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Physiological variation among Populus fremontii populations: short- and long-term relationships between delta13C and water availability
Authors:Leffler A J  Evans A S
Affiliation:Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.
Abstract:Different populations of widely distributed species can experience dramatically different climatic conditions that may influence physiological activity, specifically carbon assimilation and water use. Populus fremontii Wats. (Fremont cottonwood) populations are found near rivers of varying size along a precipitation gradient from New Mexico to northern California. Climatic differences among populations may lead to physiological differences because P. fremontii is sensitive to water availability. To assess physiological variation among populations, we collected foliage and wood samples from 13 populations that experience different precipitation and stream flow regimes and analyzed the samples for carbon isotope composition (delta13C). Wood delta13C served as a lifetime-averaged indicator of water-use efficiency (WUE), whereas foliage delta13C provided as an estimate of WUE during the growing season of collection. We found approximately 3.4 per thousand variation in delta13C among populations for both foliage (-31.1 to -27.9 per thousand) and wood (-28.3 to -24.7 per thousand). Wood delta13C was, on average, 2.8 per thousand more enriched than foliage. Some of the variation in wood delta13C can be explained by variation in elevation of the study sites. We constructed total precipitation and mean stream flow variables based on the length of the growing season at each study site and analyzed for a relationship between delta13C, precipitation and stream flow. A significant relationship between foliage delta13C and precipitation was found, but water availability did not explain a significant fraction of the variation in wood delta13C. The data suggest that water availability can account for some of the delta13C variation among populations but, given the large residual variances, other factors are important.
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