BIOME SHIFTS IN SIBERIAN ARCTIC TUNDRA: EVIDENCE FROM FIVE DECADES OF SPACE-BASED EARTH OBSERVATION. Gerald V. Frost and Howard E.

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1 BIOME SHIFTS IN SIBERIAN ARCTIC TUNDRA: EVIDENCE FROM FIVE DECADES OF SPACE-BASED EARTH OBSERVATION Gerald V. Frost and Howard E. Epstein (advisor) Department of Environmental Sciences, University of Virginia, Charlottesville, VA ABSTRACT Increasing abundance of tall, canopy-forming shrubs is one of the primary changes expected in the Arctic tundra biome with recent climate warming, but virtually all evidence for shrub increase comes from North America. Here we demonstrate a novel technique for assessing changes in the extent of tall shrublands in northwest Siberian tundra since the 1960s, using satellite imagery dating to the early Space Age. First, we directly quantified changes in tall shrub cover by comparing high-resolution satellite imagery from the 1960s and recent years for five ~60 km 2 study areas. We then determined temporal trends in the Normalized Difference Vegetation Index (NDVI) a spectral metric of the abundance of green vegetation for these sites for , using imagery from Landsat satellites. We found that Landsat pixels with strong, positive NDVI trends tended to correspond to areas which have developed new shrub cover since the 1960s. We exploited the sensitivity of Landsat NDVI time-series to changes in tundra vegetation to estimate recent changes in shrubland extent for six much larger (~1,000 km 2 ) study areas. We conclude that alder abundance has increased at all eleven sites distributed across this large and littlestudied region, and that high potential exists for future shrub increase. INTRODUCTION Rapid changes in climate are driving a suite of changes to the physical and biological properties of Arctic lands and seas. The dramatic reduction in the extent of summer sea-ice in recent decades has garnered the most attention and is a key driver of a phenomenon known as Arctic amplification, by which climate-induced changes to the Arctic land- and sea surface create positive feedbacks that promote further warming (Serreze and Barry 2011). On land, one of the most commonly reported trends in Arctic tundra is an increase in the abundance of shrub vegetation (Myers-Smith et al 2011). This form of land-cover change is of global significance, because the development of an erect plant canopy strongly modifies biophysical system-properties of tundra landscapes, with impacts to the Earth s climate system at regional and potentially global scales. For example, low-growing tundra vegetation is completely covered by snow in the winter season and has very high albedo, such that most incoming solar energy is reflected back into space. Tall shrub vegetation, however, protrudes above the snowpack and reduces surface albedo, such that more solar energy is absorbed and can be exchanged with the lower atmosphere. Other important biophysical properties affected by tundra shrub increase include the temperature and stability of permafrost, which currently stores large amounts of carbon. Direct evidence for tundra shrub increase primarily comes from field-based observations (e.g., Tape et al 2006) and experiments (Walker et al 2006); however, virtually all of this evidence comes from the North American Arctic. In contrast, very little information exists for the Eurasian Arctic, which encompasses a large proportion of the Arctic tundra biome. In this paper, we demonstrate a novel application for satellite remote-sensing datasets that date to the early Space Age to elucidate recent land-cover Frost 1

2 changes in a large and little-studied region the northwest Siberian Low Arctic. Earth-observing satellites offer an effective means of evaluating land-cover changes at regional to circumpolar scales. Widespread increases in remotely-sensed measures of Arctic vegetation productivity, such as the Normalized Difference Vegetation Index (NDVI), have been observed at high latitudes since 1981 (e.g., Bhatt et al 2010, Myneni et al 1997); this phenomenon has been termed the greening of the Arctic. The NDVI takes advantage of the unique spectral properties of leaf pigments, which preferentially absorb visible wavelengths, but strongly reflect infrared wavelengths. NDVI is calculated from the reflectance values (ρ) for the red and near-infrared (NIR) portions of the electromagnetic spectrum, which are commonly recorded by multi-spectral satellite instruments: NDVI = (ρ red ρ NIR ) / (ρ red + ρ NIR ) (Eq. 1) The NDVI is a unitless ratio ranging from 0 to 1, with high values corresponding to dense vegetation with high aboveground biomass. Increasing trends in NDVI are commonly referred to as greening, and decreasing trends are referred to as browning. Most high-latitude