures hiF TF R TF hiT r ¼ ð3Þ for development, and accumulate all the temperatures between the 1þexp DHL 1 1 þexp DHH 1 1 R TL T R TH T lower and upper limits for all the days within one year. As mentioned

above, we set [TL, TH]asthelowerandupperlimits.UCIPMOnline Table 2 describes how to calculate the degree days in detail (http://www.ipm. Supercooling points of larvae of S. incertulas (Zhang, 1990). ucdavis.edu/WEATHER/ddconcepts.html). Measuring time Sample size Mean of SCP (1C) SE of SCP (1C) Step 3: Divide the total accumulated degree days per year by the sum of effective temperatures (k)ofS. incertulas to obtain the Nov 16, 1986 30 10.82 0.65 average number of generations at any location among the 186 sites. Dec 17, 1986 34 12.21 0.62 Jan 14, 1987 28 10.61 0.37 Step 4: Use the LOESS procedure to predict NGEN at any Feb 17, 1987 42 10.35 0.32 speciﬁed location within the region spanned by the 186 study sites.

Table 3 Fitted results by the Ikemoto-Takai and Sharpe-Schoolﬁeld-Ikemoto models for the temperature-dependent development rates.

Stage Ikemoto–Takai model Sharpe–Schoolﬁeld–Ikemoto model

1 1 1 1 2 2 t (1C) k (degree-days) T^ (K) r^ (days ) DHA (cal mol ) DHL (cal mol ) DHH (cal mol ) TL (K) TH (K) w R

Egg 14.40 85.17 295.30 0.0910 22167 85500 54500 287.55 306.79 0.0035 0.98 Larva 13.92 317.29 295.70 0.0272 20545 81000 73000 287.07 305.64 0.0011 0.97 Pupa 15.28 131.88 295.40 0.0528 26118 91500 63500 288.43 304.99 0.0046 0.96 Total 14.46 529.84 295.50 0.0149 22406 85000 66500 287.61 305.55 0.0007 0.97 P. Shi et al. / Journal of Thermal Biology 37 (2012) 130–137 133

Fig. 3. Effect of temperature on the preimaginal development of S. incertulas. (a) Comparison between the observed and theoretical values of the temperature-dependent development rates. The gray curve is the predicted values by the Sharpe–Schoolfield–Ikemoto model; the dark solid line shows predictions from the Ikemoto-Takai linear model; the open and closed circles are the observed values used in the non-linear fitting; the closed circles are observed values used in the linear fitting; three open squares from left to right represent the development rates at TL, T^,andTH. (b) Probability of enzyme being in the active state (i.e., P2). At the intrinsic optimum temperature (T^) the probability is maximal. (c) Comparison between the Ikemoto–Takai linear model and the tangent of the Sharpe-Schoolfield-Ikemoto model at T^. The solid line is the Ikemoto-Takai linear model; the dashed line is the tangent of the Sharpe-Schoolfield-Ikemoto model at T^; the open square represents the development rate at T^. (d) Comparison between the observed and theoretical values of the temperature-dependent development rates on the Arrhenius plot. L1 is the tangent of the Sharpe–Schoolfield–Ikemoto model at T^;L2is the line passing by the two points of (1/TL,ln[r(TL)]) and (1/TH,ln[r(TH)]). L1 approximately parallels L2 (see Shi et al. [2011] for details).

3. Results plain. The 0.9 PLLT isoline can be taken as an approximation for the maximum northern distribution limits of S. incertulas. Thus it 3.1. Normality of minimum annual temperatures made sense that the 0.7 PLLT isoline was a good match with the observed northern distribution limits of S. incertulas (see Fig. 4 The probability density functions for minium annual tempera- published in Cai (1959) and the description of Zhang and Zhao ture in China approximated a normal distribution. MAT for 136 (1996)). Some deviations reflect the constraints of host plant among 186 sites (E73%) passed the rather strict normality test availability. For example, the middle of the Shandong province in defined by p-values of the Shapiro–Wilk W statistic greater eastern China and the western Sichuan province in south-western than 0.05. China are apparently climatically suitable but lack S. incertulas because there is little or no rice agriculture in this region (Xiao 3.2. Spatial patterns in the probability of reaching the lower lethal et al., 2005; Sun et al., 2009). temperature 3.3. Spatial patterns in the average number of generations per year The average minimum annual temperature (AMAT) decreased with increasing latitude (Fig. 4) while the standard deviation of The expected number of generations per year (NGEN) of minimum annual temperatures (SDMAT) increased with increas- S. incertulas decreased with increasing latitude (Fig. 7). In Hainan ing latitude (Fig. 5). The spatial patterns of AMAT and SDMAT province in southern China, the predicted number of generations described spatial patterns in the probabilities of reaching a lower per year is 6–7 (Xu, 1991); in provinces such as Jiangsu (Kong lethal temperature of 11 1C (PLLT) (Fig. 6). PLLT differed in et al., 2006), Anhui (Zhao and Xue, 2001), Henan (Song et al., eastern and western China. The Hengduan Mountains and Mount 2005), and Sichuan (Chen, 1996), near the northern distribution Qinling in south-western China yielded a band of rapid change in limits, the predicted NGENs were usually 3. The coefficients of PLLT isolines in south-western China. In contrast, the band of PLLT determination (R2) of AMAT, SDMAT, PLLT, and NGEN were 0.93, isolines was quite broad in eastern China where there is a large 0.71, 0.94, and 0.88, respectively. 134 P. Shi et al. / Journal of Thermal Biology 37 (2012) 130–137

