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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.
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Ecosystem feedbacks constrain the effect of day‐to‐day weather variability on land–atmosphere carbon exchange
Abstract Whole‐ecosystem interactions and feedbacks constrain ecosystem responses to environmental change. The effects of these constraints on responses to climate trends and extreme weather events have been well studied. Here we examine how these constraints respond to changes in day‐to‐day weather variability without changing the long‐term mean weather. Although environmental variability is recognized as a critical factor affecting ecological function, the effects of climate change on day‐to‐day weather variability and the resultant impacts on ecosystem function are still poorly understood. Changes in weather variability can alter the mean rates of individual ecological processes because many processes respond non‐linearly to environmental drivers. We assessed how these individual‐process responses to changes in day‐to‐day weather variability interact with one another at an ecosystem level. We examine responses of arctic tundra to changes in weather variability using stochastic simulations of daily temperature, precipitation, and light to drive a biogeochemical model. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates in our model. However, responses of some processes (e.g., respiration) were inconsistent with expectations because ecosystem feedbacks can moderate, or even reverse, direct process responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere; less variability led to higher carbon sequestration on land. The magnitude of modeled ecosystem response to weather variability was comparable to that predicted for the effects of climate mean trends by the end of the century.
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- PAR ID:
- 10467669
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 29
- Issue:
- 21
- ISSN:
- 1354-1013
- Format(s):
- Medium: X Size: p. 6093-6105
- Size(s):
- p. 6093-6105
- Sponsoring Org:
- National Science Foundation
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