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Abstract Constrained carbon allocation toward secondary metabolites involved in chemical defense is a common explanation for widespread drought-related beetle-kill in conifers—we challenge the generality of this explanation. While monitoring drought stress (ψpd), we tracked both carbon reserves (non-structural carbohydrates) and chemical defenses (terpenes, phenolics, resin flow) in mature Pinus edulis Englem. trees experiencing either short-term (3-year) or a ‘legacy’ long-term (13-year) throughfall exclusion treatments, plus a control. We also quantified the Δ14C-age of resin to measure past allocation to current defense. While 72% of trees in short-term throughfall exclusion plots died (attacked by bark beetles, Ips confusus LeConte), mortality patterns were unrelated to throughfall exclusion intensity and all ‘legacy’ trees survived. We thus assessed trees in four survivorship categories: control, ‘legacy’, surviving, and dying trees. We found concentrations of certain defense compounds (leaf phenolics, twig monoterpenes) increased with drought stress, particularly in dying trees. In the main stem, dying trees exhibited similar terpene concentrations (94%) and phenolic concentrations (139%) relative to control trees. Compared with control trees, only ‘legacy’ trees had reduced stem terpenes (−49%, P < 0.05) after a decade of drought. Δ14C-age of resin could be up to 10.2 ± 0.5 years old, where the oldest resin was exuded from trees with low sugar concentrations and more negative Ψpd. Our results suggest that drought imposes a weak constraint on carbon allocation to resin-based defense. Instead, we primarily found evidence of increased concentrations of terpene and phenolic compounds under drought, even in dying trees, and only observed reductions in resin-based defenses after 10+ years of drought. Δ14C-ages demonstrate limited resin turnover and/or synthesis of resin from old reserves, suggesting that long-term drought is required to reduce resin-based defenses. Persistent allocation coupled with past investments appears to preserve or enhance concentrations of resin-based defenses even under lethal drought stress in P. edulis. 

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." 

Abstract: 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 SEV297, "Extreme Drought in Grassland Ecosystems (EDGE) Net Primary Production Quadrat Data" and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data." 

Summary Stomatal closure during drought inhibits carbon uptake and may reduce a tree's defensive capacity. Limited carbon availability during drought may increase a tree's mortality risk, particularly if drought constrains trees' capacity to rapidly produce defenses during biotic attack.We parameterized a new model of conifer defense using physiological data on carbon reserves and chemical defenses before and after a simulated bark beetle attack in maturePinus edulisunder experimental drought. Attack was simulated using inoculations with a consistent bluestain fungus (Ophiostomasp.) ofIps confusus, the main bark beetle colonizing this tree, to induce a defensive response.Trees with more carbon reserves produced more defenses but measured phloem carbon reserves only accounted forc.23% of the induced defensive response. Our model predicted universal mortality if local reserves alone supported defense production, suggesting substantial remobilization and transport of stored resin or carbon reserves to the inoculation site.Our results show thatde novoterpene synthesis represents only a fraction of the total measured phloem terpenes inP. edulisfollowing fungal inoculation. Without direct attribution of phloem terpene concentrations to available carbon, many studies may be overestimating the scale and importance ofde novoterpene synthesis in a tree's induced defense response. 

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":["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 Climate forecasts project change not only in the mean of climate variables but also in their variance. If these dual changes interact, then future ecological dynamics will be difficult to predict using current experimental approaches, which typically change the mean or impose a single extreme event, such as drought. We designed a new field experiment to factorially reduce mean precipitation and increase its interannual variability. Across 4 years, drier, more variable precipitation additively reduced aboveground primary productivity by 48%–69% and interactively reduced the dominant plant species, but had no effect on the plant species predicted to dominate in the future, which could lead to state transition. Drier, more variable precipitation also interactively reduced biodiversity more than either climate factor alone, with 37%–42% fewer plant species than under ambient conditions, a pattern that matched declining richness during the past 20 years of ongoing climate change. Drier, more variable precipitation restructured the composition and spatiotemporal variation of the plant community. Altered precipitation mean or variance affected 14% of plant species, with eight species sensitive to the mean × variance interaction. Results suggest that future forecasts of plant community structure may be inadequate if they fail to incorporate climate mean × variance interactions. 

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.