Predictive Analysis of Metcalfa Pruinosa

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55 Contents lists available at ScienceDirect 56 57 Journal of Asia-Pacific Biodiversity 58 59 60 journal homepage: http://www.elsevier.com/locate/japb 61 62 63 Original article 64 65 1 Predictive analysis of Metcalfa pruinosa (Hemiptera: Flatidae) 66 2 67 3 distribution in South Korea using CLIMEX software 68 4 69 a a a a 5 Q28 Dae-Hyeon Byeon , Jae-Min Jung , Santosh Lohumi , Byoung-Kwan Cho , 70 6 Sunghoon Jung b,*, Wang-Hee Lee a,* 71 7 72 a 8 Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea 73 b Department of Applied Biology, Chungnam National University, Daejeon, Republic of Korea 9 74 10 75 11 article info abstract 76 12 77 13 Article history: 78 14 Q1 Climate change has caused various environmental and ecological problems worldwide, as well as trig- Received 31 May 2017 gered invasions by alien insects that cause damage to the local ecology and agriculture. Metcalfa pruinosa, 79 15 Received in revised form a globally dispersed American native insect species, has caused significant damage to agriculture and 80 16 15 June 2017 human life and South Korea is no exception. This species is spreading rapidly in South Korea and causing 81 Accepted 16 June 2017 17 persistent damage with the change in climate; thus, there is an urgent need for effective monitoring of 82 Available online xxx 18 the potential distribution of this species. This study aimed to predict the potential distribution of 83 19 M. pruinosa in response to climate change. CLIMEX software, specialized software for predicting species Keywords: 84 20 climate change scenario distribution, was used to evaluate the future distribution pattern of M. pruinosa by combining biological 85 21 CLIMEX information and climatic conditions. According to our simulation, the distribution of M. pruinosa has 86 22 Metcalfa pruinosa expanded on a national scale; however, its invasion will decrease with the current trend in climate 87 change. Nevertheless, a nationwide distribution was predicted to be maintained until 2040, requiring 23 potential distribution 88 24 risk assessment establishment of countermeasures to manage this species in advance. Ó 89 25 2017 National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA), Publishing Services by Elsevier. This is an open access article under the CC BY-NC-ND license (http:// 90 26 creativecommons.org/licenses/by-nc-nd/4.0/). 91 27 92 28 93 29 94 30 Introduction stage by midJly (Kil et al 2011). In the larval stage, it produces a 95 31 large amount of wax and honeydew, which damages vegetables, 96 32 Invasive species are the species that are released into a nonna- fruits, and flowers (Alma et al 2005). The life cycle consists of only 97 33 tive area intentionally or accidentally (IUCN 2000). Among global one generation per year in North America and Europe; however, 98 34 environmental drawbacks caused by climate change, invasive two generations per year have been observed in central regions in 99 35 species have been identified as a major issue (Hulme 2009). Global the Korean Peninsula. Additionally, in South Korea, adult 100 36 invasive species are estimated to be w200,000 in number, and the M. pruinosa have been observed in midOctober, suggesting its 101 37 number of invasive species introduced to South Korea has increased adaptation to new environment (Kil et al 2011). In South Korea, this 102 38 from 894 in 2009 to 2,167 in 2013 (ME 2014). Among the invasive pest attacked 145 plant species from 62 families, including elm 103 39 species, Metcalfa pruinosa (Say, 1830) is a polyphagous pest, causing trees, redbuds, and false acacias in forest areas as well as 104 40 significant damage to agriculture and hygiene issues in downtown persimmon, grapevine, and ginseng in agricultural areas (Kim and 105 41 areas. This species is native to North America but has spread to Kil 2014). Significant damage by M. pruinosa has been predicted in 106 42 most parts of Europe except southern Europe (Dean and Bailey Europe and North America owing to