Photosynthetica

, Volume 48, Issue 3, pp 370–376 | Cite as

Photosynthetic traits of Carex cinerascens in flooded and nonflooded conditions

Original Papers
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Abstract

Gas exchange of Carex cinerascens was carried out in Swan Islet Wetland Reserve (29°48′ N, 112°33′ E). The diurnal photosynthetic course of C. cinerascens in the flooded and the nonflooded conditions were analyzed through the radial basis function (RBF) neural network approach to evaluate the influences of environmental variables on the photosynthetic activity. The inhibition of photosynthesis induced by soil flooding can be attributed to the reduced stomatal conductance (g s), the deficiency of Rubisco regeneration and decreased chlorophyll (Chl) content. As revealed by analysis of artificial neural network (ANN) models, g s was the dominant factor in determining the photosynthesis response. Weighting analysis showed that the effect of water pressure deficit (VPD) > air temperature (T) > CO2 concentration (C a) > air humidity (RH) > photosynthetical photon flux density (PPFD) for the nonflooded model, whereas for the flooded model, the factors were ranked in the order VPD > C a > RH > PPFD > T. The different photosynthetic response of C. cinerascens found between the nonflooded and flooded conditions would be useful to evaluate the flood tolerance at plant species level.

Additional key words

ANN Carex cinerascens photosynthesis 

Abbreviations

ANN

artificial neural network

AQY

apparent quantum yield

Ca

CO2 concentration

Ci

intercellular CO2 concentration

CE

carboxylation efficiency

Chl

chlorophyll

E

transpiration rate

ETR

electron transport rate

Fm

maximum fluorescence of dark state

Fm′

maximum fluorescence of light-adapted state

Fo

minimum fluorescence of dark state

Fo′

minimum fluorescence of light-adapted state

Fs

steady-state fluorescence

Fv

variable fluorescence

Fv/Fm

maximum quantum yield of PSII

Fv/Fo

the ratio of variable fluorescence to minimum fluorescence

gs

stomatal conductance

Jmax

the light saturated rate of electron transport

Lc

light compensation point

Ls

light saturation point

PN

net photosynthetic rate

PAR

photosynthetically active radiation

PPFD

photosynthetic photon flux density

PSII

photosystem II

qN

non-photochemical quenching coefficient

qP

photochemical quenching coefficient

RD

dark respiration rate

Rday

day respiration

RBF

radial basis function

RH

air humidity

Rubisco

ribulose-1,5-bisphosphate carboxylase/oxygenase

T

air temperature

Tl

leaf temperature

Vcmax

maximum rate of carboxylation

VPD

water-pressure deficit

WUE

water-use efficiency

ΦPSII

effective quantum yield of PSII

Γ

CO2 compensation point

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Notes

Acknowledgement

This work was financed by Innovation Key project of CAS (O754551B 03), Innovation Key project of CAS (KSCX2-YW-Z-1023-5), grant (30700083) from Natural Sciences Foundation of China and project (CN2357) funded by WWF.

