pp 1–11 | Cite as

Extending the osmometer method for assessing drought tolerance in herbaceous species

  • Robert J. Griffin-NolanEmail author
  • Troy W. Ocheltree
  • Kevin E. Mueller
  • Dana M. Blumenthal
  • Julie A. Kray
  • Alan K. Knapp
Physiological ecology – original research


Community-scale surveys of plant drought tolerance are essential for understanding semi-arid ecosystems and community responses to climate change. Thus, there is a need for an accurate and rapid methodology for assessing drought tolerance strategies across plant functional types. The osmometer method for predicting leaf osmotic potential at full turgor (πo), a key metric of leaf-level drought tolerance, has resulted in a 50-fold increase in the measurement speed of this trait; however, the applicability of this method has only been tested in woody species and crops. Here, we assess the osmometer method for use in herbaceous grassland species and test whether πo is an appropriate plant trait for understanding drought strategies of herbaceous species as well as species distributions along climate gradients. Our model for predicting leaf turgor loss point (πTLP) from πo (πTLP = 0.80πo–0.845) is nearly identical to the model previously presented for woody species. Additionally, πo was highly correlated with πTLP for graminoid species (πtlp = 0.944πo–0.611; r2 = 0.96), a plant functional group previously flagged for having the potential to cause erroneous measurements when using an osmometer. We report that πo, measured with an osmometer, is well correlated with other traits linked to drought tolerance (namely, leaf dry matter content and leaf vulnerability to hydraulic failure) as well as climate extremes linked to water availability. The validation of the osmometer method in an herb-dominated ecosystem paves the way for rapid community-scale surveys of drought tolerance across plant functional groups, which could improve trait-based predictions of ecosystem responses to climate change.


Osmotic potential Climate change Grasslands Plant traits Drought 



We would like to thank Victoria Klimkowski, Daniel Spitzer, Dan LeCain, Julie Bushey, and Mary Carlson for helping with data collection and three anonymous reviewers for providing comments that greatly improved this manuscript. This work was funded by the NSF Emerging Frontiers Macrosystem Biology Program (EF-1137378, EF-1137363, EF-1137342 and EF-1137293).

Author contribution statement

All authors contributed substantially to data collection and the conception of the experiment. RJGN and TWO conducted the analyses and wrote the initial draft of this manuscript; other authors provided editorial advice.

Supplementary material

442_2019_4336_MOESM1_ESM.docx (371 kb)
Supplementary material 1 (DOCX 371 kb)