changes in its potential dis- 107 43 1961; Souliotis et al 2008); its presence in South Korea was tribution caused by climatic conditions (Strauss 2010); this study 108 44 Q4 confirmed in 2009 (Kim et al 2011). M. pruinosa overwinters in the also predicted that M. pruinosa would establish in similar areas in 109 45 egg stage, emerges as nymphs in midMay, and reaches the adult which it is already distributed, and because the main route of 110 46 introduction is the trade of woody plants, it is essential to monitor 111 47 it during the import stage to control its invasion. However, most of 112 48 the recent studies in South Korea have focused on crop damage and 113 49 * þ þ Q2 Corresponding authors. Tel.: 82 42 821 6720; fax: 82 42 823 6246. local pest control rather than the potential distribution of this pest, 114 50 E-mail addresses: [email protected] (S. Jung), [email protected] (W.-H. Lee). despite its wide dispersion throughout the country (Kim and Kil 115 51 Peer review under responsibility of National Science Museum of Korea (NSMK) and 116 Korea National Arboretum (KNA). 2014). Thus, it is essential to evaluate the current and future 52 117 53 http://dx.doi.org/10.1016/j.japb.2017.06.004 118 54 pISSN2287-884X eISSN2287-9544/Ó 2017 National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA), Publishing Services by Elsevier. This is an open 119 access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article in press as: Byeon D-H, et al., Predictive analysis of Metcalfa pruinosa (Hemiptera: Flatidae) distribution in South Korea using CLIMEX software, Journal of Asia-Paciﬁc Biodiversity (2017), http://dx.doi.org/10.1016/j.japb.2017.06.004 JAPB235_proof ■ 7 July 2017 ■ 2/7

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1 66 2 67 3 68 4 69 5 70 6 71 7 72 8 73 9 74 10 75 11 76 12 77 13 78 14 79 15 80 16 81 17 82 18 83 19 84 20 85 21 86 22 87 23 88 24 89 25 90 26 91 27 92 28 93 29 Figure 1. Locations of 74 major cities in South Korea for CLIMEX, including city name, latitude, and longitude. 94 30 95 31 96 32 distribution of M. pruinosa in South Korea for establishing effective distributed intensively in the eastern part of North America. In 97 33 monitoring and control methods. Europe, M. pruinosa was first introduced in Italy in 1970 (Zangheri 98 34 Q5 CLIMEX is specialized software used for predicting potential and Donadini 1980), from where it rapidly dispersed to other Eu- 99 35 distribution of species based on climate data (Jung et al 2016; ropean countries, including Spain (Pons et al 2002), Austria (Kahrer 100 36 Sutherst et al 2007). This software matches the biological charac- and Moosbeckhofer 2003), Hungary (Orosz and Der 2004), 101 37 teristics of target species with climatic conditions in areas where Switzerland (Jermini et al 1995), Greece (Drosopoulos et al 2004), 102 38 species distribution and invasive risk are assessed. This software and France (Della Giustina 1986). In South Korea, M. pruinosa was 103 39 currently emerges as an efficient and economical tool for insect first identified in Suwon in Gyeonggi-do, Gimhae in 104 40 management; studies on the potential distribution of Lantana Gyeongsangnam-do, and around Umyeon Mountain in Seoul in 105 41 camara L. in Australia (Taylor and Kumar 2013), Harmonia axyridis 2009 (Kil et al 2011). In 2013, M. pruinosa was reported to spread to Q6 106 42 as a natural enemy of aphid and coccid (Poutsma et al 2008); and the southern part of South Korea, according to the observations 107 43 the anticipated scale of damage caused by Guignardia citricarpa recorded in the central and southern provinces of 108 44 (Yonow et al 2013), have used Climex for the prediction. Although Chungcheongbuk-do, Chungcheongnam-do, Gyeongsangnam-do, 109 45 worldwide application of CLIMEX has increased, only a limited and Jeollanam-do (Kim and Kil 2014). However, it was not 110 46 number of studies have used CLIMEX in Asian countries, except observed in Gangwon-do and Jeju-do, which are geographically 111 47 China. The potential distribution of Thrips palmi according to the separated by a mountain and a sea, respectively. 