Niwot Ridge Research Articles

Temporal variability and predictability predict alpine plant community composition and distribution patterns.

One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species-environment associations. In this study, we evaluated how two components of temporal environmental variation-variability and predictability-impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long-Term Ecological Research site, we used in situ, high-resolution temporal measurements of soil moisture and temperature from 13 locations ("nodes") distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within-day) and seasonal (2- to 4-month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes.

Scaling Individual Tree Transpiration With Thermal Cameras Reveals Interspecies Differences to Drought Vulnerability

AbstractUnderstanding tree transpiration variability is vital for assessing ecosystem water‐use efficiency and forest health amid climate change, yet most landscape‐level measurements do not differentiate individual trees. Using canopy temperature data from thermal cameras, we estimated the transpiration rates of individual trees at Harvard Forest and Niwot Ridge. PT‐JPL model was used to derive latent heat flux from thermal images at the canopy‐level, showing strong agreement with tower measurements (R2 = 0.70–0.96 at Niwot, 0.59–0.78 at Harvard at half‐hourly to monthly scales) and daily RMSE of 33.5 W/m2 (Niwot) and 52.8 W/m2 (Harvard). Tree‐level analysis revealed species‐specific responses to drought, with lodgepole pine exhibiting greater tolerance than Engelmann spruce at Niwot and red oak showing heightened resistance than red maple at Harvard. These findings show how ecophysiological differences between species result in varying responses to drought and demonstrate that these responses can be characterized by deriving transpiration from crown temperature measurements.

Topographic Heterogeneity and Aspect Moderate Exposure to Climate Change Across an Alpine Tundra Hillslope

AbstractAlpine tundra ecosystems are highly vulnerable to climate warming but are governed by local‐scale abiotic heterogeneity, which makes it difficult to predict tundra responses to environmental change. Although land models are typically implemented at global scales, they can be applied at local scales to address process‐based ecological questions. In this study, we ran ecosystem‐scale Community Land Model (CLM) simulations with a novel hillslope hydrology configuration to represent topographically heterogeneous alpine tundra vegetation across a moisture gradient at Niwot Ridge, Colorado, USA. We used local observations to evaluate our simulations and investigated the role of topography and aspect in mediating patterns of snow, productivity, soil moisture, and soil temperature, as well as the potential exposure to climate change across an alpine tundra hillslope. Overall, our simulations captured observed gradients in abiotic conditions and productivity among heterogeneous, hydrologically connected vegetation communities (moist, wet, and dry). We found that south facing aspects were characterized by reduced snowpack and drier and warmer soils in all communities. When we extended our simulations to the year 2100, we found that earlier snowmelt altered the timing of runoff, with cascading effects on soil moisture, productivity, and growing season length. However, these effects were not distributed equally across the tundra, highlighting potential vulnerabilities of alpine vegetation in dry, wind‐scoured, and south facing areas. Overall, our results demonstrate how land model outputs can be applied to advance process‐based understanding of climate change impacts on ecosystem function.

Sampling a pika's pantry: Temporal shifts in nutritional quality and winter preservation of American pika food caches

AbstractClimate change is increasing temperature, decreasing precipitation, and increasing atmospheric CO2 concentrations in many ecosystems. As atmospheric carbon rises, plants may increase carbon‐based defenses, such as phenolics, thereby potentially affecting food quality, foraging habits, and habitat suitability for mammalian herbivores. In alpine habitats, the American pika (Ochotona princeps) is a model species for studying effects of changing plant chemistry on mammals. To survive between growing seasons, pikas cache “haypiles” of plants rich in phenolics. Although they are toxic to pikas, phenolic compounds facilitate retention of plant biomass and nutrition during storage, and they degrade over time. Alpine avens (Geum rossii, Rosales: Rosaceae) is a high‐phenolic plant species that comprises up to 75% of pika haypiles in Colorado. Here, we tested the hypothesis that contemporary climate change has affected the nutritional value of alpine avens to pikas in the last 30 years. Specifically, we compared phenolic activity, nutritional quality, and overwinter preservation of plants collected at Niwot Ridge, Colorado (USA), in 1992 to those collected between 2010 and 2018, spanning nearly three decades of climate change. Phenolic activity increased in alpine avens since 1992, while fiber and nitrogen content decreased. Importantly, overwinter preservation of plant biomass also increased, particularly on windblown slopes without long‐lasting snow cover. Previous studies indicate that pikas at this site still depend on alpine avens for their winter food caches. Higher phenolic content in alpine avens could therefore enhance the preservation of haypiles over winter; however, if pikas must further delay consuming these plants to avoid toxicity or invest extra energy in detoxification, then the nutritional gains from enhanced preservation may not be beneficial. This study provides important insights into how climate‐driven changes in plant chemistry will affect mammalian herbivores in the future.

