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Title: Plant traits, stoichiometry and microbes as drivers of decomposition in the rhizosphere in a temperate grassland

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [2];  [5];  [6];
  1. Hawkesbury Institute for the Environment, University of Western Sydney, Penrith South NSW 2751 Australia
  2. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins CO 80523 USA
  3. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins CO 80523 USA, Department of Biology, Algoma University, ON P6A 2G4 Canada
  4. Center for Carbon, Water and Food, University of Sydney, Camden NSW 2750 Australia
  5. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins CO 80523 USA, Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins CO 80523 USA
  6. Hawkesbury Institute for the Environment, University of Western Sydney, Penrith South NSW 2751 Australia, Department of Botany, University of Wyoming, Laramie WY 82071 USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1400620
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Ecology
Additional Journal Information:
Journal Volume: 105; Journal Issue: 6; Related Information: CHORUS Timestamp: 2017-10-20 15:06:16; Journal ID: ISSN 0022-0477
Publisher:
Wiley-Blackwell
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Carrillo, Yolima, Bell, Colin, Koyama, Akihiro, Canarini, Alberto, Boot, Claudia M., Wallenstein, Matthew, Pendall, Elise, and de Vries, ed., Franciska. Plant traits, stoichiometry and microbes as drivers of decomposition in the rhizosphere in a temperate grassland. United Kingdom: N. p., 2017. Web. doi:10.1111/1365-2745.12772.
Carrillo, Yolima, Bell, Colin, Koyama, Akihiro, Canarini, Alberto, Boot, Claudia M., Wallenstein, Matthew, Pendall, Elise, & de Vries, ed., Franciska. Plant traits, stoichiometry and microbes as drivers of decomposition in the rhizosphere in a temperate grassland. United Kingdom. doi:10.1111/1365-2745.12772.
Carrillo, Yolima, Bell, Colin, Koyama, Akihiro, Canarini, Alberto, Boot, Claudia M., Wallenstein, Matthew, Pendall, Elise, and de Vries, ed., Franciska. Thu . "Plant traits, stoichiometry and microbes as drivers of decomposition in the rhizosphere in a temperate grassland". United Kingdom. doi:10.1111/1365-2745.12772.
@article{osti_1400620,
title = {Plant traits, stoichiometry and microbes as drivers of decomposition in the rhizosphere in a temperate grassland},
author = {Carrillo, Yolima and Bell, Colin and Koyama, Akihiro and Canarini, Alberto and Boot, Claudia M. and Wallenstein, Matthew and Pendall, Elise and de Vries, ed., Franciska},
abstractNote = {},
doi = {10.1111/1365-2745.12772},
journal = {Journal of Ecology},
number = 6,
volume = 105,
place = {United Kingdom},
year = {Thu Apr 13 00:00:00 EDT 2017},
month = {Thu Apr 13 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1111/1365-2745.12772

Citation Metrics:
Cited by: 2works
Citation information provided by
Web of Science

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  • A modeling study evaluated photosynthetic pathways (C[sub 3], C[sub 4], or both) and management strategies in the foliage productivity and soil carbon characteristics of a semihumid temperate grassland with various combinations of climate change. Model values for plant and soil characteristics were obtained near Manhattan, Kansas, and the Manhattan climate record was used for actual monthly temperature and precipitation data for a 100-yr interval and average weather conditions. Monthly temperatures were increased 2[degrees]C, left unchanged, or decreased 2[degrees]C; annual precipitation was increased 6 cm, left unchanged, or decreased 6 cm. All possible combinations of temperature and precipitation were then usedmore » in 100-yr simulations. Regardless of climate, plant production was lowest for C[sub 3] grasses and highest for the mixed C[sub 3]-C[sub 4] community. The nominal seasonal pattern of precipitation favored an active C[sub 3] plant community in early to late spring, prior to the emergence of the C[sub 4] vegetation. However, the higher growth and water use efficiencies of C[sub 4] vegetation during summer contributed to the maximization response of the grasslands containing both C[sub 3] and C[sub 4] grasses. The relative importance of climate, photosynthetic pathways, and management activities (annually burned, burned every 4 yr, unburned, or lightly grazed) to plant production and soil carbon values were evaluated. Photosynthetic pathway and precipitation were the most significant single variables; the interaction between photosynthetic pathway and temperature was the most significant interaction term. Management treatments were by far the most important variable affecting soil carbon values, but 3[degrees]C warming did produce substantial soil carbon losses from C[sub 3] grasslands. Enhanced carbon fixation by the C[sub 4] and C[sub 3]-C[sub 4] plant communities negated the losses of soil carbon caused by enhanced soil respiration at warmer temperatures.« less
  • No abstract prepared.
  • Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon, water, and energy fluxes. However, we lack robust and efficient ways to monitor the temporal dynamics of leaf traits. Here we assessed the potential of leaf spectroscopy to predict and monitor leaf traits across their entire life cycle at different forest sites and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. In addition, the dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyllmore » a and b), carotenoids, mass-based nitrogen concentration (N mass), mass-based carbon concentration (C mass), and leaf mass per area (LMA)]. All leaf traits varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) modeling approach to estimate leaf traits from spectra, and found that PLSR was able to capture the variability across time, sites, and light environments of all leaf traits investigated (R 2 = 0.6–0.8 for temporal variability; R 2 = 0.3–0.7 for cross-site variability; R 2 = 0.4–0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the seasonal patterns. Compared with the estimation of foliar pigments, the performance of N mass, C mass and LMA PLSR models improved more significantly with sampling frequency. Our results demonstrate that leaf spectra-trait relationships vary with time, and thus tracking the seasonality of leaf traits requires statistical models calibrated with data sampled throughout the growing season. In conclusion, our results have broad implications for future research that use vegetation spectra to infer leaf traits at different growing stages.« less