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This long-term study at the Sevilleta LTER measures net primary production (NPP) across four distinct ecosystems: creosote-dominant shrubland (Site C, est. winter 1999), black grama-dominant grassland (Site G, est. winter 1999), blue grama-dominant grassland (Site B, est. winter 2002), and pinon-juniper woodland (Site P, est. winter 2003), which is now in its own dataset, SEV278 (Pinon-Juniper (Core Site) Quadrat Data). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots collected in SEV129, "Core Research Site Web Quadrat Data" and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data." 

We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM. 

We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And How does micro-environmental variation among plots influence how treatments alter soil moisture profiles over three soil depths? This data package includes sensor data from the Mean x Variance experiment in the Plains grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM, which is dominated by the grass species Bouteloua gracilis (blue grama). 

Abstract. Future global changes will impact carbon (C) fluxes andpools in most terrestrial ecosystems and the feedback of terrestrial carboncycling to atmospheric CO2. Determining the vulnerability of C in ecosystems to future environmental change is thus vital for targeted land management and policy. The C capacity of an ecosystem is a function of its C inputs(e.g., net primary productivity – NPP) and how long C remains in the systembefore being respired back to the atmosphere. The proportion of C capacitycurrently stored by an ecosystem (i.e., its C saturation) provides informationabout the potential for long-term C pools to be altered by environmental andland management regimes. We estimated C capacity, C saturation, NPP, andecosystem C residence time in six US grasslands spanning temperature andprecipitation gradients by integrating high temporal resolution C pool andflux data with a process-based C model. As expected, NPP across grasslandswas strongly correlated with mean annual precipitation (MAP), yet Cresidence time was not related to MAP or mean annual temperature (MAT). We linksoil temperature, soil moisture, and inherent C turnover rates (potentiallydue to microbial function and tissue quality) as determinants of carbon residence time. Overall, we found that intermediates between extremes in moisture andtemperature had low C saturation, indicating that C in these grasslands maytrend upwards and be buffered against global change impacts. Hot and drygrasslands had greatest C saturation due to both small C inputs through NPPand high C turnover rates during soil moisture conditions favorable formicrobial activity. Additionally, leaching of soil C during monsoon eventsmay lead to C loss. C saturation was also high in tallgrass prairie due tofrequent fire that reduced inputs of aboveground plant material.Accordingly, we suggest that both hot, dry ecosystems and those frequentlydisturbed should be subject to careful land management and policy decisionsto prevent losses of C stored in these systems. 

{"Abstract":["This dataset includes estimated plant aboveground live biomass data\n measured in 1 m x 1 m quadrats at several sites and experiments\n under the Sevilleta LTER program. Quadrat locations span four\n distinct ecosystems and their ecotones: creosotebush dominated\n Chihuahuan Desert shrubland (est. winter 1999), black\n grama-dominated Chihuahuan Desert grassland (est. winter 1999), blue\n grama-dominated Plains grassland (est. winter 2002), and\n pinon-juniper woodland (est. winter 2003). Data on plant cover and\n height for each plant species are collected per individual plant or\n patch (for clonal plants) within 1 m x 1 m quadrats. These data\n inform population dynamics of foundational and rare plant species.\n Biomass is estimated using plant allometries from non-destructive\n measurements of plant cover and height, and can be used to calculate\n net primary production (NPP), a fundamental ecosystem variable that\n quantifies rates of carbon consumption and fixation. Estimates of\n plant species cover, total plant biomass, or NPP can inform\n understanding of biodiversity, species composition, and energy flow\n at the community scale of biological organization, as well as\n spatial and temporal responses of plants to a range of ecological\n processes and direct experimental manipulations. The cover and\n height of individual plants or patches are sampled twice yearly\n (spring and fall) in permanent 1m x 1m plots within each site or\n experiment. This dataset includes core site monitoring data (CORE,\n GRIDS, ISOWEB, TOWER), observations in response to wildfire (BURN),\n and experimental treatments of extreme drought and delayed monsoon\n rainfall (EDGE), physical disturbance to biological soil crusts on\n the soil surface (CRUST), interannual variability in precipitation\n (MEANVAR), intra-annual variability via additions of monsoon\n rainfall (MRME), additions of nitrogen as ammonium nitrate\n (FERTILIZER), additions of nitrogen x phosphorus x potassium\n (NutNet), and interacting effects of nighttime warming, nitrogen\n addition, and El Niño winter rainfall (WENNDEx). To build allometric\n equations that relate biomass to plant cover or volume, the dataset\n "SEV-LTER quadrat plant cover and height data all sites and\n experiments" is used with a separate dataset of selectively\n harvested plant species "SEV-LTER Plant species mass data for\n allometry." Together, these datasets produced \u201cSEV-LTER quadrat\n plant species biomass all sites and experiments\u201d using the scripts\n posted with the allometry dataset. Data from the CORE sites in this\n dataset were designated as NA-US-011 in the Global Index of\n Vegetation-Plot Databases (GIVD). Data from the TOWER sites in this\n dataset are linked to Ameriflux sites:\n ameriflux.lbl.gov/doi/AmeriFlux/US-Seg and\n ameriflux.lbl.gov/sites/siteinfo/US-Ses."]} 

