1 Species interactions slow warming-induced upward shifts of treelines
2 on the Tibetan Plateau
3
4 Eryuan Lianga, b, c, 1, Yafeng Wanga, Shilong Piaoa, c, Xiaoming Lua, Jesús Julio Camarerod,
5 Haifeng Zhua, Liping Zhub, c, Aaron M. Ellisone, Philippe Ciaisf, Josep Peñuelasg, h
6 aKey Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research,
7 Chinese Academy of Sciences, Beijing 100101, China
8 bKey Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of
9 Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
10 cCAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
11 dInstituto Pirenaico de Ecología (IPE-CSIC), Consejo Superior de Investigaciones Científicas,
12 Avda. Montañana 1005, 50059 Zaragoza, Spain
13 eHarvard Forest, 324 North Main Street, Petersham, MA 01366, USA
14 fLaboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, 91191 Gif sur
15 Yvette, France
16 gCREAF, Cerdanyola del Valles, Barcelona 08193, Catalonia, Spain
17 hCSIC, Global Ecology Unit CREAF- CSIC-UAB, Cerdanyola del Vallès, 08193, Catalonia,
18 Spain
19 1To whom correspondence may be addressed. Email: [email protected]
20 Author contributions: E.L. designed research; E.L., Y.W., and X.L. performed research; E.L.,
21 Y.W., S.P., X.L., J.J.C., H.Z., L.Z., A.M.E., P.C., and J.P. analyzed data, and wrote the paper.
22 The authors declare no conflict of interest
1 23 Abstract
24 The alpine treeline is commonly regarded as being sensitive to climatic warming because
25 regeneration and growth of trees at treeline generally are limited by low temperature. The alpine
26 treelines of the Tibetan Plateau (TP) occur at the highest elevations (4900 m a.s.l.) in the Northern
27 Hemisphere. Ongoing climatic warming is expected to shift treelines upward. Studies of treeline
28 dynamics at regional and local scales, however, have yielded conflicting results, indicating either
29 unchanging treeline elevations or upward shifts. To reconcile this conflict, we reconstructed in
30 detail a century of treeline structure and tree recruitment at sites along a climatic gradient of 4 °C
31 and mean annual rainfall of 650 mm on the eastern TP. Species interactions interacted with effects
32 of warming on treeline and could outweigh them. Densification of shrubs just above treeline
33 inhibited tree establishment, and slowed upward movement of treelines on time scale of decades.
34 Interspecific interactions are major processes controlling treeline dynamics that may account for
35 the absence of an upward shift at some TP treelines despite continued climatic warming.
36
37 Keywords: alpine treeline ∣treeline dynamics∣ climate change ∣interspecific competition
38 ∣Tibetan Plateau
39
40 Significance Statement
41 Climatic warming is expected to shift alpine treelines upward because regeneration and growth of
42 trees there are limited by low temperature. However, treeline displacement is spatially
43 heterogeneous despite warming climate. Using data on undisturbed spruce and fir treeline plots
44 located on the Tibetan Plateau between 28.4 and 38.5°N, we showed that climatic warming tended
2 45 to promote an upward shift of alpine treelines at local and regional scales. However, upslope
46 migration rates were controlled largely by interspecific interactions. Our findings both help to
47 explain why many treelines have not advanced in response to climatic warming and highlight that
48 predictions of treeline shifts based solely on climate may be misleading because interspecific
49 interactions can temper effects of climatic change.
3 50 \body
51 Introduction
52 The boundary of vegetation formed by alpine treelines is expected to be sensitive to effects of
53 climatic warming on subalpine and alpine ecosystems (1-3). Despite complex mechanisms
54 controlling treeline ecotones (4), the mean root-zone and air temperature is thought to be the
55 primary constraint on tree growth at the high elevations reached by particular tree species because
56 reduced soil temperatures limit the use of carbohydrates to sustain cell division and create woody
57 plant tissues (3). As the temperature warms, therefore, treelines are expected to increase in
58 elevation (“shift upward”) (e.g., 5-7).
59 In a global meta-analysis, however, Harsch et al. (8) found that treelines shifted upward
60 during the last century at only 52% of 166 locations examined; the majority of treelines upward
61 shifts was attributed to improved winter conditions. Elsewhere, changes in treeline (“treeline
62 displacement”) were spatially heterogeneous and slow despite accelerating warming (9, 10). In
63 general, treelines are not always keeping pace with climatic warming on multi-decadal time scales,
64 suggesting that upward migration and adjustment of alpine trees to warmer climate conditions may
65 take from several decades to centuries (11), given biotic and climatic factors (e.g. drought,
66 changes in frost damage and insect and pathogen attacks, soil nutrients or water availability
67 limitations). However, little is known about processes that control upward displacement of
68 treelines in response to long-term warming, and whether alpine tree lines will respond quickly or
69 not to climate warming occurring since the mid-1800s and accelerating today.
70 Treeline displacement results from changes in tree recruitment, growth and mortality (11-13).
71 These demographic processes are controlled by different drivers and involve biotic and climatic
4 72 responses and limitations (4, 14-17). In addition to physiological responses to temperature
73 warming (3), geomorphic or topographic conditions also limit upward shifts in alpine treelines
74 (18-20). Species competition often is an important force driving stand dynamics and suppression
75 (e.g., 21-27). Theoretically, altered disturbance regimes and interactions between trees and shrubs
76 or grasses also can affect demographic parameters and determine whether treelines shift upward.
77 Interactions among plants also may shift from competition to facilitation as environmental
78 stress increases (28), but the role of species interactions as regulators of alpine treeline shifts has
79 been little studied. There are some data that suggest that the presence of shrubs may limit
80 recruitment and tree establishment above the treeline (16, 29-31), but regional studies and
81 transplant experiments have not allowed for quantitative assessment of long-term changes in
82 species interactions at treeline.
