Sevilleta Long Term Ecological Research

Abstract We investigated long‐term and seasonal patterns of N imports and exports, as well as patterns following climate perturbations, across biomes using data from 15 watersheds from nine Long‐Term Ecological Research (LTER) sites in North America. Mean dissolved inorganic nitrogen (DIN) import–export budgets (N import via precipitation–N export via stream flow) for common years across all watersheds was highly variable, ranging from a net loss of − 0·17 ± 0·09 kg N ha −1 mo −1 to net retention of 0·68 ± 0·08 kg N ha −1 mo −1 . The net retention of DIN decreased (smaller import–export budget) with increasing precipitation, as well as with increasing variation in precipitation during the winter, spring, and fall. Averaged across all seasons, net DIN retention decreased as the coefficient of variation (CV) in precipitation increased across all sites ( r 2 = 0·48, p = 0·005). This trend was made stronger when the disturbed watersheds were withheld from the analysis ( r 2 = 0·80, p < 0·001, n = 11). Thus, DIN exports were either similar to or exceeded imports in the tropical, boreal, and wet coniferous watersheds, whereas imports exceeded exports in temperate deciduous watersheds. In general, forest harvesting, hurricanes, or floods corresponded with periods of increased DIN exports relative to imports. Periods when water throughput within a watershed was likely to be lower (i.e. low snow pack or El Niño years) corresponded with decreased DIN exports relative to imports. These data provide a basis for ranking diverse sites in terms of their ability to retain DIN in the context of changing precipitation regimes likely to occur in the future. Copyright © 2008 John Wiley & Sons, Ltd.

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.