Electronic Supplementary Material s25

Electronic Supplementary Material

Title: Can changes in the distribution of lizard species over the past

50 years be attributed to climate change?

Journal: Theoretical and Applied Climatology

Authors: Jianguo Wu

Affiliations:

The Center for Climate Change

Chinese Research Academy of Environmental Sciences

No. 8 Da Yang Fang, Beiyuan, Anwai, Chaoyang District, Beijing, China, 100012

*Corresponding author: Jianguo Wu, e-mail:[email protected];Telephone :86-10-84915152;

Fax: 86-10-84915152

Appendix A The sources of date and records for the lizard distributions

1.National level distribution date and records

Hu SQ,Zhao EM (1987) China animal atlas-amphibians and reptiles. Science Presee,Beijing,China

Institute of Biology Research in Sichuan Province (1977) The reference reptiles in China. Science

Presee,Beijing,China

Li DM,Wen SS (2002) Chinese reptile atlas. Henan Science and Technology Press,Zhenzhou,China.

Liu MY (2000) Chinese vertebrate list .Liaoning University Press, Shenyang, China.

Pope CH (1927)Notes on Reptiles from Fukien and other Provinces.American Museum of Natural

History.

Pope CH(1935) The Reptiles of China.New York: Amer Mus Nat Hist.

Tian WS,Jiang YM (1986)The identifying handbook Chinese amphibians and reptiles. Science Press,

Beijing, China

Wang S,Xie Y(2009)China species red list. Vol.Ⅱ .Vertebrates.Higher Education Press, Beijing,China.

Zhang RZ (1979) China's natural geography. Animal Geography. Science Press, Beijing,China.

Zhang RZ(1999)China Animal Geography. Science Press,Beijing,China

1 Zhang RZ, Zhao KT (1978)China zoogeography( modified). Animal J.24 (2): 196-202.

Zhao EM,Adler K(1993)Herpetology of China.Oxford,Ohio, USA

Zhao,EM (1998) China Red Data Book of Endangered Animals - amphibians and reptiles.Science Press,

Beijing, China

Zhao,EM,Zhang XW,Zhao H et al(2000) Revised checklist of Chinese amphibia and reptilia.J Sichuan

Zoo,19(3):196-207．

Zhao EM et al (1999).Fauna Sinica,Reptilian Vol.2.Science Presee,Beijing,China.

Zheng ZX, Zhang RZ(1959)China zoogeography (draft). Science Press,Beijing,China.

Zhao KT (1998) Geckoes (gekkonidae)in western china. Chin J. Zoo 33(1):20-24 2.Local or regions level distribution date and records

Cheng BH(1991) Anhui Amphibians and reptiles.Anhui Science and Technology Press,Hefei,China.

Cheng Yl, Zhang JQ,Xu H (2009) Reptilian fauna and zoogeographic division of fujian province.

Sichuan J Zoo 29 (6):. 928-932

Dai Q, Liu XS,Zhou Q,Dai ZX (2011)Resources of reptiles in hubei province.Chin J Zoo 46( 5) : 132-

Deng XJ(2003)Dupangling vertebrate background resources and their diversity. Hunan Science and

Technology Publishing House,Changsha,China.

Deng XJ(2007)Monitoring of vertebrate and bird resources in Dongting Lake.Hunan Normal University

Press, Changsha,China.

Department of Biology,Jilin Normal University(1962)Common vertebrate in Jilin.Jilin People's

Publishing House,Changchun,China.

Ding HB, Zhen J (1981) studied the fauna of northern Fujian Wuyi crawling sci (2): 137 -139.

Ding HB, Zhen J, Cai MZ (1980) floristic study of amphibians and reptiles in Fujian geographic

distribution. J Fujian Normal Univ (1): 57-74.

Fan LS,Guo CW,Liu HJ(1998)Shanxi amphibians and reptiles.China Forestry Publishing

House,Beijing,China.

Gang Y,Fang BH (2004) Investigation and protection of wildlife resources in Henan Province. Yellow

River Conservancy Press, Zhengzhou,China

Gao XY(2005)Xinjiang Vertebrate species and subspecies classification and distribution list.Xinjiang

2 Science and Technology Press,Wulumqi,China.

Guizhou fauna Editorial(1980)Vertebrate distribution list in Guizhou. Guizhou People's Publishing

House,Guiyang,China.

Hu BQ, Huan MH, He SX, Zhou SA (1965) Zhejiang reptiles investigation report. J.Zoo 22,22-26

Hu SQ, Zhao EM, Liu CZ (1966) survey of amphibians and reptiles in Qinling and Daba Mountain

Animal J.18 (1): 57-89

Hu SQ,Zhao EM(1973)a survey of amphibians and reptiles in Kweichow province. including a herpe to

faunal analysis. Acta zoo.sin 19(2):149-178

Huan ZJ, Zhen J, Ding HB (1982) Analysis of Fujian Nanjing Amphibians and Reptiles.Wuyi Sci 2: 91-

Huang MH,Cai CM,Jing YL(1990) Zhejiang Fauna.amphibians,reptiles.Zhejiang Science and

Technology Press,Hangzhou,China.

Institute of Tibetan Plateau Comprehensive Scientific Expedition series(1987)Amphibians and Reptiles

of Tibet. Science Press, Beijing, China.

Hu SQ, Zhao Em, Liu CZ (1966)Investigation and analysis of amphibians and reptiles fauna .J Guizhou

anim 19 (2): 149-178

Ji DM, Zhou YF(1987) Liaoning Fauna • amphibians, reptiles. Liaoning Science and Technology Press:

Shenyang,China.

Kang JG (1985) Beijing amphibian and reptile fauna.J Amphibians.Reptiles 4 (2): 120-122

Li HF (1985) Tianjin preliminary investigation amphibians and reptiles. J Amphibians. Reptiles 85 (4): 2

9 - 30

Li PZ,Zhao EM,Dong BJ(2010) Amphibians and reptiles of Tibet. Science Presee, Beijing, China.

LI YK,Shan JH,Gong Y (2013) The amphibian species diversity and zoogeographic division in jiangxi

province. Chin J Zoo 48( 6):919 -925

Li ZC,Xiao Z,Liu SR (2011)Amphibians and reptiles of guandong. guangdong Science and Technology

Press,Guangzhou,China.

Li ZZ(2011)List of wildlife guizhou.Guizhou Science and Technology Press,Guiyang,China.

Liu CZ, Hu SQ (1962) Amphibians and reptiles guangxi preliminary investigation report.Animal J 14

(Suppl): 73-104

3 Liu MY, Ji DM, Chang FX et al (1985) Liaoning geographic distribution of amphibians and reptiles.

J.Amphibians. Reptiles Divisions (4): 325-327

Luo J, Gao HY, Zhou YY (2004) Chongqing reptiles species diversity and protection.J Sichuan Zoo ,, 23

(3):. 249-256

Shao WJ,Henan Province Local Chronicles Compilation Committee(1992)Henan blog(Vol 8)

Fauna.Henan People's Publishing House, Zhengzhou,China.

Shi HT(2001)terrestrial vertebrates retrieve Hainan.Hainan Publishing House, Haikou,China.

Shi HT,Zhao EM,Wang LJ(2011) Hainan amphibians and reptiles. Science Press, Beijing,China

Sichuan Institute of Biology (1974) List of amphibians and reptiles in china and the geographic

distribution.amphibians and reptiles research data (Sichuan Science and Technology, amphibians

and reptiles research monograph Second Series) 2: 1-40

Sichuan Institute of Biology (1974) Survey of Anhui amphibians and reptiles.Amphibians and reptiles

research data (Sichuan Science and Technology, amphibians and reptiles research monograph

Second Series), 2: 48-57

Sichuan Institute of Biology (1974) Sichuan Erlangshan amphibians and reptiles.Amphibians and

Reptiles survey research data (Sichuan Science and Technology, amphibians and reptiles research

monograph Second Series) 2: 58-65

Sichuan Institute of Biology (1976) Reptiles in western Hubei Province.Amphibians and reptiles survey

research data (Sichuan Science and Technology, amphibians and reptiles research monograph third

series), 3: 49-53

Sichuan Institute of Biology (1976) Reptiles in western Hunan Province.Amphibians and reptiles survey

research data (Sichuan Science and Technology, amphibians and reptiles research monograph third

series), 3: 54-60

Sichuan Institute of Biology (1976) Fujian reptiles.Amphibians and Reptiles List correction studies

(Sichuan Science and Technology, amphibians and reptiles research monograph third series), 3: 30-

Sichuan Institute of Biology (1977) Tibet reptile fauna survey and description of new species .Animals J

23 (1): 64-71

Sichuan Province Institute of Biology (1976) The preliminary list of Hunan reptiles. amphibians.reptiles

4 geographical distribution studies 3: 54-60.

