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Michael Pocock: Citizen Science Project Design

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Michael Pocock (Centre for Ecology and Hydrology) talk for Into the Night Citizen Science Training Day, February 2017

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Michael Pocock: Citizen Science Project Design

  1. 1. Citizen science project design Michael Pocock (Centre for Ecology & Hydrology) michael.pocock@ceh.ac.uk @mjopocock
  2. 2. What is ‘citizen science’? ‘real’ science excellent engagement citizen science?
  3. 3. What is ‘citizen science’? ‘real’ science excellent engagement excellent engagement ‘real’ science citizen science Has greater impact and value because of the science Made possible and better because of wide engagement citizen science?
  4. 4. Overview 1. My story 2. Anyone can organise citizen science 3. But maybe you shouldn’t do citizen science 4. Think like a participant 5. Ask the very best questions 6. Be excellent and have fun!
  5. 5. Public engagement with science, with Darren Evans (University of Hull)
  6. 6. • Hypothesis-driven citizen science • Observational and experimental • Engagement and science equally important • ‘Real’ science: greater spatial extent and fine resolution than would be possible otherwise With Darren Evans Funded by:
  7. 7. Horse-chestnut leaf-miner Cameraria ohridella • Discovered as new to science in 1970s • Spread rapidly through Europe since 1980s • Reached London in 2002 • Spreads at about 30km per year Is this the single species of moth that is familiar to more people than any other? 2002 2003 2004 2005 2006 2007 2008 2009 2010
  8. 8. Horse-chestnut tree Aesculus hippocastanum • Seeds known as conkers • Widely planted in towns in Britain over past 200 years • Well recognised • Regarded as quintessentially British
  9. 9. Yes: damage does increase with length of time Cameraria has been present • It takes 4 years to reach maximum damage  a novel result
  10. 10. Mission: pest controllers Levels of ‘pest control’ are greatest where the moth has been longest
  11. 11. Mass participation & hypothesis-led With Darren Evans Funded by: The highlights • Engaged c. 18, 000 people • Reached several million people • Received 10, 000+ data points • Addressed hypotheses about an invasive insect • Discovered new biology about the insect • Real science and good engagement • Article in PLOS ONE (2014)
  12. 12. Biological recording www.brc.ac.uk/apps for list of current apps Early detection of invasives Cascading impacts of species loss Trends & indicators Pocock et al. (2015) Biological Journal of the Linnean Society Record Research Respond Available at www.brc.ac.uk
  13. 13. Recording lots of taxa… Pocock et al. (2015) Biol. Journal Linnean Soc
  14. 14. …over a long time… Pocock et al. (2015) Biol. Journal Linnean Soc 0 20 40 60 80 100 120 1960 1970 1980 1990 2000 2010 Taxa with atlases Taxa with repeat atlases
  15. 15. …by lots of people Pocock et al. (2015) Biol. Journal Linnean Soc 0 20 40 60 80 100 120 1960 1970 1980 1990 2000 2010 Taxa with atlases Taxa with repeat atlases
  16. 16. Slide from: Karolis Kazlauskis. Icons from Flaticons Several BRC apps were developed by: www.brc.ac.uk/apps
  17. 17. Large scale, long term data  contributes to ‘grand challenges’, e.g. biodiversity loss, food security, invasive species & climate change
  18. 18.  contributes to policy and management Large scale, long term data UK NGO’s State of Nature 2373 species trends from volunteer data Priority species indicator
  19. 19. 2. Anyone can run citizen science • Discuss: in 2s/3s come up with as many different types of citizen science as possible
  20. 20. Citizen science is diverse Mass participation Elaborate approach Simple approach Systematic sampling Entirely online Multivariate analysis of traits of 507 projects in ecology & environment
  21. 21. Citizen science is diverse Mass participation Hypothesis-led Elaborate approach Simple approach Systematic sampling Entirely online
  22. 22. Citizen science is diverse Long-term monitoring + Engagement, informal education etc. Ad hoc recording Mass participation Hypothesis testing
  23. 23. It is changing over time
  24. 24. • There is no single thing of ‘citizen science’! • It doesn’t always have to be huge
  25. 25. Short article in a naturalists journal About 12 people took part Run by Nik Charlton for his PhD – formed half a chapter Trialling pheromone traps for longhorn beetles About 12 keen naturalists took part Recruited via twitter Both provided greater spatial coverage than otherwise possible
  26. 26. 3. Maybe you shouldn’t do citizen science?
  27. 27. Citizen science • A multitool or a toolbox?
  28. 28. By Michael Pocock, Dan Chapman, Lucy Sheppard & Helen Roy Discuss in 2s/3s: what things do you need to think about if wondering whether to begin a citizen science project?
  29. 29. • Should you consider a citizen science approach? Maybe not • The generation of citizen science data is different to professional science • Unstructured and uncontrolled • Data of unknown quality (= varying measurement error) Before you do citizen science Available to download. Search for “CEH citizen science”