83 Treelines provide a valuable place for investigating interactions between plant species since
84 prevailing environmental conditions are harsh and this could lead to shifts from negative
85 (competition) to positive interactions (facilitation; cf. 28, 32, 33). The natural treeline on the
86 Tibetan Plateau (TP) is at the highest elevation 4900 m a.s.l. in the Northern Hemisphere (34).
87 Several climatic proxies show that the past century was the warmest period on the TP over the last
88 millennium (SI Appendix, Fig. S1). The TP treelines also are virtually undisturbed by human
89 activities, and both edaphic constraints and slope features allow trees to colonize new areas above
90 the current TP treeline, making the area a good locale for investigating climatic and biotic factors
91 influencing treeline changes (35, 36). Along the eastern TP, tree growth of alpine trees is limited
92 primarily by spring and summer temperatures (see SI Appendix, Fig. S1 and references therein). In
93 the past 100 years, the region experienced a warming of 1.2-1.5 °C (SI Appendix, Fig. S1). Based
5 94 on an observed temperature lapse rate of 0.65°C /100 m on the southeastern TP, an upward
95 displacement of more than 200 m in elevation should be expected on the TP if spring and summer
96 temperature were the single factor limiting tree growth and regeneration, and if treelines were in
97 equilibrium with climate.
98 Recent studies have reported a range of different changes in treelines on the TP. For example,
99 Baker and Moseley (37) estimated an upward shift of the fir treeline by 67 m from comparisons of
100 repeated photographs from 1923 and 2003 taken at the same points in the Baima Snow Mountains
101 (southeastern margin of TP). Plot-based dendroecological reconstructions, however, showed little
102 changes of fir and spruce treelines in the past 200 years on the eastern and southeastern TP (36,
103 38). Both local and regional studies are needed to determine why upward treeline shifts occur in
104 some sites and not in others. Based on data from 14 treeline plots located on the eastern TP
105 between 28.4 and 38.5°N (Fig. 1), we addressed two questions: (1) has climatic warming been
106 associated with an upward shift of treeline during the past 100 years; and (2) could biotic
107 interactions between trees and other plants modulate treeline responses to warming and account
108 for the inconsistent observations of previous studies?
109
110 Results and Discussion
111 A pronounced warming trend is apparent across the TP (Figs. 1, SI Appendix Fig. S1, S2, S3). A
112 total of 14 treeline fir and spruce sites in the eastern part of the region showed increased density of
113 tress (“densification”) during the past 100 years (SI Appendix, Fig. S4), in line with previous
114 studies in that region (36, 38). A global meta-analysis of treeline response to climate warming also
115 found an increase in stand density during the last century (8). Based on stand age structure of the
6 116 studied treeline plots in the TP, we found that tree recruitment increased by more than 50% across
117 all sites in the 1950s and 1960s (Fig. 2). Decadal tree recruitment was significantly correlated (P <
118 0.01; SI Appendix, Table S2) with summer and winter temperatures reconstructed from climatic
119 proxies based on 18O/16O ratios in ice cores and tree-ring widths (39, 40; Zhu et al. 2008) (Fig. 2,).
120 Given that the establishment of new trees at treeline usually is limited more by climate than by the
121 distance that seeds can disperse upwards (41), our null hypothesis is that climatic warming of the
122 TP has accelerated and increased tree establishment at the studied sites, and that the treeline
123 moved upwards.
124 Based on population age structure in the investigated plots across the treeline ecotone, we
125 reconstructed treeline elevation based on the presence of the tallest trees (≥ 2 m) in 50-year
126 intervals. A treeline shift of > 10 m (equal to the maximum tree height in all study plots) in the last
127 100 years was considered to be biologically meaningful. Except in the Sygera Mountains, treelines
128 shifted upslope by > 10 m during the past century (Table 1, SI Appendix, Fig. S4). At one extreme,
129 treeline of a fir-dominated plot in the Ranwu Lake area (RW3) advanced upward in elevation by
130 69 m, a spruce-dominated plot by 65 m, and three of four Picea crassifolia-dominated plots in the
131 northeastern TP by 52 – 80 m.
132 However, the average rate of upward shift overall was only 2.9 ± 2.9 m per decade (range: 0 -
133 8.0 m per decade), far less than rates reported in previous meta-analyses (6.1-11.0 m per decade)
134 of alpine plants (42, 43). The average rate over the eastern TP also was much less than the rapid
135 upward shift of 91-119 m reported since the 1960s for the Green Mountains in Vermont, U.S.A.
136 (8). It also is somewhat less than the upward shift of 4-15 m per decade in High Asia (southern
137 Siberia and along the Urals mountain rangel) (Shiyatov 2003; Kharuk et al. 2010; Hagedorn et al.
7 138 2014[AME1]). The average treeline shift rate we found for the TP, however, was close to that
139 reported for other alpine treelines in Spanish Pyrenees and northern Siberia (9, Kirdyanov et al.
140 2012[AME2]), With a warming in summer and annual mean temperature of around 1.2 - 1.5 °C in
141 the past 100 years on the TP (SI Appendix, Fig. S1), a slower upward shift implies that drivers
142 other than temperature must be considered to understand treeline dynamics on the TP.
143 Species interactions could modulate treeline responses to climatic change. Based on the
144 coverage by shrubs (mainly Rhododendron species) or herbs and their mean heights above the
145 treeline, we defined a thickness index (TI) of short vegetation as the product of shrub and grass
146 cover multiplied by their mean height (see Methods). A generalized linear model that included
147 species above the treeline, TI, treeline elevation, mean slope of treeline ecotone, changes in winter
148 temperature and precipitation, and mean winter temperature and precipitation near the study sites
149 explained 97.2% of the variance in treeline dynamics (SI Appendix, Table S3). However, only two
150 predictor variables – either TI and species composition or TI and vegetation height – were needed
151 to explain 86.6% of the variance in treeline dynamics. Thickness index alone accounted for over
152 50% of the variance in treeline dynamics in either model (SI Appendix, Table S3). Thus, it appears
153 to be reasonable to use TI as a predictor of the rate of upward motion of treelines in response to
154 warming and shrub-herb abundance (Fig. 3).
155 A higher coverage index implies stronger interspecific competition that may counteract the
156 effects of warming on tree establishment. The TI ranged from 0.9 to 3.0 in five of the six treeline
157 plots near the Ranwu Lake and in the Sygera Mountains, where the alpine vegetation is dominated
158 by dense Rhododendron mats 1-3 m in height, and the treelines were stable over the past 100 years.
