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GeoInformatica

, Volume 16, Issue 4, pp 675–690 | Cite as

Assessing the changing flowering date of the common lilac in North America: a random coefficient model approach

  • Chris BrunsdonEmail author
  • Lex Comber
Article

Abstract

A data set consisting of Volunteered geographical information (VGI) and data provided by expert researchers monitoring the first bloom dates of lilacs from 1956 to 2003 is used to investigate changes in the onset of the North American spring. It is argued that care must be taken when analysing data of this kind, with particular focus on the issues of lack of experimental design, and Simpson’s paradox. Approaches used to overcome this issue make use of random coefficient modelling, and bootstrapping approaches. Once the suggested methods have been employed, a gradual advance in the onset of spring is suggested by the results of the analysis. A key lesson learned is that the appropriateness of the model calibration technique used given the process of data collection needs careful consideration.

Keywords

Phenology Random effects models Citizen science 

References

  1. 1.
    Appleton D, French J, Vanderpump M (1996) Ignoring a covariate: an example of Simpson’s paradox. Am Stat 50(4):340–341Google Scholar
  2. 2.
    Baayen R, Davidson D, Bates D (2008) Mixed-effects modeling with crossed random effects for subjects and items. J Mem Lang 59:390–412CrossRefGoogle Scholar
  3. 3.
    Caprio J (1957) Phenology of lilac bloom in Montana. Science 126:1344–1345CrossRefGoogle Scholar
  4. 4.
    Carr DB (1991) Looking at large data sets using binned data plots. In: Buja A, Tukey P (eds) Computing and graphics in statistics. Springer, BerlinGoogle Scholar
  5. 5.
    Cayan D, Kammerdiener S, Dettinger M, Caprio J, Peterson D (2001) Changes in the onset of spring in the western united states. Bull Am Meteorol Soc 82(3):399–415CrossRefGoogle Scholar
  6. 6.
    Cohn JP (2008) Citizen science: can volunteers do real research? BioScience 58(3):192–197. doi: 10.1641/B580303 CrossRefGoogle Scholar
  7. 7.
    Coleman D (2010) The potential and early limitations of volunteered geographic information. Geomatica 64(2):27–39Google Scholar
  8. 8.
    Cooper CB, Dickinson J, Phillips T, Bonney R (2007) Citizen science as a tool for conservation in residential ecosystems. Ecol Soc 12(2):11Google Scholar
  9. 9.
    Davison A, Hinkley D (1997) Bootstrap methods and their application. Cambridge University Press, CambridgeGoogle Scholar
  10. 10.
    Emery W, Baldwin D, Schlüssel P, Reynolds R (2000) Accuracy of in situ sea surface temperatures used to calibrate infrared satellite measures. J Geophys Res 106:2387–2405. doi: doi:10.1029/2000JC000246 CrossRefGoogle Scholar
  11. 11.
    Gelfand A, Banerjee S, Sirmans C, Tu Y, Eng Ong S (2007) Multilevel modeling using spatial processes: application to the Singapore housing market. Comput Stat Data Anal 51:3567–3579CrossRefGoogle Scholar
  12. 12.
    Goldstein H (1986) Multilevel mixed linear model analysis using iterative generalised least squares. Biometrika 73:43–56CrossRefGoogle Scholar
  13. 13.
    Goldstein H (1987) Multilevel covariance component models. Biometrika 74:430–431CrossRefGoogle Scholar
  14. 14.
    Goldstein H (1987) Multilevel models in educational and social research. Griffin, LondonGoogle Scholar
  15. 15.
    Goodchild M (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221. doi: 10.1007/s10708-007-9111-y CrossRefGoogle Scholar
  16. 16.
    Haklay M (2010) How good is volunteered geographical information? A comparative study of openstreetmap and ordnance survey datasets. Environ Plann B, Plann Des 37(4):682–703CrossRefGoogle Scholar
  17. 17.
    Hand E (2010) Citizen science: people power. Nature 466(7307):685–687. doi: 10.1038/466685a CrossRefGoogle Scholar
