Climate-induced die-offaffects plant-soil-microbe ecological relationship and functioning

Lloret F., Mattana S., Yuste J.C. (2015) Climate-induced die-offaffects plant-soil-microbe ecological relationship and functioning. FEMS Microbiology Ecology. 91: 0-0.
Link
Doi: 10.1093/femsec/iu014

Abstract:

This study reports the relationship between the diversity and functioning of fungal and bacterial soil communities with vegetation in Mediterranean woodland that experienced severe die-offafter a drought episode. Terminal restriction fragment length polymorfism (TRFLP) was used to describe microbial community structure and diversity five years after the episode in different habitats (Juniperus woodland, shrubland, grassland), when the vegetation had not yet recovered. Vegetation diversity was positively related to TRF bacterial richness under unaffected canopies and was higher in diverse grassland. Fungal TRF richness correlated with vegetation type, being greater in Juniperus woodland. Microbial respiration increased in grassland, whereas microbial biomass, estimated from soil substrate-induced respiration (SIR), decreased with bacterial diversity. Die-offincreased bacterial richness and changed bacterial composition, particularly in Juniperus woodland, where herbaceous species increased, while fungal diversity was reduced in Juniperus woodland. Die-offincreased microbial respiration rates. The impact on vegetation from extreme weather episodes spread to microbial communities by modifying vegetation composition and litter quantity and quality, particularly as a result of the increase in herbaceous species. Our results suggest that climate-induced die-offtriggers significant cascade effects on soil microbial communities, which may in turn further influence ecosystem C dynamics. © FEMS 2014. All rights reserved.

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Non-structural carbohydrates in woody plants compared among laboratories

Quentin A.G., Pinkard E.A., Ryan M.G., Tissue D.T., Baggett L.S., Adams H.D., Maillard P., Marchand J., Landhäusser S.M., Lacointe A., Gibon Y., Anderegg W.R.L., Asao S., Atkin O.K., Bonhomme M., Claye C., Chow P.S., Clément-Vidal A., Davies N.W., Dickman L.T., Dumbur R., Ellsworth D.S., Falk K., Galiano L., Grünzweig J.M., Hartmann H., Hoch G., Hood S., Jones J.E., Koike T., Kuhlmann I., Lloret F., Maestro M., Mansfield S.D., Martínez-Vilalta J., Maucourt M., McDowell N.G., Moing A., Muller B., Nebauer S.G., Niinemets U., Palacio S., Piper F., Raveh E., Richter A., Rolland G., Rosas T., Joanis B.S., Sala A., Smith R.A., Sterck F., Stinziano J.R., Tobias M., Unda F., Watanabe M., Way D.A., Weerasinghe L.K., Wild B., Wiley E., Woodruff D.R. (2015) Non-structural carbohydrates in woody plants compared among laboratories. Tree Physiology. 35: 1146-1165.
Link
Doi: 10.1093/treephys/tpv073

Abstract:

Non-structural carbohydrates (NSC) in plant tissue are frequently quantified to make inferences about plant responses to environmental conditions. Laboratories publishing estimates of NSC of woody plants use many different methods to evaluate NSC. We asked whether NSC estimates in the recent literature could be quantitatively compared among studies. We also asked whether any differences among laboratories were related to the extraction and quantification methods used to determine starch and sugar concentrations. These questions were addressed by sending sub-samples collected from five woody plant tissues, which varied in NSC content and chemical composition, to 29 laboratories. Each laboratory analyzed the samples with their laboratory-specific protocols, based on recent publications, to determine concentrations of soluble sugars, starch and their sum, total NSC. Laboratory estimates differed substantially for all samples. For example, estimates for Eucalyptus globulus leaves (EGL) varied from 23 to 116 (mean = 56) mg g-1 for soluble sugars, 6-533 (mean = 94) mg g-1 for starch and 53-649 (mean = 153) mg g-1 for total NSC. Mixed model analysis of variance showed that much of the variability among laboratories was unrelated to the categories we used for extraction and quantification methods (method category R2 = 0.05-0.12 for soluble sugars, 0.10-0.33 for starch and 0.01-0.09 for total NSC). For EGL, the difference between the highest and lowest least squares means for categories in the mixed model analysis was 33 mg g-1 for total NSC, compared with the range of laboratory estimates of 596 mg g-1. Laboratories were reasonably consistent in their ranks of estimates among tissues for starch (r = 0.41-0.91), but less so for total NSC (r = 0.45-0.84) and soluble sugars (r = 0.11-0.83). Our results show that NSC estimates for woody plant tissues cannot be compared among laboratories. The relative changes in NSC between treatments measured within a laboratory may be comparable within and between laboratories, especially for starch. To obtain comparable NSC estimates, we suggest that users can either adopt the reference method given in this publication, or report estimates for a portion of samples using the reference method, and report estimates for a standard reference material. Researchers interested in NSC estimates should work to identify and adopt standard methods. © The Author 2015.

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