Global climate is changing more rapidly in alpine and arctic regions than in other areas, and the average temperature in alpine areas is expected to continue to rise faster than the average global increase (Intergovernmental Panel on Climate Change (IPCC), 2007, 2014). Changes in mountainous vegetation phe- nology are considered an important and observable trace of mountainous ecosystem response to these cli- matic changes (Jonas et al., 2008; Menzel et al., 2006), as well as a key determinant of coupled water and energy exchange (White et al., 2009), landsurface carbon ﬂuxes (Barrio et al., 2013; Richardson et al., 2010), and species distributions (Chuine & Beaubien, 2001). As a climate driver, snow is one of the most important controlling factors in mountainous ecosystems (Cornelius et al., 2013; Wipf et al., 2009). It shields harsh winds and provides frost protection in winter (Chen, An, et al., 2015; Desai et al., 2016; Groffman et al., 2006; Wahren, et al., 2005; Wipf et al., 2006) and nutrient mobilization and water supply in spring (Keller & Körner, 2003). Variations in timing and accumulation of snow have been reported to signiﬁcantly inﬂuence vegetation phenology, as well as the energy balance (Euskirchen et al., 2007), water cycling (Barnett et al., 2005; Rawlins et al., 2006), and soil carbon cycling (Dorrepaal et al., 2003; Monson et al., 2006). For these reasons, it is critical to understand the response of alpinelandsurfacephenology to the variation of the timing and accumulation of snow, which can change ecological interactions and thereby reshape alpine ecosystems. Many studies have documented that the timing and accumulation of snow inﬂuence the start and length of mountainous landsurfacephenology (Chen, Liang, et al., 2015; Dunne, 2003; Jonas et al., 2008; Paudel & Andersen, 2013; Trujillo et al., 2012; Yu et al., 2013). For instance, a larger snowpack and longer snow cover duration can result in later snowmelt and timing of phenological events (Cooper et al., 2011; Inouye, 2008). In contrast, shorter snow cover duration and earlier snowmelt often advance plant development (Chen et al., 2011; Dunne, 2003; Hu et al., 2010; Wipf et al., 2009; Wipf & Rixen, 2010). Moreover, both the timing and accumulation of snow (Beniston et al., 2003; Hüsler et al., 2014; Trujillo et al., 2012) and phenological events (Benadi et al., 2014; Cornelius et al., 2013; Deﬁla & Clot, 2005; Lambert et al., 2010; Schuster et al.,
Different levels of response to environmental drivers can be observed according to the vegetation type. For broadleaved deciduous forests and shrublands, the phenological variations are relatively stable in all regions, whereas needle-leaved evergreen forests or grasslands present greater variability between biogeographical regions and phenophases. The variation of OG of deciduous trees is grouped into two categories: Alpine regions and northern latitudes, such as Atlantic, Continental, and Steppe regions starting during the first week of April; and the southern biogeographical regions (Black Sea, Mediterranean, and Panonian), which start four days later. EOS dates are more diverse and ranged from 16 November for Alpine, Steppe, and Continental regions until 30 November and 2 December for the Black Sea and Atlantic Regions, respectively. In terms of LS, it can be observed that deciduous trees of regions with higher values of EOS produce slightly longer growing seasons (Atlantic and Black Sea), equal to 240 and 232 days, respectively, perhaps due to the oceanic influence keeping the temperature warmer for a relatively longer period. The timing of grasslands was estimated for the Atlantic and Continental regions only, as large homogeneous sites were not found for the remaining biogeographical regions. Grasslands present similar OG dates in both regions. However, there are important discrepancies regarding the EOS and LS times. Continental grasslands have a median EOS date equal to 196 Julian days (15 July), more than four months earlier than Atlantic grasslands (26 December). This results in a LS equal to five-and-a-half months for Continental grasslands against a LS of almost a year for Atlantic grasslands (11.8 months). Additionally, the variance in estimated EOS for Continental grasslands was significantly greater than for other categories. This can be due to the wide longitudinal range of the Continental region, but also to greater difficulty in estimating the autumnal phenophases in temperate regions .
