Skip to main content
Log in

Meta-analysis and its application in global change research

  • Review
  • Published:
Chinese Science Bulletin

Abstract

Meta-analysis is a quantitative synthetic research method that statistically integrates results from individual studies to find common trends and differences. With increasing concern over global change, meta-analysis has been rapidly adopted in global change research. Here, we introduce the methodologies, advantages and disadvantages of meta-analysis, and review its application in global climate change research, including the responses of ecosystems to global warming and rising CO2 and O3 concentrations, the effects of land use and management on climate change and the effects of disturbances on biogeochemistry cycles of ecosystem. Despite limitation and potential misapplication, meta-analysis has been demonstrated to be a much better tool than traditional narrative review in synthesizing results from multiple studies. Several methodological developments for research synthesis have not yet been widely used in global climate change researches such as cumulative meta-analysis and sensitivity analysis. It is necessary to update the results of meta-analysis on a given topic at regular intervals by including newly published studies. Emphasis should be put on multi-factor interaction and long-term experiments. There is great potential to apply meta-analysis to global climate change research in China because research and observation networks have been established (e.g. ChinaFlux and CERN), which create the need for combining these data and results to provide support for governments’ decision making on climate change. It is expected that meta-analysis will be widely adopted in future climate change research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. IPCC. Climate change 2001-synthesis report: third assessment report of the Intergovernmental Panel on Climate Change. 2001

  2. Rustad L E, Campbell J L, Marion G M, et al. A meta-analysis of the response of soil respiration, net nitrogen mineralization, and above-ground plant growth to experimental ecosystem warming. Oecologia, 2001, 126: 543–562

    Article  Google Scholar 

  3. Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature, 2003, 421: 37–42

    Article  Google Scholar 

  4. van Wijk M T, Clemmensen K E, Shaver G R, et al. Long-term eco-system level experiments at Toolik Lake, Alaska, and at Abisko, Northern Sweden: generalizations and differences in ecosystem and plant type responses to global change. Glob Change Biol, 2003, 10(1): 105–123

    Article  Google Scholar 

  5. Hedges L V, Olkin I. Statistical Methods for Meta-analysis. New York: Academic Press, 1985

    Google Scholar 

  6. Stuhlmacher A F, Gillespie T L. Managing conflict in the literature: Meta-analysis as a research method. Int Negot, 2005, 10(1): 67–78

    Article  Google Scholar 

  7. Glass G V. Primary, secondary, and meta-analysis of research. Educ Res, 1976, 5: 3–8

    Google Scholar 

  8. Gurevitch J, Curtis P S, Jones M H. Meta-analysis in ecology. Adv Ecol Res, 2001, 32: 199–247

    Article  Google Scholar 

  9. Gurevitch J, Hedges L V. Meta-analysis: combining the results of independent experiments. In: Scheiner SM, Gurevitch J, eds. Design and Analysis of Ecological Experiments. London: Chapman and Hall, 1993. 378–425

    Google Scholar 

  10. Amqvist G, Wooster D. Meta-analysis: synthesizing research findings in ecology and evolution. Trends Ecol Evol, 1995, 10: 236–240

    Article  Google Scholar 

  11. Osenberg C W, Samelle O, Cooper S, et al. Resolving ecological questions through meta-analysis: goals, metrics and models. Ecology, 1999, 80: 1105–1117

    Article  Google Scholar 

  12. Zhao N, Yu S Z. Meta-analysis: A new quantitative synthesis method. Chin J Prev Contr Chron Non-Commun Dis (in Chinese), 1993, 1(6): 277–281

    Google Scholar 

  13. Peng S L, Tang X Y. Meta-analysis and its application in ecology. J Ecol (in Chinese), 1998, 17(5): 74–79

    Google Scholar 

  14. Liu J, Peng S L. Meta-analysis in ecology and medical science. Acta Ecol Sin (in Chinese), 2004, 24(11): 2627–2634

    Google Scholar 

  15. Zheng F Y, Peng S L. Meta-analysis of prey relationships. Acta Ecol Sin (in Chinese), 1999, 19(4): 448–452

    Google Scholar 

  16. Zheng F Y, Peng S L. Meta-analysis of the response of plant ecophysiological variables to doubled atmospheric CO2 concentrations. Acta Botan Sin (in Chinese), 2001(11): 1101–1109