Song MT (1987) analysis of Shaanxi fauna amphibians and reptiles J amphibians. reptiles (4): 63-73

Song MT (1987) in southern Shaanxi animal studies reptiles. J amphibians .reptiles 6 (1): 59-64.

Wang XT (1990) The Fauna of Vertebrates in Ningxia Province. Gansu Science and Technology Press,

Lanzhou,China

Wang XT (1991) The fauna of vertebrates in gansu province.Gansu Science and Technology Press,

Lanzhou,China

Wang YY(2010)Color iconographs for terrestrial vertebrate of Mount Yangjifeng in JiangXi.Science

Press,Beijing,China.

Wu J,Li JD(1985) Guizhou reptiles blog. Guizhou People's Publishing House

Publishing.Guiyang,China.

Wu MC (1993) Guangxi wildlife.Guangxi People's Publishing House,Nanning,China.

Wu YF,Wu ML,Cao YP(2009)Hebei mammalian fauna of amphibians and reptiles.Hebei Science and

Technology Press, Shijiazhuang,China

Xu J(2002) The vertebrate conservation and ecology in northern Guangdong province. China

Environmental Science Press, Beijing,China.

Xu TQ,Cao YH(1996)List of vertebrate animal in shangxi province.Shangxi Science And Technological

Press,Xian,China.

Yang DT(2008) Amphibia and reptilia of Yunnan. Yunnan Science and Technology

Press,Kunming,China.

Yao CY,Gong DJ(2012)Amphibians and reptiles of Gansu.Gansu Science and Technology

Presee,Lanzhou,Gansu,China.

Yu YZ, Zhang XL (1990) Ningxia amphibian and reptile fauna analysis and geographic divisions of

.J.Ningxia Univ 11 (2): 82-89.

Zhang MW (1961) Heilongjiang Province Reptilia Fauna (draft). Heilongjiang University, Harbin

Normal University Publishing, heerbin, China

Zhang P,Yuan GY(2005) Xinjiang Amphibians and Reptiles.Xinjiang Science and Technology

Press,Wulumq,China.

Zhang YX(2009).Guangxi reptiles.Guangxi Normal University Press,Nangning,China.

5 Zhao EM(2003)Sichuan reptiles colors illustrations.China Forestry Publishing House ,Beijing,China.

Zhao EM, Huang KC(1982) Liaoning Province survey of amphibians and reptiles.amphibians and

reptiles J 1 (1): 1-23

Zhao EM, Jian YM (1976) Fujian reptiles and amphibians.Reptiles List correction studies 3: 30 to 48.

Zhao EM, Jiang YM (1966) Yunnan reptile surveys and supplementary list.Zoo 8 (3): 127-130

Zhao EM, Zhao H, Zhou ZY(2004)Herpeto diversity of Northeastern China and their distribution.

Sichuan J Zoo 23(3),165-168

Zhao EM,Yang DT(1997) Hengduan Mountains amphibians and reptiles.Science Presee,Beijing,China

Zhao KT (1978) survey of amphibians and reptiles in Inner Mongolia.Inner Mongolia Univ 15 (2): 65-

Zhao KT (1981) Hexi Corridor in Gansu.Inner Mongolia University lizard survey 22 (3): 71-75.

Zhao KT (1985) Survey of amphibians and reptiles.lizards Xinjiang J 4 (1): 25-29.

Zhao KT (1993) reptile fauna and geographical divisions of Inner Mongolia desert.Suzhou Railway

Teachers College 10 (4): 1-7.

Zhao WG( 2008) Heilongjiang Amphibians and Reptiles Chi.Science Press,Beijing,China.

Zhao WG,Xu CZ, Liu P, Liu ZC(2010)A sort of vertebrate retrieve in Heilongjiang province.

Heilongjiang People's Publishing House,Haerbing,China.

Zhao ZJ(1988) Jilin wildlife atlas (amphibians, reptiles, mammals) Jilin Science and Technology

Press,Changchun,China.

Zhou F(2011) Guangxi terrestrial vertebrates distribution list.Chinese forestry publish house,

Beijing,China

3.Natural reserves level distribution date and records

Deng J, Yi l, Tian L, et al (2006) A preliminary study Daguisi National Forest Park amphibians and

reptiles Forest Inventory Planning 31 (2): 21--23.

Fang XB,Zhou YL,Yang LY,Zhao Y,Zheng WH,Liu JS(2013 )Amphibian and reptilian resources of

gutian mountain national nature reserve in zhejiang province.Sich J Zoo 32(56):125-130

Dai ZX, Yang QR, Zhang RS et al (1996) Reptiles of Hubei Province.Central China Normal Univ 30

(1): 92-95.

Li HQ, Liu XL (2010) Chongqing jinfo nature reserve herpetological resources survey.Anhui

6 Agricultural Sciences, 38 (5): 2391-2392,2395

Li SB, Su ZL, Tong P et al (2010) National nature reserve xingdoushan herpetological resources survey.J

Sichuan Zoo 29 (1):. 130-133.

Liu H, Ai H, Ren S, et al (2010) Fangxian yerengu nature amphibians and reptiles diversity survey .J

Sichuan Zoo 29 (5): 560 - 563. \

Shi S, Dai ZX, Wu FQ, et al (2007) Wanchaoshan nature reserve in hubei amphibians, reptiles resources

survey . Central China Normal University 41 (2): 278--281.

Wang K (2003) China national level natural reserve. An Hui science and technology press.hefei,China.

Xu N,Gao X M,Wu KY,Wei G (2007)Study on the distribution of reptiles in eight natural reserves in

guizhou province. Chin J.Zoo 42( 3) : 106-113

Yang DD,Liu S,Fei DB,et al (2008) nature reserve, jiangxi qiyunshan herpetological resources survey

and fauna .J Zoo 43 (6): 68-76.

Yang GJ, Qin WC(2013)Vertebrate of ningxia luo mountain.Sunshine Press,Yinchuan,China.

4.New records of distribution date

Deng,X.J.et al.(1998) Hunan Province two New Records species of Reptile..Sichuan J. Zoo.17,53.

Deng QX (1988) South slippery clarity found in Sichuan Miyi.J Nanchong teachers college 9 (3): 184

Fei DB,Yang DD,Song YC,Fu Q,Yang XL (2010)New records of tropidophorus hainanus and

amphiesma bitaeniata in hunan,China. Chin J Zoo 45(2) :162-164

Gong DJ, Zhang YH, Sun LX, Ling LJ,Yan LI (2012) Eumeces capito,a new record of lizard in Guizhou

Province.Chin.Jour. Zoo 47,127-129

Guo,L.(2002).Eumeces capito:a new reptilia record of Neimongol..Acta Scien.Natura.Univer.

Neimongol.33,1

Hang JQ, Ma HQ, Wang HJ Lu YM (2007) Hebei new record of reptiles - takydromus.J Zoo. 3,113,119.

Huang HY (2002) A new record of lizard in guangdong province.J Sichuan Zoo,21 (1): 37

Jian QF (1986)New records of lizards species in liaoning province-Eumeces Capito. Jour.Amphi. Repti.

5,159.

Li JL (1999) Two reptile records new to Liaoning province.Cultum Herpetological Sin 8,337-338.

Li ZC, Xiao Z,Lau WN ,Chan PL, Lee KS (2002)Five new records of amphibians and reptiles in

7 Guangdong Province .J South China Normal Univ 2003,2:80-84

Lin XL,Zhao H,Shi L,Lin,XL..et al (2010)A new records of reptile in Gansu province.J Xinjiang

Agrieultural Univ.33,4 0-4 1

Lu YY,Jia YL, Liu JX et al.(2002) A new reptilians record in Shandong province. Sichuan J. Zoo.21,35-

Lu,YY.et al.(2000) Tak Ydromus Septentrionalis,a new record to shandong province. Sichuan J.

