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

GeoInformatica

Volumn 16, Issue 4, 2012, Pages 675-690

  Brunsdon, Chris ^a   Comber, Lex ^b  

^a UNIVERSITY OF LIVERPOOL   (United Kingdom)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Citizen science; Phenology; Random effects models

Indexed keywords

SPRINGS (COMPONENTS);

CITIZEN SCIENCE; DATA COLLECTION; GEOGRAPHICAL INFORMATION; MODEL CALIBRATION; NORTH AMERICAN; PHENOLOGY; RANDOM COEFFICIENTS; RANDOM EFFECTS MODEL;

RANDOM PROCESSES;

ANGIOSPERM; BOOTSTRAPPING; FLOWERING; GIS; NUMERICAL MODEL; PHENOLOGY;

NORTH AMERICA;

SYRINGA; SYRINGA VULGARIS;

EID: 84867041820     PISSN: 13846175     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10707-012-0159-6     Document Type: Article

References (39)

1. Appleton D, French J, Vanderpump M (1996) Ignoring a covariate: an example of Simpson's paradox. Am Stat 50(4): 340-341.
2. Baayen R, Davidson D, Bates D (2008) Mixed-effects modeling with crossed random effects for subjects and items. J Mem Lang 59: 390-412.
3. Caprio J (1957) Phenology of lilac bloom in Montana. Science 126: 1344-1345.
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, Berlin.
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): 399-415.
6. Cohn JP (2008) Citizen science: can volunteers do real research? BioScience 58(3): 192-197. doi: 10. 1641/B580303.
7. Coleman D (2010) The potential and early limitations of volunteered geographic information. Geomatica 64(2): 27-39.
8. Cooper CB, Dickinson J, Phillips T, Bonney R (2007) Citizen science as a tool for conservation in residential ecosystems. Ecol Soc 12(2): 11.
9. Davison A, Hinkley D (1997) Bootstrap methods and their application. Cambridge University Press, Cambridge.
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.
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: 3567-3579.
12. Goldstein H (1986) Multilevel mixed linear model analysis using iterative generalised least squares. Biometrika 73: 43-56.
13. Goldstein H (1987) Multilevel covariance component models. Biometrika 74: 430-431.
14. Goldstein H (1987) Multilevel models in educational and social research. Griffin, London.
15. Goodchild M (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4): 211-221. doi: 10. 1007/s10708-007-9111-y.
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): 682-703.
17. Hand E (2010) Citizen science: people power. Nature 466(7307): 685-687. doi: 10. 1038/466685a.
18. Lister M Adrian, the Climate Change Research Group (2011) Natural history collections as sources of long-term datasets. Trends Ecol Evol 26(4): 153-154.
19. Longford N (1993) Random coefficient models. Clarendon Press, Oxford.
20. McCaffrey RE (2005) Using citizen science in urban bird studies. Urban Habitats 3(1): 70-86.
21. Menzel A, Fabian P (1999) Growing season extended in europe. Nature 397: 659.
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: 1667-1674.
23. Myers R, Montgomery D, Vining G, Borror C, Kowalski S (2004) Response surface methodology: a retrospective and literature survey. J Qual Technol 36: 53-78.
24. Myers JL, Well A, Lorch RF (2010) Research design and statistical analysis, 3rd edn. Routledge, New York.
25. R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
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.
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): 235-241.
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 23-38.
29. Schwartz MD (1994) Monitoring global change with phenology-the case of the spring green wave. Int J Biometeorol 38(1): 18-22.
30. Schwartz M (1998) Green-wave phenology. Nature 394(6696): 839-840.
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, USA.
32. Schwartz M, Reiter B (2000) Changes in north american spring. Int J Climatol 20(8): 929-932.
33. Simpson E (1951) The interpretation of interaction in contingency tables. J R Stat Soc, Ser B Stat Methodol 13(2): 238-241.
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. The Guardian (2011) Weatherwatch: phenology in the UK. URL http://www. guardian. co. uk/news/2011/apr/11/weatherwatch-phenology.
36. Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46: 234-240.
37. USA National Phenology Network (2011) History of lilac and honeysuckle phenological observations in the USA. http://www. usanpn. org/q=node/36.
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): 1545-1555.
39. Wagner CH (1982) Simpson's paradox in real life. Am Stat 36(1): 46-48. URL http://www. jstor. org/stable/2684093.