Chinese Science Bulletin

, Volume 52, Issue 3, pp 289–302 | Cite as

Meta-analysis and its application in global change research

  • Lei XiangDong 
  • Peng ChangHui 
  • Tian DaLun 
  • Sun JianFeng 
Review

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.

Keywords

meta-analysis global climate change 

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References

  1. 1.
    IPCC. Climate change 2001-synthesis report: third assessment report of the Intergovernmental Panel on Climate Change. 2001Google Scholar
  2. 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: 543–562CrossRefGoogle Scholar
  3. 3.
    Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature, 2003, 421: 37–42CrossRefGoogle Scholar
  4. 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): 105–123CrossRefGoogle Scholar
  5. 5.
    Hedges L V, Olkin I. Statistical Methods for Meta-analysis. New York: Academic Press, 1985Google Scholar
  6. 6.
    Stuhlmacher A F, Gillespie T L. Managing conflict in the literature: Meta-analysis as a research method. Int Negot, 2005, 10(1): 67–78CrossRefGoogle Scholar
  7. 7.
    Glass G V. Primary, secondary, and meta-analysis of research. Educ Res, 1976, 5: 3–8Google Scholar
  8. 8.
    Gurevitch J, Curtis P S, Jones M H. Meta-analysis in ecology. Adv Ecol Res, 2001, 32: 199–247CrossRefGoogle Scholar
  9. 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. 378–425Google Scholar
  10. 10.
    Amqvist G, Wooster D. Meta-analysis: synthesizing research findings in ecology and evolution. Trends Ecol Evol, 1995, 10: 236–240CrossRefGoogle Scholar
  11. 11.
    Osenberg C W, Samelle O, Cooper S, et al. Resolving ecological questions through meta-analysis: goals, metrics and models. Ecology, 1999, 80: 1105–1117CrossRefGoogle Scholar
  12. 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): 277–281Google Scholar
  13. 13.
    Peng S L, Tang X Y. Meta-analysis and its application in ecology. J Ecol (in Chinese), 1998, 17(5): 74–79Google Scholar
  14. 14.
    Liu J, Peng S L. Meta-analysis in ecology and medical science. Acta Ecol Sin (in Chinese), 2004, 24(11): 2627–2634Google Scholar
  15. 15.
    Zheng F Y, Peng S L. Meta-analysis of prey relationships. Acta Ecol Sin (in Chinese), 1999, 19(4): 448–452Google Scholar
  16. 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): 1101–1109Google Scholar
  17. 17.
    Rosenthal R, DiMatteo M R. Meta-analysis: recent developments in quantitative methods for literature review. Ann Rev Psychol, 2001, 52: 59–82CrossRefGoogle Scholar
  18. 18.
    Guo L B, Gifford R M. Soil carbon stocks and land use change: a meta-analysis. Glob Change Biol, 2002, 8: 345–360CrossRefGoogle Scholar
  19. 19.
    Kotiaho J S, Tomkins J L. Meta-analysis: can it ever fail? Oikos, 2002, 96: 551–553CrossRefGoogle Scholar
  20. 20.
    Jennions M D, Møller A P, Curie M, et al. Meta-analysis can “fail”: reply to Kotiaho and Tomkins. Oikos, 2004, 104: 191–193CrossRefGoogle Scholar
  21. 21.
    Noble G H. Meta-analysis: methods, strengths, weaknesses, and political uses. J Lab Clin Med, 2006, 147: 7–20CrossRefGoogle Scholar
  22. 22.
    Song F, Eastwood AJ, Gilbody S, et al. Publication and related biases. Health Technol Asses, 2000, 4: 1–115Google Scholar
  23. 23.
    Møller A P, Jennions M D. Testing and adjusting for publication bias. Trends Ecol Evol, 2001, 16: 580–586CrossRefGoogle Scholar
  24. 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: 455–463CrossRefGoogle Scholar
  25. 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: 89–98CrossRefGoogle Scholar
  26. 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: 629–634Google Scholar
  27. 27.
    Macaskill P, Walter S, Irwig L. A comparison of methods to detect publication bias in meta-analysis. Stat Med, 2001, 20: 641–654CrossRefGoogle Scholar
  28. 28.
    Rothstein H R, Sutton A J, Borenstein M. Publication Bias in Meta-Analysis-Prevention, Assessment and Adjustments. Chichester: John Wiley & Sons Ltd, 2005. 1–374CrossRefGoogle Scholar
  29. 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: 127–137CrossRefGoogle Scholar
