The potential of Citizen Science (8 Comments)

CS

Photo courtesy of Andrew Pringle

Never doubt that a small group of thoughtful, committed citizens can change the world; indeed, it’s the only thing that ever has.

-Margaret Mead

What if it’s not a small group, but an army of thousands? Citizen science may lead the way to a greater understanding of causes and consequences of biodiversity change.

When you look at our diagram of biodiversity science, you might reflect on where the bulk of research to date lies. I think it’s mostly in the upper right “dimensions” circle.  We’ve come a long way toward cataloging biodiversity and documenting patterns. The frontiers of our field concern the connections between patterns and human or abiotic drivers, diversity and ecosystems function (and services). To identify patterns in the first place often requires datasets collected at large scales of space and/or time. In their 2010 paper, Devictor et al. gave the first (to my knowledge) evaluation of citizen science by biodiversity scientists as a vehicle for filling this gap.  Using examples of citizen science (CS) programs, they present a case for using CS data to answer biogeographic questions and provide guidelines for creating and maintaining successful programs.  As a graduate student utilizing citizen science, I’m particularly fond of this paper because it represents a true endorsement of CS by the established scientific community.  While there continues to be some skepticism among scientists about the validity and usefulness of CS data, examples provided in this and other papers present a powerful case for citizen science as a tool for doing solid science. Previous papers on the subject mainly came from individual case studies or education professionals trumpeting the potential of the field.  The fact that these influential scientists recognize the value of the CS approach and provide good examples of its successes is really exciting.   They demonstrate that citizen science is not limited to the documentation of biodiversity indicators, but has also contributed to the understanding of the drivers of change (examples given include land use, climate, and acid rain) and mechanisms of response.  They also touch on the theory (so far largely untested) that involvement in citizen science will result in more scientifically literate, conservation minded people.  If that turns out to be true, then citizen science could be the key to studying and understanding biodiversity, while at the same time saving it.

As I was developing the study for my Master’s thesis, I focused on the skills that I wanted to learn as much as the questions that I was interested in.  Citizen science seemed like the perfect opportunity to hone an ability to communicate and engage with the public, while gathering data at volumes and locations that I could not do alone.  I was fortunate to find “Beyond Scarcity…” (Devictor et al, 2010) and the roadmap that it provides for creating a successful citizen science program, and also for working with the resulting, potentially imperfect, dataset. The result is Project E-PIG, where I work with volunteers to monitor pollinator abundance and diversity in residential gardens, investigating the role of individual stewardship in augmenting habitat and how that relates to the surrounding landscape.  You can check it out here, and find results there soon.

Further reading on citizen science:

Dickinson, J., Zuckerberg, B. & Bonter, D.N. 2010, “Citizen Science as an Ecological Research Tool: Challenges and Benefits.”, Annual Review of Ecology, Evolution & Systematics, vol. 41, no. 1.  pp149-172

Shirk, J. L., H. L. Ballard, C. C. Wilderman, T. Phillips, A. Wiggins, R. Jordan, E. McCallie, M. Minarchek, B.V. Lewenstein, M. E. Krasny, and R. Bonney. 2012. Public participation in scientific research: a framework for deliberate design. Ecology and Society 17(2): 29.

Hochachka, W.M., Fink, D., Hutchinson, R.A., Sheldon, D., Wong, W.K., Kelling, S. & Ecological and evolutionary informatics 2012, “Data-intensive science applied to  broad-scale citizen science”, Trends in Ecology & Evolution, vol. 27, no. 2, pp. 130-137.

January 12, 2013

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  • Hillary, I enjoyed reading your short and upbeat introduction to the idea of Citizen Science! I had even more fun checking out your E-PIG website! Pretty cool to see what ordinary citizens can do in their own backyards for science. Perhaps CS could also stand for “Crowdsourcing Science”?

  • I have a feeling that your time in the EG has given you some of the impetus to work on this!!! I loved this blog and the way you wrote it!

    (Fondly) Kathleen Konicek-Moran

  • CS certainly has it’s challenges, but as you point out, several benefits which are hard to ignore. Personally, I look forward to creative innovations with new technology, which could improve data accuracy and better realize CS’s potential.

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  • Nice insights, Hillary. I just read this paper for the first time last week and I was particularly taken by their discussion of statistical tools for managing citizen science data, particularly in dealing with issues of species detectability. Have you seen other citizen scientists recognize such issues in their own data? Biodiversity monitoring is the goal of many CS programs, yet they are guilty of collecting exactly the kinds of data that are warned about by Devictor. Are you worried that the lack of awareness of statistical biases may lead CS scientists to incorrect inferences?

  • Hey Jon. I think lack of awareness or appropriately dealing with statistical bias is a concern for any study, but is particularly true of CS. That’s why it’s important, as others have argued, to have a thoughtful design that draws on expertise from the start. Hopefully as the field continues to grow, there will be more and more resources about best practices for doing that, and for techniques in dealing with bias. I’ve been reading a lot about mixed-effects modeling lately and though I haven’t yet come across any examples, it seems like a promising technique for dealing with site-specific or collector-specific error. Generalized Estimating Equations were also recently brought to my attention. It would be really cool to see someone publish about how these techniques can be applied to CS.

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