    Google Scholar 

  17. Rosenthal R, DiMatteo M R. Meta-analysis: recent developments in quantitative methods for literature review. Ann Rev Psychol, 2001, 52: 59–82

    Article  Google Scholar 

  18. Guo L B, Gifford R M. Soil carbon stocks and land use change: a meta-analysis. Glob Change Biol, 2002, 8: 345–360

    Article  Google Scholar 

  19. Kotiaho J S, Tomkins J L. Meta-analysis: can it ever fail? Oikos, 2002, 96: 551–553

    Article  Google Scholar 

  20. Jennions M D, Møller A P, Curie M, et al. Meta-analysis can “fail”: reply to Kotiaho and Tomkins. Oikos, 2004, 104: 191–193

    Article  Google Scholar 

  21. Noble G H. Meta-analysis: methods, strengths, weaknesses, and political uses. J Lab Clin Med, 2006, 147: 7–20

    Article  Google Scholar 

  22. Song F, Eastwood AJ, Gilbody S, et al. Publication and related biases. Health Technol Asses, 2000, 4: 1–115

    Google Scholar 

  23. Møller A P, Jennions M D. Testing and adjusting for publication bias. Trends Ecol Evol, 2001, 16: 580–586

    Article  Google Scholar 

  24. Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 2000, 56: 455–463

    Article  Google Scholar 

  25. Duval S, Tweedie R. A nonparametric “Trim and Fill” method of accounting for publication bias in meta-analysis. J Am Stat Assoc, 2000, 5: 89–98

    Article  Google Scholar 

  26. Egger M, Davey S G, Schnedier M, et al. Bias in meta-analysis detected by a simple graphical test. Br Med J, 1997, 315: 629–634

    Google Scholar 

  27. Macaskill P, Walter S, Irwig L. A comparison of methods to detect publication bias in meta-analysis. Stat Med, 2001, 20: 641–654

    Article  Google Scholar 

  28. Rothstein H R, Sutton A J, Borenstein M. Publication Bias in Meta-Analysis-Prevention, Assessment and Adjustments. Chichester: John Wiley & Sons Ltd, 2005. 1–374

    Book  Google Scholar 

  29. Curtis P S. A meta-analysis of leaf gas exchange and nitrogen in trees grown under elevated carbon dioxide. Plant Cell Environ, 1996, 19: 127–137

    Article  Google Scholar 

  30. Hoorens B, Aerts R, Stroetenga M. Is there a trade-off between the plant’s growth response to elevated CO2 and subsequent litter decomposability? Oikos, 2003, 103: 17–30

    Article  Google Scholar 

  31. Norby R J, Luo Y Q. Evaluating ecosystem responses to rising atmospheric CO2 and global warming in a multi-factor world. New Phytol, 2004, 162: 281–293

    Article  Google Scholar 

  32. Curtis P S, Wang X. A meta-analysis of elevated CO2 effects on woody plant growth, form, and function. Oecologia, 1998, 113: 299–313

    Article  Google Scholar 

  33. Wand S J E, Midgley G Y F, Jones M H, et al. Response of wild C4 and C3 grass (Poaceae) species to elevated atmospheric CO2 concerntration: a meta-analytic test of current theories and perceptions. Glob Change Biol, 1999, 5: 723–741

    Article  Google Scholar 

  34. Kerstiens G. Meta-analysis of the interaction between shade-tolerance, light environment and growth response of woody species to elevated CO2. Acta Oecol, 2001, 22: 61–69

    Article  Google Scholar 

  35. Poorter H, Navas M L. Plant growth and competition at elevated CO2: on winners, losers and functional groups. New Phytol, 2003, 157: 175–198

    Article  Google Scholar 

  36. Medlyn B E, Badeck F W, De Pury D G, et al. Effects of elevated CO2 on photosysthesis in European forest species: a meta-analysis of model parameters. Plant Cell Environ, 1999, 22: 1475–1495