Weekly high-resolution multi-spectral and thermal uncrewed-aerial-system mapping of an alpine catchment during summer snowmelt, Niwot Ridge, Colorado

Abstract. Alpine ecosystems are experiencing rapid change as a result of warming temperatures and changes in the quantity, timing and phase of precipitation. This in turn impacts patterns and processes of ecohydrologic connectivity, vegetation productivity and water provision to downstream regions. The fine-scale heterogeneous nature of these environments makes them challenging areas to measure with traditional instrumentation and spatiotemporally coarse satellite imagery. This paper describes the data collection, processing, accuracy assessment and availability of a series of approximately weekly-interval uncrewed-aerial-system (UAS) surveys flown over the Niwot Ridge Long Term Ecological Research site during the 2017 summer-snowmelt season. Visible, near-infrared and thermal-infrared imagery was collected. This unique series of 5–25 cm resolution multi-spectral and thermal orthomosaics provides a unique snapshot of seasonal transitions in a high alpine catchment. Weekly radiometrically calibrated normalised difference vegetation index maps can be used to track vegetation health at the pixel scale through time. Thermal imagery can be used to map the movement of snowmelt across and within the near sub-surface as well as identify locations where groundwater is discharging to the surface. A 10 cm resolution digital surface model and dense point cloud (146 points m−2) are also provided for topographic analysis of the snow-free surface. These datasets augment ongoing data collection within this heavily studied and important alpine site; they are made publicly available to facilitate wider use by the research community. Datasets and related metadata can be accessed through the Environmental Data Initiative Data Portal, https://doi.org/10.6073/pasta/dadd5c2e4a65c781c2371643f7ff9dc4 (Wigmore, 2022a), https://doi.org/10.6073/pasta/073a5a67ddba08ba3a24fe85c5154da7 (Wigmore, 2022c), https://doi.org/10.6073/pasta/a4f57c82ad274aa2640e0a79649290ca (Wigmore and Niwot Ridge LTER, 2021a), https://doi.org/10.6073/pasta/444a7923deebc4b660436e76ffa3130c (Wigmore and Niwot Ridge LTER, 2021b), https://doi.org/10.6073/pasta/1289b3b41a46284d2a1c42f1b08b3807 (Wigmore and Niwot Ridge LTER, 2022a), https://doi.org/10.6073/pasta/70518d55a8d6ec95f04f2d8a0920b7b8 (Wigmore and Niwot Ridge LTER, 2022b). A summary of the available datasets can be found in the data availability section below.

Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data

AbstractThe oil and gas (O&G) sector is estimated to be the largest contributor to anthropogenic methane (CH4) emissions in Colorado. Since 2004, the State of Colorado has implemented multiple regulations to significantly reduce emissions from the O&G sector. The Denver‐Julesburg Basin (DJ Basin) is a significant O&G producing region in northern Colorado, and O&G production here has steadily increased over the last decade. To assess CH4 trends in Northern Colorado, we selected CH4 retrievals from the NASA Atmospheric Infrared Sounder (AIRS) instrument for 2003–2020. The study grid cell includes Denver, Boulder, and much of the dense O&G production in the DJ Basin. We computed mean June‐August ascending node AIRS 700 hPa CH4 for each year and subtracted mean June–August CH4 sampled at NOAA's Niwot Ridge (NWR) station, a high‐altitude background site. Differences represent estimated enhancement over background. Linear regression shows an annual change of −2.84 ppb ± 0.8 ppb from 2012 to 2020 (R2 0.90) and an estimated reduction of 56% for 2012–2020, despite substantial increases in O&G production. Local CH4 enhancement is strongly correlated with surface measurements of ethane at Platteville which is in the center of the O&G fields (correlation coefficient 0.96), and this is evidence that reductions in O&G emissions are driving reductions in CH4. We conclude that AIRS CH4 can be used to measure the efficacy of emissions control programs in this region and that regulatory requirements are having an effect.

Estimating phenological sensitivity in contemporary vs. historical data sets: Effects of climate resolution and spatial scale.