remote-sensing studies to date have utilized datasets obtained by the Advanced Very High Resolution Radiometer (AVHRR), onboard the NOAA family of polar-orbiting satellites (e.g., Bhatt et al 2010). AVHRR provides a global dataset with high temporal resolution (daily) and a 33 year period-of-record (1981- present), thus making it well-suited for circumpolar-scale applications. The spatial resolution of global AVHRR data, however, is ~7-km; at this coarse scale, most forms of land-cover change are rendered as a subpixel effect that is, what appears as a homogeneous trend across a single large pixel, typically results from multiple types of change that are occurring at much finer scales within the pixel. Arctic greening is therefore ambiguous with respect to landcover change, because a positive NDVI trend can be caused by increased productivity of existing tundra vegetation, and/or more fundamental changes to land-cover, such as shrub expansion. The lack of overlap in the spatial scale of field- and remote-sensing based approaches makes it difficult to reconcile the heterogeneity of vegetation responses observed at the field-scale (i.e., sub-pixel level), with the more uniform trends seen at regional and circumpolar scales. Bridging this gap is a critical component of efforts to understand how tundra vegetation is changing, where it is changing, and how to more accurately project future changes. Several recent studies (Fraser et al 2011, McManus et al 2012) have applied multi-spectral data from the Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) instruments, which have operated aboard the Landsat family of satellites since Landsat data have much higher spatial resolution (30 m per pixel) than AVHRR and offer an intermediate spatial scale that allows greater integration and contextualization of information obtained at the field-scale (meters) and the circumpolar scale (kilometers). Figure 1. Ground-photo of expanding shrubland near Kharp, northwest Siberia. Siberian alder is the dominant tall shrub. The development of alder shrublands in tundra represents a biome shift and alters a range of system properties in Arctic landscapes. Frost 2

3 In this paper, we sought to assess recent changes in the extent of tall shrublands dominated by Siberian alder (Alnus viridis ssp. fruticosa) in a network of tundra-dominated study areas spanning the northwest Siberian Low Arctic region. We chose alder as a focal species, because it has a wide geographic distribution and forms a tall canopy (> 2 m); the establishment of alder shrublands in tundra represents a fundamental change to vegetation structure and a biome shift from tundra to shrubland (Figure 1). Our specific objectives were to (1) directly quantify changes in alder shrubland cover in study areas for which overlapping, high-resolution satellite images exist for the 1960s and recent years; (2) isolate the NDVI trends for shrublands in these study areas using Landsat time-series data from ; (3) use the attributes of the NDVI time-series for recently-expanded alder shrublands to estimate changes in alder shrub cover over much larger study areas; and (4) integrate field-based observations made at two of the study sites to assess the potential for future shrub increase in the region. Data Sources METHODS We utilized three sources of satellite imagery: (1) declassified VHR photographs from 1960s-era satellite surveillance systems; (2) commercial VHR satellite imagery from recent years; and (3) Landsat time-series for the period alder shrublands (shrubs > 2 m tall) are readily distinguished in Keyhole imagery, because their dark leaves, tall stature, and extensive intra-canopy shadowing produces a dark photo-signature that contrasts strongly with low-growing tundra vegetation (Figure 2). A single Corona satellite acquired extensive imagery for the Yamal and Tazovskiy peninsulas in August 1968; Gambit imagery is far less extensive, but useful imagery was acquired in the Taymyr Peninsula region in July We obtained Keyhole imagery from the U.S. Geological Survey (USGS) Earth Explorer website Present-day commercial VHR satellites, such as Quickbird, Worldview-2, and GeoEye-1, have produced archives of contemporary imagery that can be coregistered with Keyhole imagery from the 1960s to visually assess changes in shrubland extent over a ~45 year period. Most modern sensors also collect imagery with multiple spectral bands red, green, blue, and infrared that facilitate automated techniques for distinguishing tall shrublands from other land-cover types over large areas. We acquired modern VHR imagery from commercial vendors, and from archives held by the National Geospatial-Intelligence Figure 2. Comparison of imagery from 1966 (Gambit; left) and 2009 (GeoEye-1; right) showing increase in tall alder shrubland extent (dark areas) at Dudinka site, northwest Siberia. VHR imagery from the 1960s come from the KH-4B Corona and KH-7 Gambit (collectively referred to as Keyhole ) Cold War satellite surveillance systems (McDonald 1995). Keyhole imagery comprises the oldest VHR dataset available for the northwest Siberian region, establishing a baseline for land-cover change studies over a ~45 year time period. Keyhole systems acquired panchromatic (i.e., black & white) photographs with spatial resolutions of 2 m (Corona) and 0.7 m (Gambit). Tall Frost 3