References

  1. Balls, G.R., Palmer-Brown, D., Sanders, G.E.: Investigating microclimatic influences on ozone injury in clover (Trifolium subterraneum) using artificial neural networks. — New Phytol. 132: 271–286, 1996.CrossRefGoogle Scholar
  2. Baruch, Z.: Responses to drought and flooding in tropical forage grasses. II. Leaf water potential, photosynthesis rate and alcohol dehydrogenase activity. — Plant Soil 164: 97–105, 1994.CrossRefGoogle Scholar
  3. Bianchini, M., Frasconi, P., Gori, M.: Learning without local minima in radial basis function networks. — IEEE Trans. Neural Networks 6: 749–756, 1995.CrossRefGoogle Scholar
  4. Bradford, K.J.: Effects of soil flooding on leaf gas exchange of tomato plants. — Plant Physiol. 73: 475–479, 1983.CrossRefPubMedGoogle Scholar
  5. Bragina, T.V., Ponomareva, Y.V., Drozdova, I.S., Grinieva, G.M.: Photosynthesis and dark respiration in leaves of different ages of partly flooded maize seedlings. — Russ. J. Plant Physiol. 51: 342–347, 2004.CrossRefGoogle Scholar
  6. Brown, C.E., Pezeshki, S.R.: A study on waterlogging as a potential tool to control Ligustrum sinense populations in western Tennessee. — Wetlands 20: 429–437, 2000.CrossRefGoogle Scholar
  7. Chen, H.J., Qualls, R.G., Blank, R.R.: Effect of soil flooding on photosynthesis, carbohydrate partitioning and nutrient uptake in the invasive exotic Lepidium latifolium. — Aquat. Bot. 82: 250–268, 2005.CrossRefGoogle Scholar
  8. Farquhar, G.D., von Caemmerer, S.: Modeling of photosynthetic responses to environmental conditions. — In: Lange, O.L., Nobel, P.S., Osmond, C.B., Ziegler, H. (ed.): Physiological Plant Ecology. II.Water Relation and Carbon Assimilation. Pp. 549–587. Springer-Verlag, Berlin — Heidelberg — New York 1982.Google Scholar
  9. Farquhar, G.D., von Caemmerer, S.V., Berry, J.A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. — Planta 149: 78–90, 1980.CrossRefGoogle Scholar
  10. Genty, B., Briantais, J.M., Baker, N.R.: The relationship between the quantum yield of photosynthetic electron-transport and quenching of chlorophyll fluorescence. — Biochim. Biophys. Acta 990: 87–92. 1989.Google Scholar
  11. Gravatt, D.A., Kirby, C.J.: Patterns of photosynthesis and starch allocation in seedlings of four bottomland hardwood tree species subjected to flooding. — Tree Physiol. 18: 411–417, 1998.PubMedGoogle Scholar
  12. Huntingford, C., Cox, P.M.: Use of statistical and neural network techniques to detect how stomatal conductance responds to changes in the local environment. — Ecol. Model. 97: 217–246, 1997.CrossRefGoogle Scholar
  13. Jeong, K.S., Joo, G.J., Kim, H.W., Ha, K., Recknagel, F.: Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network. — Ecol. Model. 146: 115–129, 2001.CrossRefGoogle Scholar
  14. Kozlowski, T.T.: Plant responses to flooding of soil. — Bioscience 34: 162–167, 1984.CrossRefGoogle Scholar
  15. Li, M., Yang, D., Li, W.: Leaf gas exchange characteristics and chlorophyll fluorescence of three wetland plants in response to long-term soil flooding — Photosynthetica 45: 222–228, 2007.CrossRefGoogle Scholar
  16. Liao, C.T., Lin, C.H.: Effect of flooding stress on photosynthetic activities of Momordica charantia. — Plant Physiol. Biochem. 32: 479–485, 1994.Google Scholar
  17. Lichtenthaler, H.K.: Chlorophyll. and carotenoids, pigments of photosynthetic biomembranes. — Methods Enzymol. 148: 350–382, 1987.CrossRefGoogle Scholar
  18. Macek, P., Rejmánková, E., Houdková, K.: The effect of longterm submergence on functional properties of Eleocharis cellulosa Torr. — Aquat. Bot. 84: 251–258, 2006.CrossRefGoogle Scholar
  19. Marshall, B., Biscoe, P.V.: A model for C3 leaves describing the dependence of net photosynthesis on irradiance. Derivation. — J. Exp. Bot. 31: 29–39, 1980.CrossRefGoogle Scholar
  20. Mauchamp, A, Méthy, M.: Submergence-induced damage of photosynthetic apparatus in Phragmites australis. — Environ. Exp. Bot. 51: 227–235, 2004.CrossRefGoogle Scholar
  21. Maxwell, K., Johnson, G.N.: Chlorophyll fluorescence — a practical guide. — J. Exp. Bot. 51: 659–668, 2000.CrossRefPubMedGoogle Scholar