  30. 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: 17–30CrossRefGoogle Scholar
  31. 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: 281–293CrossRefGoogle Scholar
  32. 32.
    Curtis P S, Wang X. A meta-analysis of elevated CO2 effects on woody plant growth, form, and function. Oecologia, 1998, 113: 299–313CrossRefGoogle Scholar
  33. 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: 723–741CrossRefGoogle Scholar
  34. 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: 61–69CrossRefGoogle Scholar
  35. 35.
    Poorter H, Navas M L. Plant growth and competition at elevated CO2: on winners, losers and functional groups. New Phytol, 2003, 157: 175–198CrossRefGoogle Scholar
  36. 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: 1475–1495CrossRefGoogle Scholar
  37. 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: 695–709CrossRefGoogle Scholar
  38. 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: 333–343CrossRefGoogle Scholar
  39. 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: 351–372CrossRefGoogle Scholar
  40. 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: 591–628CrossRefGoogle Scholar
  41. 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): 53–63Google Scholar
  42. 42.
    Morison J I L. Increasing atmospheric CO2 and stomata. New Phytol, 2001, 149: 154–158CrossRefGoogle Scholar
  43. 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: 247–264CrossRefGoogle Scholar
  44. 44.
    Wang X Z, Curtis P. A meta-analytical test of elevated CO2 effects on plant respiration. Plant Ecol, 2002, 161: 251–261CrossRefGoogle Scholar
  45. 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: 145–155CrossRefGoogle Scholar
  46. 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: 609–639CrossRefGoogle Scholar
  47. 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: 1–14Google Scholar
  48. 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: 331–346CrossRefGoogle Scholar
  49. 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: 9–26CrossRefGoogle Scholar
  50. 50.
    Jastrow J D, Miller R M, Matamala R, et al. Elevated atmospheric carbon dioxide increases soil carbon. Glob Change Biol, 2005, 11: 2057–2064CrossRefGoogle Scholar
  51. 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): 167–181CrossRefGoogle Scholar
  52. 52.
    Rustad L E, Huntington T G, Boone R D. Controls on soil respiration: implications for climate change. Biogeochemistry, 2000, 48: 1–6CrossRefGoogle Scholar
  53. 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: 1–23CrossRefGoogle Scholar
  54. 54.
    Barnard R, Leadley P W, Hungate B A. Global change, nitrification, and denitrification: A review. Glob Biogeochem Cycle, 2005, 19: 2282–2298Google Scholar
  55. 55.
    Norby R J, Cotrufo M F, Ineson P, et al. Elevated CO2, litter chemistry, and decomposition: a synthesis. Oecologia, 2001, 127: 153–165CrossRefGoogle Scholar
  56. 56.
    Knorr W, Prentice I C, House J I, et al. Long-term sensitivity of soil carbon turnover to warming. Nature, 2005, 433: 298–301CrossRefGoogle Scholar
  57. 57.
    Treseder K K. A meta-analysis of mycorrhizal responses to nitrogen, phosphorus, and atmospheric CO2 in field studies. New Phytol, 2004, 164: 347–355CrossRefGoogle Scholar
  58. 58.
    Alberton O, Kuyper T W, Gorissen A. Taking mycocentrism seriously: mycorrhizal fungal and plant responses to elevated CO2. New Phytol, 2005, 167(3): 859–868CrossRefGoogle Scholar
  59. 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): 491–511CrossRefGoogle Scholar
  60. 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: 1342–1346CrossRefGoogle Scholar
  61. 61.
    Root T L, Price J T, Hall K R, et al. Fingerprints of global warming on wild animals and plants. Nature, 2003, 421: 57–60CrossRefGoogle Scholar
  62. 62.
    Davidson E A, Davidson E A, Janssens I A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 2006, 440: 165–173CrossRefGoogle Scholar
  63. 63.
    Lloyd J, Taylor J A. On the temperature dependence of soil respiration. Func Ecol, 1994, 8: 315–323CrossRefGoogle Scholar
  64. 64.
    Grace J, Rayment M. Respiration in the balance. Nature, 2000, 404: 819–820CrossRefGoogle Scholar
