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We designed novel field experimental infrastructure to resolve the relative importance and interactions among changes in precipitation 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 in 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. We collected samples of soils, biological soil crusts, leaves of the foundation plant species, and roots of the two dominant grass species each year during peak productivity (September-October). These samples enable us to address the question: How do interactions between the mean and variance of precipitation alter the biogeochemistry and microbiomes of plants and soils. This data package includes accession numbers for all samples collected from the Mean x Variance Experiment at the Sevilleta National Wildlife Refuge, Socorro, NM. 

Nutrient augmentation is one major global change disturbance that could have cascading effects on local plant and microbial communities thus altering biogeochemical properties (Peñuelas et al. 2012). While many studies have investigated fertilization effects on community change and ecosystem processes, less work has been done in dryland ecosystems (Schimel 2010), where nutrient availability often comes as pulses correlated with rain events (Collins et al. 2008). We leveraged an ongoing fertilization experiment (NutNet) at the Sevilleta to answer the question: How does fertilization alter dryland biogeochemical processes, and how does this effect change seasonally? To explore this topic, we specifically measure three important soil hydrolase enzymes, N-acetyl- glycosaminidase (NAG), phosphatase (AP), and β- glucosidase (BG), microbial biomass, and soil nitrogen levels at 5 points along a seasonal gradient within the NutNet plots. 

Abstract Extensive ecological research has investigated extreme climate events or long‐term changes in average climate variables, but changes in year‐to‐year (interannual) variability may also cause important biological responses, even if the mean climate is stable. The environmental stochasticity that is a hallmark of climate variability can trigger unexpected biological responses that include tipping points and state transitions, and large differences in weather between consecutive years can also propagate antecedent effects, in which current biological responses depend on responsiveness to past perturbations. However, most studies to date cannot predict ecological responses to rising variance because the study of interannual variance requires empirical platforms that generate long time series. Furthermore, the ecological consequences of increases in climate variance could depend on the mean climate in complex ways; therefore, effective ecological predictions will require determining responses to both nonstationary components of climate distributions: the mean and the variance. We introduce a new design to resolve the relative importance of, and interactions between, a drier mean climate and greater climate variance, which are dual components of ongoing climate change in the southwestern United States. The Mean × Variance Experiment (MVE) adds two novel elements to prior field infrastructure methods: (1) factorial manipulation of variance together with the climate mean and (2) the creation of realistic, stochastic precipitation regimes. Here, we demonstrate the efficacy of the experimental design, including sensor networks and PhenoCams to automate monitoring. We replicated MVE across ecosystem types at the northern edge of the Chihuahuan Desert biome as a central component of the Sevilleta Long‐Term Ecological Research Program. Soil sensors detected significant treatment effects on both the mean and interannual variability in soil moisture, and PhenoCam imagery captured change in vegetation cover. Our design advances field methods to newly compare the sensitivities of populations, communities, and ecosystem processes to climate mean × variance interactions.