  18. 18.
    Lister M Adrian, the Climate Change Research Group (2011) Natural history collections as sources of long-term datasets. Trends Ecol Evol 26(4):153–154CrossRefGoogle Scholar
  19. 19.
    Longford N (1993) Random coefficient models. Clarendon Press, OxfordGoogle Scholar
  20. 20.
    McCaffrey RE (2005) Using citizen science in urban bird studies. Urban Habitats 3(1):70–86Google Scholar
  21. 21.
    Menzel A, Fabian P (1999) Growing season extended in europe. Nature 397:659CrossRefGoogle Scholar
  22. 22.
    Miller-Rushing A, Primack R, Primack D, Mukunda S (2006) Photographs and herbarium specimens as tools to document phenological changes in response to global warming. Am J Bot 93:1667–1674CrossRefGoogle Scholar
  23. 23.
    Myers R, Montgomery D, Vining G, Borror C, Kowalski S (2004) Response surface methodology: a retrospective and literature survey. J Qual Technol 36:53–78Google Scholar
  24. 24.
    Myers JL, Well A, Lorch RF (2010) Research design and statistical analysis, 3rd edn. Routledge, New YorkGoogle Scholar
  25. 25.
    R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  26. 26.
    Rayner N, Parker D, Horton E, Folland C, Alexander L, Rowell D, Kent E, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407. doi: doi:10.1029/2002JD002670 CrossRefGoogle Scholar
  27. 27.
    Robbirt K, Davy A, Hutchings M, Roberts D (2010) Validation of biological collections as a source of phenological data for use in climate change studies: a case study with the orchid ophrys sphegodes. J Ecol 99(1):235–241CrossRefGoogle Scholar
  28. 28.
    Schwartz M (1997) Phenology of seasonal climates. In: Lieth H, Schwartz M (eds) Spring index models: an approach to connection satellite and surface phenology. Backhuys, Netherlands, pp 23–38Google Scholar
  29. 29.
    Schwartz MD (1994) Monitoring global change with phenology—the case of the spring green wave. Int J Biometeorol 38(1):18–22CrossRefGoogle Scholar
  30. 30.
    Schwartz M (1998) Green-wave phenology. Nature 394(6696):839–840CrossRefGoogle Scholar
  31. 31.
    Schwartz M, Caprio J (2003) North american first leaf and first bloom lilac phenology data. IGBP PAGES/World Data Center for Paleoclimatology Data; Contribution Series # 2003-078; NOAA/NGDC Paleoclimatology Program, Boulder CO, USAGoogle Scholar
  32. 32.
    Schwartz M, Reiter B (2000) Changes in north american spring. Int J Climatol 20(8):929–932CrossRefGoogle Scholar
  33. 33.
    Simpson E (1951) The interpretation of interaction in contingency tables. J R Stat Soc, Ser B Stat Methodol 13(2):238–241Google Scholar
  34. 34.
    The Guardian (2011) Spring’s here: skylarks overhead, moles in the garden, moths in the bathroom. URL http://www.guardian.co.uk/environment/2011/mar/27/spring-wildlife-black-mountains-wales
  35. 35.
    The Guardian (2011) Weatherwatch: phenology in the UK. URL http://www.guardian.co.uk/news/2011/apr/11/weatherwatch-phenology
  36. 36.
    Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240CrossRefGoogle Scholar
  37. 37.
    USA National Phenology Network (2011) History of lilac and honeysuckle phenological observations in the USA. http://www.usanpn.org/?q=node/36
  38. 38.
    van Oort P, Zhang T, de Vries M, Heinemann A, Meinke H (2012) Correlation between temperature and phenology prediction error in rice (Oryza sativa L.). Agric For Meteorol 151(12):1545–1555Google Scholar
  39. 39.
    Wagner CH (1982) Simpson’s paradox in real life. Am Stat 36(1):46–48. URL http://www.jstor.org/stable/2684093 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.School of Environmental SciencesUniversity of LiverpoolLiverpoolUK
  2. 2.Department of GeographyUniversity of LeicesterLeicesterUK

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