112 48 representative concentration pathway (RCP) 8.5 climate change 113 49 scenario was simulated in South Korea using CLIMEX by Park et al 114 50 (2014), and Lycorma delicatula distribution was tested using CLI- Current climate data and climate change scenarios 115 51 MEX, as reported in the review article by Jung et al (2016). 116 52 The objective of this study was to predict the potential distri- To simulate the current and potential distribution of M. pruinosa, 117 53 bution of M. pruinosa in South Korea under the current climate a CLIMEX-specific climate database for 74 major cities in South 118 54 conditions and climate change scenario. The parameters for Korea was developed using historical data of 30 years (1981e2010) 119 55 running CLIMEX were determined using data collected for climate provided by the Korea Meteorological Administration (KMA) 120 56 and biological information of M. pruinosa. Thereafter, a map (Figure 1). The data comprised average values of five variables: 121 57 calculating the adaptability of M. pruinosa at the specific location maximum monthly temperature, minimum monthly temperature, 122 58 was constructed for evaluating the risk of M. pruinosa distribution. monthly precipitation, relative humidity at 9:00 AM and 3:00 PM. 123 59 This database was used for estimating parameter values required 124 60 Materials and methods for running CLIMEX (described later). Representative concentration 125 61 pathway (RCP) 8.5 climate change scenario was obtained from KMA 126 62 Current distribution of Metcalfa pruinosa to predict future distribution of M. pruinosa in response to climate 127 63 change. The RCP 8.5 scenario reflects the impact of the highest 128 64 Metcalfa pruinosa (Say, 1830) (Hemiptera: Flatidae) is native to greenhouse gas emissions used in the HadGEM3-RA model (Diallo 129 65 North America (Dean and Bailey 1961; Souliotis et al 2008), and et al 2014). To obtain a resolution applicable for small areas using 130

DH Byeon et al. / Journal of Asia-Pacific Biodiversity xxx (2017) 1e7 3 Q3 1 CLIMEX, the RCP 8.5 database was reconstructed by extracting data Table 1. Parameter definitions and values used for Metcalfa pruinosa CLIMEX 66 Q25,26 2 at 20-year intervals from 2020 to 2100, with a resolution of 1 km. running. 67 3 Parameters Parameter description Values 68 4 69 CLIMEX software Temperature parameters 5 DV0 Lower temperature threshold 13C 70 6 CLIMEX software has been used for comparing climatic simi- DV1 Lower optimal temperature 22C 71 7 ’ DV2 Upper optimal temperature 28 C 72 larity in different areas for evaluating species geographical distri- 8 bution and seasonal abundance based on climatic conditions as DV3 Upper temperature threshold 31 C 73 PDD Degree-days to complete 1 generation 500 9 well as for predicting future dispersion of species in response to Moisture parameters 74 10 climate change (Jung et al 2016; Kriticos et al 2015). CLIMEX yields SM0 Lower soil moisture threshold 0.25 75 11 the ecoclimatic index (EI), at a scale from 0 (the least climatic SM1 Lower optimal soil moisture 0.5 76 12 suitability indicating impossibility of habitation) to 100 (the SM2 Upper optimal soil moisture 1.0 77 SM3 Upper soil moisture threshold 1.5 13 78 maximum climatic suitability indicating most favorable climate for Heat stress 14 habitation), in order to designate climatic suitability for the target TTHS Heat stress temperature threshold 31C 79 15 species in a specific region. EI is calculated by multiplying Growth THHS Heat stress temperature rate 0.002/wk week1 80 16 Index (GI), Stress Index (SI), and Stress Interaction (SX) (Kriticos Cold stress 81 17 et al 2015). GI is positively related to population growth during TTCS Cold stress temperature threshold 1 C 82 THCS Cold stress temperature rate 0.0001/wk week1 18 favorable seasons, whereas SI and SX are indices which limit set- Dry stress 83 19 tlement and growth of a target species. The details for each index in SMDS Dry stress threshold 0.25 84 20 terms of its definition, calculation, and the