Abstract. Climatic extreme events are expected to occur more frequently in the future, increasing the likelihood of unprecedented climate extremes (UCEs) or record-breaking events. UCEs, such as extreme heatwaves and droughts, substantially affect ecosystem stability and carbon cycling by increasing plant mortality and delaying ecosystem recovery. Quantitative knowledge of such effects is limited due to the paucity of experiments focusing on extreme climatic events beyond the range of historical experience. Here, we present a road map of how dynamic vegetation demographic models (VDMs) can be used to investigate hypotheses surrounding ecosystem responses to one type of UCE: unprecedented droughts. As a result of nonlinear ecosystem responses to UCEs that are qualitatively different from responses to milder extremes, we consider both biomass loss and recovery rates over time by reporting a time-integrated carbon loss as a result of UCE, relative to the absence of drought. Additionally, we explore how unprecedented droughts in combination with increasing atmospheric CO2 and/or temperature may affect ecosystem stability and carbon cycling. We explored these questions using simulations of pre-drought and post-drought conditions at well-studied forest sites using well-tested models (ED2 and LPJ-GUESS). The severity and patterns of biomass losses differed substantially between models. For example, biomass loss could be sensitive to either drought duration or drought intensity depending on the model approach. This is due to the models having different, but also plausible, representations of processes and interactions, highlighting the complicated variability of UCE impacts that still need to be narrowed down in models. Elevated atmospheric CO2 concentrations (eCO2) alone did not completely buffer the ecosystems from carbon losses during UCEs in the majority of our simulations. Our findings highlight the consequences of differences in process formulations and uncertainties in models, most notably related to availability in plant carbohydrate storage and the diversity of plant hydraulic schemes, in projecting potential ecosystem responses to UCEs. We provide a summary of the current state and role of many model processes that give way to different underlying hypotheses of plant responses to UCEs, reflecting knowledge gaps which in future studies could be tested with targeted field experiments and an iterative modeling–experimental conceptual framework. 

Two of the most pervasive human impacts on ecosystems are alteration of global nutrient budgets and changes in the abundance and identity of consumers. Fossil fuel combustion and agricultural fertilization have doubled and quintupled, respectively, global pools of nitrogen and phosphorus relative to pre-industrial levels. In spite of the global impacts of these human activities, there have been no globally coordinated experiments to quantify the general impacts on ecological systems. This experiment seeks to determine how nutrient availability controls plant biomass, diversity, and species composition in a desert grassland. This has important implications for understanding how future atmospheric deposition of nutrients (N, S, Ca, K) might affect community and ecosystem-level responses. This study is part of a larger coordinated research network that includes more than 40 grassland sites around the world. By using a standardized experimental setup that is consistent across all study sites, we are addressing the questions of whether diversity and productivity are co-limited by multiple nutrients and if so, whether these trends are predictable on a global scale. 