Our results show that the correlations between ΔSCD and ΔSOS and between ΔSCD and ΔLOS differ consid- erably between the four subregions (Figure 3 and Table 1), being generally stronger in the northern than in the southern Alpine subregions. These ﬁndings are in agreement with the fact that the correlation strength between SCD and onset of spring is dependent on the climate of a geographical region [Jönsson et al., 2010]. We conclude that the correlation between SCD and alpinephenology is stronger in geographical regions with longer SCD than in regions with shorter SCD. Our study goes one step further in showing that the cor- relation between ΔSCD and ΔSOS and between ΔSCD and ΔLOS vary between vegetation types (Table 1). We found that ΔSCD has a strong positive correlation with ΔSOS and a negative correlation with ΔLOS for NG, MH, SV, and midaltitude TWS (Table 1 and Figures S6 and S7). These results indicate that a later start of grow- ing season and a shorter length of growing season are always in parallel with a longer snow cover duration in high and middle altitudinal vegetation types, and vice versa. These ﬁndings support the suggestion by Julitta et al.  that snow cover plays an important role in determining the start of phenological development in alpine grasslands and agree with the ﬁnding by Galvagno et al.  that snow cover limits the length of the growing season in high-altitude grasslands. Yu et al.  report that the winter snow depth has stronger effect on grasslands and shrubs than on broadleaf deciduous forests and needleleaf forests in temperate China, which is what we also ﬁnd for the Alps. Our results provide evidence that SCD is correlated with forest phenology in middle and low altitudes across the Alps. Indeed, the phenology of forests (i.e., 10–22% of the total area of BF, CF and MF) and low-altitude TWS signiﬁcantly correlates (|R| < 0.5) with snow cover duration at middle and low altitudes (Figures S6 and S7). Although temperature strongly regulates the start of the growing season of both temperate deciduous broadleaf and coniferous forest [Yu et al., 2013], our results consequently support the suggestion that phenological events of most temperate tree species are not solely driven by air temperature [Yu et al., 2013] but also by snow cover duration. Furthermore, in European Alps, the dynamic of forest ecosystem could be affected by the variation of interannual snow cover duration. Our choice of land cover data set entails that changes in land cover type within the study period are not taken into account in our analysis. However, excluding pixels with land cover change between 2000 and 2012 had no signiﬁcant inﬂuence on our results.
and thermal time to peak (TTP), in the subsequent growing season. Then we evaluated the role of terrain features in shaping the relationships between snow cover and pasture phenology using exact multinomial tests for equivalence. Results revealed a positive relationship between snow covered dates (SCD) and PH occurred in over 1,664 km 2 at p <0.01 and 5,793 km 2 at p<0.05, which account for more than 8% of 68,881 km 2 of pasturelands analyzed in Kyrgyzstan. Also, more negative than positive correlations were found between snow cover onset and PH, and more positive correlations were observed between snowmelt timing and PH. Thus, a longer snow season can positively influence PH. Significant negative correlations between TTP and SCD appeared in 1,840 km 2 at p<0.01 and 6,208 km 2 at p <0.05, and a comparable but smaller area showed negative correlations between TTP and snowmelt date (1,538 km 2 at p<0.01 and 5,188 km 2 at p <0.05). Furthermore, terrain had a stronger influence on the timing of snowmelt than on the number of snow covered dates, with slope being more important than aspect, and the strongest effect appearing from the interaction of aspect with steeper slopes. In this study, we characterized the snow-phenology interactions in highland pastures and revealed strong dependencies of pasture phenology on timing of snowmelt and snow onset and snow cover duration and. Under changing climatic conditions toward earlier spring warming, decreased duration of snow cover may lead to lower pasture productivity threatening the sustainability of montane agropastoralism.
analyses that used image segmentation focused on extracting land use/land cover classes from basic grayscale or multi-spectral imagery where a priori class labels were manually assigned by the analyst during the segmentation process. For example, Corcoran and Winstanley (2007) used grayscale imagery of a suburban residential area to compare human-created vs. computer-created image segments. Volunteer test subjects were instructed to draw boundaries around objects in the image, which included streets, vehicles, houses, etc. The optimal computer segmentation strategy produced much smaller segments on average than the human-made segments. This indicates that the computer segments are relatively conservative in their approximation of real- world objects.
previous study comparing the spatial and temporal patterns among snow-cover and the NDVI trends in northern Eurasia (1982-1999), and linking the derived “greening” trends both to more favourable conditions for growth (through rising temperatures) and to declining snow- cover effects on NDVI (Dye & Tucker, 2003). A promising step forward for the study of vegetation dynamics in this region is the reconstruction of long-term AVHRR NDVI time series that aim to correct for the effect of confounding abiotic factors such as snow or bare soil, such as presented in (Zhang, 2015). However, the presence of snow within an AVHRR pixel does not rule out that vegetation may be active. It is therefore difficult to exclude snow contamination to some extent, particularly at large spatial scales and in a boreal biome.