parameters used for HDS Dry stress rate 0.005/wk week1 85 21 calculation are as described by Jung et al 2016 and Kriticos et al Wet stress 86 SMWS Wet stress threshold 1.5 22 87 2015. In the present study, we adopted the EI categories for cli- HWS Wet stress rate 0.002/wk week 1 23 matic suitability used by Hill et al (2014):EI¼ 0 (unsuitable), 0 < EI Diapause parameters 88 24 < 10 (marginal), 10 < EI < 30 (suitable), and EI > 30 (optimal). DPD0 Diapause induction day length 9 h 89 25 DPT0 Diapause induction temperature 0C 90 26 Parameter estimation DPSW Diapause summer or winter indicator 0 91 27 92 28 To determine reliable parameter values, it is important that the 93 29 predicted distribution is consistent with the currently known dis- and part of the Sobaek Mountains (Jiri Mountain area), with the 94 30 tribution of a target species (Kriticos et al 2015). The initial highest EI values occurring in Seoul, Gyeonggi-do, and 95 31 parameter values were adopted from a previous study by Strauss Chungcheong-do. The simulation indicates that the survival of 96 32 (2010), in which the risk of M. pruinosa invasion in Austria was M. pruinosa would be limited in high-altitude regions and other 97 33 evaluated using CLIMEX. Furthermore, additional M. pruinosa dis- areas with low average winter temperature (e.g. Taebaek Moun- 98 34 tribution data from a study in North America in 2007 were used for tains and Jiri Mountain area in South Korea) due to high cold stress. 99 35 fine tuning the parameter values (Strauss 2010). This process of According to Wladimir Köppen’s climatic classification (Peel et al 100 36 parameter estimation is a repetitive task which ends when con- 2007), North America, Europe, and Northeast Asia, including Ko- 101 37 sistency between predicted and observed distribution is estab- rea and Japan, have temperate climates and temperature ranges 102 38 lished. Finally, the simulation was found consistent with the actual from 22 Cto28 C, which is suitable for M. pruinosa establishment. 103 39 distribution of M. pruinosa in the eastern United States and in Average temperatures from May to October were assumed to be the 104 40 Europe, except Nordic countries, indicating that the evaluation actual temperatures experienced by M. pruinosa living in South 105 41 parameters are reliable. The description of parameters and their Korea because the species is active during this period. It was esti- 106 42 values are listed in Table 1. Details of all parameters in Table 1 are as mated that the lowest average temperature was 16.1 C in October, 107 43 reported by Jung et al (2016) and Kriticos et al (2015). whereas the highest average temperature was 27.8 C in August 108 44 (Table 2). Based on the RCP 8.5 climate change scenario, it is 109 45 Results anticipated that the annual average temperature in the Korean 110 46 Peninsula will increase by 0.63C every 10 years resulting in 3.2C 111 47 Potential distribution of M. pruinosa in South Korea over 30 and 5.3 C increases in temperature in 2040 and 2100, respectively. 112 48 years (1981e2010) and its future dispersion from 2020 to 2100 was This indicates that South Korea will start experiencing subtropical 113 49 simulated using CLIMEX by applying RCP 8.5 climate change sce- climate near the mid-21st century (2041e2070) and will be defined 114 50 nario (Figures 2 and 3). The simulation showed that all 74 cities in as a subtropical region by the end of the 21st century, except 115 51 South Korea had an EI value > 0, suggesting the current climate is Gangwon-do and northwestern Gyeonggi-do (KMA 2012). The 116 52 highly suitable for M. pruinosa. However, M. pruinosa has not been distribution of M. pruinosa was predicted to decrease largely in the 117 53 reported in a few provinces such as Gangwon-do and Jeju-do, southern provinces, such as Gyeongsang-do and Jeolla-do pre- 118 54 indicating inconsistency between prediction and actual observa- sumably because the heat stress temperature for M. pruinosa 119 55 tion. This inconsistency may be due to the fact that CLIMEX does inhabitation in these