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

1. Citizen science project design
Michael Pocock (Centre for Ecology & Hydrology)
michael.pocock@ceh.ac.uk @mjopocock

2. What is ‘citizen science’?
‘real’
science
excellent
engagement
citizen science?

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. 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. Public engagement with science, with Darren Evans (University of Hull)

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. 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. 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

10. Yes: damage does increase with length
of time Cameraria has been present
• It takes 4
years to reach
maximum
damage 
a novel result

11. Mission: pest controllers
Levels of ‘pest control’ are
greatest where the moth has
been longest

13. 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)

14. 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

15. Recording lots of taxa…
Pocock et al. (2015) Biol. Journal Linnean Soc

16. …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

17. …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

18. Slide from: Karolis Kazlauskis. Icons from Flaticons
Several BRC apps were developed by:
www.brc.ac.uk/apps

19. Large scale, long term data
 contributes to ‘grand challenges’, e.g. biodiversity loss,
food security, invasive species & climate change

20.  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

23. 2. Anyone can run citizen science
• Discuss: in 2s/3s come up with as many
different types of citizen science as possible

24. Citizen science is diverse
Mass
participation
Elaborate
approach
Simple
approach
Systematic
sampling
Entirely online
Multivariate analysis
of traits of 507 projects
in ecology & environment

25. Citizen science is diverse
Mass
participation
Hypothesis-led
Elaborate
approach
Simple
approach
Systematic
sampling
Entirely online

26. Citizen science is diverse
Long-term
monitoring
+ Engagement, informal education etc.
Ad hoc
recording
Mass participation
Hypothesis
testing

27. It is changing over time

28. • There is no single thing of ‘citizen science’!
• It doesn’t always have to be huge

29. 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

30. 3. Maybe you shouldn’t do citizen science?

31. Citizen science
• A multitool or a toolbox?

32. 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?

33. • 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”

34. • 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]

36. 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?

37. 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

38. • 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

39. Motivations and triggers
www.yellowhammers.net https://www.zooniverse.org/
Involvement primarily for the sake of science

40. What is a volunteer anyway?
Opportunities for citizen science in East Africa, June 2016

41. 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

42. 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.

43. What are the triggers for involvement?
Avian flu monitoring
Pigeon behaviour

44. Have realistic expectations
050100150200
Recordsperday
Jun
Jul
Aug
Sep
Oct
Jun
Jul
Aug
Sep
Oct
Jun
Jul
Aug
Sep
Oct
2010 2011 2012

45. Have realistic expectations
050100150200
Recordsperday
Jun
Jul
Aug
Sep
Oct
Jun
Jul
Aug
Sep
Oct
Jun
Jul
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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

46. 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

47. 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

48. 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

49. 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

50. Monitoring spread
• “Tell us if you see it”
Presence only data,
With mis-identifications

51. Monitoring spread
• “Tell us if you see it” • “Tell us whether you see it”
Presence only data,
With mis-identifications
Presence-absence data?

52. Monitoring spread
• “Tell us whether you see it”
Presence-absence data?

53. • Discuss:
• Do you have examples of questions which
could/have been improved?

54. • 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

55. 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

56. Roy et al. (2016) PLoS ONE

58. 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!

59. 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”)