  30. 30. • If you do citizen science then decide: • What does ‘success’ look like? • Success is entirely context-specific • Defining success helps you to evaluate • Formative • Ongoing • Summative • [story about schools projects with Conker Tree Science]
  31. 31. 4. Think like a participant • Have you ever taken part in citizen science? • Try out different projects • You will always be contributing – plus you’ll be gaining insights! • Discuss in 2s/3s: Why did you participate in this project?
  32. 32. What is your pitch? We need your help to record the abundance of leaf mines of the Gracillarid micro-moth Cameraria ohridella and the normalised abundance of its parasitoids
  33. 33. • Motivations can clash! Responsibility Concern Fun activity Personal interest Fear of a threat Duty What is the motivation? An excuse to get into the woods Generosity Sense of discovery
  34. 34. Motivations and triggers www.yellowhammers.net https://www.zooniverse.org/ Involvement primarily for the sake of science
  35. 35. What is a volunteer anyway? Opportunities for citizen science in East Africa, June 2016
  36. 36. Motivations may differ from expectations! Deguines N, Julliard R, de Flores M, Fontaine C (2012) The Whereabouts of Flower Visitors: Contrasting Land-Use Preferences Revealed by a Country-Wide Survey Based on Citizen Science. PLoS ONE 7(9): e45822
  37. 37. Motivations • Different values optimise recruitment v retention Blackmore et al. 2013. Common Cause for Nature: Finding values and frames in the conservation sector. Rotman et al. 2012. Dynamic changes in motivation in collaborative citizen-science projects. Proc. ACM 2012 Conf. on Computer Supported Cooperative Work: 217. Grove-White et al. 2007. Amateurs as experts: harnessing new networks for biodiversity’. Lancaster University.
  38. 38. What are the triggers for involvement? Avian flu monitoring Pigeon behaviour
  39. 39. Have realistic expectations 050100150200 Recordsperday Jun Jul Aug Sep Oct Jun Jul Aug Sep Oct Jun Jul Aug Sep Oct 2010 2011 2012
  40. 40. Have realistic expectations 050100150200 Recordsperday Jun Jul Aug Sep Oct Jun Jul Aug Sep Oct Jun Jul Aug Sep Oct 2010 2011 2012 - National media call the shots! - Local radio & press is good - Difficult to involve schools… unless you visit them - 0.1% recruitment via mass media is reasonable - 10% uptake for each extra step of involvement
  41. 41. 4. Think like a participant • What is your story? • What are the motivations? • Will anyone intend to participate? • What are the triggers? • Will anyone actually participate? • Provide feedback • A thank you • Contextual information • A summary
  42. 42. 5. Ask the very best questions • Be clear about your questions • They may change… Maybe they should change? • Keep your aims simple. Keep instructions simple. And then simplify them. And again. • Scientists often ask poor questions • Be clear about analysis • Know how you will analyse the data in advance of running the project • Examples of asking better questions
  43. 43. Using citizen science data in ecology • Icons: The Noun Project (parkjisun, Luis Prado, Prosymbols, Icon Mafia, Creative Stall, Luke Anthony Firth) Citizen science dataset Outputs Analyst / Scientist Outcomes Reporting biases Accounting for biases
  44. 44. People are the ‘data generating process’ • Icons: The Noun Project (parkjisun, Luis Prado, Prosymbols, Icon Mafia, Creative Stall, Luke Anthony Firth) Citizen science dataset Outputs Observers / Reporters Analyst / Scientist Outcomes Observation Reporting
  45. 45. Monitoring spread • “Tell us if you see it” Presence only data, With mis-identifications
  46. 46. Monitoring spread • “Tell us if you see it” • “Tell us whether you see it” Presence only data, With mis-identifications Presence-absence data?
  47. 47. Monitoring spread • “Tell us whether you see it” Presence-absence data?
  48. 48. • Discuss: • Do you have examples of questions which could/have been improved?
  49. 49. • Error and bias • Data don’t need to be perfect, as long as… • Fit-for-purpose • Accuracy is known (or estimated) • How can accuracy be quantified? • Data entry portals, e.g. don’t enter grid refs, data format is consistent • Verifying, e.g. photos (a conservative & time- consuming approach) • Pilot data and test data • Testing through protocol design & re-design
  50. 50. 0 10 20 30 40 50 01020304050 'True' counts Children'scounts 0 5 10 15 20 05101520 'True' counts Children'scounts 0 2 4 6 8 10 0246810 'True' counts Children'scounts • Children can count mines and moths accurately • Parasitoids are very small and rare, so harder to count accurately – but we modelled the mis-counting and took it into account Dotted lines = 1:1 Solid lines = line of best fit
  51. 51. Roy et al. (2016) PLoS ONE
  52. 52. Top tips? • Define success, and evaluate • Be creative – think big or small, innovate - engage • Be scientifically rigorous (and think like a participant) • You will under-estimate the investment required • Learn from and share with others • Have fun!
  53. 53. Join JISCmail: BES-citizenscience Activities planned for 2017 include: • Meeting on crowd-sourcing in ecology • Citizen science data hackathon • Bringing participants and professionals together • We need student reps! Citizen Science Group Guides available from CEH website and UKEOF website. (Search “CEH citizen science” and “UKEOF citizen science”)

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