regions will exceed the threshold value (31 C) 120 56 not consider geographical barriers, such as mountains and sea, owing to the increase in temperature. EI values predicted in 2060 in 121 57 preventing the migration of M. pruinosa into these areas, and also Gyeonggi-do and Chungcheong-do indicate that the population of 122 58 indicates the high possibility of survival of this species if it is M. pruinosa would gradually diminish, but in the mountainous 123 59 introduced in these areas. The wide potential distribution under areas such as Gangwon-do, there would be noticeable distribution 124 60 the current climate suggests that M. pruinosa will rapidly disperse with higher EI values than in other areas. The decrease in future EI 125 61 and cause problems. For example, it is already established in the in the currently suitable areas is likely be due to high heat stress 126 62 Seoul metropolitan area, where it is reported to cause uncontrol- caused by the increased temperature limits. By contrast, heat stress 127 63 lable sanitation issues. will reduce in Gangwon-do and the eastern coastal regions due to a 128 64 The CLIMEX simulation expected that M. pruinosa would be high moisture index in accordance with a cool climate and pre- 129 65 distributed nationally in future except in the Taebaek Mountains cipitation by climate change. In 2080 and 2100, the potential 130

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1 66 2 67 3 68 4 69 5 70 6 71 7 72 8 73 9 74 10 75 11 76 12 77 13 78 14 79 15 80 16 81 17 82 18 83 19 84 20 85 21 86 22 87 23 88 24 89 25 90 26 91 27 92 28 93 29 94 30 95 31 96 32 97 33 98 34 99 35 100 36 101 37 102 38 103 39 104 40 105 41 106 42 107 43 Figure 2. Potential distribution maps of Metcalfa pruinosa in South Korea evaluated based on historical climate data for 30 years (1981 latitude). 108 44 109 45 110 46 111 47 distribution of M. pruinosa in Korea is predicted to sharply decrease, Discussion 112 48 showing only a few areas suitable for habitation around the 113 49 mountainous regions. The average temperature in August in the The CLIMEX model is used to predict the potential geographical 114 > w 50 regions with an EI 10 was 33.6 C, but it was 36.9 C for regions distribution of M. pruinosa in South Korea in response to RCP 8.5 115 < 51 having EI 10 in 2080. Furthermore, the average temperature of Scenario. The results indicate that although the distribution of 116 > 52 suitable areas in 2100 (EI 10) decreased to 31.6 C, whereas that of M. pruinosa will decrease with time, it will spread throughout 117 < 53 marginally suitable areas (EI 10) was as high as 35.9 C. CLIMEX South Korea by 2040. M. pruinosa being a polyphagous species at- 118 ﬂ 54 data indicate that heat stress will be the main factor in uencing tacks many host plants globally, especially fruit trees such as ap- 119 55 M. pruinosa distribution in future. EI of major provinces in South ples, pears, and grapes, and caused major damage to persimmon, 120 56 Korea under current climate and by applying climate change sce- grapes, and ginseng in South Korea (Barbattini et al 1991; Della Q7 121 57 nario until 2100 (Table 3) were assessed using CLIMEX. The results Giustina and Navarro 1993; Kim and Kil 2014). M. pruinosa has 122 58 show that EI of all the provinces except Gangwon-do decreased and invaded majority of areas of apple and ginseng production in South 123 59 would become unsuitable climatically for M. pruinosa habitation. Korea, including Chungju-siin Chungcheongbuk-do, and Gyeonggi- 124 60 However, Gangwon-do was predicted to consistently have a climate do. For example, Chungju-si is a major area for apple cultivation in 125 61 suitable for M. pruinosa survival, with the minimum EI of 6.7 in South Korea and has an EI of 20, which indicates that it is a suitable 126 ﬁ 62 2080. Taken together, the ndings of this study indicate that cli- habitat for M. pruinosa; thus, severe damage to apple production 127 63 matic suitability for M. pruinosa will keep decreasing in South Korea has been caused in this area and is consistent with the actual report. 128 64 because of climate change, from the existing temperate to sub- The distribution of M. pruinosa in this area is expected to gradually 129 st 65 tropical conditions, by the end of the 21 century. decrease due to increased heat stress caused by temperature rise 130