Total snow cover days in a year (SCDs hereafter) is an im- portant index that represents the environmental features of climate (Ye and Ellison, 2003; Scherrer et al., 2004), and is directly related to the radiation and heat balance of the Earth– atmosphere system. The SCDs vary in space and time and contribute to climate change over short timescales (Zhang, 2005), especially in the Northern Hemisphere. Bulygina et al. (2009) investigated the linear trends of SCDs observed at 820 stations from 1966 to 2007, and indicated that the dura- tion of snow cover decreased in the northern regions of Eu- ropean Russia and in the mountainous regions of southern Siberia, while it increased in Yakutia and the Far East. Peng et al. (2013) analysed trends in the snow cover onset date (SCOD) and snow cover end date (SCED) in relation to tem- perature over the past 27 years (1980–2006) from over 636 meteorological stations in the Northern Hemisphere. They found that the SCED remained stable over North America, whereas there was an early SCED over Eurasia. Satellite- derived snow data indicated that the average snow season duration over the Northern Hemisphere decreased at a rate of 5.3 days per decade between 1972/1973 and 2007/2008 (Choi et al., 2010). Their results also showed that a major change in the trend of snow duration occurred in the late 1980s, especially in western Europe, central and East Asia, and mountainous regions in western United States.
water near the surface can refreeze with night-time cooling and thaw during day (Bargtsson, 1980). This refreeze and thaw cycle can continue for days if the liquid water does not exceed the water-holding capacity of the snowpack. Dur- ing the day, this cycle might need several hours to warm up and resume melting again (Dingman, 1994). Snowmelt starts once the liquid water in the snowpack exceeds the water- holding capacity. Initially, snow melting is more uniform (“matrix flow” in porous media) but with increase in liquid water content and growth of snow grains, melt flow rate ac- celerates (“preferential flow”). Theoretical representation of preferential flow is difficult which can advance below freez- ing temperature, so snowmelt algorithm even in the sophis- ticated snow models (e.g., like SNTHERM; Jordan, 1991) is based on liquid water flow under isothermal conditions (Waldner et al., 2004). However, the parameter “liquid-water holding capacity” is difficult to measure from wet snow be- cause, during the snowmelt metamorphism, snow can be su- persaturated yet be below the liquid-water holding capacity due to the freeze-thaw cycle (Livneh et al., 2009). In vari- ous studies, the liquid water-holding capacity is quantified as 3–9 % of the volume of the snowpack (Denoth et al., 1984; Kattlemann, 1987; Kendra et al., 1994; and Albert and Krajeski, 1998). Jordan (1991) used 4 % of the pore volume in SNTHERM, Lynch-Stieglitz (1994) used 5.5 % height of the compacted snow layer, while Dingman (1994) suggested 6 % of the pore space as the liquid water-holding capac- ity. Following Jordan (1991) and Denoth (2003), Livneh et al. (2009) applied 4 % of the pore volume of the liquid-water holding capacity in the Noah model. Because the density of the snowpack is different for fresh snow compared to old snow, Livneh et al. (2009) showed that 4 % of the pore vol- ume can range from approximately 2.5 % of SWE depth for old snow to approximately 10 % of SWE depth for fresh snow. Here, we have used 5 % of the total mass of the snow- pack (liquid and ice) as the liquid water-holding capacity (Tarboton and Luce, 1996), and the fraction of liquid water L f is estimated as:
derived from original LAI V2 (smoothing and gap filling was already included in the retrieval algorithm) outperforms the phenology derived from the seasonal trajectories derived after smoothing LAI V1 ( Table 3 ). We tested four state of the art methods to extract phenological metrics: thresholds, logistic function, derivative and moving average. Each method has its own strengths and limitations ( de Beurs and Henebry, 2010 ). The threshold approach based on a percentage of the annual amplitude is simple and robust but it is sensitive to the minimum and maximum values that may be affected by noise in the signal. The logistic function approach has been widely used (e.g. Zhang et al., 2003 ) but it is limited to the performance of the model fitting ( Beck et al., 2006 ) and it may fail when the curvature function is too flat to determine the phenophases ( de Beurs and Henebry, 2010 ). The derivative approach based on the maximum increase and decrease of the vegetation variable is very sensitive to the noise in the signal and the temporal smoothing and composition approach and it cannot re- present short growing seasons well, especially when the increase and decrease in the annual time series occur rapidly and abruptly ( Beck et al., 2006 ). The moving average approach is based on the assumption that vegetation growth follows a well-defined temporal profile and it may fail in cases of disturbances and abrupt changes. Further, the se- lection of the time lag is arbitrary.