Zoo.19,155.

Rao JT,Pan HJ, Jiang K,Hu HJ et al.(2011) A new record of poorly known lizard-Tropidophorus

hainanus in Guangdong Province,China. Chin.Jour. Zoo.46,142-143

Shi L,Lin XL,Zhao H.(2011)New record of lizards in Tibet. Chin Jour Zoo 46(1) :131-133

Wu GJ, Xiao L, Zhu J, Wang BJ (2001) A new record of lizards in Sichuan. Sichuan J Zoo 20 (2):. 86-

Wu,YF,Wu ML(2001).Eumeces chinensis is found in the suburb of shijiazhuang. Chin.Jour. Zoo. 36,46.

Yang JH,Wang HC MA XX,Wang YY(2009) New distribution records of sphenomorphus maculatus and

additional description of the species. Chin J Zoo 44( 6) : 156~ 159

Zhang L,He SY,He SF (2012) A new record species of Genus Eumeces(Squamata,Scincidae)from

Henan,China-Eumeces capito. Henan Sci 30,63-64

Zhang YH,Gong DJ,Yan L,Sun LX (2012) A new lizard record in guizhou province:Tropidophorus

hainanus. Chin.Jour. Zoo47,138-140

Zhao HP,Wang HL,Lu JQ.(2012) Japalura micangshanensis,A Lizard New to Henan Province,China.

Chin.Jour. Zoo.47,129-131.

5 .Others sources of distribution date and records

China Animal Themes Database（http://www.zoology.csdb.cn）.

Reptiles database (http://www.reptiledatabase. org)

Chinese Academy of Sciences Herbarium Amphibians and Reptiles [Museum of Herpetology, Chengdu

Institute of Biology (CIB), Chinese Academyof Sciences] Appendix B The information on observed density and frequency of the observation network of lizard species in China

Table S1 The density and frequency of the observation network of lizard species in China

8 No Province Area Number observation observation frequency in different periods/time or regions Million of Density square country number/ Before 1951-1960 1961-1970 1971-1980 1980-1990 1991-2000 2001-2010 kilometers Million 1951 (Km)2 square kilometers 1 Beijing 1.68 18 11 3-6 6-8 8-10 9 9 9-10 10 2 Hebei 19 172 9 3-7 6-8 8-10 9 9 9-10 10 3 Tianjin 1.1 18 16 3-6 6-8 8 9 9 9-10 10 4 Heilongjiang 46 130 3 3-7 6-8 8 9 9 9-10 10 5 Jilin 18 60 3 3-7 6-8 8 9 9 9-10 10 6 Liaoning 15 100 7 3-6 6-8 8 9 9 9-10 10 7 Neimengu 110 101 1 3-7 6-8 8 9 9 9-10 10 8 Xinjian 160 99 1 3-6 6-8 8 9 9 9-10 10 9 Gansu 39 86 2 3-7 6-8 8 9 9 9-10 10 10 Qinghai 72 43 1 3-7 6-8 8 9 9 9-10 10 11 Xizang 120 73 1 3-6 6-8 8 9 9 9-10 10 12 Yunnan 38 129 3 3-7 6-8 8 9 9 9 10 13 Guizhou 17 88 5 3-6 6-8 8 9 9 9 10 14 Sichuan 48 181 4 3-7 6-8 8 9 9 9 10 15 Guangxi 23 109 5 5 6-8 8 9 9 9 10 16 Chongqiing 8.23 40 5 5 6-8 8 9 9 9 10 17 Hunan 21 122 6 5 6-8 8 9 9 9 10 18 Hubei 18 102 6 5 6-8 8 9 9 9 10 19 Guangdong 18 122 7 5 6-8 8 9 9 9 10 20 Jiangxi 16 99 6 5 6-8 8 9 9 9 10 21 Jiangsu 10 106 11 5 6-8 8 9 9 9 10 22 Shangdong 15 139 9 5 6-8 8 9 9 9 10 23 Shanxi 19 107 6 5 6-8 8 9 9 9 10 24 Shanxix 15 119 8 5 6-8 8 9 9 9 10 25 Ningxia 6.6 21 3 5 6-8 8 9 9 9 10 26 Henan 16 158 10 5 6-8 8 9 9 9 10 27 Hainan 3.4 20 6 5 6-8 8 9 9 9 10 28 Fujian 12 85 7 5 6-8 8 9 9 9 10 29 Anhui 13 105 8 5 6-8 8 9 9 9 10 30 Zhejian 10 90 9 5 6-8 8 9 9 10 10 31 Taiwan 3.6 25 7 7 6-8 8 9 9 10 10 32 Shanghai 0.58 19 33 5 6-8 8 9 9 10 10 33 Macao 0.00154 1 649 7 6-8 8 9 10 10 10 34 Hong Kong 0.1101 1 9 7 6-8 8 9 9 10 10

The observed network of vertebrates in China is set based on the administrative regions. At the end of

2010, there were a total of 34 provincial-level administrative regions, 2856 county-level administrative units, and 40906 township administrative units. Therefore, there were 2856 county-level locations for vertebrate observations in China at this time. Because the area and frequency of observation differs among different administrative units and different periods from 1951 to 2010, the density and frequency

9 of the animal observation network vary over space and time. Therefore, some bias may exist from temporal and spatial variation.

The observed density in each province or region was calculated from the number of counties and the area in each province or region, and the observed frequency in each province or region was estimated from the observed time for every 10 years in each province or region from 1951 to 2010. The sample area and sample frequency before 1951 were estimated from historical reports or the literature. The sample frequency of 10 years and the sample time, sample area and sample methods were considered.

Because the areas and observation frequencies are different among different administrative units and different periods from 1951 to 2010, the density and frequency of the animal observation network is different (Table S1). Appendix C Climate change in China over the past 50 years

To analyze the correlation between the distribution change of lizards and climatic factors, we analyze the climate change trends in China over the past 50 years. The climate change trends from 1961 to 2013 in

China have been reported (China climate change bulletin, Chinese Administration of Meteorology

2014). However, some climate factors related to change in distribution of lizards have not been analyzed.

Table S2 The climatic factors

No Climatic factors unit 1 mean annual air temperature ℃ 2 mean air temperature in January ℃ 3 mean air temperature in July ℃ 4 maximum temperature in warmest month ℃ 5 minimum temperature in coldest month ℃ 6 sums of cumulative temperature above 0℃ ℃ 7 the annual mean bio-temperature ℃ 8 annual precipitation mm 9 annual potential evapotranspiration rate

Because lizard distributions are likely to have direct or indirect relationships with the macroclimate and microclimate, the mean annual temperature, mean temperatures in January and July, sum of the cumulative temperatures above 0°C, minimum temperature in the coldest month and maximum temperature in the warmest month were calculated. In addition, annual precipitation and the Holdridge

10 index (Holdridge 1967; Zhange 1993), including the mean annual bio-temperature (BT) and the annual potential evapotranspiration rate (PER), were selected (See Table S2).

) -6.5

y = 0.0313x - 55.957 e y = 0.038x - 84.62 a ) b r ℃ 7.0

2 u ( 2 r t ℃

i R = 0.7026

e ( R = 0.3016

a -8.0 a r r y

u 6.5 r e n t a p a a u r e -9.5 m e 6.0 n e a M p t J

r m i 5.5 -11.0n e i t A 5.0 -12.5 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 year year

e -12.5 ) r 20.5 c y = 0.0213x - 23.172 d y = 0.0456x - 106.1 u ) t ℃ r

a 2 i 20.0 2 ( -14.0 ℃ r R = 0.3316

( R = 0.3544 a e

y 19.5 t p l s u t e u -15.5 m a J 19.0 r w e t e n o

i 18.5 r p

i L -17.0 m

A 18.0 e 17.5 t -18.5 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 year year

26.0 3700 ) e y = 0.0254x - 27.423 ) f y = 6.0786x - 8705.3 ℃ r 25.0 i ℃ ( 2

( 2 a e R = 0.3972 R = 0.5731

r 0 3500 t

s 24.0 u e t e a v h r

23.0 o g e i b 3300 p a H

m 22.0 T e t 21.0 C 3100 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 year year

750 10.3 g y = 0.3091x - 1.5455 h y = 0.0167x - 23.956 2

n 700 R = 0.0198 9.8 2 o

) R = 0.5761 i ) t a ℃ m t

650 ( 9.3 p i m T ( c B e r 600 8.8 P 550 8.3 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 year year

3.0 i y = -0.0087x + 19.302 R2 = 0.1793 2.5 R E P 2.0

1.5 1960 1970 1980 1990 2000 2010 year

Fig S1. Climate change trend over the past 50 years in China

Note: a, b, c, d, e, f, g, h, and i represent the mean annual air temperature, mean air temperature in January, mean air temperature in July, the highest temperature in the warmest month, the lowest temperature in the coldest month, sums of cumulative temperature above 0°C, annual precipitation, BT and PER, respectively.

