The Cooling Capacity of Mosses: Controls on Water and Energy Fluxes in a Siberian Tundra Site
- 615 Downloads
Arctic tundra vegetation composition is expected to undergo rapid changes during the coming decades because of changes in climate. Higher air temperatures generally favor growth of deciduous shrubs, often at the cost of moss growth. Mosses are considered to be very important to critical tundra ecosystem processes involved in water and energy exchange, but very little empirical data are available. Here, we studied the effect of experimental moss removal on both understory evapotranspiration and ground heat flux in plots with either a thin or a dense low shrub canopy in a tundra site with continuous permafrost in Northeast Siberia. Understory evapotranspiration increased with removal of the green moss layer, suggesting that most of the understory evapotranspiration originated from the organic soil layer underlying the green moss layer. Ground heat flux partitioning also increased with green moss removal indicating the strong insulating effect of moss. No significant effect of shrub canopy density on understory evapotranspiration was measured, but ground heat flux partitioning was reduced by a denser shrub canopy. In summary, our results show that mosses may exert strong controls on understory water and heat fluxes. Changes in moss or shrub cover may have important consequences for summer permafrost thaw and concomitant soil carbon release in Arctic tundra ecosystems.
Keywordsmoss evaporation ground heat flux shrub permafrost tundra Arctic climate change
This study is partly financed by the Darwin Center for Biogeosciences and the Wageningen Institute for Environment and Climate Research (WIMEK). We are grateful to the staff of the BioGeoChemical Cycles of Permafrost Ecosystems Lab in Yakutsk for logistic support and to the staff of the Kytalyk State Resource Reservation for their permission and hospitality to conduct research in the Kytalyk reserve. We thank Roman Sofronov, Elena Ivanova and Lena Poryadina for help with plant species identification. We thank Annelein Meisner and both referees for their helpful comments on the manuscript.
- ACIA. 2004. Future climate change: modelling and scenarios for the Arctic. In: Kattsov VM, Källén E, Eds. Arctic climate impact assessment: impacts of a warming arctic. Cambridge: Cambridge University Press. p 99–150.Google Scholar
- Aubinet M, Grelle A, Ibrom A, Rannik Ü, Moncrieff J, Foken T, Kowalski AS, Martin PH, Berbigier P, Bernhofer C, Clement R, Elbers J, Granier A, Grünwald T, Morgenstern K, Pilegaard K, Rebmann C, Snijders W, Valentini R, Vesala T. 2000. Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. In: Fitter AH, Raffaelli DG, Eds. Advances in ecological research. New York: Academic Press. p 113–75.Google Scholar
- Bates D, Maechler M. 2009. Lme4: linear mixed-effects models using S4 classes. R package version 0.99. http://CRAN.R-project.org/package=lme4.
- Chapin FSIII, Sturm M, Serreze MC, McFadden JP, Key JR, Lloyd AH, McGuire AD, Rupp TS, Lynch AH, Schimel JP, Beringer J, Chapman WL, Epstein HE, Euskirchen ES, Hinzman LD, Jia G, Ping CL, Tape KD, Thompson CDC, Walker DA, Welker JM. 2005. Role of land-surface changes in arctic summer warming. Science 310:657–60.PubMedCrossRefGoogle Scholar
- Hobbie SE, Chapin FSIII. 1998. The response of tundra plant biomass, aboveground production, nitrogen, and CO2 flux to experimental warming. Ecology 79:1526–44.Google Scholar
- IPCC. 2007. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL, Eds. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, p 996.Google Scholar
- Klein Tank AMG, Wijngaard JB, Können GP, Böhm R, Demarée G, Gocheva A, Mileta M, Pashiardis S, Hejkrlik L, Kern-Hansen C, Heino R, Bessemoulin P, Müller-Westermeier G, Tzanakou M, Szalai S, Pálsdóttir T, Fitzgerald D, Rubin S, Capaldo M, Maugeri M, Leitass A, Bukantis A, Aberfeld R, Van Engelen AFV, Forland E, Mietus M, Coelho F, Mares C, Razuvaev V, Nieplova E, Cegnar T, Antonio López J, Dahlström B, Moberg A, Kirchhofer W, Ceylan A, Pachaliuk O, Alexander LV, Petrovic P. 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int J Climatol 22:1441–53.CrossRefGoogle Scholar
- R. 2008. A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.Google Scholar
- Rocha AV, Shaver GR. 2011. Postfire energy exchange in arctic tundra: the importance and climatic implications of burn severity. Global Change Biol. doi: 10.1111/j.1365-2486.2011.02441.x
- Rogers RR, Yau MK. 1989. A short course in cloud physics. Woburn (MA): Butterworth-Heinemann.Google Scholar
- Walker MD, Wahren CH, Hollister RD, Henry GHR, Ahlquist LE, Alatalo JM, Bret-Harte MS, Calef MP, Callaghan TV, Carroll AB, Epstein HE, Jonsdottir IS, Klein JA, Magnusson B, Molau U, Oberbauer SF, Rewa SP, Robinson CH, Shaver GR, Suding KN, Thompson CC, Tolvanen A, Totland O, Turner PL, Tweedie CE, Webber PJ, Wookey PA. 2006. Plant community responses to experimental warming across the tundra biome. Proc Natl Acad Sci USA 103:1342–6.PubMedCrossRefGoogle Scholar