    Article  Google Scholar 

  37. Ainsworth E A, Davey P A, Bernacchi C J, et al. A meta-analysis of elevated CO2 effects on soybean (Glycine max) physiology, growth and yield. Glob Change Biol, 2002, 8: 695–709

    Article  Google Scholar 

  38. Morgan P B, Mies T A, Bollero G A, et al. Season-long elevation of ozone concentration to projected 2050 levels under fully open-air conditions substantially decreases the growth and production of soybean. New Phytol, 2006, 170: 333–343

    Article  Google Scholar 

  39. Ainsworth E A, Long S P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol, 2005, 165: 351–372

    Article  Google Scholar 

  40. Long S P, Ainsworth E A, Rogers A, et al. Rising atmospheric carbon dioxide: Plants FACE the future. Annu Rev Plant Biol, 2004, 55: 591–628

    Article  Google Scholar 

  41. Luo Y Q, Hui D F, Zhang D Q. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land eosystems: A meta-analysis. Ecology, 2006, 87(1): 53–63

    Google Scholar 

  42. Morison J I L. Increasing atmospheric CO2 and stomata. New Phytol, 2001, 149: 154–158

    Article  Google Scholar 

  43. Medlyn B E, Barton C V M, Broadmeadow M S J, et al. Effects of elevated CO2 on photosysthesis in European forest species: A meta-analysis of model parameters. New Phytol, 2001, 149: 247–264

    Article  Google Scholar 

  44. Wang X Z, Curtis P. A meta-analytical test of elevated CO2 effects on plant respiration. Plant Ecol, 2002, 161: 251–261

    Article  Google Scholar 

  45. Dermody O, Long S P, DeLucia E H. How does elevated CO2 or ozone affect the leaf-area index of soybean when applied independently? New Phytol, 2006, 169: 145–155

    Article  Google Scholar 

  46. Drake B G, Gonzalez-Meler M A, Long S P. More efficient plants: A consequence of rising atmospheric CO2. Ann Rev Plant Physiol, 1997, 48: 609–639

    Article  Google Scholar 

  47. Cowling S A, Field C B. Environmental control of leaf area production: implications for vegetation and land-surface modeling. Glob Biogeochem Cycle, 2003, 17: 1–14

    Google Scholar 

  48. Peterson A G, Ball J T, Luo Y Q, et al. The photosynthesis-leaf nitrogen relationship at ambient and elevated atmospheric carbon dioxide: a meta-analysis. Glob Change Biol, 1999, 5: 331–346

    Article  Google Scholar 

  49. Jablonski L M, Wang X Z, Curtis P S. Plant reproduction under elevated CO2 conditions: a meta-analysis of reports on 79 crop and wild species. New Phytol, 2002, 156: 9–26

    Article  Google Scholar 

  50. Jastrow J D, Miller R M, Matamala R, et al. Elevated atmospheric carbon dioxide increases soil carbon. Glob Change Biol, 2005, 11: 2057–2064

    Article  Google Scholar 

  51. Eliasson P E, McMurtrie R E, Pepper D A, et al. The response of heterotrophic CO2 flux to soil warming. Glob Change Biol, 2005, 11(1): 167–181

    Article  Google Scholar 

  52. Rustad L E, Huntington T G, Boone R D. Controls on soil respiration: implications for climate change. Biogeochemistry, 2000, 48: 1–6

    Article  Google Scholar 

  53. Subke J A, Inglimaw I, Cotrufow F. Trends and methodological impacts in soil CO2 efflux partitioning: A metaanalytical review. Glob Change Biol, 2006, 12: 1–23

    Article  Google Scholar 

  54. Barnard R, Leadley P W, Hungate B A. Global change, nitrification, and denitrification: A review. Glob Biogeochem Cycle, 2005, 19: 2282–2298

    Google Scholar 

  55. Norby R J, Cotrufo M F, Ineson P, et al. Elevated CO2, litter chemistry, and decomposition: a synthesis. Oecologia, 2001, 127: 153–165