159 In Plot LZ1-3, fir saplings in dense Rhododendron patches cannot survive more than three years
8 160 because the saplings are smothered by fallen shrub litter. Only one treeline in these two regions
161 (RW3) with a low coverage of herbs (TI= 0.30) shifted significantly upward (69 m). In this case,
162 low shrub cover may have facilitated seedling establishment. Overall, the rate of upward shift in
163 treeline was associated significantly and negatively with TI, suggesting that in addition to climatic
164 amelioration, competition with shrubs and grass controls tree recruitment at treeline on the TP.
165 Under a warming climate, increasing shrub coverage will restrict treeline upward shifts.
166 In the four regions where the TI ranged from 0.14 to 0.64, we observed upward shifts in
167 treeline from 13 to 80 m over the past century. These low to moderate TI values represented
168 conditions where shrubs did not prevent establishment of fir and spruce seedlings above the
169 treeline, but rather created environmental conditions suitable for recruitment and growth. Species
170 above the treeline (tall shrub, short shrub, grass) was used as separate predictor in evaluating
171 treeline changes. Shrubs seem to be stronger inhibitors of tree seedling growth than grasses
172 because the former begin to grow earlier than the latter (45) and outcompete small tree seedlings.
173 At the site with the lowest TI (0.14) spruce treelines moved upslope by ≈80 m. Our results overall
174 illustrate that tree-shrub competition is unlikely to limit recruitment of tree seedlings when TI <
175 0.8; under these conditions, upward movement of treeline as a consequence of climatic warming is
176 likely to be observed. Unfortunately, coverage by shrub or grass species above the treeline across
177 all the investigated plots was never sparse enough to capture a lower TI threshold (which we
178 hypothesize that TI is << 0.14), i.e. most alpine communities studied did not act as “tree nurseries”,
179 and tree recruitment was prevented.
180 In summary, spatially explicit and quantitative assessments of empirical data on TP treeline
181 dynamics in the past century illustrate that climatic warming tended to promote an upward shift of
9 182 alpine treelines at local and regional scales. Upslope migration rates, however, were controlled
183 largely by interspecific interactions. Given the lack or low intensity of local disturbances to the TP
184 treelines by herbivores, land use, and the lack of local geomorphic constraints (i.e., availability of
185 sites with regolith above the treeline) on tree regeneration at most sites, interactions between trees
186 and short-statured vegetation could well account for the discrepancy between treeline dynamics
187 and climatic warming. Species interactions not only help to explain why many treelines have not
188 advanced in response to climatic warming on a global scale (8), but also highlight that predictions
189 of treeline shifts based on climate envelopes may be misleading because interspecific interactions
190 can temper effects of climatic change.
191
192 Material and Methods
193 The study area encompassed six regions along a latitudinal transect between the southernmost and
194 northernmost regions of the eastern TP (28.4-38.5°N; linear distance = 1150 km). The climates of
195 these two regions are influenced strongly by the southern (Baima Snow Mountains, Ranwu Lake
196 and Sygera Mountains) and East Asian (the source region of Yangtze River) monsoons and
197 westerlies (Qilian Mountains), respectively. Annual precipitation decreases from ≈1000 mm in the
198 south to ≈350 mm in the north. Annual mean air temperature ranges from 8.5 °C at Bomi (2800 m)
199 in the south to 4.7 °C at Wulan (2600 m) in the north. Neither annual nor summer precipitation
200 showed significant trends across all six regions, whereas annual, summer and winter temperatures
201 showed significant warming trends (Fig. 1, SI Appendix, Fig. S2, S3). An automated weather
202 station situated at the treeline (4360 m) in the Sygera Mountains, southeastern Tibetan Plateau
203 recorded a mean air temperatures during the growing season of 6.0 ±0.3°C from 2007–2014. This
10 204 is in agreement with the definition of the growing season since soil temperatures at 10 cm depth
205 are higher than 3.2°C, which is regarded as a global thermal threshold for treelines (3).
206 The treeline research plots were established on gentle to moderately steep slopes with
207 substrates dominated by regolith and located away from talus slopes and avalanche paths (i.e.,
208 study plots were in locations more likely to experience warming-induced changes in treeline in
209 absence of biotic factors). The elevations of the alpine treelines declined from 4478 m on the
210 southeastern TP to 3386 m on the northeastern TP as latitude increased. These treelines are much
211 lower than the permafrost zone, which as a lower altitudinal limit (i.e. the zone above which
212 permafrost occurs) running from 4800 m a.s.l. in the south to 4200 m a.s.1. in the north (Wang and
213 French 1995[AME3]).. These treelines are usually diffuse in shape and are characterized by
214 dominant tree species in each region (Fig. S5), including Abies georgei, A. georgei var. smithii,
215 Picea likiangensis var. balfouriana and Picea crassifolia (Table 1). There typically are no
216 krummholz mat formation or flagged trees in the investigated treeline ecotones.
217 The position of the upper treeline was defined by the presence of upright trees with a
218 minimum height of 2 m at the maximum altitude and a continuous distribution above the
219 timberline (forest coverage of more than 30%) in the plot (2, 9, 11). The treeline ecotone refers to
220 the transition from the timberline to the treeless alpine vegetation (3). The upper species limit
221 refers to the highest elevation at which species height is generally shorter than 2 m. One to three
222 spatially independent and rectangular plots (30 ×150 m or 30 ×200 m) were established in each
223 region on a topographically uniform area of the treeline ecotone to include the timberline and the
224 species’ upper limit (9). The longest side (y-axis) of each plot was parallel to the elevational
225 gradient formed by the transition from subalpine forest to alpine shrubland or tundra. The bottom
11 226 left corner of each plot was designated the origin (x, y) = (0, 0). The altitudes of the lower and
227 upper parts of the plots were determined by GPS at a resolution of ± 5 m. The location of each tree
228 within the plots was mapped by recording the x and y coordinates at the center of the main stem.