and landsurfacephenology of the region. Others have focused on the discrimination between weather changes and human impacts (e.g. Kariyeva et al 2012 , Dubovyk et al 2016 ), but those studies did not iden- tify the effect of large scale climate oscillations and regional climate patterns. Combining five climate oscil- lation indices into one regression model and then identifying the relative importance of each of these indices on precipitation and temperature and, subse- quently, landsurfacephenology allowed us to identify where each of the climate indices displays its strongest influence. Our analysis demonstrates that the landsurfacephenology across Central Asia is affected by several climate modes, both those that are strongly linked to far northern weather patterns and those that are forced by southern weather patterns, making this region a ‘climate change hotspot’ (Bothe et al 2012 ) with strong spatial variations in weather patterns. We found that SCAND and EAWR, both regional cli- mate patterns, played a significant role in Central Asia indicating that global climate patterns might not be suf- ficient to predict weather patterns and subsequent landsurface changes in these regions (Chen et al 2016 ). These findings may also explain, in part, why both the CMIP3 and CMIP5 model cohorts exhibited large inter-model spread in montane Central Asia and why the CMIP5 models did not significantly improve pre- cipitation estimates in this sensitive region (Flato et al
On the very small scale (m), the local advection of sen- sible heat from adjacent bare ground to snow covered areas was observed to cause strongly increased ablation rates at the upwind edges of snow patches. While for conditions with moderate wind velocities, the significant advection effect ap- peared to be active over a rather short distance of a few me- ters, for high wind velocities the advection effect could be observed for a distance of about 20 m. Thus, to capture this effect in energy balance models, a very high horizontal reso- lution is required. Neglecting the local advection of sensible heat, model results demonstrate that snow ablation is mainly controlled by net radiation fluxes. In some wind-exposed ar- eas, however, net turbulent exchange of sensible and latent heat contributes up to 50 % of the net melt energy. More im- portant is the effect of a SIBL over snow patches. Based on the constant flux layer assumption, energy balance models tend to overestimate snow ablation later in the ablation sea- son due to the fact that most energy balance models do not consider the existence of stable internal boundary layers. The development of a SIBL close to the snow-covered ground significantly alters the local energy balance above melting snow by suppressing turbulence, hence reducing snow abla- tion. Measured turbulent fluxes above a melting snow cover support this hypothesis by giving evidence of the existence of a SIBL and relatively small turbulent fluxes of sensible heat compared to modeled results.
The publication of  had a great impact and captured the imagination of many. It encouraged others to identify data sets containing phenological data and brought forth data from a wide range of sources. It became obvious that a huge amount of information existed on the arrival times of migrant birds. The majority of these species are insectivorous so a linkage with insect emergence times might be expected and there would be advantages for a species to return to the UK as soon as food supplies were adequate in spring in order to resume breeding. The main sources of data are i) coastal bird observatories, ii) county bird records and iii) individual recorders. A combination of data from these sources led to the publication of . The majority of species were showing some tendency to get earlier in recent years (negative correlations between arrival date and year) and an encouraging number of these were statistically significant despite the greater variability in these data series compared to those of plants. Most series suggested earlier arrival in warmer years. Notable among the species was sand martin which showed a strong trend towards earlier arrival and a remarkable synchrony between records from the counties of Leicestershire and Sussex. This work identified two potential problems with bird migration data. The first was the presence of a greater recording effort at weekends in county records and the second the possibility of arrival dates affected by population size, albeit only likely to be a problem amongst species at low population density. One feature of bird migration phenology is that the response to temperature seems to be of the order of 2 days per °C, much lower than that apparent in plants and invertebrates. It is not known why migrating species are not taking more advantage of earlier springs. If they are being conservative, because of the danger when arriving too early for food supplies to be available, we might expect a lag in their response to earlier springs before they take advantage of the opportunities for earlier breeding.
