INSTITUTE FOR MARINE AND ANTARCTIC STUDIES

www.imas.utas.edu.au

Amanda Bates, Gretta Pecl, Tomas Bird, Rick Stuart-Smith, Jemina Stuart-Smith,

Stewart Frusher, Graham Edgar, Neville Barrett

Amanda.Bates@utas.edu.au

Exploring the full potential of citizen science

EMERGENCE OF CITIZEN SCIENCE

EMERGENCE OF CITIZEN SCIENCE A new dawn for citizen science Silverton, 2010 TREE

www.seachangelife.net

TRAINING CITIZEN SCIENTISTS TRAINING CITIZEN SCIENTISTS

www.jellywatch.org

OPPORTUNISTIC REPORTS

MOBILE DEVICES IN CITIZEN SCIENCE

reeflifesurvey.com

• Dive surveys by trained volunteers • Produces high-quality survey information at spatial and

temporal scales beyond those possible by scientific teams • Global and growing • Dataset feeds into local, state, Commonwealth planning

FROM CITIZEN SCIENCE DATA TO POLICY

FROM CITIZEN SCIENCE DATA TO POLICY

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• Range Extension Database & Mapping

• Record ‘out-of-range’ species

• Started Dec 2009

• Currently being developed Australia-wide

UTAS VC’s Outstanding Community Engagement

Award 2011

Gretta Pecl, Jemina Stuart-Smith, Peter Walsh, Stewart Frusher, Graham Edgar, Peter Last, Jeremy

Lyle, & Rick Stuart-Smith

Gretta.Pecl@utas.edu.au

Why does Redmap work? • Engaging website

• Immediate display data

• Individual feedback provided

• Recognition of contributions

• Clear acknowledgement of industry & community

• Fishers love talking about what they caught

• Divers love taking photos

For the scientist: validating species ID and verification of sighting

details is simple

Providing feedback to the observer (fisher or diver) is easy - sent an automatic

acknowledgement

Several dozen new and out-of-range species in Tasmania (since 1970)

Citizen Data To Science To Policy

UNCERTAINTY IN CITIZEN SCIENCE DATA

UNCERTAINTY IN CITIZEN SCIENCE DATA

Navigating the uncertainties in citizen science data for robust analyses Amanda Bates1, Tomas Bird2, Gretta Pecl1, Martin Krkosek3, Rick Stuart-Smith1, Jemina Stuart-Smith1, Graham Edgar1, Neville Barrett1, Stewart Frusher1 and other workshop participants 1 Institute of Marine and Antarctic Studies, University of Tasmania 2 Department of Botany, University of Melbourne 3 Zoology Department, University of Otago

Observers Affect Error and Bias quality e.g., some observers are better at identifying fish than others honesty e.g., fishers report less catch learning e.g., communities are at different learning stages at any one time unequal effort e.g., divers pick the most interesting sites

• Common sources of error and bias • How to characterize our trust in the data • Ways to account for error

– Meta-data (e.g. effort expended, training) – Generalized models (e.g. trust metric) – Field techniques (e.g. validation) – Meta-analytical-style approach (e.g. cross

compatibility of studies)

UNCERTAINTY IN CITIZEN SCIENCE DATA

1. What existing tools are suitable to analyse citizen science data?

2. How can we characterize confidence in the data?

3. What types of tools are currently available to analyse citizen science data?

4. Is there value in generalizing models for analysing citizen science data?

5. What analytical challenges remain?

DISCUSION QUESTIONS