229 Coordinates were measured to the nearest 0.1 m and then corrected by accounting for the mean
230 slope of each plot. Diameter at breast height (DBH, 1.3 m), tree height, and horizontal projection
231 of the tree canopy along the x- and y-axes were measured for all trees. Tree height was measured
232 directly for trees shorter than 2 m or with a clinometer for trees taller than 2 m.
233 The cover and height of the vegetation above the treelines were quantified using the
234 point-intercept method (46, 47). Six transects along the elevational (y-) axis of each plot were
235 established from the upper treeline to 20 m above the treeline. Transects were spaced 6 m apart
236 perpendicular to the slope, i.e., along the axis perpendicular to the maximum slope (x-axis) at 0, 6,
237 12, 18, 24 and 30 m. Percent vegetation cover was calculated based on the number of contacts
238 between plants and a rod located every meter along the elevational axis, and the height of the
239 vegetation was calculated as the mean height of the vegetation at each sample point. A total of 126
240 points were recorded above the treeline for each plot. To ensure that maximum cover was 100%,
241 multiple contacts at a single sample point were considered to be one contact. The vegetation was
242 dominated by sparse Rhododendron shrubs above the fir treeline in the Baima Snow Mountains
243 (Table 1). Near Ranwu Lake and in the Sygera Mountains, however, Rhododendron shrubs had an
244 extremely high coverage (> 90%), and the height of the shrubs ranged from 1.0 to 3.1 m. In Yushu
245 and the Qilian Mountains, the area above the treeline was occupied by dwarf-shrub species with a
246 mean height of 0.45 to 0.70 m, respectively.
247 The thickness index (TI) of short vegetation was calculated as percent cover × vegetation
12 248 height (m) (Table 1). Cover of plants above treelines can act either as nurseries or biotic
249 bottlenecks for seedling survival, depending on cover and plant height (29, 31). The TI contains
250 the effects of both shrub/herb cover and their height on interspecific interactions. However, the
251 shrub or herb cover above the treeline was > 20% at all sites. A generalized linear model was used
252 to estimate effects of each predictor variable on treeline changes. We estimated the variance
253 explained by each predictor variable using the R software system and the package relaimpo (48).
254 Based on the “successive sweep method,” we determined whether TI was a significant predictive
255 variable.
256 Age structures of tree populations at each site were reconstructed using several methods. First,
257 trees with a DBH > 5 cm were cored to the pith at 1.3 m above ground using a Pressler increment
258 borer. When cores could not reach the pith, we estimated the number of missing innermost rings
259 with a geometric pith locator. Second, ages of saplings and seedlings (height <2.5 m and DBH ≤5
260 cm) were non-destructively determined in the field by counting terminal bud scars (internodes or
261 branch whorls) along the main stem (9, 36). Counting internodes, however, may underestimate
262 true seedling ages by up to four years (36). Third, to obtain a more accurate estimate of tree age in
263 two height classes – 1.3-2 m and ≥2 m – we determined the age of 20 individuals by counting the
264 internodes (SI Appendix, Table S1). As in previous studies (9, 36), we assumed that the times
265 required for seedlings to reach 1.3 or 2 m were statistically the same within each plot. Finally,
266 DBH-age relationships established for each plot were used to estimate ages of trees with rotten
267 stems (SI Appendix, Fig. S6).
268 A simple descriptor of an upward shift of an alpine treeline ecotone is the change in elevation
269 at which the uppermost tree ≥ 2 m was found during successive time periods. The location of the
13 270 treeline was reconstructed at 50-year intervals using standard dendrochronological methods (36).
271 Based on population age structures in each plot and the uppermost tree ≥ 2 m corresponding to an
272 estimated maximum age, treeline position and recruitment in the following 50-year intervals were
273 reconstructed: 1611-1660, 1661-1710, 1711-1760, 1761-1810, 1811-1860, 1861-1910, 1911-1960
274 and 1961-2011/2012/2013. Fieldwork was done from mid-May to mid-October in 2011, 2012 and
275 2013. On this timescale, the expected 5-year error associated with the estimated age for trees 2 m
276 tall is negligible. Maximum tree height was approximately 10 m at the treelines, so we regarded an
277 upward shift of the treeline of more than 10 m in the past 100 years to be significant (36). The
278 presence of old trees at some sites allowed for reconstruction of treeline position back to the early
279 17th century (SI Appendix, Fig. S4). It was evident that there were no remains of dead trees at or
280 above current treeline at all 14 study sites.
281
282 ACKNOWLEDGEMENTS. We thank Dieter Eckstein for comments on an early version, and the
283 fieldwork support from the Southeast Tibet Station for Alpine Environment, Observation and
284 Research, Chinese Academy of Sciences. This work was supported by the National Basic
285 Research Program of China (2012FY111400), the National Natural Science Foundation of China
286 (41525001, 41130529, 41301207), and Youth Innovation Promotion Association，CAS.
287
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367 conservation in Yunnan, PR China. Arc, Antarc Alp Res 39:200–209.
368 38. Lv L (2011) Timberline dynamics and its response to climate change on the Tibetan Plateau
369 [dissertation]. Institute of Botany, Chinese Academy of Sciences, Beijing.
370 39. Thompson LG, et al. (2006) Abrupt tropical climate change: past and present. Proc Natl Acad
371 Sci USA 103: 10536–10543.
372 40. Zhu H, et al. (2011) August temperature variability in the southeastern Tibetan Plateau since
373 AD 1385 inferred from tree rings. Palaeogeogr Palaeoclimatol Palaeoecol 305:84–92.
374 41. Dullinger S, Dirnböck T, Grabherr G. (2004) Modelling climate change-driven treeline shifts:
375 relative effects of temperature increase, dispersal and invasibility. J Ecol 92:241–252.
376 42. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across
377 natural systems. Nature 421:37–42.
378 43. Lenoir J, Gégout J-C, Marquet PA, de Ruffray P, Brisse H (2008) A significant upward shift
379 in plant species optimum elevation during the 20th century. Science 320:1768–1771.
18 380 44. Shiyatov SG. (2003) Rates of change in the upper treeline ecotone in Polar Ural mountains.
381 PAGES News 11: 8 –10.
382 45. Chapin FS, et al. (2005) Role of land-surface changes in arctic summer warming. Science
383 310:657-660.