from general circulation models. How much does MAT tend to change per 1 1C change in average annual temperature? What are the reliable predictions from general circulation models that are the best predictors of MAT? Connecting climate change scenarios to projections regarding insect distributionsalsorequirescharacteriz- ing the form of the probability density function for MAT, because this permits estimation of the annual probability of exceeding the lower lethal temperature (PLLT). It is convenient when MAT tends to approximate a normal distribution, as seems to be true in at least China (Fig. 2) and the eastern United States (Ungerer et al., 1999). It will require analyses from other global regions to determine if this is generally the case. MATs can influence pest population dynamics (Tran et al., 2007) because overwintering mortality influences preliminary population size of the coming spring. Our analyses implied that MAT should have an important influence on the population Fig. 4. Average minimum annual temperature (AMAT). dynamics of S. incertulas living in the areas near the northern distribution limits such as Shandong, Henan, Shaanxi and Sichuan provinces, but not in the more southerly warmer regions such as Jiangxi, Hunan, Zhejiang, Guangxi, Guangdong, Yunnan, Guizhou and Hainan provinces. To evaluate this, we analyzed a historic dataset of S. incertulas captures at light traps in Anyuan county of Jiangxi province (25107.8350N, 115123.3640E) from 1969 to 2004 (Fig. 8). The site was well south from the climatic region where our model predicts effects of MAT on populations of S. incertulas. For testing this, we used a model proposed by Turchin (1990):

Nt ¼ Nt1expðr0 þa1Nt1 þa2Nt2 þetÞð5Þ

Fig. 5. Standard deviation of minimum annual temperatures (SDMAT).

Fig. 7. Predicted average number of generations per year (NGEN) based on the Ikemoto–Takai and Sharpe–Schoolﬁeld–Ikemoto models.

Fig. 6. Annual probability of reaching the lower lethal temperature of S. incertulas (PLLT).

4. Discussion

4.1. Inﬂuence of minimum annual temperature of the population dynamics

Our results add to the evidence that terrestrial arthropods are restricted in their poleward distributions by overwinter mortality patterns that are affected by minimum annual temperature (MAT). This highlights that there is a challenge at the interface of climatol- Fig. 8. The population density of S. incertulas at Anyuan county of Jiangxi province, ogy and ecology to project future patterns in MAT under predictions China. P. Shi et al. / Journal of Thermal Biology 37 (2012) 130–137 135

Table 4 Fitted results by using the semi-parametric generalized additive model fothe population dynamics.

2 2 Parameter Estimate Standard error t-value p-value R Radj

Intercept 0.79 0.61 1.31 0.20 0.42 0.30 MAT 0.20 0.16 1.24 0.23

2 2 Here, R represents the coefﬁcient of determination; Radj represents the adjusted coefﬁcient of determination; MAT represents the annual minimum temperature.

Here, Nt, Nt1, Nt2 are the population densities at time t, t1, t2, respectively; r0, a1, a2 are constant; and et is the random error at time t. This model can be linearized as: rt ¼ r0 þa1Nt1 þa2Nt2 þet ð6Þ

Here, rt ¼ lnðNt=Nt1Þ represents the annual growth rate of popu- Fig. 9. Predicted number of generations per year (NGEN) given an increase of 4 1C lation. If we relax the restriction of the linear relationship in daily maximum and minimum temperatures. between the dependent variable and two variables, there is: rt ¼ r0 þs1ðNt1Þþs2ðNt2Þþet ð7Þ