Comparisons between the transect product and the data sets are presented in Fig. 2 for each data set, with the cor- relation coefficient ρ in the left column and 1, the relative bias, on the right. The months May to August were excluded and grid cells with less than 30 pairs available for correla- tion are not shown. GlobSnow achieved the best agreement with the transect data, with over 80 % of the grid cells hav- ing a correlation coefficient exceeding 0.6; this is expected since the Eurasian data were used in the calibration of Glob- Snow. However, a region of low correlation occurs around the southern border of FSU, probably as the higher relief of the area is not accounted for by the retrieval. In contrast, only 54 % of the grid cells had ρ ≥ 0.6 in the LEGOS EO- based SWE product for which data were available only for latitudes north of 50 ◦ N. As has been demonstrated (Foster et al., 1997; Pulliainen, 2006; Takala et al., 2011), tempo- ral and spatial biases in radiometry mean that a single al- gorithm, like that used by LEGOS, cannot provide accurate and global SWE estimates without assimilating ground data or using forward modelling, as in GlobSnow. The similar- ities between the LPJ-WM and SDGVM correlation maps can be attributed to their use of the same climate drivers. For these two models, 79 % and 74 % respectively of grid cells have ρ ≥ 0.6, and, like GlobSnow, both exhibit lower corre- lation in the south of the FSU. Approximately 78 % of the CLM4CN grid cells have ρ ≥ 0.6, with poorer correlation in southeastern Siberia, while for JULES 79 % of the grid cells have ρ ≥ 0.6.
information in the visible and near infrared portions of the electromagnetic spectrum, observations can only be obtained under clear-sky, daytime conditions.
Data assimilation (DA) algorithms attempt to utilize the information in such
discontinuous observations by integrating them into a numerical model. The model provides spatial and temporal continuity as well as physically-based schemes that allow an observation of one quantity to inform predictions of other modeled variables. The observation system provides reliable, independent information on a variable that the model simulates imperfectly, preventing model drift and improving the accuracy of the simulation. Our hypothesis is that data assimilation is the most effective method for utilizing MODIS-derived measurements of snow cover in studies of climate and hydrology. Further, as LSMs are frequently employed in a coupled mode with atmospheric models, a MODIS SCA assimilation system can be used to improve the initialization of numerical forecasts, a powerful predictive application of MODIS-derived information.
The altitude effect of stable water isotopes in precipita- tion is a well-known effect since the benchmark paper of Dansgaard (1964). Moser and Stichler (1970, 1971) showed that the altitude effect of precipitation by orographic up- lift of air masses and the related decrease in the conden- sation temperature leads to a depletion of heavy isotopes with altitude and can also be observed in fresh snow. In their work they sampled fresh snow in the European East- ern and Western Alps and found an average elevation gra- dient of about − 3 ‰ per 100 m altitude for δ 2 H, with varia- tions between − 2 to − 10 ‰ per 100 m. Other authors such as Renaud (1969) in Greenland, Gonfiantini (1970) at the Kili- manjaro or Friedman and Smith (1970) in the Sierra Nevada also observed a depletion of heavy isotopes with altitude in fresh snow. These authors examined a depletion of δ 18 O between − 0.25 to − 1.25 ‰ per 100 m. Niewodniczanski et al. (1981) presented a comprehensive study on the altitudinal gradient of the 18 O isotope in mountains regions of the world. In the South American Andes, the Central Asian Hindu Kush and Himalaya, as on Mount Kenya and Mount Kilimanjaro in Africa, they took fresh snow samples 5 to 10 cm below the snowsurface. They found an elevation gradient for δ 18 O between − 0.6 and − 1.0 ‰ per 100 m. However, the sam- ples were subject to a wide variation with small-scale inverse gradient and were thus only partly attributable to a linear elevation gradient. Niewodniczanski et al. (1981) attributed the variation to the conditions during and after deposition of snow, such as wind drift and fractionation by melting pro- cesses, and to topography and climatic conditions of the sam- pled areas. Moran et al. (2007) collected fresh snow sam- ples in the Canadian Rocky Mountains during two periods of snowaccumulation and examined the δ 18 O isotope content. They determined elevation gradients ranging from −0.3 to +1.8 ‰ per 100 m and, as with the Niewodniczanski study, the data collected was subject to a wide variation. It can thus be concluded that an elevation gradient of the isotopic con- tent in fresh snow is only partially observable or very weak. A non-existing elevation gradient can be explained by the fact that air masses in which snow is formed undergo no small-scale orographic uplift and secondly that the source and the trajectory of air masses are essential to the average