11 In particular, the Holdridge life zones system is a global bioclimatic scheme for the classification of land areas. It was first published by Leslie Holdridge in 1947 and was updated in 1967. It is a relatively simple system based on little empirical data, and it provides objective mapping criteria. A basic assumption of the system is that both the soil and climax vegetation can be mapped once the climate is known. The bio-temperature is based on the growing season length and the temperature. It is measured as the mean of all of the temperatures above freezing, with all of the temperatures below freezing adjusted to 0°C, as plants are dormant at these temperatures. The potential evapotranspiration (PET) is the amount of water that would be evaporated and transpired if there was sufficient water available.

Evapotranspiration (ET) is the sum of evaporation and plant transpiration from the Earth's land surface to the atmosphere. The annual potential evapotranspiration rate (PER) is the ratio of Precipitation to PET

(Holdridge 1967; Zhange 1993).

Climatic data for the last 50 years in China were provided by the climate center of the Chinese

Administration of Meteorology as 17,625 grid cells with a resolution of 0.5°×0.5°. These data included daily air temperature, maximum and minimum temperatures, and daily precipitation. The climate indices(Table S2) in every grid cells were calculated from 1961 to 2010 using Visual Fortran (Compaq

Visual Fortran Professional Edition 6.5, Compaq Computer Corporation (2000), Harris County, Texas,

To analyze the relationship between climate change and the changes in lizard distributions, the climate data over the last 50 years were analyzed for each decade from 1961 to 2010. We used ordinary kriging methods to convert the climate variables into records of lizard distributions based on a digital elevation model (DEM) and produced gridded data for China that were used to overlay the distribution of lizards and climate factors in ARCGIS (Vers. 9.2 for Windows, Esri Corp. (2008), Redlands, CA, US). Climatic variables were generated for each distribution point for each lizard in each decade.

Over the last 50 years, the mean air temperature in China has generally increased from 5.62°C in the

1960s to 6.89°C in the 2000s. The air temperature in January showed a similar trend, increasing by approximately 1.6°C. Similarly, the air temperature in July increased by approximately 0.82°C. The annual minimum air temperature increased by 1.73°C, and the annual maximum air temperature increased by 1.05°C. In addition, the cumulative temperatures above 0°C increased from 3289.4°C in the 1960s to 3533.9°C in the 2000s. The annual precipitation in China has become increasingly variable,

12 showing a decrease from 603.41 mm in the 1960s to 597.64 mm in the 1970s, an increase to 627.52 mm in the 1990s, and then a decrease to 605.09 mm in the 2000s. The BT tracked the cumulative temperature above 0°C from 8.96°C in the 1960s to 9.62°C in the 2000s. The PER declined from 2.36 in the 1960s to 1.87 in the 1980s and then increased to 2.06 in the 2000s (Fig. S1).

Our results demonstrate that regional climate change in China displays a trend that is similar to the changes in the global climate. Over the period from 1880 to 2012, the global average surface temperatures warmed by 0.85°C [0.65 to 1.06°C] (IPCC 2013). Our results show that over the past 50 years, the annual precipitation in China fluctuated considerably (Fig. S1). The precipitation averaged over the mid-latitude land areas of the Northern Hemisphere has increased since 1901, whereas at other latitudes, there is low confidence in the area-averaged long-term positive or negative trends (IPCC

2013). Our results also indicate that increases in the extreme low and high temperatures are consistent with global warming (IPCC 2013). Appendix D Calculating the correlations between climatic factors and change in the distribution of lizards

To analyze the relationship between changes in the distribution of lizards and climate factors, the northern, southern, western and eastern limits and the coordinates of the centers of the lizard distributions were calculated. The limits were calculated based on the coordinates of the 5 % outermost occupied grid cells, which were considered to be the limit of a species’ range; the coordinates for the centers of the distributions were determined using the occupied grid cells. Changes in the range margin between two survey periods, which included the intervals from 1961 to 1970, 1971 to 1980, 1981 to

1990, 1991 to 2000, and 2001 to 2010, were estimated based on the changes in the mean longitude and latitude of the 5% most marginally occupied grid cells.

Because conventional statistical methods require that there be a relationship of mutual influence between the variables, and the functional relationship can only be elucidated under the condition of large quantities of data, which should conform to the normal distribution (Tsokos and Ramachandran 2009).

In some cases, the conditions for these statistical methods are not met. While grey relational analysis

(GRA),a multi-attribute method, overcome the shortage of data for regression analysis and factor analysis, with advantages such as the ability to treat small samples, no normal distribution requirement, no independence requirement and a small number of calculations (Deng 1987). GRA has been

13 demonstrated to be a simple and accurate method, especially for problems with unique characteristics, and has been applied successfully in many engineering and managerial fields, such as industrial applications, ecology, meteorology, geography applications etc(Deng 1987). Given the errors that would be generated using conventional statistical analysis for the small sample size and non-normal distribution, we chose GRA to analyze the degree of grey incidence (DGI) of changes in lizard distributions and climate factors using time series data of climate factor changes at the range limits or centers of the distributions .Following the methods of GRA (Deng 1987), the DGI of the changes in lizard distributions and climate factors are calculated as follows.

First, the time series data of the climate factors, range limits or centers of distribution of lizards were normalized as follows.

x0 (s)  min(x0 (s)) X 0 (s)  , s  1,2,...5 (S1a) max(x0 (s))  min(x0 (s))

xi (s)  min(xi (s)) X i (s)  ,s  1,2,...5;i  1,2,3...,9 (S1b) max(xi (s))  min(xi (s))

where x0 (s) and xi (s) are the original time series data on changes in the range limits or centers of

lizards distributions and the time series data on the ith climatic factors(Table S2), respectively; X 0 (s)

and X i (s) are the normalized data points for changes in the range limits and centers of distributions

and the ith climate factors, respectively; and min(x0 (s)) , max(x0 (s)), min(xi (s)) , and

max(xi (s)) are the minimum or maximum values for the range limits or centers of lizards distributions, and climatic factors, respectively; i is the ith climate variable (Table S2) ;s is the sth time interval such as from 1961 to 1970, 1971 to 1980, 1981 to 1990, 1991 to 2000, and 2001 to 2010 respectively.

Second, the absolute differences of the change in the lizard distributions and climate factors were calculated as follows:

 (s)   (s)   (s) , s  1,2,...,5;i  1,2,...,9 i 0 i (S2)

Where  i (s) is the absolute distance between the normalized data sequence of change in the lizard distributions and climatic factors; i and s are the ith climate variable and sth time interval, respectively.

Third, the grey correlation coefficients of the changes in lizard distributions and climate factors were

14 calculated as follows:

min min  i (s)  0.5 max max  i (s) i s i s  0i (s)  , s  1,2,...,5;i  1,2,...,9 (S3)  i (s)  0.5 max max  i (s) i s

where 0i (s) is the grey correlation coefficient of changes in lizard distributions and climate factors;

min min i (s) is the minimum value of the ith minimum value of the  (s) sequence; i s i

max max i (s) is the maximum value of the ith maximum value of the sequence  (s) ; i and s are i s i the ith climate variable and sth time interval, respectively.

Finally, the DGI of change in lizard distributions and climate factors was calculated as follows.

