Infrastructure to factorially manipulate the mean and variance of precipitation in the field

Jennifer A. Rudgers, Anthony Luketich, Melissa Bacigalupa, Lauren E. Baur, Scott L. Collins, Kristofer M. Hall, Enqing Hou, Marcy E. Litvak, Yiqi Luo, Tom E.X. Miller, Seth D. Newsome, William T. Pockman, Andrew D. Richardson, Alex Rinehart, Melissa Villatoro-Castañeda, Brooke E. Wainwright, Samantha J. Watson, Purbendra Yogi, Yu Zhou

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article numbere4603
Issue number7
StatePublished - Jul 2023


  • drought
  • experiment
  • legacy effect
  • precipitation
  • rainfall
  • stochasticity
  • variability

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology


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