  22. McKevlin, M.R., Hook, D.D., McKee, W.H.: Growth and nutrient use efficiency of water tupelo seedlings in flooded and well drained soil. — Tree Physiol. 15: 753–758, 1995.PubMedGoogle Scholar
  23. Melesse, A.M., Hanley, R.S.: Artificial neural network application for multi-ecosystem carbon flux simulation — Ecol. Model. 189: 305–314, 2005.CrossRefGoogle Scholar
  24. Mielke, M.S., de Almeida, A.-A.F., Gomes, F.P., Aguilar, M.A.G., Mangabeira, P.A.O.: Leaf gas exchange, chlorophyll fluorescence and growth responses of Genipa americana seedlings to soil flooding. — Environ. Exp. Bot. 50: 221–231, 2003.CrossRefGoogle Scholar
  25. Mishra, S.K., Patro, L., Mohapatra, P.K., Biswal, B: Response of senescing rice leaves to flooding stress. — Photosynthetica 46: 315–317, 2008.CrossRefGoogle Scholar
  26. Mommer, L., Pons, T.L., Visser, E.J.W.: Photosynthetic consequences of phenotypic plasticity in response to submergence: Rumex palustris as a case study. — J. Exp. Bot. 57: 283–290, 2006.CrossRefPubMedGoogle Scholar
  27. Pallas, J.E., Kays, S.J.: Inhibition of photosynthesis by ethylene — a stomatal effect. — Plant Physiol. 70: 598–601, 1982.CrossRefPubMedGoogle Scholar
  28. Park, J., Sandberg, I.W.: Universal approximation using radial basis functions network. — Neural Computation 3: 246–257, 1991.CrossRefGoogle Scholar
  29. Pezeshki, S.R.: Responses of baldcypress (Taxodium distichum) seedlings to hypoxia: leaf protein content, ribulose-1, 5-bisphosphate carboxylase/oxygenase activity and photosynthesis. Photosynthetica 30: 59–68, 1994.Google Scholar
  30. Pezeshki, S.R.: Wetland plant responses to soil flooding. — Environ. Exp. Bot. 46: 299–312, 2001.CrossRefGoogle Scholar
  31. Pezeshki, S.R, Chambers, J.L.: Responses of cherrybark oak seedlings to short-term flooding. — Forest Sci. 31: 760–771, 1985.Google Scholar
  32. Pezeshki, S.R., Pardue, J.H., DeLaune, R.D.: The influence of soil oxygen deficiency on alcohol dehydrogenase activity, root porosity, ethylene production and photosynthesis in Spartina patens. — Environ. Exp. Bot. 33: 565–573, 1993.CrossRefGoogle Scholar
  33. Ponnamperuma, F.N.: Effects of flooding on soils. — In: Kozlowski, T.T.(ed.): Flooding and Plant Growth. Pp. 1–44. Academic Press, Orlando- San Diego — San Francisco — New York — London — Toronto — Montreal — Sydney — Tokyo — São Paulo 1984.Google Scholar
  34. Poorter, H.: Do slow-growing species and nutrient-stressed plants respond relatively strongly to elevated CO2? — Glob. Change Biol. 4: 693–697, 1998.CrossRefGoogle Scholar
  35. Schreiber, U., Bilger, W., Neubauer, C.: Chlorophyll fluorescence as a nonintrusive indicator for rapid assessment of in vivo photosynthesis. — In: Schulze, E.-D., Caldwell, M.M. (ed.): Ecophysiology of Photosynthesis. Pp. 49–70. Springer- Verlag, Berlin 1994.Google Scholar
  36. Taylor, G.E., Gunderson, C.A.: Physiological site of ethylene effects on carbon dioxide assimilation in Glycine max. L. Merr. — Plant Physiol. 86: 85–92, 1988.CrossRefPubMedGoogle Scholar
  37. Wasserman, P.D.: Neural Computing: Theory and Practice, Van Nostrand Reinhold, New York 1989.Google Scholar
  38. Yamori, W., Noguchi, K., Terashima, I.: Temperature acclimation of photosynthesis in spinach leaves: analyses of photosynthetic components and temperature dependencies of photosynthetic partial reactions. — Plant Cell Environ. 28: 536–547, 2005.CrossRefGoogle Scholar
  39. Yordanova, R.Y., Alexieva, V.S., Popova, L.P.: Influence of root oxygen deficiency on photosynthesis and antioxidant status in barley plants. — Russ. J. Plant Physiol. 50: 163–167, 2003.CrossRefGoogle Scholar

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© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Laboratory of Aquatic Plant Biology, Wuhan Botanical GardenThe Chinese Academy of SciencesWuhan, HubeiP. R. China
  2. 2.Key Laboratory of Aquatic Botany and Watershed EcologyChinese Academy of SciencesWuhan, HubeiP. R. China
  3. 3.College of Environment Sci. & Eng.Huazhong University of Sci. and Tech.WuhanP. R. China
  4. 4.Hubei Key Laboratory of Wetland Evolution & Ecological RestorationWuhan, HubeiP. R. China

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