Phenological sensitivity, or the degree to which a species' phenology shifts in response to warming, is an important parameter for comparing and predicting species' responses to climate change. Phenological sensitivity is often measured using herbarium specimens or local studies in natural populations. These approaches differ widely in spatiotemporal scales, yet few studies explicitly consider effects of the geographic extent and resolution of climate data when comparing phenological sensitivities quantified from different data sets for a given species. We compared sensitivity of flowering phenology to growing degree days of the alpine plant Silene acaulis using two data sets: herbarium specimens and a 6 yr observational study in four populations at Niwot Ridge, Colorado, USA. We investigated differences in phenological sensitivity obtained using variable spatial scales and climate data sources. Herbarium specimens underestimated phenological sensitivity compared to observational data, even when herbarium samples were limited geographically or to nearby weather station data. However, when observational data were paired with broader-scale climate data, as is typically used in herbarium data sets, estimates of phenological sensitivity were more similar. This study highlights the potential for variation in data source, geographic scale, and accuracy of macroclimate data to produce very different estimates of phenological responses to climate change. Accurately predicting phenological shifts would benefit from comparisons between methods that estimate climate variables and phenological sensitivity over a variety of spatial scales.

Influence of Snowpack Cold Content on Seasonally Frozen Ground and Its Hydrologic Consequences: A Case Study From Niwot Ridge, CO

AbstractFrozen ground influences subsurface hydrology by reducing soil permeability, impeding infiltration, and inducing surface runoff. As the seasonal snowpack strongly controls the evolution of the ground thermal regime, it is necessary to represent the snowpack in models simulating frozen ground and its hydrologic consequences. Conventional understanding of the relationship between snowcover and frozen ground considers the snowpack as an insulator, shielding the ground from cold winter temperatures and thus inhibiting frozen ground. However, observations and modeling at Niwot Ridge, a seasonally snow‐covered alpine catchment in the headwaters of the Boulder Creek watershed, illustrate that snowpack cold content can promote and preserve frozen ground beneath the snowpack during the snowmelt season, unlike bare ground patches that thaw relatively rapidly due to exposure to solar radiation and warm spring temperatures. We present results from a coupled snowpack and subsurface thermo‐hydrologic model including freeze‐thaw processes that captures these contrasts reasonably well. Our results suggest that the cold snowpack at this site acts as a heat sink and promotes frozen ground, in contrast to the conventional understanding of the snowpack as an insulator. In the subalpine zone, infiltration simulations show that shallow freezing beneath snow‐covered ground has a much stronger effect on infiltration than shallow freezing beneath bare ground because the soil beneath the snow remained frozen during snowmelt, whereas bare patches thawed by the time they received excess snowmelt run‐on.

Snowmelt-Driven Seasonal Infiltration and Flow in the Upper Critical Zone, Niwot Ridge (Colorado), USA

The hydrology of alpine and subalpine areas in the Colorado Front Range (USA) is evolving, driven by warming and by the alteration of precipitation patterns, the timing of snowmelt, and other components of the hydrologic budget. Field measurements of soil hydraulic conductivity and moisture along 30-m transects (n = 13) of representative soils developed in surficial deposits and falling head slug tests of shallow groundwater in till demonstrate that hydraulic conductivity in the soil is comparable to hydraulic conductivity values in the shallow aquifer. Soil hydraulic conductivity values were variable (medians ranged from 5.6 × 10−7 to 4.96 × 10−5 m s−1) and increased in alpine areas underlain by periglacial deposits. Hydraulic conductivities measured by a modified Hvorslev technique in test wells ranged from 4.86 × 10−7 to 1.77 × 10−4 m s−1 in subalpine till. The results suggest a gradient from higher hydraulic conductivity in alpine zones, where short travel paths through periglacial deposits support ephemeral streams and wetlands, to lower hydraulic conductivity in the till-mantled subalpine zone. In drier downstream areas, streambed infiltration contributes substantially to near-channel groundwater. As summer temperatures and evapotranspiration (ET) increase and snowmelt occur earlier, alpine soils are likely to become more vulnerable to drought, and groundwater levels in the critical zone may lower, affecting the connectivity between late-melting snow, meltwater streams, and the areas they affect downstream.