384 46. Barbour MG, Burk JH, Pitts WD (1987) Terrestrial plant ecology (Benjamin Cummings,
385 Menlo Park).
386 47. Camarero JJ, Gutiérrez E, Fortin M-J (2006) Spatial patterns of plant richness across treeline
387 ecotones in the Pyrenees reveal different locations for richness and tree-cover boundaries.
388 Glob Ecol Biogeog 15:182-191.
389 48. R Development Core Team (2013) R: a language and environment for statistical computing.
390 R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/.
391
392
393 (References will added, several previous references will be removed)
394 Kharuk VI, Ranson KJ, Im ST, Vdovin AS (2010) Spatial distribution and temporal dynamics of
395 high-elevation forest stands in southern Siberia. Glob Ecol Biogeog 19: 822–830
396 Kirdyanov AV, et al. (2012) 20th century tree-line advance and vegetation changes along an
397 altitudinal transect in the Putorana Mountains, northern Siberia. Boreas 41: 56–67.
398 Hagedorn F, et al. (2014) Treeline advances along the Urals mountain range – driven by improved
399 winter conditions? Global Change Biology, 20: 3530–3543.
400 Zhu H, Zheng Y, Shao X, Liu X, Xu Y, Liang E (2008) Millennial temperature reconstruction
401 based on tree-ring widths of Qilian juniper from Wulan, Qinghai Province, China. Chin Sci Bull
19 402 53:3914–3920.
403 Wang B, French HM (1995) permafrost on the Tibet plateau, China. Quaternary Science Reviews
404 14: 255-274:
20 405 Tables
406 Table 1. Characteristics of the 14 treeline plots covering six regions from the southeastern to the
407 northern margins of the Tibetan Plateau (site locations in Fig. 1). The main tree species, the mean
408 covers and heights of the dominant alpine shrub or herb species above the treeline, and the
409 corresponding vegetation thickness index (TI), are given. The last column shows the elevational
410 treeline shift estimated for the past 100 years. Tree species include: 1, Picea crassifolia; 2, Picea
411 likiangensis var. balfouriana; 3, Abies georgei var. smithii and 4, Abies georgei. Dominant shrub
412 or herb species above the treeline include: S1, Salix cupularis; S2, Caragana jubata; S3, Stipa spp.;
413 S4, Rhododendron aganniphum var. schizopeplum; S5, Rhododendron nivale; S5, Artemisia spp.
414 and S5, Rhododendron platyphyllum.
Study Tree Vegetation Treeline Vegetation Height of Vegetation TI Treeline shift (m)
site species above elevation cover (%) vegetation over past 100
treeline (m) (m) years (m)
QL1 1 S1 3385.8 90 0.70 0.63 51.5
QL2 1 S2 3496 20 0.70 0.14 79.9
WL1 1 S1 3876.9 70 0.80 0.56 13.2
WL2 1 S1 3846.5 80 0.80 0.64 53.6
DZ1 2 S3 4194.8 90 0.45 0.41 64.6
LZ1 3 S4 4390 90 3.0 2.70 0.0
LZ2 3 S4 4387.3 90 3.1 2.79 0.8
LZ3 3 S4 4376.7 90 3.30 2.97 0.0
21 RW1 3 S5 4471.3 85 1.0 0.85 3.6
RW2 3 S5 4447.8 83 1.1 0.91 5.4
RW3 2 S6 4478.3 85 0.40 0.34 68.5
BM1 4 S7 4396.8 76 0.60 0.46 28.1
BM2 4 S7 4397.9 82 0.55 0.45 18.7
BM3 4 S7 4427.9 89 0.70 0.62 19.5
415
22 416 Figure legends
417 Figure 1. Location of treeline plots along a latitudinal transect across the eastern Tibetan Plateau
418 (top). Variations in the mean temperature and total precipitation in summer (June-August) since
419 the year with available instrumental records at five meteorological stations located close to the
420 study treeline plots (bottom). A, Zhangye in the middle Qilian Mountains; B, Yushu in the source
421 region of the Yangtze River; C, Nyingchi in the valley of the Sygera Mountains; D, Bomi close to
422 the Ranwu Lake; E, Deqin close to the Baima Snow Mountains. Variations in summer (June –
423 August) mean temperature (low left) and total precipitation (low right) from instrumental records
424 at five meteorological stations located close to the study treeline plots. The instrumental records at
425 Wulan only began in 2001 and are thus not shown. The mean summer temperature at the five
426 stations showed significant and sustained warming trends (statistics are presented), but summer
427 precipitation did not present significant trends.
428
429 Figure 2. Trends in (A), winter-temperature reconstruction based on timberline tree rings on the
430 northeastern Tibetan Plateau (Zhu et al. 2008); (B), ice-core δ18O (18O/16O ratios) series integrated
431 from the Dunde, Guliya, Puruogangri and Dasuopu glaciers on the Tibetan Plateau (39) indicating
432 summer temperature; (C) summer-temperature reconstruction based on timberline tree rings on the
433 southeastern Tibetan Plateau (40); and decadal tree regeneration at treelines in the six studied
434 regions (D-I) across the Tibetan Plateau and related proxies. The two summer climatic proxies are
435 correlated for decadal intervals back to 1601 (r=0.77; p<0.001). See Table 1 and Figure 1 for
436 research regions and forest plots. Red lines in A and C shows decadal moving averages.