4 Agency (NGA). Landsat imagery has a relatively coarse spatial resolution (30 m), but unlike VHR sensors, which capture detailed snapshots of relatively small areas at a single point in time, Landsat acquires imagery of the entire globe at frequent intervals and therefore permits time-series analysis that is, the examination of land surface changes on a year-to-year basis. Landsat data are wellsuited for this application, because the data are well-calibrated over time and a robust method has been developed to correct for atmospheric conditions at the time of image acquisition, such as variation in aerosols and water vapor content (Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Masek et al 2006)). Additionally, the 30 m spatial resolution of Landsat data is adequate for resolving temporal trends within individual land-cover patches. The entire Landsat archive is georeferenced to a common grid of pixels; that is, Landsat data from the 1980s can be stacked with data from later years to create a continuous, overlapping grid of pixels. The observational period-of-record for Landsat is ; however, useful, cloudfree imagery was not available for all study areas for all years (Table 1). Additionally, Landsat data acquisition was suspended for most Arctic regions, including northwest Siberia, during We compiled Landsat imagery for each study landscape using the USGS Global Visualization Viewer (GLOVIS; We restricted our search to imagery acquired during a 4-week period in mid-summer (late July to mid-august), when aboveground biomass is at its peak. We converted the topof-atmosphere reflectance values in the raw imagery to surface reflectance using the LEDAPS algorithm (Masek et al 2006). VHR acquisitions for bioclimate subzone E of the Circumpolar Arctic Vegetation Map (Walker et al 2005) in the northwest Siberian region (Figure 2); bioclimate subzone E refers to the warmest, southernmost part of the Arctic tundra biome, where the proliferation of tall shrubs is an important land-cover change issue. VHR coverage of the northwest Siberian region is relatively sparse, so we made opportunistic use of available, cloudfree imagery that was acquired during the growing season (June-early September). The locations of intensive study sites were also constrained by the availability of Keyhole VHR imagery that overlapped with the footprints of modern VHR imagery, and by the availability of cloud-free, peak growing season Landsat imagery particularly for , because this period is critical for establishing a baseline for time-series analysis due to the lack of Landsat imagery for We were able to delineate eleven tundra-dominated study areas distributed across the northwest Siberian Low Arctic (Figure 2). Study areas included five relatively small intensive sites, where 1960s VHR Figure 3. Overview of study areas in northwest Siberia. Intensive study areas with 1960s satellite imagery are outlined in red; study areas with Landsat imagery only are outlined in black. The colored area indicates the southernmost Low Arctic tundra (Walker et al., 2005). Study areas We identified potential study areas by examining available archives of modern Frost 4

5 Table 1. Summary of study area location, attributes of historical and modern very-highresolution imagery, and observational period-of-record for Landsat. Study area Latitude (ºN) Longitude (ºE) Area (km 2 ) Historical VHR imagery Modern VHR imagery 1 Landsat period of record Kharp Aug 1968 (Corona) 21 June 2010 (WV-1) Obskaya Aug 1968 (Corona) 3 June 2011 (WV-2) Laborovaya none 11 Jul 2005 (QB-2) Tanlova Aug 1968 (Corona) 22 Jul 2011 (WV-2) Taz Aug 1968 (Corona) 20 Aug 2010 (WV-1) Mesoyakha none 2 Sep 2005 (QB-2) Gydan ,080 none 9 Jul 2009 (GE-1) Tanama ,223 none 23 Aug 2009 (QB-2) Yenisey ,259 none 6 Jul 2010 (GE-1) Dudinka Jul 1966 (Gambit) 9 Jul 2009 (GE-1) Taymyr ,226 none 9 Jul 2010 (GE-1) WV-1 = WorldView-1, WV-2 = WorldView-2, QB-2 = QuickBird, GE-1 = GeoEye-1 imagery was available, and six much larger sites. Additionally, suitable Landsat data exist from at least three years in the mid-1980s for all sites. The study landscapes are primarily composed of gently-sloping, upland terrain; some landscapes also encompass welldeveloped river floodplains. All sites experience a