  1. Baker M (2016) Is there a reproducibility crisis? A nature survey lifts the lid on how researchers view the ‘crisis’ rocking science and what they think will help. Nature 533(7604):452–455CrossRefGoogle Scholar
  2. Bartlett MK, Scoffoni C, Sack L (2012a) The determinants of leaf turgor loss point and prediction of drought tolerance of species and biomes: a global meta-analysis. Ecol Lett 15(5):393–405CrossRefGoogle Scholar
  3. Bartlett MK, Scoffoni C, Ardy R, Zhang Y, Sun S, Cao K, Sack L (2012b) Rapid determination of comparative drought tolerance traits: using an osmometer to predict turgor loss point. Methods Ecol Evol 3(5):880–888CrossRefGoogle Scholar
  4. Bartlett MK, Zhang Y, Kreidler N, Sun S, Ardy R, Cao K, Sack L (2014) Global analysis of plasticity in turgor loss point, a key drought tolerance trait. Ecol Lett 17:1580–1590CrossRefGoogle Scholar
  5. Bartlett MK, Zhang Y, Yang J et al (2016) Drought tolerance as a driver of tropical forest assembly: resolving spatial signatures for multiple processes. Ecology 97:503–514CrossRefGoogle Scholar
  6. Beck J, Böller M, Erhardt A, Schwanghart W (2014) Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecol Inf 19:10–15CrossRefGoogle Scholar
  7. Blackman CJ, Brodribb TJ, Jordan GJ (2010) Leaf hydraulic vulnerability is related to conduit dimensions and drought resistance across a diverse range of woody angiosperms. New Phytol 188:1113–1123CrossRefGoogle Scholar
  8. Brodribb TJ (2017) Progressing from ‘functional’ to mechanistic traits. New Phytol 215(1):9–11CrossRefGoogle Scholar
  9. Brodribb TJ, Holbrook NM (2003) Stomatal closure during leaf dehydration, correlation with other leaf physiological traits. Plant Physiol 132(4):2166–2173CrossRefGoogle Scholar
  10. Dai A (2011) Drought under global warming: a review. WIREs Clim Change 2:45–65CrossRefGoogle Scholar
  11. Dai A (2013) Increasing drought under global warming in observations and models. Nat Clim Change 3:52–58CrossRefGoogle Scholar
  12. Edwards EJ, Still CJ (2008) Climate, phylogeny and the ecological distribution of C4 grasses. Ecol Lett 11(3):266–276CrossRefGoogle Scholar
  13. Esperón-Rodríguez M, Curran TJ, Camac JS, Hofmann RW, Correa-Metrio A, Barradas VL (2018) Correlation of drought traits and the predictability of osmotic potential at full leaf turgor in vegetation from New Zealand. Austral Ecol 43:397–408CrossRefGoogle Scholar
  14. Evert RF, Eichhorn SE (2013) Raven biology of plants. Freeman and Co, Chicago, pp 598–599CrossRefGoogle Scholar
  15. Field CB, Behrenfeld MJ, Randerson JT, Falkowski P (1998) Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281(5374):237–240CrossRefGoogle Scholar
  16. Griffin-Nolan RJ, Bushey JA, Carroll CJW et al (2018) Trait selection and community weighting are key to understanding ecosystem responses to changing precipitation regimes. Funct Ecol 00:1–11. Google Scholar
  17. Huxman TE, Smith MD, Fay PA, Knapp AK, Shaw MR, Loik ME, Smith SD, Tissue DT, Zak JC, Weltzin JF, Pockman WT (2004) Convergence across biomes to a common rain-use efficiency. Nature 429(6992):651CrossRefGoogle Scholar
  18. IPCC (2013) Climate Change 2013. The physical science basis. Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. Stocker TF, Qin D, Plattner GK, Tignor MMB, Allen SK, Boschung J, Nauels A, Xia Y, Bex V Midgley PM, eds. Cambridge University Press, CambridgeGoogle Scholar
  19. Karl TR, Melillo JM, Peterson TC (2009) Global climate change impacts in the United States: a state of knowledge report from the US Global Change Research Program. Cambridge University Press, CambridgeGoogle Scholar
  20. Kikuta SB, Richter H (1992) Leaf discs or press saps? A comparison of techniques for the determination of osmotic potentials in freeze-thawed leaf material. J Exp Bot 43(8):1039–1044CrossRefGoogle Scholar
  21. Knapp AK, Carroll CJ, Denton EM, La Pierre KJ, Collins SL, Smith MD (2015) Differential sensitivity to regional-scale drought in six central US grasslands. Oecologia 177(4):949–957CrossRefGoogle Scholar
  22. Knapp AK, Ciais P, Smith MD (2017) Reconciling inconsistencies in precipitation–productivity relationships: implications for climate change. New Phytol 214(1):41–47CrossRefGoogle Scholar
  23. Koide RT, Robichaux RH, Morse SR, Smith CM (1989) Plant water status, hydraulic resistance and capacitance. Plant physiological ecology. Springer, New York, pp 161–183CrossRefGoogle Scholar
  24. Kramer PJ, Boyer JS (1995) Water relations of plants and soils. Academic Press, San DiegoGoogle Scholar
  25. Kunkel KE, Stevens LE, Stevens SE et al (2013) Regional Climate Trends and Scenarios for the U.S. National Climate Assessment. Part 4. Climate of the U.S. Great Plains, NOAA Technical Report NESDIS 142-4, 82Google Scholar
  26. Levitt J (1980) Responses of plants to environmental stresses, Volume II. Water, radiation, salt, and other stresses. Academic Press, New YorkGoogle Scholar
  27. Liu MZ, Osborne CP (2008) Leaf cold acclimation and freezing injury in C3 and C4 grasses of the Mongolian Plateau. J Exp Bot 59(15):4161–4170CrossRefGoogle Scholar