position), and environmental constraints (e.g., age and other grouping policies, family influence, popularity of sport, coach influence). For example, considering an individual constraint such as sex, researchers have suggested that RAEs are not consistently observed in female sport. While some studies on female athletes have shown significant RAEs (e.g., Delorme & Raspaud, 2009; Dixon, Liburdi, Horton, & Weir, 2013; Smith & Weir, 2013; Weir, Smith, Paterson, & Horton, 2010), others have found no such effect (e.g., Delorme, Boiché, & Raspaud, 2009; Vincent & Glamser, 2006; Wattie, Baker, Cobley, & Montelpare, 2007). Furthermore, compared to the male RAE profile, the female pattern has been found to be non-linear, with many studies exhibiting a peak in the number of athletes in Q2 (e.g., Baker, Schorer, Cobley, Bräutigam, & Büsch, 2009; Delorme et al., 2010a; Weir et al., 2010). The influence of a task constraint, such as participation level, can be seen in Barnsley and Thompson’s (1988) analysis of minor league ice hockey players when they noticed reversals in the RAE in the lowest tiers of minor league ice hockey. This reversal in the RAE is characterized by a greater
between windward and leeward slopes (Pomeroy and Brun, 2001). In order to be able to combine data sets from differ- ent areas, elevation was changed to relative elevation (dE), which is the difference between the absolute and the mini- mum elevation of the area. This mapping respects the fact that all data sets are from the alpine zone at or above tree line but that this corresponds to different absolute elevation in the different areas. The slope (SL) represents gravitational pro- cesses such as sloughing and avalanching, which can have a significant effect on the snow distribution (Bl¨oschl and Kirnbauer, 1992; Gruber, 2007; Sovilla et al., 2010; Bern- hardt and Schulz, 2010). In combination with northing (NO), the slope also describes the amount of solar energy which is available for the ablation and settling of snow. NO is also important for the deposition and redistribution of snow by wind (Seyfried and Wilcox, 1995; Lehning et al., 2008), as more snow is usually accumulated in the leeward sides of slopes and mountains. An additional parameter, which rep- resents the effect of the wind, is the mean sheltering index SX (Winstral et al., 2002), which has been calculated for the direction of the main flow (see site descriptions) and for a maximum distance of 100 m. Several studies showed that SX is a good measure for sheltering and exposure of the lo- cal terrain, which gives a simple but reasonable representa- tion of the local flow field and therefore of the redistribution of snow by wind (e.g. Winstral and Marks, 2002; Winstral et al., 2002; Anderton et al., 2004; Erickson et al., 2005; Molotch et al., 2005; Litaor et al., 2008; Schirmer et al., 2011). Finally, different measures for the surface roughness are applied. The standard deviation of the elevation (σ (E)) and slope (σ (SL)) represent classical morphometric rough- ness measures (Evans, 1972; Speight, 1974; Shepard et al., 2001). The surface roughness strongly affects the redistribu- tion of snow by wind and gravitational processes (Jost et al., 2007), and can be seen as the capability of the surface to trap snow. As suggested by Lehning et al. (2011), we also tested the fractal dimension D and the ordinal intercept γ of the semi-variogram, which have been identified as good mea- sures for the surface roughness (Goodchild and Mark, 1987; Power and Tullis, 1991; Klinkenberg, 1992; Klinkenberg and Goodchild, 1992; Xu et al., 1993; Sun et al., 2006; Taud and Parrot, 2006).
days had all four assemblages represented. Both taxonomic and phylogenetic analyses of the amplicon data suggested temporal shifts in snow algae communities.
However, the taxonomic shift in Bagley Basin (Figure 6) didn’t follow a uniform pattern that could be described by the collected bioclimate variables. Hierarchical clustering based on the KR distance (Supplemental Figure 1) supported this claim, as the clusters of pairwise phylogenetic distances between samples were not based on collection date. This suggests that small-scale variability was similar to large-scale variability, such sample similarity (β-diversity measured by phylogenetic distance) was not solely based on time of collection. The variation in microenvironment affected the phylogenetic composition of microbial communities on a very small scale. At the taxonomic resolution of this analysis, most of the variation between communities is explained by landscape-level variation in environmental variables and not by factors that may differ across the entire mountain range. Additionally, at a basin scale, the bioclimate analyses described patterns that only scratch the surface of the factors that govern these assemblages and biocoenoses. Though samples were collected in relative close proximity, many parameters that likely affect diversity were not measured. Recent studies show that the surface underlying the snow ecosystem plays an important role in snow algal growth and community composition. Studies have looked at the effect of adjacent or underlying bedrock (Hamilton and Havig 2017) or lakes underlying the snow environment (Procházková et al. 2018). To describe a snow algae biocoenosis more completely, future work should describe