continental Arctic climate; longterm means of annual temperature range from C and means of growing-season temperature (June-August) range from C. Imagery analysis At the five intensive study sites, we compared Keyhole VHR imagery from with VHR imagery from recent years to quantify changes in tall shrub cover; a detailed description of methods can be found in (Frost et al 2013). Briefly, we co-registered the VHR image pairs in a Geographic Information System (ArcMap v. 10.0; ESRI, Redlands, CA) and overlaid a uniform grid of vegetation sampling-points at 30 m spacing. The sampling grid matched the spatial scale of Landsat, and was aligned to the center of individual Landsat pixels. We then recorded the presence/absence of tall shrub cover at each sampling-point for 1960s and modern VHR imagery, using visual photointerpretation. From the grid data, we calculated the total area of tall shrublands for each time-step by determining the fraction of points with tall shrub cover, and multiplying by the total area of each site. We also computed the percent change in total shrub cover, relative to the total cover of shrubs in the baseline imagery. For the six regional study areas, we exploited the multi-spectral capability of modern VHR imagery to automatically delineate tall shrub stands. We used the Interactive Supervised Classification function in ArcMap v 10.0 to automatically delineate tall shrublands, based on the spectral attributes of small training polygons in which we manually delineated representative areas of shrublands. We used the same technique to delineate lakes and other waterbodies, which were excluded from subsequent NDVI timeseries analysis. The remaining tundra landcover classes were collapsed into a single category. For all sites, we compiled Landsat imagery for the summers of In order to minimize the effects of seasonal phenology on the time-series, we restricted our search to imagery obtained during a 4- week period during the peak of the growing Frost 5

6 season (~late July mid August; Julian days ). For the five intensive sites, we used the vegetation sampling grid data to isolate the NDVI time-series for Landsat pixels that corresponded to three landcover classes: (1) newly-established shrublands (i.e., shrubs that did not exist in the 1960s); pre-existing shrub stands (i.e., where shrubs were already present in the 1960s); and (3) tundra land-cover types. For the six regional study areas, we distinguished tall shrublands as a single class (because the shrub extent in the 1960s was unknown), and a tundra land-cover class. We then applied pixel-based regression for all landcover classes, wherein we conducted least-squares linear regression for each Landsat pixel stack, with NDVI as a response variable and year as the independent variable. We then calculated the slope of the trendline (i.e., the temporal rate of greening and browning ) for all pixels with significant trends (p 0.05). For the five intensive study sites, we determined the percent of total pixels for each land-cover class that had significant NDVI trends. Next, we determined the frequency distributions of the rate greening or browning, by plotting the % of all pixels for each landcover class that had significant (p<0.05) trends within 30 bins of NDVI trend. We then used these frequency distributions to calculate the probability that shrub pixels in regional study areas corresponded to newlyestablished alder cover, based on their NDVI trend. Intensive sites RESULTS Table 2. Summary of changes in tall shrub canopy cover at the five intensive study sites, expressed on an area and percent basis. Site 1960s shrub cover (ha) 2000s shrub cover (ha) Δ (ha) %Δ Kharp Obskaya Tanlova Taz 1,213 1, Dudinka 1,284 1, Time-series analysis of Landsat NDVI at the intensive sites indicate that significant trends are overwhelmingly positive (i.e., greening) for four of five sites (Table 3). The one exception is the Tanlova site, where browning was observed in many areas of upland tundra. Time-series of Landsat NDVI also corroborate the shrub expansion observed in VHR imagery comparisons, with strong greening observed in known shrub expansion areas (Figure 4). Across all sites, ~69% of Landsat pixels in shrub expansion areas have significant, positive NDVI trends, while only ~30% of pre-existing shrublands have significant NDVI trends. Additionally, the mean NDVI trend for shrub expansion pixels is significantly higher than that of pre- Figure 2. Corona (1968; left) and QuickBird (2003; center) imagery at Kharp site. Red markers indicate points with new shrub cover. Pixel-based linear regression of Landsat NDVI for (right) show significant greening, mostly in shrublands; pixels with non-significant trends (p>0.05) are not shown. Darker tones of green indicate higher rates of greening. Comparison of 1960s Keyhole and modern VHR imagery indicates that tall alder shrublands have expanded at all five intensive study sites (Table 2). Additionally, we observed virtually no die-back of preexisting shrub cover, except locally due to erosional processes in riparian areas. Frost 6