437
23 438 Figure 3. The vegetation thickness index (TI) relates changes in treeline elevation across the
439 Tibetan Plateau in the past 100 years to the product of cover and height of shrubs and herbs.
24 440 Figures
441 25 200 A: Zhangye
20 100
r = 0.65, p < 0.001 15 0 15 600 B: Yushu 400
r = 0.67, p < 0.001 200 10 18 600
C) C: Nyingchi
400 16
200 r = 0.73, p<0.001
14 Precipitation(mm) Temperature( 18 600 D: Bomi 400 16
r = 0.62, p < 0.001 200 14 15 600 E: Deqin 400
r = 0.75, p < 0.001 200 10 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year 442 443 Figure 1
25 A: Tree-ring based winter temperature reconstruction 2 0 -2
18 B: Ice core O Composite 2
0
Z-scores -2
C: Tree-ring based summer temperature reconstruction 2 0 -2 1600 1650 1700 1750 1800 1850 1900 1950 2000 75 D: Qilian Mts. QL1 50 QL2 25
0 75 E: Wulan WL1 50 WL2
25
0 45 F: Yushu DZ1 30
15
0
120 G: Sygera Mts. LZ1 80 LZ2
Number of trees of Number LZ3 40
0 120 H: Ranwu Lake RW1 80 RW2 RW3 40
0 480 I: Baima Snow Mts. BM1 360 BM2 240 BM3 120 0
1701-1710
1661-1670 1771-1780 1931-1940 1971-1980
1641-1650 1721-1730
1731-1740 1951-1960
1681-1690 1831-1840 1891-1900 1911-1920 1991-2000
1791-1800 1851-1860 1871-1880
1601-1620 1621-1630 1751-1760 1811-1820
444 445 Figure 2
26 446 447 448 449 450 451 452
453 454 455 456 Figure 3 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474
27 475 476 Supporting Information Appendix 477 478
479 Table S1. Ages of trees reaching heights of 1.3 and 2 m in the treeline plots across the eastern
480 Tibetan Plateau. Values are means ± one SE of the mean.
481
Region Treeline plot Tree age at 1.3 Tree age at 2.0 m (years) m (years) Qilian Mountains QL1 19 ± 2 26 ± 2 QL2 17 ± 2 26 ± 3 Wulan WL1 23 ± 4 31 ± 4 WL2 20 ± 3 30 ± 4
Yushu, source region of Yangtze River DZ1 19 ± 3 25 ± 2 Sygera Mountains LZ1 31 ± 7 34 ± 5 LZ2 31 ± 7 34 ± 5 LZ3 32 ± 4 36 ± 3 Ranwu Lake RW1 22 ± 4 27 ± 3 RW2 23 ± 5 28 ± 4 RW3 21 ± 3 28 ± 4 Baima Snow Mountains BM1 28 ± 3 33 ± 5 BM2 29 ± 4 36 ± 6 BM3 30 ± 4 39 ± 6 482 483 484
28 485 Table S2. Pearson correlation coefficients (first row for each plot) and significance levels (second
486 row for each plot) calculated by relating tree recruitment data at 14 treeline plots and reconstructed
487 mean summer and winter temperatures in decadal intervals. Temperatures were reconstructed from
488 either tree-ring or ice-core δ18O data (see Fig. 2).
489 490 Treeline plot Summer-temperature proxy Winter-temperature proxy Tree-ring width data Oxygen isotopes Tree-ring width data QL1 0.599 0.566 0.771 0.007 0.012 < 0.001 QL2 0.749 0.726 0.482 0.001 0.001 0.042 WL1 0.797 0.717 0.774 < 0.001 < 0.001 < 0.001 WL2 0.674 0.588 0.764 < 0.001 0.003 < 0.001 DZ1 0.633 0.440 0.184 0.127 0.323 0.662 LZ1 0.634 0.553 0.708 < 0.001 0.002 < 0.001 LZ2 0.737 0.660 0.764 < 0.001 < 0.001 < 0.001 LZ3 0.683 0.646 0.746 < 0.001 < 0.001 < 0.001 RW1 0.584 0.557 0.567 < 0.001 0.001 < 0.001 RW2 0.563 0.541 0.575 < 0.001 < 0.001 < 0.001 RW3 0.682 0.635 0.588 0.001 0.003 < 0.001 BM1 0.553 0.549 0.542 0.002 0.002 < 0.001 BM2 0.677 0.636 0.713 < 0.001 < 0.001 < 0.001 BM3 0.647 0.610 0.673 < 0.001 < 0.001 < 0.001 491 492 493 494
29 495 Table S3. Percentage of variance explained by the models predicting treeline migration rate. All
496 the models included species above the treeline, height or vegetation cover (VC) of species above
497 the treeline, the thickness index (TI) of short vegetation, treeline elevation, mean slope of treeline
498 ecotone (Slope), mean/sum annual, summer and winter temperature (AT, ST, WT)/precipitation
499 (AP, SP, WP), changes in annual, summer and winter temperature (CAT, CST and CWT), changes
500 in annual, summer and winter precipitation (CAP, CSP and CWP) of the study areas. A generalized
501 linear model (GLM) was used to predict treeline dynamics. The relative contribution of the top
502 three or two variables is indicated by the different asterisks (*, ** and *** mean p<0.05, p<0.01
503 and p<0.001, respectively).
504 Dependent Independent variables Model R2 variable (percentage) Species TI Height VC Elevation Slope AT AP CAT CAP 97.1 ** *** * * Treeline Species TI Height VC Elevation Slope ST SP CST CSP 96.8 Changes ** *** *** *
Species TI Height VC Elevation Slope WT WP CWT CWP 97.2 ** ** * * Species TI Height VC 82.8 *** *** * Species TI Height 86.6 * *** ** TI Height 85.9 ** * Species TI Height Species*TI 82.8 ** *** Species TI Height TI*height 82.8 *** ** Species TI Height Species*TI 82.8 * *** Species TI Species*TI 82.7 ** *** Species TI 86.6 * **
30 A: Temperature-sensitive tree-ring chronology in the Qilian Mountains 2
0
-2
Tree-ringwidthindex 3 B: Annual mean temperature on the northeastern TP
2
C)