    Article  Google Scholar 

  56. Knorr W, Prentice I C, House J I, et al. Long-term sensitivity of soil carbon turnover to warming. Nature, 2005, 433: 298–301

    Article  Google Scholar 

  57. Treseder K K. A meta-analysis of mycorrhizal responses to nitrogen, phosphorus, and atmospheric CO2 in field studies. New Phytol, 2004, 164: 347–355

    Article  Google Scholar 

  58. Alberton O, Kuyper T W, Gorissen A. Taking mycocentrism seriously: mycorrhizal fungal and plant responses to elevated CO2. New Phytol, 2005, 167(3): 859–868

    Article  Google Scholar 

  59. Arft A M, Walker M D, Gurevitch J, et al. Response of tundraplants to experimental warming: meta-analysis of the international tundra experiment. Ecol Monogr, 1999, 69(4): 491–511

    Article  Google Scholar 

  60. Walker M D, Wahrenb C H, Hollisterc R D, et al. Plant community responses to experimental warming across the tundra biome. Proc Natl Acad Sci USA, 2006, 103: 1342–1346

    Article  Google Scholar 

  61. Root T L, Price J T, Hall K R, et al. Fingerprints of global warming on wild animals and plants. Nature, 2003, 421: 57–60

    Article  Google Scholar 

  62. Davidson E A, Davidson E A, Janssens I A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 2006, 440: 165–173

    Article  Google Scholar 

  63. Lloyd J, Taylor J A. On the temperature dependence of soil respiration. Func Ecol, 1994, 8: 315–323

    Article  Google Scholar 

  64. Grace J, Rayment M. Respiration in the balance. Nature, 2000, 404: 819–820

    Article  Google Scholar 

  65. Jarvis P, Linder S. Constraints to growth of boreal forests. Nature, 2000, 405: 904–905

    Article  Google Scholar 

  66. Giardina C P, Ryan M G. Evidence that decomposition rates of organic carbon in mineral soil do not vary with temperature. Nature, 2000, 404: 858–861

    Article  Google Scholar 

  67. Luo Y, Wan S, Hui D, et al. Acclimatization of soil respiration to warming in a tall grass prairie. Nature, 2001, 413: 622–625

    Article  Google Scholar 

  68. Sanderman J, Amundson R G, Baldocchi D D. Application of eddy covariance measurements to the temperature dependence of soil organic matter mean residence time. Glob Biogeochem Cycle, 2003, 17: 1061–1075

    Article  Google Scholar 

  69. Dormann C F, Woodin S J. Climate changes in the Arctic: using plant functional types in a meta-analysis of field experiments. Func Ecol, 2002, 16: 4–17

    Article  Google Scholar 

  70. Raich J W, Russell A E, Kitayama K, et al. Temperature true influences carbon accumulations in moist tropical forests. Ecology, 2006, 87(1): 76–87

    Google Scholar 

  71. Blenckner T, Hillebrand H. North Atlantic oscillation signatures in aquatic and terrstrial ecosystems-a meta-analysis. Glob Change Biol, 2002, 8: 203–212

    Article  Google Scholar 

  72. Zvereva E L, Kozlov M V. Consequences of simultaneous elevation of carbon dioxide and temperature for plant-herbivore interactions: A meta-analysis. Glob Change Biol, 2006, 12: 27–41

    Article  Google Scholar 

  73. Morgan P B, Ainsworth E A, Long S P. How does elevated ozone impact soybean? A meta-analysis of photosynthesis, growth and yield. Plant Cell Environ, 2003, 26: 1317–1328

    Article  Google Scholar 

  74. Searles P S, Flint S D, Caldwell M M. A meta-analysis of plant field studies simulating stratospheric ozone depletion. Oecologia, 2001, 127: 1–10

    Article  Google Scholar 

  75. Ogle S M, Breidt F J, Paustian K. Agricultural management impacts on soil organic carbon storage under moist and dry climatic conditions of temperate and tropical regions. Biogeochemistry, 2005, 72: 87–121