1 n s0 i   0 i (s), s  1,2,..., n;i  1,2,..., m (S4) n s1

where s0i the DGI for the change in lizard distributions and the ith climatic factors; i and s are the ith climate variable and sth time interval, respectively. The DGI reflects the correlation between individual climate change factors and changes in the distribution of lizards, with a high value indicating a strong correlation between the two. Generally, DGI ≥ 0.7 is considered a strong correlation, 0.7 > DGI ≥ 0.5 a moderate correlation, and DGI < 0.5 a weak correlation (Deng 1987). The DGI can be used to identify the most important climate factor influencing changes in the distributions of lizard species.

The DGI relating changes in the southern and northern distribution limits to climate factors is shown in

Table S3. The DGI relating changes in the eastern or western limits to climate factors is shown in Table

S4. And The DGI between the centers of distribution and climate factors is shown in Table S5.

Table S3 The DGI of the observed changing in distribution southern or northern boundary of lizards with climatic factors over the past 50 years

Species The DGI of the observed changing in distribution boundary of lizards with climatic factors T1 T2 T3 T4 T5 T6 T7 T8 T9 T1 T2 T3 T4 T5 T6 T7 T8 T9 name Southern boundary Northern boundary

AP 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61

JM 0.40 0.45 0.38 0.39 0.54 0.37 0.67 0.37 0.59 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61

TS 0.40 0.45 0.38 0.39 0.54 0.37 0.67 0.37 0.59 0.77 0.70 0.83 0.79 0.66 0.86 0.56 0.85 0.59

EC 0.40 0.45 0.38 0.39 0.54 0.37 0.67 0.37 0.59 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61

ECH 0.55 0.46 0.55 0.56 0.45 0.56 0.50 0.56 0.76 0.80 0.74 0.84 0.80 0.79 0.85 0.69 0.85 0.51

SR 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61 0.79 0.83 0.78 0.76 0.88 0.79 0.74 0.79 0.40

15 SM 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61

TH 0.38 0.41 0.37 0.38 0.41 0.37 0.54 0.37 0.67 0.81 0.74 0.86 0.82 0.80 0.87 0.69 0.87 0.48

HY 0.40 0.45 0.38 0.39 0.54 0.37 0.67 0.37 0.59 0.77 0.70 0.83 0.79 0.66 0.86 0.56 0.85 0.59 Note:T1,T2,T3,T4,T5,T6,T7,T8,T9 represents mean annual air temperature(℃), mean air temperature in January(℃), mean air temperature in

July(℃) , the highest temperature in warmest month(℃) , the lowest temperature in coldest month (℃), sums of cumulative temperature above

0℃(℃), annual precipitation(mm) ,BT (℃) and PER respectively.

AP(Alsophylax przewalskii),JM(Japalura micangshanensis),TS(Takydromus septentrionalis), EC(Eumeces capito), ECH(Eumeces chinensis),SR(Scincella reevesii),SM(Sphenomorphus maculatus), TH(Tropidophorus hainanus),HY(Hemiphyllodactylus yunnanensis).

Table S4 The DGI of the observed changing in distribution western or eastern boundary of lizards with climatic factors over the past 50 years

Species The DGI of the observed changing in distribution boundary of lizards with climatic factors T1 T2 T3 T4 T5 T6 T7 T8 T9 T1 T2 T3 T4 T5 T6 T7 T8 T9 name Western boundary Eastern boundary

AP 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61 0.77 0.70 0.83 0.79 0.66 0.86 0.56 0.85 0.59

JM 0.38 0.41 0.37 0.38 0.41 0.37 0.54 0.37 0.67 0.77 0.70 0.83 0.79 0.66 0.86 0.56 0.85 0.59

TS 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61

EC 0.39 0.43 0.38 0.39 0.44 0.38 0.50 0.38 0.68 0.75 0.75 0.75 0.73 0.83 0.76 0.82 0.76 0.38

ECH 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61 0.80 0.74 0.84 0.80 0.79 0.85 0.69 0.85 0.51

SR 0.45 0.43 0.49 0.48 0.40 0.50 0.43 0.49 0.82 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61

SM 0.64 0.57 0.70 0.66 0.56 0.72 0.61 0.72 0.61 0.77 0.70 0.83 0.79 0.66 0.86 0.56 0.85 0.59

TH 0.46 0.51 0.43 0.43 0.47 0.41 0.60 0.41 0.61 0.82 0.81 0.79 0.78 0.84 0.80 0.72 0.81 0.41

HY 0.40 0.45 0.38 0.39 0.54 0.37 0.67 0.37 0.59 0.80 0.74 0.84 0.80 0.79 0.85 0.69 0.85 0.51 Note:T1,T2,T3,T4,T5,T6,T7,T8,T9 represents mean annual air temperature(℃), mean air temperature in January(℃), mean air temperature in

AP(Alsophylax przewalskii),JM(Japalura micangshanensis),TS(Takydromus septentrionalis), EC(Eumeces capito), ECH(Eumeces chinensis),SR(Scincella reevesii),SM(Sphenomorphus maculatus), TH(Tropidophorus hainanus),HY(Hemiphyllodactylus yunnanensis). Table S5 The DGI of the observed changing in latitude and longitude of distribution center of lizards with climatic factors over the past 50 years

Lizard The DGI of the observed changing in distribution center of lizards with climatic factors T1 T2 T3 T4 T5 T6 T7 T8 T9 T1 T2 T3 T4 T5 T6 T7 T8 T9 Species Center longitude Center latitude

AP 0.77 0.70 0.83 0.79 0.66 0.86 0.56 0.85 0.59 0.77 0.70 0.83 0.79 0.66 0.86 0.56 0.85 0.59

JM 0.38 0.42 0.37 0.38 0.41 0.38 0.56 0.38 0.69 0.44 0.46 0.44 0.45 0.44 0.46 0.56 0.46 0.64

TS 0.39 0.43 0.38 0.40 0.42 0.39 0.59 0.39 0.70 0.69 0.70 0.65 0.68 0.61 0.65 0.48 0.64 0.57

EC 0.73 0.67 0.69 0.69 0.75 0.71 0.80 0.71 0.52 0.64 0.65 0.63 0.62 0.77 0.65 0.90 0.65 0.44

ECH 0.58 0.55 0.64 0.63 0.50 0.62 0.43 0.63 0.81 0.60 0.60 0.61 0.60 0.53 0.59 0.45 0.60 0.80

SR 0.50 0.45 0.47 0.51 0.42 0.48 0.44 0.48 0.77 0.67 0.72 0.61 0.69 0.70 0.66 0.71 0.65 0.55

SM 0.77 0.70 0.83 0.79 0.66 0.86 0.56 0.85 0.59 0.40 0.45 0.38 0.39 0.54 0.37 0.67 0.37 0.59

TH 0.86 0.90 0.81 0.82 0.81 0.81 0.65 0.81 0.42 0.82 0.91 0.76 0.83 0.82 0.80 0.56 0.80 0.47

16 HY 0.84 0.77 0.87 0.83 0.71 0.83 0.62 0.84 0.54 0.82 0.74 0.89 0.85 0.68 0.93 0.58 0.92 0.56 Note:T1,T2,T3,T4,T5,T6,T7,T8,T9 represents mean annual air temperature(℃), mean air temperature in January(℃), mean air temperature in

AP(Alsophylax przewalskii),JM(Japalura micangshanensis),TS(Takydromus septentrionalis),EC(Eumeces capito), ECH(Eumeces chinensis),SR(Scincella reevesii),SM(Sphenomorphus maculatus), TH(Tropidophorus hainanus),HY(Hemiphyllodactylus yunnanensis). Appendix E Prediction distribution of Lizards driven by climate factors

Considering the risk of generating a false absence in an area with a favorable lizards habitat, presence- only data on lizards distributions were used in this study. The fuzzy envelope model can be used to predict the potential distribution of organisms using presence-only locality records and a set of environmental predictor variables (Robertson et al.2004). Specifically, fuzzy models are appropriate for species distribution modeling due to their transparency and their ability to consider the uncertainty inherent to both biotic and abiotic variables (Van Broekhoven et al. 2006). So, the fuzzy-set classification techniques were used to analyze the lizard distributions influenced by climate factors over the past 50 years as follows.

