Electronic Supplementary Information

Electronic Supplementary information

Study Site

Over 3000 median-small size alpine lakes have been identified on the SE margin of Tibet Plateau, many lakes lie above 4000m, with the local treeline ranging between 3800-4200m. The 9 alpines form a west-east (94°27′E to 102°20′E) and a north-south (33°13′ N to 26°38′N) transect in SE Tibet (Figure 1). The present-day annual precipitation for this area ranges from 500 to 850 mm, with around 77-90% of the annual total falling between May and September. Due to the mountainous landscape, there are variable regional temperature and moisture reconstructions for the area (Mischke et al., 2008; Herzschuh et al., 2009). Regional summer temperature reconstructions from tree-rings for the last 200 years, for example, from Deqin County, NW Yunnan (Li et al., 2011), southeast Tibet (Bräuning and Mantwill, 2004) and from Gongga Mountain, western Sichuan (Duan et al., 2010), have all showed there were no clear warming trend until 1980s.

Chronology Dried sediment samples were analyzed for 210Pb and 137Cs by direct gamma spectrometry using the OrtecHPGe GWL series of well-type, coaxial, low background, intrinsic germanium detectors (Appleby, 2002) at the Nanjing Institute of Geography and Limnology,

210 Chinese Academy of Sciences.The Pbex profiles show nonlinear change in both lakes, more precisely, the lower parts of the profiles are irregular, which may indicate variable depositional conditions, while137Cs peaks were identified at 5 cm, 12 cm, 7 cm and 13 cm of sediment cores from Heihai, Jiren, Cuoqia and Tiancai Lake, respectively (Fig. 1). The age- depth model was based on a composite dating model (Appleby, 2002): the depth of the maximum peak of 137Cs was set as 1963, and then CRS models were used to calculate the chronologies for the lower section of the sediment cores constrained by the137Cs peak depth.

210 137 Considering the Pbex activity profiles above the depth of Cs were almost linear, CIC model was used to get the age for the upper section. 210 Age (AD) 137 210 Age (AD) 137Cs activity (Bq/kg) Pb activity (Bq/kg) Cs activity (Bq/kg) Pbex activity (Bq/kg) ex 0 100 200 300 400 2020 2000 1980 1960 1940 1920 1900 1880 1860 0 20 40 60 80 100 120 1 10 100 1000 10000 2020 2000 1980 1960 1940 1920 1900 1880 1860 1 10 100 1000 10000 0 0 0 0 2 2 2 4 2 4 4 6 1963 6 1963 4 8 6 10 8 8 6 12 10 10 14 12 8 16 12 14 D D 18 D D e e p e p e 10 16 p p

14 t t 20 h h t t h h

c c ( ( 22 m c m c 16 m m 12 ) )

) 24 20 ) 18 26 14 22 20 28 Heihai Lake Cuoqia Lake 24 30 22 16 0 20 40 60 80 100 1 10 100 1000 10000 0 20 40 60 80 100120140 1 10 100 1000 10000 2020 2000 1980 1960 1940 1920 1900 1880 0 2020 2000 1980 1960 1940 1920 1900 1880 0 0 0 2 2 2 2 4 4 4 4 6 6 6 6 8 8 8 8 10 10 10 12 12 1963 10 12 14 1963 14 12 14 16

16 14 D D 16 D e D

18 p e e p e p t h p 16 18 t t 18 h h t

20 ( h

m c c ( 20 18 20 m m c

22 ) m ) )

) 22 22 20 24 24 22 26 24 26 24 28 Tiancai Lake 26 28 Jiren Lake 30

137 210 Fig. S1 The chronology of Heihai, Jiren, Cuoqia and Tiancai Lake: variation of Cs and Pbex, the age-depth results using composite model Statistical Analysis Due to the decay rate of 210Pb, determining a chronology prior to 1860 is difficult (Appleby, 2004) and dates are often derived from linear interpolation. The restriction of 210Pb combined with the confounding effects of the ecological response to the Little Ice Age (LIA) (around 1820-1860), only ecological trends post-1860 are considered here. Nevertheless, the “natural warming” associated with the post-LIA climate changes undoubtedly contributed to the ecosystem recovery observed.