  28. Maréchaux I, Bartlett MK, Sack L, Baraloto C, Engel J, Joetzjer E, Chave J (2015) Drought tolerance as predicted by leaf water potential at turgor loss point varies strongly across species within an Amazonian forest. Funct Ecol 29(10):1268–1277CrossRefGoogle Scholar
  29. Maréchaux I, Bartlett MK, Gaucher P, Sack L, Chave J (2016) Causes of variation in leaf-level drought tolerance within an Amazonian forest. J Plant Hydraul 31(3):e004Google Scholar
  30. Mart KB, Veneklaas EJ, Bramley H (2016) Osmotic potential at full turgor: an easily measurable trait to help breeders select for drought tolerance in wheat. Plant Breed 135(3):279–285CrossRefGoogle Scholar
  31. Meinzer FC, Woodruff DR, Marias DE, McCulloh KA, Sevanto S (2014) Dynamics of leaf water relations components in co-occurring iso-and anisohydric conifer species. Plant, Cell Environ 37(11):2577–2586CrossRefGoogle Scholar
  32. Meinzer FC, Woodruff DR, Marias DE, Smith DD, McCulloh KA, Howard AR, Magedman AL (2016) Mapping ‘hydroscapes’ along the iso-to anisohydric continuum of stomatal regulation of plant water status. Ecol Lett 19(11):1343–1352CrossRefGoogle Scholar
  33. Mitchell PJ, O’grady AP, Pinkard EA et al (2016) An ecoclimatic framework for evaluating the resilience of vegetation to water deficit. Glob Change Biol 22(5):1677–1689CrossRefGoogle Scholar
  34. Noy-Meir I (1973) Desert ecosystems: environment and producers. Annu Rev Ecol Syst 4(1):25–51CrossRefGoogle Scholar
  35. Ocheltree TW, Nippert JB, Prasad PV (2016) A safety vs efficiency trade-off identified in the hydraulic pathway of grass leaves is decoupled from photosynthesis, stomatal conductance and precipitation. New Phytol 210(1):97–107CrossRefGoogle Scholar
  36. Paine CT, Deasey A, Duthie AB (2018) Towards the general mechanistic prediction of community dynamics. Funct Ecol 32:1681–1692CrossRefGoogle Scholar
  37. Peuke AD, Rokitta M, Zimmermann U, Schreiber L, Haase A (2001) Simultaneous measurement of water flow velocity and solute transport in xylem and phloem of adult plants of Ricinus communis over a daily time course by nuclear magnetic resonance spectrometry. Plant, Cell Environ 24:491–503CrossRefGoogle Scholar
  38. Poorter H, Garnier E (1999) Ecological significance of inherent variation in relative growth rate and its components. Handbook Funct Plant Ecol 20:81–120Google Scholar
  39. Reich PB (2014) The world-wide ‘fast–slow’plant economics spectrum: a traits manifesto. J Ecol 102(2):275–301CrossRefGoogle Scholar
  40. Ricklefs RE, Latham RE (1992) Intercontinental correlation of geographical ranges suggests stasis in ecological traits of relict genera of temperate perennial herbs. Am Nat 139(6):1305–1321CrossRefGoogle Scholar
  41. Rosenberg NJ (1987) Climate of the Great Plains region of the United States. Great Plains Quarterly, 22–32Google Scholar
  42. Sack L, Melcher PJ, Zwieniecki MA, Holbrook NM (2002) The hydraulic conductance of the angiosperm leaf lamina: a comparison of three measurement methods. J Exp Bot 53(378):2177–2184CrossRefGoogle Scholar
  43. Schulte PJ, Hinckley TM (1985) A comparison of pressure-volume curve data analysis techniques. J Exp Bot 36(10):1590–1602CrossRefGoogle Scholar
  44. Sage RF, Monson RK (1999) C4 plant biology. Academic, New YorkGoogle Scholar
  45. Scoffoni C, Rawls M, McKown A, Cochard H, Sack L (2011) Decline of leaf hydraulic conductance with dehydration: relationship to leaf size and venation architecture. Plant Physiol. Google Scholar
  46. Shipley B, De Bello F, Cornelissen JH, Laliberté E, Laughlin DC, Reich PB (2016) Reinforcing loose foundation stones in trait-based plant ecology. Oecologia 180(4):923–931CrossRefGoogle Scholar
  47. Suding KN, Lavorel S, Chapin FS et al (2008) Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Glob Change Biol 14(5):1125–1140CrossRefGoogle Scholar
  48. Turner NC (1988) Measurement of plant water status by the pressure chamber technique. Irrig Sci 9(4):289–308CrossRefGoogle Scholar
  49. Weaver JE (1958) Classification of root systems of forbs of grassland and a consideration of their significance. Ecology 39(3):393–401CrossRefGoogle Scholar
  50. Wright IJ, Reich PB, Westoby M et al (2004) The worldwide leaf economics spectrum. Nature 428(6985):821CrossRefGoogle Scholar
  51. Zhu SD, Chen YJ, Ye Q et al (2018) Leaf turgor loss point is correlated with drought tolerance and leaf carbon economics traits. Tree Physiol. Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of BiologyColorado State UniversityFort CollinsUSA
  2. 2.Graduate Degree Program in EcologyColorado State UniversityFort CollinsUSA
  3. 3.Department of Forest and Rangeland StewardshipColorado State UniversityFort CollinsUSA
  4. 4.Department of Biological, Geological, and Environmental SciencesCleveland State UniversityClevelandUSA
  5. 5.Rangeland Resources and System Research UnitUSDA Agricultural Research ServiceFort CollinsUSA

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