First, the membership functions of 9 climate factors (Table S2) were constructed based on the suitability of climate variables for lizard survival and generation, and the membership of different climatic factors shows the suitability of various climate factors for lizards. The symmetric membership function of the fuzzy Cauchy distribution was used to describe the mean annual temperature, the mean temperatures in

January and July, the sum of the cumulative temperatures above 0°C, the annual precipitation, the BT and the PER. In addition, the monotonically increasing function and monotonically decreasing function of the fuzzy Cauchy distribution were used to describe the minimum temperature in the coldest month and maximum temperature in the warmest month, respectively (Table S6). The mean, minimum and maximum of the suitable climate range for 9 different climate factors for various lizards were computed based on the distribution and were related to the climatic factors from 1951 to 1960, which were used as training dates (Table S7). The parameter of the membership function of different climatic factors was calculated as an optimal value by analyzing the membership of the most suitable point or unsuitable point using the training dates (Table S8).

Second, the suitability of the every climatic factor for lizards in each grid cell for each year from 1961 to

2010 was calculated using the membership function based on the date of every climatic factor from 1961 to 2010.

17 Third, the total membership of the various climatic factors was computed using the sum of the weighting coefficients multiplied by the membership of every climatic factors in each grid cell from 1961 to 2010, and the weighting coefficients of every climatic factor were computed using the coefficient of variation of the 33,211 location records for the climatic variables (Table S9).

Fourth, a multivariate set representing the potential distribution of organisms was produced using the total membership of different climate factors in each grid cell from 1961 to 2010, and the locations that had high membership were interpreted as having conditions more favorable for lizards compared with the locations that had low- members

Table S6 The membership functions for different climatic factors

18 Climate type The membership function *parameter factors Air temperature(℃) symmetric 1 α β Ax  （   0,  is positive even 1 x  a number ） ,x is the mean air temperature, a is the mean of the mean air temperature Air symmetric 1 α β temperature （ is positive even Ax     0,  in January(℃) 1 x  a number）,x is the mean air temperature in January ,a is the mean of the air temperature in January Air temperature in symmetric 1 α β July(℃) Ax  （   0,  is positive even 1 x  a number）,x is the mean air temperature in July, a is the mean air temperature in July The highest temperature in monotonically α β decreasing  1 the warmest month(℃)  x  a Ax   1 （   0,   0 ）,x is  x  a 1 x  a the highest air temperature, a is the mean of the highest air temperature The lowest temperature in monotonically α β increasing  1 the coldest month(℃)  x  a Ax   1 （   0,   0 ）,x is  x  a 1 x  a the lowest air temperature, a is the mean of the lowest air temperature Sums of cumulative symmetric 1 α β temperature above 0℃(℃) Ax  （   0,  is positive even 1 x  a number ） ,x is the mean of the cumulate temperature, a is the mean of the cumulate air temperature Annual precipitation(mm) symmetric 1 α β Ax  （   0,  is positive even 1 x  a number）,x is the mean precipitation, a is the mean of the annual precipitation. BT(℃) symmetric 1 α β Ax  （   0,  is positive even 1 x  a number）,x is the mean of BT, a is the mean of BT. PER symmetric 1 α β Ax  （   0,  is positive even 1 x  a number）,x is the mean of the PER, a is the mean of the PER.

Note: Ax is the membership of the membership function; There are no unites for the *parameter.

Table S7 Climate parameter of different lizard species

19 Climate Lizard species parameter A B C D E F G H I

TP(℃) 11.22 12.57 15.03 11.49 18.17 21.77 17.48 22.41 17.04

TX(℃) 9.11 6.79 3.41 5.31 10.61 12.25 9.46 12.45 12.73

TD(℃) 12.5 14.96 19.48 16.94 25.8 25.04 20.64 24.77 20.64

PY(℃) -7.01 0.91 3.6 -2.44 7.92 14.76 10.36 15.51 9.13

XY(℃) -10.59 -5.6 -15.44 -12.49 -4.48 4.75 1.41 -1.49 4.92

DY(℃) -4.41 3.78 11.03 5.89 21.59 20.29 13.92 19.58 13.73

PQ(℃) 23.41 24.13 25.06 23.94 27.11 26.87 21.25 28.2 23.06

XQ(℃) 22.23 20.87 18.36 18.36 21.23 19.75 13.53 25.1 19.89

DQ(℃) 24.96 25.21 28.56 26.24 28.75 28.91 23.74 28.51 28.32

GP(℃) 39.58 38.56 37.79 38.62 38.27 37.46 34.3 37.92 35.7

GX(℃) 38.08 34.98 32.57 32.57 35.7 33.53 27.83 37.34 33.56

GD(℃) 41 39.71 40.56 40.52 40.04 38.9 37.12 39.12 39.31

XP(℃) -24.23 -13.97 -10.08 -18.13 -7.2 0.68 -2.56 0.6 -4.74

XX(℃) -29.01 -24.38 -35.47 -31.43 -21.31 -11.21 -12.66 -16.32 -10.27

XD(℃) -20.13 -9.19 -0.3 -5.05 6.1 5.8 0.24 5.45 0.24

JP(℃) 4222.59 4758.68 5502.24 4446.88 6640.48 7938.91 6259.29 8265.2 6263.68

JX(℃) 3805.99 3789.24 2768.31 3267.51 4169.51 4736.02 2959.78 4632.92 4947.84

JD(℃) 4575.59 5403.84 7006.06 6074.69 9391.84 9168.95 7379.92 9023.67 7483.15

SP(mm) 45.63 755.23 1100.71 671.45 1365.29 1524.02 1298.88 1763.69 1193.58

SX(mm) 32.88 640.62 37.62 382.15 512.44 839.04 796 653.03 845.41

SD(mm) 69.09 900.37 1832.48 1229.85 2430.9 2090.81 1524.91 2259.87 1660.61

SP(℃) 10.93 13.09 14.81 12.12 17.9 21.38 16.65 22.49 17.21

SX(℃) 9.67 11.55 7.4 9.15 11.19 12.43 6.49 12.57 14.42

SD(℃) 12.17 14.34 18.49 16.23 24.99 24.54 19.77 24.52 20.82 ZP 16.76 1.05 1.23 1.14 0.86 0.92 0.81 0.83 0.91 ZX 11.31 0.63 0.16 0.63 0.42 0.67 0.59 0.62 0.59 ZD 23.77 1.37 16.37 1.66 1.55 1.53 0.94 1.14 1.4

Note:A,B,C,D,E,F,G,H,I represents Alsophylax przewalskii,Japalura micangshanensis,Takydromus septentrionalis,Eumeces capito,Eumeces chinensis,Scincella reevesii,Sphenomorphus maculates,Tropidophorus hainanus,Hemiphyllodactylus yunnanensis,respectively.

TP,TX,TD represents the mean, minimum and maximum value of mean annual air temperature;PY,XY,DY represents the mean, minimum and maximum value of mean annual air temperature mean air temperature in January; PQ,XQ,DQ represents the mean, minimum and maximum value of mean air temperature in July ;GP,GX,GD represents the mean, minimum and maximum value of highest temperature in the warmest month;

XP,XX,XD represents the mean, minimum and maximum value of lowest temperature in the coldest month; JP,JX,JD represents the mean, minimum and maximum value of sums of cumulative temperature above 0℃;SP,SX,SD represents the mean, minimum and maximum value of annual precipitation ;WP,WX,WD represents the mean, minimum and maximum value of BT;ZP,ZX, ZD represents the mean, minimum and maximum value of PER, respectively.

Table S8 Fuzzy set parameter of different lizards parameter Lizard species

20 A B C D E F G H I

TP 6 4 4 4 4 4 4 4 4

TX 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

YP 6 6 4 4 4 4 4 4 4

YX 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02

QP 8 8 6 6 6 6 6 16 6

QX 0.04 0.04 0.03 0.03 0.03 0.03 0.03 0.08 0.03

DP 3.5899 3.9679 2.67 3.141 3.2429 3.5659 2.651 3.8879 2.401

DX 3.5899 3.9679 2.67 3.141 3.2429 3.5659 2.651 3.8879 2.401

XP 2.7761 2.7761 2.7761 2.7761 2.7761 2.7761 2.7761 2.7761 2.7761

XX 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003

JP 2 2 2 2 2 2 2 2 2

JX 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

SP 4 2 2 2 2 2 2 2 2

SX 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

SP 6 6 4 4 4 4 4 4 4

SX 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2

ZP 4 14 8 12 12 16 22 18 14

ZX 0.02 0.07 0.04 0.06 0.06 0.08 0.11 0.09 0.07

Note:Note:A,B,C,D,E,F,G,H,I represents Alsophylax przewalskii,Japalura micangshanensis,Takydromus septentrionalis,Eumeces capito,Eumeces chinensis,Scincella reevesii,Sphenomorphus maculates,Tropidophorus hainanus,Hemiphyllodactylus yunnanensis,respectively.