Influence of Snowpack Cold Content on Seasonally Frozen Ground and its Hydrologic Consequences: a Case Study from Niwot Ridge, CO

Influence of Snowpack Cold Content on Seasonally Frozen Ground and its Hydrologic Consequences: a Case Study from Niwot Ridge, CO

Influence of Snowpack Cold Content on Seasonally Frozen Ground and its Hydrologic Consequences: a Case Study from Niwot Ridge, CO

Influence of Snowpack Cold Content on Seasonally Frozen Ground and its Hydrologic Consequences: a Case Study from Niwot Ridge, CO

Estimating species misclassification with occupancy dynamics and encounter rates: A semi‐supervised, individual‐level approach

Abstract Large‐scale, long‐term biodiversity monitoring is essential to conservation, land management and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false‐positive/false‐negative detection and misclassification, where a species is thought to have been encountered but not correctly identified. Previous methods assume an imperfect classifier produces species‐level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including ‘unknown’, morphospecies designations and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic rates, occurrence and species richness. Here we present a joint classification‐occupancy model that accounts for species non‐detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain ‘morphospecies’ designations and makes use of individual specimens with known species identities in a semi‐supervised setting. We compare the performance of our model to a classification‐only model that discards information about occupancy and encounter rate. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, near Boulder, CO, USA. We also use simulations to evaluate model performance through validation metrics where varying fractions of the data are confirmed. The model supported imperfect classifier accuracy and favoured certain true species classifications strongly for some morphospecies. The model outperformed (e.g. precision) the reduced model that discarded occupancy information, and these differences were most pronounced for abundant species. Spatial and temporal dynamics from modelled occupancy and encounter rates may inform species misclassification probability, but this idea has not yet been tested. Our statistical framework explores this opportunity, and can be applied to datasets with imperfect species detection and classification, limited verification data and non‐species classifications.

Mass balance of two perennial snowfields: Niwot Ridge, Colorado, and the Ulaan Taiga, Mongolia

ABSTRACT Perennial snowfields are generally receding worldwide, though the precise mechanisms causing recessions are not always well understood. Here we apply a numerical snowpack model to identify the leading factors controlling the mass balance of two perennial snowfields that have significant human interest: Arapaho Glacier, located at Niwot Ridge in the Colorado Rocky Mountains (United States), and a snowfield located in the Ulaan Taiga (Mongolia). The two locations were chosen because they differ in elevation, slope, and aspect. However, both have subarctic climates and are located within semi-arid regions. We show that for these two locations the snowfield mass balance is primarily sensitive to air temperature and wind speed, followed by precipitation and dust deposition amounts. The lack of sensitivity to dust deposition was most likely due to timing of deposition and the overall small dust amounts. We find that the sensitivities are similar for the center of the snowfield as well as the margins.

Gross primary production (GPP) and red solar induced fluorescence (SIF) respond differently to light and seasonal environmental conditions in a subalpine conifer forest

The phenology of montane conifer forests is likely to shift in response to climate change and altered seasonal dynamics of light, temperature, and moisture. Solar-induced fluorescence (SIF) is expected to provide substantial improvement for mapping temporal changes in evergreen gross primary production (GPP) over greenness-based remote sensing indices. The utility of SIF to monitor seasonal changes in the phenology of conifer photosynthesis depends on the degree to which GPP and SIF respond in synchrony to key environmental drivers. However, to what extent SIF and GPP become decoupled by responding differently to the combined effects of light and other environmental conditions remains unknown. The goal of this study was to characterize the responses of GPP and SIFred to a suite of environmental drivers at the half-hour time scale and determine how these relationships change across seasons. We analyzed one year of tower-based SIFred and eddy covariance-derived GPP data from a conifer forest at Niwot Ridge, Colorado. We compared the light responses of GPP and SIFred across the year, finding that SIFred increased in response to light earlier in the year than did GPP. The light response of GPP had a positive temperature dependence in spring, and this dependency reversed in summer due to increased evaporative demand, while the light response of SIFred was less temperature dependent. Using artificial neural network ensemble analysis, we found that from spring to summer, SIFred did not exhibit a parallel response to the seasonally dynamic temperature and moisture controls on GPP. In summer SIFred was not correlated with canopy conductance, suggesting that SIF is less sensitive to stomatal control than GPP. Our results suggest that, in conifers, photosystems begin to activate in spring prior to when water becomes available for photosynthesis, presenting a challenge for the use of SIF as a phenological indicator in conifer forests.