-5 C: Mean September-April tempeature in Wulan -6
Temperature ( -7
-8
16
C) D: Mean early-summer temperature in the Hengduan Mountains
14
12
Temperature (
1 E: Mean summer minimum temperature on the southeastern TP
0
-1
2 18 Z-score F: Mean ice-core O series on the TP 1
0
-1 1000 1200 1400 1600 1800 2000
Year 505
506 Fig. S1. Variations in temperature on the Tibetan Plateau as indicated by tree-ring and ice-core δ18O series.
507 A. Standardized December-April temperature-sensitive tree-ring-width index in the Qilian Mountains of
508 the northeastern Tibetan Plateau (1); B. Annual-temperature reconstruction based on tree rings (40-year
509 moving average) on the northeastern Tibetan Plateau (2); C. Reconstructed mean temperatures from the
31 510 previous September to the current April for the Wulan area based on tree-ring chronologies (3); D. Mean
511 early-summer (June-July) temperature reconstruction based on tree rings in the Hengduan Mountains of
512 the southeastern Tibetan Plateau (4); E. Mean summer minimum temperature based on tree rings on the
513 southeastern Tibetan Plateau (5); F. decadal ice-core δ18O series (an indicator of summer temperature)
514 from the Dunde, Guliya, Puruogangri and Dasuopu glaciers on the Tibetan Plateau (6). Except for B, the
515 thick gray curves represent the 10-year low-pass-filtered values, and the horizontal lines represent the
516 long-term means. The Z-score is calculated by dividing the score deviation (data of each year - mean of the
517 series) by the standard deviation of the series.
518 In addition to the research presented above, tree growth at the treelines/timberlines in our study area
519 responded significantly and positively to summer temperature in the Baima Snow Mountains (7) and the
520 Sygera Mountains (8) of the southeastern TP, Yushu in the source region of the Yangtze River (9) and
521 Wulan of the northeastern TP (10).
522
523 References
524 1. Liu X, Qin D, Shao X, Chen T, Ren J (2005) Temperature variations recovered from tree-rings in the 525 middle Qilian Mountain over the last millennium. Sci China Ser D – Earth Sci 48:521–529. 526 2. Liu Y, et al. (2009) Annual temperatures during the last 2485 years in the Eastern Tibetan Plateau 527 inferred from tree rings. Sci China Ser D – Earth Sci 52:348-359. 528 3. Zhu H, Zheng Y, Shao X, Liu X, Xu Y, Liang E (2008) Millennial temperature reconstruction based on 529 tree-ring widths of Qilian juniper from Wulan, Qinghai Province, China. Chin Sci Bull 53:3914–3920. 530 4. Deng Y, Gou X, Gao L, Yang T, Yang M (2014) Early-summer temperature variations over the past 531 563 yr inferred from tree rings in the Shaluli Mountains, southeastern Tibet Plateau. Quat Res 532 81:513–519. 533 5. Zhu H, Shao X, Yin Z, Xu P, Xu Y, Tian H (2011) August temperature variability in the southeastern 534 Tibetan Plateau since AD 1385 inferred from tree rings. Palaeogeogr Palaeoclimatol Palaeoecol 535 305:84–92. 536 6. Thompson LG, et al. (2006) Abrupt tropical climate change: past and present. Proc Natl Acad Sci USA 537 103:10536–10543. 538 7. Li Z, Zhang Q, Ma K (2012) Summer temperature variations basedon tree-ring data in the central 539 Hengduan Mountains, Northwestern Yunnan, China, AD1475-2003. Clim Change 110:455-467 540 8. Liang E, Shao X, Xu Y (2009) Tree-ring evidence of recent abnormal warming on the southeast 541 Tibetan Plateau. Theor App Climatol 98:9-18. 542 9. Liang E, Shao X, Qin N (2008) Tree-ring based summer temperature reconstruction for the source 543 region of the Yangtze River on the Tibetan Plateau. Glob Planet Change 61:313-320 544 10. Liang E, Shao X, Eckstein D, Huang L, Liu X (2006) Topography- and species-dependent growth 545 responses of Sabina przewalskii and Picea crassifolia to climate on the northeast Tibetan Plateau. For 546 Ecol Manag 236:268-277. 547 548 549 550 551 552
32 553
10 300 9 A: Zhangye 8 200 7 100 6 r = 0.79, p < 0.001 5 0 6 800 5 B: Yushu 4 600 3 2 400 1 r = 0.66, p< 0.001 0 200
C) 12 1000 C: Nyingchi 11 800 10 9 600 8 400 Temperature( r = 0.78, p< 0.001
7 Precipitation(mm) 12 1200 D: Bomi 1000 10 800 600 8 r = 0.75, p<0.001 400 r = 0.40, p < 0.01
8 E: Deqin 1200 1000 6 800 4 600 r = 0.68, p< 0.001 400 2 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 554 Year 555
556 Fig. S2. The annual mean temperatures showed a significant and sustained warming tendency at five
557 meteorological stations located close to the study treeline plots, whereas precipitation values did not such
558 trend (see Figure. 1) except for a slight increasing trend in Bomi. The instrumental records at Wulan only
559 began in 2001 and are thus not shown[C4].
560 561
33 1 0 A: Zhangye 40 -1 -2 -3 20 -4 r = 0.62, p < 0.001 -5 0 0 100 -1 B: Yushu -2 -3 50 -4 -5 r = 0.62, p < 0.001 r = 0.35, p < 0.01 -6 0
C)
6 150 C: Nyingchi 5 100 4 3 50 r = 0.69, p<0.001
Temperature( 2 Precipitation(mm) 5 400 D: Bomi 4 3 200 2 r = 0.71, p < 0.001 r = 0.47, p < 0.001 1 3 400 E: Deqin 2 1 200 0 -1 r = 0.64, p < 0.001 -2 0 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Year 562 563 564
565 Fig. S3. The mean temperatures during the half year when study sites were covered by snow
566 (November-April) showed a significant and sustained warming tendency at five meteorological stations
567 located close to the study treeline plots (see Figure. 1). Except for a significant and increasing trend in
568 precipitation during winter-half year at Yushu and Bomi, the other stations did not show significant trends.