TP,TX represents α,β value of mean annual air temperature;YP,YX represents α, β value of mean air temperature in January; QP,QX represents α,β value of mean air temperature in July ; DP,DX represents α,β value of highest temperature in the warmest month; XP,XX represents α,β value of lowest temperature in the coldest month; JP,JX represents α,β value of sums of cumulative temperature above 0℃;SP,SX represents α,β value of annual precipitation ;WP,WX represents α,β value of BT;ZP,ZX represents α,β value of PER respectively for the membership function parameters.

Table S9.The weight coefficient of climate factor

21 year weight coefficient of climate factor year weight coefficient of climate factor tp yp qp zd zx jw js sw gz tp yp qp zd zx jw js sw gz

1961 0.147 lizard0.102 species0.232 0.272 0.091 0.005 0.131 0.005 0.014 Kappa(k)1986 0.147 0.105 Degree0.227 of0.273 agreement0.094 0.005 0.132 0.005 0.013 Rousettus leschenaultii 0.69 good 1962 0.147 Rhinolophus0.103 0.231 ferrumequinum0.269 0.094 0.005 0.133 0.005 0.013 0.198760 0.149 0.107 good0.228 0.271 0.093 0.005 0.133 0.005 0.010 1963 0.149 Rhinolophus0.102 0.230 pusillus 0.269 0.094 0.005 0.133 0.005 0.013 0.198854 0.146 0.105 fair0.230 0.275 0.093 0.005 0.130 0.005 0.010 Rhinolophus luctus 0.84 very good 1964 0.147 0.105 0.228 0.272 0.093 0.005 0.134 0.005 0.011 1989 0.148 0.105 0.225 0.275 0.094 0.005 0.132 0.005 0.011 Rhinolophus rex 0.67 good 1965 0.148 Rhinolophus0.107 0.228 pearsonii0.268 0.093 0.005 0.134 0.005 0.012 0.719909 0.150 0.106 very0.228 good0.269 0.094 0.005 0.133 0.005 0.011 Hipposideros bicolor 1966 0.148 0.105 0.227 0.274 0.091 0.005 0.133 0.005 0.011 0.199165 0.148 0.105 good0.231 0.272 0.091 0.005 0.132 0.005 0.011 Coelops frithiii 0.75 very good 1967 0.147 Vespertilio0.103 sinensis0.231 0.271 0.091 0.005 0.134 0.005 0.013 0.199267 0.148 0.106 good0.225 0.271 0.097 0.005 0.133 0.005 0.010 1968 0.148 0.104 0.229 0.271 0.092 0.005 0.133 0.005 0.013 1993 0.150 0.105 0.228 0.265 0.095 0.005 0.135 0.005 0.010

1969 0.147 0.103 0.229 0.273 0.092 0.005 0.134 0.005 0.012 1994 0.147 0.104 0.231 0.274 0.093 0.005 0.129 0.005 0.012

1970 0.147 0.104 0.229 0.271 0.092 0.005 0.134 0.005 0.012 1995 0.148 0.104 0.227 0.275 0.096 0.005 0.131 0.005 0.010

1971 0.147 0.104 0.230 0.271 0.094 0.005 0.133 0.005 0.011 1996 0.149 0.105 0.229 0.269 0.095 0.005 0.133 0.005 0.011

1972 0.146 0.106 0.225 0.277 0.093 0.005 0.132 0.005 0.011 1997 0.146 0.104 0.229 0.278 0.093 0.005 0.129 0.005 0.012

1973 0.148 0.105 0.228 0.271 0.094 0.005 0.132 0.005 0.012 1998 0.151 0.103 0.232 0.271 0.092 0.005 0.132 0.005 0.011

1974 0.147 0.105 0.231 0.268 0.093 0.005 0.133 0.005 0.013 1999 0.147 0.105 0.227 0.280 0.093 0.005 0.128 0.005 0.011

1975 0.148 0.106 0.226 0.272 0.093 0.005 0.133 0.005 0.012 2000 0.144 0.101 0.233 0.283 0.091 0.005 0.128 0.005 0.010

1976 0.148 0.107 0.224 0.270 0.093 0.005 0.135 0.005 0.012 2001 0.146 0.102 0.233 0.279 0.090 0.005 0.128 0.005 0.012

1977 0.148 0.100 0.230 0.277 0.090 0.005 0.133 0.005 0.012 2002 0.148 0.106 0.229 0.278 0.091 0.005 0.129 0.005 0.010

1978 0.148 0.102 0.231 0.274 0.091 0.005 0.132 0.005 0.013 2003 0.150 0.106 0.228 0.271 0.094 0.005 0.132 0.005 0.010

1979 0.149 0.107 0.224 0.271 0.094 0.005 0.133 0.005 0.012 2004 0.148 0.104 0.227 0.278 0.092 0.005 0.129 0.005 0.012

1980 0.147 0.104 0.229 0.273 0.092 0.005 0.133 0.005 0.013 2005 0.147 0.103 0.231 0.279 0.093 0.005 0.129 0.005 0.009

1981 0.146 0.103 0.232 0.276 0.092 0.005 0.132 0.005 0.009 2006 0.148 0.103 0.231 0.279 0.091 0.005 0.128 0.005 0.010

1982 0.148 0.107 0.227 0.270 0.095 0.005 0.132 0.005 0.011 2007 0.150 0.105 0.228 0.273 0.096 0.005 0.129 0.005 0.010

1983 0.148 0.105 0.225 0.273 0.093 0.005 0.134 0.005 0.011 2008 0.149 0.102 0.233 0.274 0.090 0.005 0.131 0.005 0.011

1984 0.149 0.104 0.228 0.269 0.093 0.005 0.135 0.005 0.012 2009 0.149 0.105 0.230 0.272 0.093 0.005 0.129 0.005 0.013

1985 0.148 0.104 0.228 0.269 0.094 0.005 0.134 0.005 0.014 2010 0.144 0.102 0.231 0.289 0.089 0.004 0.126 0.004 0.009

Note :tp,yp,qp,zd,zx,jw,js,sw,gz press the mean, minimum and maximum of mean annual air temperature, mean air temperature in January, mean

air temperature in July , maximum temperature in warmest month , minimum temperature in coldest month , sums of cumulative temperature

above 0℃ , annual precipitation ,the annual mean bio-temperature, annual potential evapotranspiration rate,respectively. There are no unites for these weight coefficient The accuracy of the models was evaluated using the Kappa-statistic (k). We used presence and absence

of distribution records over 1951-1960 as the baseline, and we used the predicted and observed presence

and absence of distribution records in 1961-1970 as an independent set for evaluating the model

performance (Robertson et al.2004) (See Table S10).

Table S10 Model performance by Kappa statistic for different lizard species

Notes: the k is calculated as follows

k  a  d  a  ca  b b  d c  d  N  N  a  ca  b b  d c  d  N 

22 where a  the number of cases predicted present when actually present b  the number of cases predicted present when actually absent c  the number of cases predicted absent when actually present d  the number of cases predicted absent when actually absent

N  a  b  c  d

There are no unites for Kappa(k) value

Using the ranges of agreement for the kappa statistic proposed by Monserud and Leemans(1992) to describe the degree of agreement.