7 Table 4. Summary of Landsat NDVI observations and NDVI trends at the study sites. Site Mean observations per pixel % pixels greening % pixels browning Kharp Obskaya Laborovaya Tanlova Taz Tanama Dudinka existing shrub stands (p<0.01) (Figure 5). Regional study areas Pixel-based linear regression of Landsat NDVI has been conducted for two of six regional study areas at the time of writing. Significant NDVI trends are overwhelmingly positive (greening) for both of these sites (Laborovaya and Tanama). As at the intensive sites, high rates of greening were generally observed for tall alder shrublands. Based on the frequency distribution curves shown in Figure 5, we estimate that Figure 5. Frequency distributions of the magnitude of significant greening trends (p<0.05) for Landsat pixels in alder shrub patches that have either expanded in size, or remained stable in comparisons of 1960s and modern satellite imagery at the five intensive landscapes. Distribution curves are plotted as the percentage of all Landsat pixels with shrubland cover that have a significant trend of given slope. shrub cover increased only 2.4% at the Laborovaya study area, but by 15.5% at the Tanama study area. Discussion In this study, we have demonstrated a novel application of remote-sensing datasets spanning five decades, to characterize landcover changes in a large and little-studied region. Our results are broadly consistent with findings from the North American Arctic: tall alder shrublands are becoming more abundant in northwest Siberian tundra. Examination of VHR imagery indicates that geomorphic processes in permafrost are a critical, landscape-scale mechanism for shrubland expansion at most of the study sites. Areas of patterned-ground are of particular importance; patterned-ground refers to small, disturbed landforms, commonly termed frost boils, that occur at regular, geometric intervals across contiguous areas (Figure 6). These landforms are annually disturbed by frost-heave processes that occur as surface soils begin to freeze in early winter. These disturbed features typically support very little vegetation; however, these microsites are favorable for the recruitment of alders. The facilitation of alder recruitment in patterned-ground has been described in detail, on the basis of field studies at two of the intensive study sites: Kharp and Obskaya (Frost et al 2013). We have not yet made systematic investigation of the degree to which alder expansion is associated with patterned-ground areas at the regional study areas; however, patterned-ground areas, similar to those observed in the field at the Kharp and Obskaya study sites, are very common within all of the regional study sites. We find it highly probable that patternedground facilitation of alder recruitment is a widespread mechanism for tall alder expansion across much of the northwest Siberian Low Arctic. Given the widespread distribution of patterned-ground in the region, Frost 7

8 there appears to be high potential for the continued expansion of tall shrublands. Long-term meteorological records from the northwest Siberian region indicate that a warming climate has probably also played a role in the shrub expansion and overall greening observed in the eleven study sites. Of particular importance is a trend toward warmer summer temperatures since the mid-1960s, as summer temperature is a key control on the growth and reproduction of tall, boreal shrubs such as alder. Three weather stations are distributed within the study area; one ~35 km southeast of Kharp and Obskaya, one ~20 km south of Mesoyakha, and one ~25 km south of Dudinka. All three stations show significant positive trends in summer temperature for the period (p<0.05; ºC decade -1 ). Growth and seed production of alder increases sharply beyond a threshold mean summer temperature of 10ºC (Lantz et al 2009); given that the long-term mean summer temperatures at all of the areas we investigated are near 10ºC, it is highly likely that the warming that has occurred in recent decades has promoted alder establishment over the satellite period-of-record. One limitation of Landsat-based analyses of Arctic vegetation dynamics is the near-total lack of data for for much of the circumpolar region. This raises the question of whether the extensive, linear increases in NDVI we recorded at most study areas would have been observed if data were available for this lost decade. We argue that although 1990s data would be desirable, it is unlikely that non-linear