{"Abstract":["The varied topography and large elevation gradients that\n characterize the arid and semi-arid Southwest create a wide range of\n climatic conditions - and associated biomes - within relatively\n short distances. This creates an ideal experimental system in which\n to study the effects of climate on ecosystems. Such studies are\n critical given that the Southwestern U.S. has already experienced\n changes in climate that have altered precipitation patterns (Mote et\n al. 2005), and stands to experience dramatic climate change in the\n coming decades (Seager et al. 2007; Ting et al. 2007). Climate\n models currently predict an imminent transition to a warmer, more\n arid climate in the Southwest (Seager et al. 2007; Ting et al.\n 2007). Thus, high elevation ecosystems, which currently experience\n relatively cool and mesic climates, will likely resemble their lower\n elevation counterparts, which experience a hotter and drier climate.\n In order to predict regional changes in carbon storage, hydrologic\n partitioning and water resources in response to these potential\n shifts, it is critical to understand how both temperature and soil\n moisture affect processes such as evaportranspiration (ET), total\n carbon uptake through gross primary production (GPP), ecosystem\n respiration (Reco), and net ecosystem exchange of carbon, water and\n energy across elevational gradients. We are using a sequence of six\n widespread biomes along an elevational gradient in New Mexico --\n ranging from hot, arid ecosystems at low elevations to cool, mesic\n ecosystems at high elevation to test specific hypotheses related to\n how climatic controls over ecosystem processes change across this\n gradient. We have an eddy covariance tower and associated\n meteorological instruments in each biome which we are using to\n directly measure the exchange of carbon, water and energy between\n the ecosystem and the atmosphere. This gradient offers us a unique\n opportunity to test the interactive effects of temperature and soil\n moisture on ecosystem processes, as temperature decreases and soil\n moisture increases markedly along the gradient and varies through\n time within sites. This dataset examines how different stages of\n burn affects above-ground biomass production (ANPP) in a mixed\n desert-grassland. Net primary production is a fundamental ecological\n variable that quantifies rates of carbon consumption and fixation.\n Estimates of NPP are important in understanding energy flow at a\n community level as well as spatial and temporal responses to a range\n of ecological processes. Above-ground net primary production is the\n change in plant biomass, represented by stems, flowers, fruit and\n foliage, over time and incorporates growth as well as loss to death\n and decomposition. To measure this change the vegetation variables\n in this dataset, including species composition and the cover and\n height of individuals, are sampled twice yearly (spring and fall) at\n permanent 1m x 1m plots. The data from these plots is used to build\n regressions correlating biomass and volume via weights of select\n harvested species obtained in SEV157, "Net Primary Productivity\n (NPP) Weight Data." This biomass data is included in SEV292,\n "Flux Tower Seasonal Biomass and Seasonal and Annual NPP\n Data.""]} 

{"Abstract":["This dataset contains pinon-juniper woodland quadrat data and is\n part of a long-term study at the Sevilleta LTER measuring net\n primary production (NPP) across four distinct ecosystems:\n creosote-dominant shrubland (Site C, est. winter 1999), black\n grama-dominant grassland (Site G, est. winter 1999), blue\n grama-dominant grassland (Site B, est. winter 2002), and\n pinon-juniper woodland (Site P, est. winter 2003). Net primary\n production is a fundamental ecological variable that quantifies\n rates of carbon consumption and fixation. Estimates of NPP are\n important in understanding energy flow at a community level as well\n as spatial and temporal responses to a range of ecological\n processes. Above-ground net primary production is the change in\n plant biomass, represented by stems, flowers, fruit and and foliage,\n over time and incorporates growth as well as loss to death and\n decomposition. To measure this change the vegetation variables in\n this dataset, including species composition and the cover and height\n of individuals, are sampled twice yearly (spring and fall) at\n permanent 1m x 1m plots within each site. A third sampling at Site C\n is performed in the winter. The data from these plots is used to\n build regressions correlating biomass and volume via weights of\n select harvested species obtained in SEV157, "Net Primary\n Productivity (NPP) Weight Data." This biomass data is included\n in SEV182, "Seasonal Biomass and Seasonal and Annual NPP for\n Core Research Sites.""]}