  65. 65.
    Jarvis P, Linder S. Constraints to growth of boreal forests. Nature, 2000, 405: 904–905CrossRefGoogle Scholar
  66. 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: 858–861CrossRefGoogle Scholar
  67. 67.
    Luo Y, Wan S, Hui D, et al. Acclimatization of soil respiration to warming in a tall grass prairie. Nature, 2001, 413: 622–625CrossRefGoogle Scholar
  68. 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: 1061–1075CrossRefGoogle Scholar
  69. 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: 4–17CrossRefGoogle Scholar
  70. 70.
    Raich J W, Russell A E, Kitayama K, et al. Temperature true influences carbon accumulations in moist tropical forests. Ecology, 2006, 87(1): 76–87Google Scholar
  71. 71.
    Blenckner T, Hillebrand H. North Atlantic oscillation signatures in aquatic and terrstrial ecosystems-a meta-analysis. Glob Change Biol, 2002, 8: 203–212CrossRefGoogle Scholar
  72. 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: 27–41CrossRefGoogle Scholar
  73. 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: 1317–1328CrossRefGoogle Scholar
  74. 74.
    Searles P S, Flint S D, Caldwell M M. A meta-analysis of plant field studies simulating stratospheric ozone depletion. Oecologia, 2001, 127: 1–10CrossRefGoogle Scholar
  75. 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: 87–121CrossRefGoogle Scholar
  76. 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): 28–40CrossRefGoogle Scholar
  77. 77.
    Johnson D W, Curtis P S. Effects of forest management on soil C and N storage: Meta analysis. For Ecol Manag 2001, 140: 227–238CrossRefGoogle Scholar
  78. 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: 239–251CrossRefGoogle Scholar
  79. 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: 41–65CrossRefGoogle Scholar
  80. 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. 2001Google Scholar
  81. 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: 1349–1365CrossRefGoogle Scholar
  82. 82.
    Curtis P S, Jablonski L M, Wang X Z. Assessing elevated CO2 responses using meta-analysis. New Phytol, 2003, 160: 6–7CrossRefGoogle Scholar
  83. 83.
    Thornton A, Lee P. Publication bias in meta-analysis: its causes and consequences. J Clin Epidemiol, 2000, 53: 207–216CrossRefGoogle Scholar
  84. 84.
    Hedges L V, Gurevitch J, Curtis P S. The meta-analysis of response ratios in experimental ecology. Ecology, 1999, 80: 1150–1156CrossRefGoogle Scholar
  85. 85.
    Körner C. Nutrients and sink activity drive plant CO2 responses-caution with literature-based analysis. New Phytol, 2003, 159: 531–538CrossRefGoogle Scholar
  86. 86.
    Lortie C J, Callaway R M. Re-analysis of meta-analysis: support for the stress-gradient hypothesis. J Ecol, 2006, 94: 7–16CrossRefGoogle Scholar
  87. 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: 748–757CrossRefGoogle Scholar
  88. 88.
    Gates S. Review of methodology of quantitative reviews using meta-analysis in ecology. J Anim Ecol, 2002, 71: 547–557CrossRefGoogle Scholar
  89. 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: 1961–1966CrossRefGoogle Scholar
  90. 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: 43–48CrossRefGoogle Scholar
  91. 91.
    Englund G, Sarnelle O, Cooper S D. The importance of data-selection criteria: meta-analyses of stream predation experiments. Ecology, 1999, 80(4): 1132–1141CrossRefGoogle Scholar
  92. 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: 253–280CrossRefGoogle Scholar

Copyright information

© Science in China Press 2007

Authors and Affiliations

  • Lei XiangDong 
    • 1
    • 2
  • Peng ChangHui 
    • 2
    • 3
  • Tian DaLun 
    • 4
  • Sun JianFeng 
    • 2
  1. 1.Institute of Forest Resource Information TechniquesChinese Academy of ForestryBeijingChina
  2. 2.ECO-MCS Lab, Institute of Environment SciencesUniversity of Quebec at Montreal (UQAM)MontrealCanada
  3. 3.Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  4. 4.College of Life Science and TechnologyCentral-South University of Forestry and TechnologyChangshaChina

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