dynamics of NDVI occurred during this period. Other time-series analyses of Arctic NDVI using AVHRR which provides a complete data record since 1981-preesnt indicate greening that is consistent with the Landsat-based findings we report here. For example, a circumpolar-scale analysis of AVHRR NDVI trends by (Bhatt et al 2010) indicates greening throughout most Figure 6. Aerial view of alder shrublands growing in association with patterned-ground. The small, bare areas offer favorable seedbeds with relatively warm soils and little competing vegetation. of our study area with the exception of the southern Yamal Peninsula, where we also observed net browning of the landscape at the Tanlova study area. Second, climatic conditions during the 1990s were warmer than those of preceding decades; thus, we would anticipate that NDVI increased during the 1990s. In this study, we have demonstrated a novel application of multi-temporal, spacebased remote-sensing datasets that collectively provide a 45-year period-of-record for the detection of landcover change. Here we have applied this technique solely on a single type of land-cover change, the expansion of tall alder shrublands; however, this technique could also be used to address a broad array of other research questions concerning tundra vegetation dynamics. This approach would be most useful for study areas with detailed landcover maps, such as maps that are informed by dedicated geobotanical field efforts that sample the full array of tundra vegetation communities. Such land-cover maps could be used to stratify NDVI trends obtained through pixel-based linear regression of Landsat data, and develop NDVI response curves, similar to those shown in Figure 5, for specific landcover types to assess the degree to which different land-cover types are sensitive to Frost 8

9 ongoing changes to Arctic environmental conditions. Such efforts could elicit new hypotheses about land-cover change issues, and inform dedicated studies that establish the nature of, and mechanisms for, tundra vegetation changes. Acknowledgements First and foremost, I thank the Virginia Space Grant Consortium (VSGC) and the NASA Land-Cover Land-Use Change Initiative (LCLUC) for financial support of this research. I also thank the NGA Commercial Imagery Program for permitting the scientific use of NGA satellite imagery archives. Special thanks go to Jaime Nickeson at NASA for querying the NGA archives and delivering useful data. Finally, I thank Kathy Holcomb at the University of Virginia Alliance for Computational Science and Engineering for her help in scripting many of the processing steps for Landsat data. References Bhatt U S, Walker D A, Raynolds M K, Comiso J C, Epstein H E, Jia G, Gens R, Pinzon J E, Tucker C J, Tweedie C E and Webber P J 2010 Circumpolar Arctic Tundra Vegetation Change Is Linked to Sea Ice Decline Earth Interactions Fraser R H, Olthof I, Carrière M, Deschamps A and Pouliot D 2011 Detecting longterm changes to vegetation in northern Canada using the Landsat satellite image archive Environmental Research Letters Frost G V, Epstein H E, Walker D A, Matyshak G and Ermokhina K 2013 Patterned-ground facilitates shrub expansion in Low Arctic tundra Environmental Research Letters Lantz T C, Kokelj S V, Gergel S E and Henry G H R 2009 Relative impacts of disturbance and temperature: persistent changes in microenvironment and vegetation in retrogressive thaw slumps Global Change Biology Masek J G, Vermote E F, Saleous N E, Wolfe R, Hall F G, Huemmrich K F, Gao F, Kutler J and Lim T-K 2006 A Landsat Surface Reflectance Dataset for North America, IEEE Geoscience and Remote Sensing Letters McDonald R A 1995 Corona: success for space reconnaissance, a look into the Cold War, and a revolution for intelligence Photogrammetric Engineering and Remote Sensing McManus K M, Morton D C, Masek J G, Wang D, Sexton J O, Nagol J R, Ropars P and Boudreau S 2012 Satellite-based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010 Global Change Biology Myers-Smith I H, et al 2011 Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities Environmental Research Letters Myneni R B, Keeling C D, Tucker C J, Asrar G and Nemani R R 1997 Increased plant growth in the northern high latitudes from 1981 to 1991 Nature Serreze M C and Barry R G 2011 Processes and impacts of Arctic amplification: A research synthesis Global and Planetary Change Frost 9

10 Tape K, Sturm M and Racine C 2006 The evidence for shrub expansion in Northern Alaska and the Pan-Arctic Global Change Biology Walker D A et al 2005 The circumpolar Arctic vegetation map Journal of Vegetation Science Walker M D et al 2006 Plant community responses to experimental warming across the tundra biome Proceedings of the National Academy of Sciences of the United States of America Frost 10