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1 66 2 67 3 68 4 69 5 70 6 71 7 72 8 73 9 74 10 75 11 76 12 77 13 78 14 79 15 80 16 81 17 82 18 83 19 84 20 85 21 86 22 87 23 88 24 89 25 90 26 91 27 92 28 93 29 94 30 95 31 96 32 97 33 98 34 99 35 100 36 101 37 102 38 103 39 104 40 105 ¼ 41 Figure 3. Future distribution maps of Metcalfa pruinosa in South Korea simulated based on RCP 8.5 Scenario. A, 2020. B, 2040. C, 2060. D, 2080. E, 2100. RCP representative 106 concentration pathway. 42 107 43 108 44 due to climate change, and the future damage in this area can be Taebaek mountain area under the yearly climate of 2071e2100. 109 45 expected to reduce. The present study predicts a change in the This indicates that the distribution of the M. pruinosa and the 110 46 climate of South Korea from temperate to subtropical by the end of cultivation area of “Fuji” apple will be consistent with each other in 111 47 the 21st century However, it should be noted that the change to 2100. Besides the orchards, forested regions are also under attack 112 48 subtropical climate may also lead to changes in the types of fruits by M. pruinosa. Based on a previous study, forested areas are 113 49 cultivated in a speciﬁc area. Thus, it is necessary to compare the observed to have the highest establishment rate of M. pruinosa 114 50 changes in cultivated areas with the distribution of the M. pruinosa. (45%), whereas orchard areas have a rate of 30% in South Korea (Kil 115 51 For example, “Fuji” is cultivated mainly in Gyeonggi-do and et al 2011). In most of the forests in South Korea, M. pruinosa ﬁrst 116 52 Chungcheong-do, but cultivated areas would gradually move to the multiplies on Robinia pseudoacacia L. and Ulmus davidiana Planch, 117 53 and causes secondary damage in neighboring farms. For example, 118 54 in the case of ginseng, one of the major host plants of M. pruinosa, 119 55 Table 2. Monthly average temperatures Metcalfa pruinosa activities in Korea. the plant has been enormously damaged because ginseng is 120 56 121 57 May June July August September October 122 58 Paju 17.1 23.5 25.4 27.2 20.5 13.3 Table 3. Average ecoclimatic index (EI) for provinces in South Korea from current to 123 59 Incheon 16.0 22.2 24.1 26.9 21.5 15.9 2100. 124 Seoul 18.2 24.4 25.5 27.7 21.8 15.8 60 Current 2020 2040 2060 2080 2100 125 Suwon 17.6 23.5 25.5 27.7 21.8 15.8 61 Gwangyang 19.3 22.9 27.3 28.4 23 18 Gyeonggi-do 13.8 15.5 14 6 0 3 126 62 Gwangju 19.1 23.9 27.1 28.4 22.6 16.8 Gangwon-do 14.6 10.6 12.7 13.3 6.7 10.7 127 63 Daejeon 18.8 23.9 26.8 27.8 21.4 15.5 Chungcheong-do 20.5 16 12 6 1 1 128 64 Kimhae 19.3 23.3 27 28.5 23.3 18.1 Jeolla-do 22 13 9 8 1 1 129 65 Mean 18.1 23.5 26.1 27.8 22 16.1 Gyeongsang-do 20.9 13 12 9 3 3 130

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1 generally cultivated in mountainous regions. M. pruinosa has also References 66 2 invaded downtown and residential areas by following light after 67 3 sunset or by hitchhiking on humans (Alma et al 2005). This has Alma A, Ferracini C, Burgio G. 2005. Development of a sequential plan to evaluate 68 Neodryinus typhlocybae (Ashmead) (Hymenoptera: Dryinidae) population 4 caused severe hygiene problems such as secretion of wax and associated with Metcalfa pruinosa (Say) (Homoptera: Flatidae) infestation in 69 5 attachment to food. This is consistent with the high EIs in most of northwestern Italy. Environmental Entomology 34:819e824. 70 6 the metropolitan areas in South Korea except for Daegu. For Barbattini R, Greatti M, Iob M, et al. 1991. Osservazioni su Metcalfa pruinosa (Say) e 71 7 indagine sulle caratteristiche del miele derivato dalla sua melata. Q12 72 example, in Gyeonggi-do and Seoul where the damage has been Burgiel SW, Muir AA. 2010. 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