Appendix F Estimate the consistency index between the observed and predicted lizard range over the past 50 years

To attribute the changes in lizard distributions over the past 50 years to climate change, it is crucial to test whether the observed changes in the distributions of lizard species are consistent with climate-driven changes. As errors would be generated using conventional statistical analysis for this dataset because of the small sample size and non-normal data distributions (Deng 1987), we defined the consistency index of the observed versus predicted changes in the distribution of lizards based on the DGI of the time- series data of these changes over the past 50 years. To analyze the consistency index, we first calculated the observed and predicted changes in the distributions of lizards, including their range limits and distribution center coordinates, using the time-series data for the observed and predicted distributions from 1961 to 2010. The northern, southern, western and eastern limits were analyzed as the means of the coordinates of the outermost 5% of occupied grid cells, which were considered to be the range limits, and the center coordinates of the distribution were analyzed as the average coordinates for all of the occupied grid cells. We then analyzed the DGI of the observed and predicted ranges over the past 50 years using the time-series data for the observed and predicted changes in lizard distributions, which represented the consistency index of the observed versus predicted changes in the ranges of lizards.Following the methods of GRA (Deng 1987), the consistency index of the observed versus predicted changes in the distribution of lizards are calculated as follows:

23 First, the observed and predicted changes in the lizard distributions were normalized as:

y0 (s)  min(y0 (s)) Y0 (s)  ,s  1,2,...5 (S5a) max(y0 (s))  min(y0 (s))

yi (s)  min(yi (s)) Yi (s)  ,s  1,2,...5 (S5b) max(yi (s))  min(yi (s))

where y0 (s) is the original sequence of the observed lizard distribution; yi (s) is the original sequence

of the predicted lizard distribution; Y0 (s) is the normalized data sequence of the observed lizard

distribution; Yi (s) is the normalized data sequence of the predicted lizard distributions; min(y0 (s))

and max(y0 (s)) are the minimum and maximum values , respectively ,of original time series data of

the observed changes in the range limits or centers of distributions; and min(yi (s)) and max(yi (s)) are the minimum and maximum values , respectively ,of the original time series data of the predicted changes in the range limits or the centers of lizards distributions; and s are the sth time interval among from 1961 to 1970, 1971 to 1980, 1981 to 1990, 1991 to 2000, and 2001 to 2010.

Second, the absolute difference between the observed and predicted changes in the lizard distributions was calculated as follows:

 (s)  Y (s)  Y (s) , s  1,2,...,5;i  1,2,...,6 i 0 i (S6)

where Bi (s) is the absolute difference between the normalized data sequences of the observed and

predicted distributions,Y0 (s) is the normalized data sequence of the observed distribution, and Yi (s) is the normalized data sequence of the predicted distribution of lizards;and i are the ith distribution index such as the northern, southern, western and eastern limits, the latitude and longitude of distributions centers of lizards;and s are the sth time interval among from 1961 to 1970, 1971 to 1980, 1981 to 1990,

1991 to 2000, and 2001 to 2010.

Thirds, the grey correlation coefficients of the observed and predicted distribution changes were calculated as follows:

min min i (s)  0.5max max i (s) i s i s  0i (s)  , s  1,2,...,5;i  1,2,...,6 (S7) i (s)  0.5max max i (s) i s where  (s) is the gray correlation coefficient of the observed and predicted changes; min min Bi (s) 0i i s

24 is the minimum value of the i minimum value of the  (s) sequence; max max i (s) is the maximum i i s

value of the i maximum value of the i (s) sequence; i are the ith distribution index such as the northern, southern, western and eastern limits ,the latitude and longitude of distributions centers of lizards ,and s are the sth time interval such as from 1961 to 1970, 1971 to 1980, 1981 to 1990, 1991 to

2000, and 2001 to 2010;and 0i is the observed changes and predicted distribution in the lizard distribution in the ith variable(such as such as the northern, southern, western and eastern limits ,the latitude and longitude of distributions centers of lizards).

Fourth, the consistency index of the observed and predicted changes in the distribution of a lizard for ith index was calculated as:

1 n  0 i   0 i (s), s  1,2,...,5;i  1,2,...,6 (S8) n s1

where where  0i is the consistency index of the observed and predicted changes in the distribution of a lizard for ith index, i are the ith distribution index such as the northern, southern, western and eastern limits ,the latitude and longitude of distributions centers of lizards. A high consistency index indicates high consistency between the observed and predicted changes in the distributions of lizards over the past years. According to the DGI of the observed and predicted distribution of species over the past 50 years

(Deng 1987), a consistency index ≥ 0.7 was considered as highly consistent, 0.7 > consistency index ≥

0.5 as moderately consistent, and a consistency index < 0.5 as weakly consistent. Appendix G Estimating attribution value of changes in lizard distributions to climate change over the past 50 years

The observed changes in distribution of lizard species cannot be attributed to past climate change if 1) there is no change in the observed or predicted distributions driven by climate factors, 2) there is no consistency between the observed and predicted changes in distribution of species, or 3) there is a poor relationship between the observed changes in lizard distributions and climate change. Therefore, the

degree of attribution to climate change of the changes in lizard distribution ( Aij ) was defined as a

function of the observed changes in lizard distribution ( Oij ), correlation between the climatic factors and

25 changes in distribution ( Rij ), predicted changes in the distribution ( Sij ), and consistency between the

observed and predicted distributions (Cij ). Mathematically, this relationship is expressed as follows:

Aij  f (Oij , Rij , Sij ,Cij ) (S9)

We assume that Oij , Rij , Sij , and Cij are of equal importance and are all required to determine Aij ;

thus, we redefine Eq. (S9): Aij  Oij  Rij  Sij  Cij 100 (S10)

0 if max( oij )  min( oij )  Oij   oij  min( oij ) (S11)  if max( oij )  min( oij ) max( o )  min( o )  ij ij

where oij , min( oij ) , and max( oij ) represent the observed changes in the range limit or distribution center of lizards and the associated minimum and maximum absolute values of the range or center coordinate, respectively; i are the ith lizard species among 9 lizard species;j are the jth distribution index such as the northern, southern, western and eastern limits, the latitude and longitude of distributions centers of lizards.

Rij  rij  wij (S12)

rij  max(si j ) (S13)

For the ith lizard species among 9 lizard species and jth distribution index among the northern, southern,

western and eastern limits, the latitude and longitude of distributions centers of lizards, si j is equal to

max(s ) s0 j ,which is calculated using Eq. (S4),and i j is the maximum value of si j among 9 lizard species;and i is the ith lizard species among 9 lizard species;j are the jth distribution index such as the northern, southern, western and eastern limits, the latitude and longitude of distributions centers of lizards.

0 if max(kij )  min(kij )  wij   kij  minkij  (S14)  if max(kij )  min(kij ) maxkij  minkij 

26 where wij is the change in the climate factor, kij represents a simple correlation between the change in

climate factors over time, and min(kij ) and max(kij ) represent the associated minimum and maximum

values among kij for all lizard species, respectively; the climate factor is chosen when the grey correlation coefficients of the changes in lizard distributions index and certain climate factor is the highest among the all grey correlation coefficients of the changes in lizard distributions index and 9 climate factors; i are the ith lizard species among 9 lizard species;j are the jth distribution index such as the northern, southern, western and eastern limits, the latitude and longitude of distributions centers of lizards.

 if min( s )  max( s ) 0 ij ij Sij   sij  min( sij ) (S15) if min( s )  max( s )  max( s )  min( s ) ij ij  ij ij

where sij represents the predicted changes in the range limit or distribution center of lizards and

min( oij ) and max( sij ) are the associated minimum and maximum absolute values among sij for all lizard species, respectively. i are the ith lizard species among 9 lizard species are the jth distribution index such as the northern, southern, western and eastern limits, the latitude and longitude of distributions centers of lizards.

For the ith lizard species among 9 lizard species and jth distribution index among the northern, southern,

western and eastern limits, the latitude and longitude of distributions centers of lizards,Cij is equal to

0 j , which is calculated using Eq. (S8).

Higher values of Aij indicate that changes in the lizard distributions can be attributed to climate change.

According to climate change trends in China, the degree of grey correlation change in lizard distributions and climate factors, the consistency index of the observed and predicted changes in the

distribution of a lizard,both Rij and Sij are always higher than 0.4. When Aij is lower than 1 and is

higher than 0, either Oij or Cij will be lower than 0.25, and the observed and predicted changes in the

27 distribution of a lizard will be minor. So, when Aij is higher than 1, the changes in the lizard

distributions can be attributed to climate change significantly. When Aij is less than or equal to 0, either

Oij or Cij will be lower than 0 or equal to 0, and the observed or predicted changes in the distribution of

a lizard will not happen. So, if Aij is less than or equal to 0, the distributions changes of lizards cannot be attributed to climate change.

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