Unravelling groundwater contributions to evapotranspiration and constraining water fluxes in a high‐elevation catchment

AbstractDespite the importance of headwater catchments for the water supply of the western United States, these regions are often poorly understood, particularly with respect to quantitative understanding of evapotranspiration (ET) fluxes. Heterogeneity of land cover, physiography, and atmospheric patterns in these high‐elevation regions lead to difficulty in developing spatially‐distributed characterization of ET. As the largest terrestrial water flux behind precipitation, ET represents a significant fraction of the water budget for any watershed. Likewise, groundwater is the largest available freshwater store and has been shown to play a large role in the water balance, even in headwater systems. Using an eddy covariance tower in the East River Catchment, a Colorado River headwaters basin, we estimated water and energy fluxes in high‐elevation, complex systems to better constrain ET estimates and calculate overall water and energy budgets, including losses from groundwater. We used the eddy covariance method to estimate ET from years 2017 through 2019 at a saturated, riparian end‐member site. Owing to complexities in near surface atmospheric structure such as stable boundary layers over snowpack and shallow terrain driven flow from surrounding landscape features, energy flux and ET estimates were limited to the warm season when energy closure residuals from the eddy‐covariance system were reliably less than 30%, a threshold commonly used in eddy covariance energy flux estimation. The resulting ET estimations are useful for constraining water budget estimates at this energy‐limited site, which uses groundwater for up to 84% of ET in the summer months. We also compared East River ET magnitudes and seasonality to two other flux towers (Niwot Ridge, CO and Valles Caldera, NM), located in the Rocky Mountains. These data are useful for constraining ET estimates in similar end‐member locations across the East River Catchment. Our results show that groundwater‐fed ET is a significant component of the water balance and groundwater may supply riparian ET even during low‐snow years.

Influence of Snowpack Cold Content on Seasonally Frozen Ground and its Hydrologic Consequences: a Case Study from Niwot Ridge, CO

Influence of Snowpack Cold Content on Seasonally Frozen Ground and its Hydrologic Consequences: a Case Study from Niwot Ridge, CO

Integrating observations and models to determine the effect of seasonally frozen ground on hydrologic partitioning in alpine hillslopes in the Colorado Rocky Mountains, USA

AbstractThis study integrated spatially distributed field observations and soil thermal models to constrain the impact of frozen ground on snowmelt partitioning and streamflow generation in an alpine catchment within the Niwot Ridge Long‐Term Ecological Research site, Colorado, USA. The study area was comprised of two contrasting hillslopes with notable differences in topography, snow depth and plant community composition. Time‐lapse electrical resistivity surveys and soil thermal models enabled extension of discrete soil moisture and temperature measurements to incorporate landscape variability at scales and depths not possible with point measurements alone. Specifically, heterogenous snowpack thickness (~0–4 m) and soil volumetric water content between hillslopes (~0.1–0.45) strongly influenced the depths of seasonal frost, and the antecedent soil moisture available to form pore ice prior to freezing. Variable frost depths and antecedent soil moisture conditions were expected to create a patchwork of differing snowmelt infiltration rates and flowpaths. However, spikes in soil temperature and volumetric water content, as well as decreases in subsurface electrical resistivity revealed snowmelt infiltration across both hillslopes that coincided with initial decreases in snow water equivalent and early increases in streamflow. Soil temperature, soil moisture and electrical resistivity data from both wet and dry hillslopes showed that initial increases in streamflow occurred prior to deep soil water flux. Temporal lags between snowmelt infiltration and deeper percolation suggested that the lateral movement of water through the unsaturated zone was an important driver of early streamflow generation. These findings provide the type of process‐based information needed to bridge gaps in scale and populate physically based cryohydrologic models to investigate subsurface hydrology and biogeochemical transport in soils that freeze seasonally.

Catchment‐scale observations at the Niwot Ridge long‐term ecological research site

AbstractThe Niwot Ridge and Green Lakes Valley (NWT) long‐term ecological research (LTER) site collects environmental observations spanning both alpine and subalpine regimes. The first observations began in 1952 and have since expanded to nearly 300 available datasets over an area of 99 km2 within the north‐central Colorado Rocky Mountains that include hydrological (n = 101), biological (n = 79), biogeochemical (n = 62), and geographical (n = 56) observations. The NWT LTER database is well suited to support hydrologic investigations that require long‐term and interdisciplinary data sets. Experimentation and data collection at the NWT LTER are designed to characterize ecological responses of high‐mountain environments to changes in climate, nutrients, and water availability. In addition to the continuation of the many legacy NWT datasets, expansion of the breadth and utility of the NWT LTER database is driven by new initiatives including (a) a catchment‐scale sensor network of soil moisture, temperature, humidity, and snow‐depth observations to understand hydrologic connectivity and (b) snow‐albedo alteration experiments using black sand to evaluate the effects of snow‐disappearance on ecosystems. Together, these observational and experimental datasets provide a substantial foundation for hydrologic studies seeking to understand and predict changes to catchment and local‐scale process interactions.