3 4 3 Dongewuka Lake Heihai Lake Jiren Lake 3 2 2 2 2 R adjust=0.35, p<0.05 R adjust=0.88, p<0.001 2 1 1 1 0 0 P

P 0 P C C C A -1 A A -1

1 1 1 -1

( ( ( 6 5 4 0 9 9 % % -2 % -2 -2 ) 1850 1900 1950 2000 ) 1850 1900 1950 2000 ) 1850 1900 1950 2000

forest lakes 3 3 2 4 Moon Lake R2 =0.79, p<0.001 Shade Co Cuoqia Lake adjust 2 2 PCA 1 R adjust=0.56 3 2 2 PCA 2 1 p<0.001 R adjust=0.74, p<0.001 1 2 1 0 0 1 0 P P

-1 P 0 C P C C A

-1 C A A

2 1 -2 -1

5 ( ( 9

7 7 ( % 2 3 % 3 -3 -2% -2 ) -2 ) % 1850 1900 1950 2000 ) 1850 1900 1950 2000 1850 1900 1950 2000 )

2 4 3 LC 6 Lake 2 2 R adjust=0.59, p<0.005 Tiancai Lake Wuxu Lake 3 2 1 1 2 1 0 1 0 0 P

P P P 0 C C C -1 C A A -1 A A

1 1 2 1 -1

( ( ( ( 4 2 2 2 8 1 9 0 % % % -2 -2 % -2 -2 ) ) 1850 1900 1950 2000 ) ) 1850 1900 1950 2000 1850 1900 1950 2000

Date (AD) Date (AD) Date (AD)

Fig. S2 PCA 1 scores of all the nine lakes, and PCA 2 scores from Moon Lake and LC6 Lake, with linear regression, or GLS (generalised least squares) and/or two segment piecewise regression There are temporal autocorrelation in both PCA 1 and PCA 2 scores of Shade Co and Heihai, PCA 1 scores of LC6 Lake. Therefore, the GLS model was carried out for the PCA scores above. Linear regression was applied for the rest PCA scores which show linear change (Table S1). At Moon Lake, the PCA-2 scores show a significant linear trend but not PCA 1. The PCA 1 scores from Moon Lake reflect the fluctuation of small Fragilaria (Fig. 4), which may associate with the habitat availability (Hu et al., 2014). For LC 6, the PCA 2 explained 21% of total diatom variance, comparing with 29% explained by PCA 1 scores which present no trend, and a better fit was on PCA-2 scores. As a result, the PCA 2 scores from both Moon and LC6 were selected to represent diatom community change at these lakes. The PCA-1 scores at Jiren and Tiancai and PCA-2 scores at Shade Co and Moon lakes all have high loading of benthic species. In contrast, the PCA-1 scores of Hei Hai, Cuoqia, LC6 and Dongerwuka lakes represent change of planktonic species. The linear change of diatom PCA-1 scores from ~1860 at Shade Co, Jiren, Cuoqia and Dongewuka Lake and PCA-2 scores at Moon and LC6 Lakes are all significant, while the two segment piecewise regression on PCA 1 scores is significant at Heihai and Tiancai (Table S1).

Table S1 Results of temporal auto-correlation, linear regression (LR) or generalised least squares (GLS) model and two segment piecewise regression (SR) of both PCA axes on 9 alpine lakes

PCA axis Temporal LR or SR Significant Site Name Davies test (explained R-squared breakpoint auto- GLS of slope variance) regression correlatio R-squared change (p value) (p value) n Shade Co 1 (57%) exist 0.56*** * 0.68 PCA 1, GLS 2 (15%) exist 0.27* *** 0.59 1926 Moon Lake 1 (73%) -- -- 0.03 PCA 2, LR 2 (9%) None 0.79*** 0.82 Heihai Lake 1 (59%) exist 0.75* *** 0.89 1958 PCA 1, SR 2 (20%) exist 0. 61 * 0.62 Jiren Lake 1 (49%) None 0.88*** ** 0.92 PCA 1, LR 2 (9%) -- -- *** 0.43 Cuoqia Lake 1 (23%) None 0.74*** / / 1999 PCA 1, LR 2 (15%) -- -- ** 0.46 Tiancai Lake 1 (20%) None 0.61*** *** 0.79 1983 PCA 1, SR 2 (7%) -- -- * 0.18 LC 6 Lake 1 (29%) exist 0.07* 0.38 PCA 2, LR 2 (21%) None 0.59** -- -- Wuxu Lake 1 (48%) ------2 (21%) -- -- 0.38 Dongewuka 1 (60%) None 0.35* / / 1993 PCA 1, LR 2 (23%) -- -- 0.28 Lake p: *** <0.001, ** <0.005, * <0.05; sites name in Italic notes the forest lakes, ‘--’ represents no linear change, ‘/’ represents the significance is not estimated with intervals less than 5 samples.

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