569 The instrumental records at Wulan began in 2001 and they are too short to analyze trends.
570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587
34 588 589 590 591 592 593
QL1 200 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2013 3496 m
175
150
125
100
Treeline 75 3416.1 m
50
(m) increasing altitude
y 25
0 30 30 30 30 0 30 30 30 30 x (m) 594 595
QL2 200 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-19601961-2013 3385.8 m
175
150
125 Treeline 3334.3 m 100
75
50
(m) increasing(m) altitude
y 25
0 30 30 30 30 0 30 30 30 30 x (m) 596
35
WL1 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2012 200
175 3876.9 m 150 3863.5 m 3863.7 m
125
100
Treeline 75 3830.4 m
50
(m) increasing altitude(m)
y
25
0 0 30 30 30 30 30 30 30 30 597 x (m)
WL2 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2013 200 3846.5 m 175
150
125
100
3792.9 m 75
3782.9 m 50
(m) increasing (m) altitude
y
25 Treeline 3762.4 m
0 0 30 30 30 30 30 30 30 30 598 x (m)
36
DZ1 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2012 200
4194.8 m 175
150
125
100
Treeline 75 4130.2 m
50
(m) increasing (m) altitude
y
25
0 0 30 30 30 30 30 30 30 30 599 x (m)
LZ1 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2012 150 4398.4 m
125
100 Treeline 4388.7 m
75
50
(m) increasing altitude(m)
y 25
0 0 30 30 30 30 30 30 30 30 600 x (m)
37
LZ2 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 150 1961-2011
125
100
4378.3 m Treeline 75 4376.7 m 4375.6 m 4377.5 m
50
(m) increasing altitude(m)
y 25
0 0 30 30 30 30 30 30 30 30 601 x (m)
LZ3 1611-1660 1661-1710 150 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2011
4387.6m
125 4385.2 m 4384 m
100
75
50 Treeline 4368.4m
(m) increasing altitude 25
y
0 0 30 30 30 30 30 30 30 30
x (m) 602 RW1 1761-1810 1811-1860 1961-2012 200 1611-1660 1661-1710 1711-1760 1861-1910 1911-1960 4471.3m 4467.6m 175
150
125
100
Treeline
75 4419.7m
50
(m) increasing (m) altitude
y
25
0 0 30 30 30 30 30 30 30 30 603 x (m)
38
RW2 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2012 200 4447.8 m 4442.3 m 175
150
125
4398.9 m 100
4388.3 m 4383.5 m 75 Treeline 4374.2 m 4372.4 m
(m) increasing (m) altitude 50
y
25
0 0 30 30 30 30 30 30 30 30 604 x (m) RW3 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2012 200 4478.3 m
175
150
125
100 4409.8 m
75
50
(m) increasing (m) altitude
y
25 Treeline 4364.0 m 0 0 30 30 30 30 30 30 30 30 605 x (m)
39 BM1 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2012 200
175
150 4396.9m
125
100
4383.4m 75
50
(m) increasing (m) altitude
y Treeline 4368.7m
25 4367.9m
0 0 30 30 30 30 30 30 30 30 606 x (m)
BM2 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2012 200
175 4394.9m 4397.9m
150
125 Treeline 4379.2m 100
75
50
(m) increasing (m) altitude
y
25
0 0 30 30 30 30 30 30 30 30 607 x (m)
40
BM3 1611-1660 1661-1710 1711-1760 1761-1810 1811-1860 1861-1910 1911-1960 1961-2012 200
175
150 4427.9m
125 4411.0m 4408.4m 100
4407.4m 75
50
(m) increasing (m) altitude
y Treeline 25 4377.7m
0 0 30 30 30 30 30 30 30 30 608 x (m) 609
610 Fig. S4. Spatiotemporal variability in tree density and treeline position (maximum elevation reached by
611 trees with stems at least 2 m high indicated in each plot) reconstructed at the alpine treeline plots along a
612 north-south latitudinal transect across the eastern Tibetan Plateau. Each closed symbol represents an
613 individual that was established during the period indicated at the top of the rectangular plot, and open
614 symbols represent trees established during periods previous to that indicated at the top. Different symbols
615 correspond to different establishment periods (e.g. triangles indicate trees established 1661–1710). Plot
616 LZ3 corresponds to the treeline dataset presented in Liang et al. (2011).
617
618
619
620
621
622
623
624
625
626
627
41 628
629
630
631
632 633
634 Fig. S5. A typical Smith fir (Abies georgei var. smithii) treeline ecotone (plot LZ1) where dense
635 rhododendron shrub (Rhododendron aganniphum var. schizopeplum) dominates above the treeline
636 ecotone (photo by Eryuan Liang).
637
42 638
639
120 200 QL1 QL2 80 100 40 2 Age = 2.5*DBH+16.9, p < 0.001, R = 0.77, n=64 Age = 3.5*DBH+14.3, p < 0.001, R2= 0.71, n=38 0 0
240 240 WL1 WL2 160 160 80 80 Age = 4.8*DBH+8.1, p < 0.001, R2= 0.73, n=60 2 0 0 Age = 3.7*DBH+14.5, p < 0.001, R = 0.86, n = 84
100 240 DZ1 LZ1 75 160 50 80 25 Age = 1.5*DBH+26.6, p < 0.001, R2= 0.60, n = 104 2 0 0 Age = 4.2*DBH+30.2, p < 0.001, R = 0.79, n = 128
240 LZ2 300 LZ3 160 200 80 2 100 2 0 Age = 4.4*DBH+23.5, p < 0.001, R = 0.78, n = 58 0 Age = 4.1*DBH+26.8, p < 0.001, R = 0.90, n=98
Tree age (year) age Tree 300 300 RW1 RW2 200 200 100 100 2 0 Age = 4.3*DBH+30.6, p < 0.001, R2= 0.66, n=93 0 Age = 4.5*DBH+17.1, p < 0.001, R = 0.77, n=105
240 150 RW3 BM1 100 160
50 2 80 2 0 Age = 2.6*DBH+19.3, p < 0.001, R = 0.69, n=43 0 Age = 5.6*DBH+12.4, p < 0.001, R = 0.78, n=53
320 300 BM2 BM3 240 200 160 80 2 100 2 0 Age = 7.7*DBH+18.8, p < 0.001, R = 0.77, n = 258 0 Age = 5.6*DBH+33.7, p < 0.001, R = 0.76, n=108 0 10 20 30 40 50 0 10 20 30 40 50 60 640 DBH (cm) 641
642 Fig. S6. Relationships between tree age and diameter at breast height (DBH, measured at 1.3 m
643 aboveground) in the treeline plots along a latitudinal transect across the eastern Tibetan Plateau.
644 645 646
43