    Article  Google Scholar 

  76. Zinn Y L, Lal R, Resck D V S. Changes in soil organic carbon stocks under agriculture in Brazil. Soil Till Res, 2005, 84(1): 28–40

    Article  Google Scholar 

  77. Johnson D W, Curtis P S. Effects of forest management on soil C and N storage: Meta analysis. For Ecol Manag 2001, 140: 227–238

    Article  Google Scholar 

  78. van Kooten G C, Eagle A J, Manley J. How costly are carbon offsets? A emta-analysis of carbon forest sinks. Environ Sci Pol, 2004, 7: 239–251

    Article  Google Scholar 

  79. Manley J, van Kooten G C, Moeltner K, et al. Creating carbon offsets in agriculture through no-till cultivation: a meta-analysis of costs and carbon benefits. Clim Change, 2005, 68: 41–65

    Article  Google Scholar 

  80. Geist H J, Lambin E F. What drives tropical deforestation? A meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence. LUCC Report Series 4. 2001

  81. Wan S Q, Hui D F, Luo Y Q. Fire effects on nitrogen pools and dynamics in terrestrial ecosystems: a meta-analysis. Ecol Appl, 2001, 5: 1349–1365

    Article  Google Scholar 

  82. Curtis P S, Jablonski L M, Wang X Z. Assessing elevated CO2 responses using meta-analysis. New Phytol, 2003, 160: 6–7

    Article  Google Scholar 

  83. Thornton A, Lee P. Publication bias in meta-analysis: its causes and consequences. J Clin Epidemiol, 2000, 53: 207–216

    Article  Google Scholar 

  84. Hedges L V, Gurevitch J, Curtis P S. The meta-analysis of response ratios in experimental ecology. Ecology, 1999, 80: 1150–1156

    Article  Google Scholar 

  85. Körner C. Nutrients and sink activity drive plant CO2 responses-caution with literature-based analysis. New Phytol, 2003, 159: 531–538

    Article  Google Scholar 

  86. Lortie C J, Callaway R M. Re-analysis of meta-analysis: support for the stress-gradient hypothesis. J Ecol, 2006, 94: 7–16

    Article  Google Scholar 

  87. Maestre F T, Valladares F, Reynolds J F. Is the change of plant-plant interactions with abiotic stress predictable? A meta-analysis of field results in arid environments. J Ecol, 2005, 93: 748–757

    Article  Google Scholar 

  88. Gates S. Review of methodology of quantitative reviews using meta-analysis in ecology. J Anim Ecol, 2002, 71: 547–557

    Article  Google Scholar 

  89. Leimu R, Koricheva J. Cumulative meta-analysis: a new tool for detection of temporal trends and publication bias in ecology. Proc R Soc Lond Ser B-Biol Sci, 2004, 271: 1961–1966

    Article  Google Scholar 

  90. Jennions M D, Møller A P. Relationships fade with time: a meta-analysis of temporal trends in publication in ecology and evolution. Proc R Soc Lond B, 2002, 269: 43–48

    Article  Google Scholar 

  91. Englund G, Sarnelle O, Cooper S D. The importance of data-selection criteria: meta-analyses of stream predation experiments. Ecology, 1999, 80(4): 1132–1141

    Article  Google Scholar 

  92. Nowak R S, Ellsworth D S, Smith S D. Functional responses of plants to elevated atmospheric CO2-do photosynthetic and productivity data from FACE experiments support early predictions? New phytol, 2004, 162: 253–280

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peng ChangHui.

Additional information

Supported by International Partnership Project “Human Activities and Ecosystem Changes”, Chinese Academy of Sciences (Grant No. CXTD-Z2005-1), Natural Science and Engineering Research Council of Canada (NSERC), and China Scholarship Council (CSC)

About this article

Cite this article

Lei, X., Peng, C., Tian, D. et al. Meta-analysis and its application in global change research. CHINESE SCI BULL 52, 289–302 (2007). https://doi.org/10.1007/s11434-007-0046-y

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11434-007-0046-y

Keywords

Navigation