Here, sj( )(j¼1, 2) is a specified smooth function in the general- Shandong province is suitable for rice growth, but rice is still not ized additive models (GAMs, Hastie and Tibshirani, 1986, 1990). popular here. Wheat and corn will probably continue to dominate Climatic factors such as MAT can be introduced into this regional agriculture in Shandong even though climatic conditions equation (e.g., Friedenberg et al., 2008; Colchero et al., 2009). become suitable for rice in the next decades. rt ¼ r0 þs1ðNt1Þþs2ðNt2ÞþaUMATþet ð8Þ 4.3. Relationship between linear and non-linear models for Here, a is a constant. Eq. (8) is a typical semi-parametric general- describing the temperature-dependent development rates ized additive model (Hastie and Tibshirani, 1990). As expected for this moderate climatic region, there was no signal of effects on There are many non-linear models for describing the effect of population growth from MAT (p-value¼0.2340.05, Table 4). temperature on the development rate of insects (Logan et al., There would be great value in similarly analyzing time series of 1976; Sharpe and DeMichele, 1977; Schoolfield et al., 1981; S. incertulas abundance at a location near the northern distribu- Taylor, 1981; Wang et al., 1982; Ratkowsky et al., 1983; Lactin tion limits (e.g., southern Shandong province) where there is a et al., 1995; Briere et al., 1999; Ikemoto, 2005, 2008; Shi and Ge, theoretical expectation of effects from MAT, but such data were 2010; Shi et al., 2011). All the non-linear models for describing not available. the temperature-dependent development rates approximate to a linear model over the mid-temperature range (Campbell et al., 4.2. Influence of climate change on NGEN of the yellow stem borer 1974). The Sharpe–Schoolfield–Ikemoto model (Sharpe and DeMichele, 1977; Schoolfield et al., 1981; Ikemoto, 2005, 2008) Climate change can result in northward migration for many is perhaps the most theoretically satisfying because it is based on insects. It seems likely that climate warming will permit an thermodynamics. This model is also convenient because it has a expansion of S. incertulas and leave China without a northern parameter for the temperature for maximum metabolic activity region where rice can be cultivated without exposure to this pest (T^). We found T^ for S. incertulas to be about 22 1C(Table 3) and insect. However, climate change might lead to more serious that the Ikemoto-Takai line approximated the tangent of the damage in southern China by increasing the number of genera- Sharpe–Schoolfield–Ikemoto model at T^ (Fig. 3c). This supported tions per year (NGEN). For example, if both the daily maximum use of the linear model for describing the temperature-dependent and minimum temperatures increased by 4 1C(Fig. 9), there is an development rates over the mid-temperature range. The Ikemoto- expectation of increased generations per year and aggravated Takai model is derived from the law of accumulative effective damage from S. incertulas in the Jiangsu and Anhui provinces (see temperatures. Although the law of accumulative effective tem- also Figs. 1 and 4 in Xiao et al. (2005)). A related issue is whether peratures is simplistic, it seems useful for reflecting on the effect rice agriculture can be moved northward given the effects of of temperature on development rate for the many terrestrial climate change. Water is a limiting factor in rice cultivation and insects and mites. Combined with the degree day model that is water is scarce in northern China. Even if the water issue was used to calculate the accumulative degree days per year for a solved, the people in northern China might not be inclined to location, it is possible to estimate the number of generations per plant rice. There has been a natural cultural difference between year of the insect in question at any specified location. southern and northern China since the start of Chinese civilization (Lawler, 2009), with societies of southern China being generally 4.4. Comparison of the northern distribution limits of Chilo supported by rice agriculture while northern societies were suppressalis and S. incertulas Walker (Lepidoptera: Pyralidae) historically dependent upon Setaria italica (L.), which has since been replaced by wheat. With a 4 1C temperature increase, C. suppressalis is another important rice pest in China, but it Shandong, Shanxi, Shaanxi and Gansu provinces would become can survive in northern China (Zhang and Zhao, 1996). The thermally suitable for rice agriculture but much of this region difference between these species in their geographical distribu- (especially Shanxi, Shaanxi and Gansu provinces) has terrain that tions may be due to differences in cold tolerance. The super- is unsuitable for crop cultivation. In fact, the present output of cooling points of C. suppressalis vary among climatic regions with rice, wheat and corn of these three provinces is low relative to populations in northern China being more cold tolerant than other provinces that lie in big plains. At present, only southern those in southern China (Wang et al., 1999; Guo et al., 2002; 136 P. Shi et al. / Journal of Thermal Biology 37 (2012) 130–137

Huang and Jiang, 2005; Zhang et al., 2005). The mean SCP of Friedenberg, N.A., Sarkar, S., Kouchoukos, N., Billings, R.F., Ayres, M.P., 2008. C. suppressalis in north-eastern China can reach 19 1C. Therefore, Temperature extremes, density dependence, and southern pine beetle (Coleoptera: Curculionidae) population dynamics in east Texas. Environ. it is natural that C. suppressalis can occur farther north than Entomol. 37, 650–659. S. incertulas. Differences between species in distribution limits Guo, H., Li, Q., Fang, J., Zhang, H., 2002. Comparison of cold hardiness in three and cold tolerance could also be related to diapause, which is species of overwintering rice stem borers in Nanjing area. Jiangsu J. Agric. Sci. related to photoperiod (Yin and Gu, 1988; Zhang and Yin, 1991; 18 (2), 85–88 (in Chinese). Guo, R., Zhao, Z., 2006. Illnesses and pest insects of crops. In: Department of Rural Xiao et al., 2010). 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