Advertisement

International Journal of Biometeorology

, Volume 58, Issue 10, pp 2159–2163 | Cite as

Citizen science: best practices to remove observer bias in trend analysis

  • Alemu GonsamoEmail author
  • Petra D’Odorico
Short Note

Abstract

Citizen science, time series records over long periods of time, and wide geographic areas offer many opportunities for scientists to answer questions that would otherwise be impractical to investigate. Citizen scientists currently play active roles in a wide range of ecological projects; however, observer biases such as varying perception of events or objects being observed and quality of observations present challenges to successfully derive interannual variability and trend statistics from time series records. It is recommended that citizen science records, particularly those involving events such as plant phenology, should not be directly averaged across sites. The interannual variability expressed as an anomaly and trend expressed as a regression slope should be calculated for each site. Only the site level anomaly and regression slopes should be averaged to suppress observer biases.

Keywords

Anomaly Citizen science Observer bias Phenology Trend analysis 

Notes

Acknowledgments

We would like to thank and commend the persistent work of John O’Keefe at Harvard Forest Experimental Forest for collecting and cataloging the unique field phenology data. We extend our thanks to the thoughtful and constructive comments of two anonymous reviewers.

References

  1. Arakawa H (1956) Climatic change as revealed by the blooming dates of the cherry blossoms at Kyoto. J Meteor 13:599–600CrossRefGoogle Scholar
  2. Bishop TR, Botham MS, Fox R, Leather SR, Chapman DS, Oliver TH (2013) The utility of distribution data in predicting phenology. Methods Ecol Evol. doi: 10.1111/2041-210×.12112 Google Scholar
  3. Clark JE (1922) Flowering dates of trees along main British railway routes. Nature 109:210–212CrossRefGoogle Scholar
  4. Dennis EB, Freeman SN, Brereton T, Roy DB (2013) Indexing butterfly abundance whilst accounting for missing counts and variability in seasonal pattern. Methods Ecol Evol 4:637–645CrossRefGoogle Scholar
  5. Dickinson JL, Zuckerberg B, Bonter DN (2010) Citizen science as an ecological research tool: challenges and benefits. Annu Rev Ecol Evol S 41:149–172CrossRefGoogle Scholar
  6. Fink D, Hochachka WM, Zuckerberg B, Winkler DW, Shaby B, Munson MA, Hooker G, Riedewald M, Sheldon D, Kelling S (2010) Spatiotemporal exploratory models for broad-scale survey data. Ecol Appl 20:2131–2147CrossRefGoogle Scholar
  7. Fitzpatrick MC, Preisser EL, Ellison AM, Elkinton JS (2009) Observer bias and the detection of low-density populations. Ecol Appl 19:1673–1679CrossRefGoogle Scholar
  8. Gonsamo A, Chen JM, Wu C (2013) Citizen science: linking the recent rapid advances of plant flowering in Canada with climate variability. Sci Rep 3:2239CrossRefGoogle Scholar
  9. Greenwood JJD (2007) Citizens, science and bird conservation. J Ornithol 148:S77–S124CrossRefGoogle Scholar
  10. Hill MO (2012) Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods Ecol Evol 3:195–205CrossRefGoogle Scholar
  11. Holt BG, Rioja-Nieto R, Aaron MacNeil M, Lupton J, Rahbek C (2013) Comparing diversity data collected using a protocol designed for volunteers with results from a professional alternative. Methods Ecol Evol 4:383–392CrossRefGoogle Scholar
  12. Jiguet F (2009) Method learning caused a first-time observer effect in a newly started breeding bird survey. Bird Study 56:253–258CrossRefGoogle Scholar
  13. Kéry M, Guillera–Arroita G, Lahoz–Monfort JJ (2013) Analysing and mapping species range dynamics using occupancy models. J Biogeog 40:1463–1474CrossRefGoogle Scholar
  14. Krejcie RV, Morgan DW (1970) Determining sample size for research activities. Educ Psychol Meas 30:607–610Google Scholar
  15. MacCallum RC, Browne MW, Sugawara HM (1996) Power analysis and determination of sample size for covariance structure modeling. Psychol Methods 1:130–149CrossRefGoogle Scholar
  16. Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-Kübler K, Bissolli P, Braslavská O, Briede A, Chmielewski FM, Crepinsek Z, Curnel Y, Dahl Å, Defila C, Donnelly A, Filella Y, Jatczak K, Måge F, Mestre A, Nordli Ø, Peñuelas J, Pirinen P, Remišová V, Scheifinger H, Striz M, Susnik A, van Vliet AJH, Wielgolaski FE, Zach S, Zust A (2006) European phenological response to climate change matches the warming pattern. Glob Change Biol 12:1969–1976CrossRefGoogle Scholar
  17. Munson MA, Caruana R, Fink D, Hochachka WM, Iliff M, Rosenberg KV, Sheldon D, Sullivan BL, Wood C, Kelling S (2010) A method for measuring the relative information content of data from different monitoring protocols. Methods Ecol Evol 1:263–273Google Scholar
  18. Newton A (1896) A dictionary of birds. A&C Black, LondonGoogle Scholar
  19. Nicholson EM (1959) The British approach to ornithology. IBIS 101:39–43CrossRefGoogle Scholar
  20. O’Keefe J (2000) Phenology of woody species at Harvard Forest since 1990. Harvard Forest Data Archive: HF003Google Scholar
  21. Schmeller DS, Henry P-Y, Julliard R, Gruber B, Clobert J, Dziock F, Lengyel S, Nowicki P, Déri E, Burdrus E, Kull T, Tali K, Bauch B, Settele J, Van Swaay C, Kobler A, Babij V, Papastergiadou E, Henle K (2009) Advantages of volunteer-based biodiversity monitoring in Europe. Conserv Biol 23:307–316CrossRefGoogle Scholar
  22. Schwartz MD, Hanes JM, Liang L (2013) Separating temperature from other factors in phenological measurements. Int J Biometeorol. doi: 10.1007/s00484-013-0723-2 1-6 Google Scholar
  23. Tweddle JC, Robinson LD, Pocock MJO, Roy HE (2012) Guide to citizen science: developing, implementing and evaluating citizen science to study biodiversity and the environment in the UK. Natural History Museum and NERC Centre for Ecology & Hydrology for UK-EOF. Available online: www.ukeof.org.uk.
  24. Worthington JP, Silvertown J, Cook L, Cameron R, Dodd M, Greenwood RM, McConway K, Skelton P (2012) Evolution MegaLab: a case study in citizen science methods. Methods Ecol Evol 3:303–309CrossRefGoogle Scholar

Copyright information

© ISB 2014

Authors and Affiliations

  1. 1.Department of Geography and Program in PlanningUniversity of TorontoOntarioCanada
  2. 2.Grassland Science Group, Institute of Agricultural SciencesSwiss Federal Institute of TechnologyZurichSwitzerland

Personalised recommendations