The sensitivity of runoff generation to spatial snowpack uniformity in an alpine watershed: Green Lakes Valley, Niwot Ridge Long‐Term Ecological Research station

AbstractSeasonal water storage in high‐elevation alpine catchments are critical sources of water for mountainous regions like the western U.S. The spatial distribution of snow in these topographically complex catchments is primarily governed by orography, solar radiation, and wind redistribution. While the effect of solar shading is relatively consistent from year‐to‐year, the redistribution of snow due to wind is more variable – capable of producing snowpacks that have varying degrees of uniformity across these hydrologically‐important catchments. A reasonable hypothesis is that a warmer climate will cause snowfall to become more dense (i.e. wetter and heavier), possibly leading to less wind redistribution and thus produce a more uniformly distributed snowpack across the landscape. In this study, we investigate the role of increasingly uniform spatial snowpack distributions on streamflow generation in the Green Lakes Valley Niwot Ridge Long Term Ecological Research station, within the headwaters of the Boulder Creek watershed in Colorado. A set of idealized hydrologic simulation experiments driven by reconstructed snowpacks spanning 2001–2014 show that more a more uniform spatial snowpack distribution leads to an earlier melt‐out of 31 days on average and tends to produce less total streamflow, with maximum decreases as large as 7.5%. Isolating the role of snowpack heterogeneity from melt‐season precipitation, we find that snowpack uniformity reduces total streamflow by as much as 13.2%. Reductions in streamflow are largely explained by greater exposure to solar radiation in the uniformly distributed case relative to a more heterogeneous snowpack, with this exposure driving shifts towards earlier snowmelt and changes in soil water storage. Overall, we find that the runoff efficiency from shallower snowpacks is more sensitive to the effects of uniformity than deeper snowpacks, which has potential implications for a warming climate where shallower snowpacks and enhanced sensitivities may be present.

Nematode community diversity and function across an alpine landscape undergoing plant colonization of previously unvegetated soils

Climate warming is a key factor driving species range shifts. While previous work has focused on shifts of aboveground plant communities, changes in climate and vegetation should affect soil communities and hence ecosystem-level nutrient cycling and ecosystem functioning. High alpine ecosystems are particularly sensitive to climate warming because snow is among the main drivers of ecosystem structure and function. Climate-warming snow cover changes at Niwot Ridge in the Colorado Rocky Mountains have resulted in a consistent plant colonization of previously unvegetated soils generating a natural gradient of soil habitats ranging from unvegetated to increasingly vegetated. We used this gradient of plant communities at different successional stages to determine if nematodes respond to climate-driven changes in this high-alpine landscape and if they play a role in changes in soil C and N. We hypothesized that: 1) there would be clear shifts in nematode communities along the gradient as a function of snow cover, plant richness and density, and water holding capacity but that these shifts would be dependent on nematode feeding habits and their positioning in the soil foodweb and 2) the shifts would be associated with accumulating soil C and N. To test these hypotheses, we measured nematodes, plants, and soil microbes, snow cover, pH, soil water holding capacity, and different forms of soil C and N in 98 plots across the plant successional gradient. As predicted, nematode communities exhibited extensive shifts from a few individuals of a single species in unvegetated soils to hundreds of individuals and tens of species within every feeding group under complex plant communities. Representatives of omnivorous and bacterivorous K-strategists preceded plants and plant parasites and root associates depended on plants most. Linear regression models indicated that plants, microbial communities and soil water holding capacity, but not snow cover, were the most predictive factors of nematode diversity and density across all trophic levels and that all nematode groups were positively related to all measures of soil C and N. Structural equation models confirmed these results, but also indicated that effects of climate warming on nematodes were indirect primarily through shifts in plant and microbial communities and changes of soil water holding capacity. Moreover, nematode trophic group densities, but not their diversity, played a potential role in the accumulation of soil N, and to a lesser degree of soil C. Because nematode communities at Niwot Ridge are largely at their early phases of assembly, with continuing climate warming, we predict their increasing abundance and diversity will likely continue, as will their impact on soil C and N processes.