Contribution of citizen science towards international biodiversity monitoring

Chandler, Mark; See, Linda; Copas, Kyle; Bonde, Astrid M.Z.; López, Bernat Claramunt; Danielsen, Finn; Legind, Jan Kristoffer; Masinde, Siro; Miller‐Rushing, Abraham J.; Newman, Greg; Rosemartin, Alyssa; Turak, Eren

doi:10.1016/j.biocon.2016.09.004

Cited by 535 publications

(436 citation statements)

References 58 publications

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“…This is a common pattern found in many citizen science intiatives [36] and likely has several explanations, including the targeting of our media outreach, English as the language of the application and the availability of high-speed internet connections. Regional mis-representation could potentially explain some of our results.…”

Section: Discussionmentioning

confidence: 86%

Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task

et al. 2016

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Abstract:The idea that closer things are more related than distant things, known as 'Tobler's first law of geography', is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen science campaigns and help to improve the overall quality of collected data. In this paper, we use classifications of satellite imagery by volunteers from around the world to test whether local familiarity with landscapes helps their performance. Our results show that volunteers identify cropland slightly better within their home country, and do slightly worse as a function of linear distance between their home and the location represented in an image. Volunteers with a professional background in remote sensing or land cover did no better than the general population at this task, but they did not show the decline with distance that was seen among other participants. Even in a landscape where pasture is easily confused for cropland, regional residents demonstrated no advantage. Where we did find evidence for local knowledge aiding classification performance, the realized impact of this effect was tiny. Rather, the inherent difficulty of a task is a much more important predictor of volunteer performance. These findings suggest that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications.

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Section: Discussionmentioning

confidence: 86%

Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task

et al. 2016

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“…There is still a high spatial and taxonomic heterogeneity in citizen science data (Chandler et al 2016). Another challenge is to provide open access to the data, as data provision to GBIF is quite poor .…”

Section: Perspectives From Eu Bon Stakeholders and Potential Usersmentioning

confidence: 99%

4th European Biodiversity Observation Network (EU BON) Stakeholder Roundtable: Pathways to sustainability for EU BONs network of collaborators and technical infrastructure

Wetzel¹,

Belmonte²,

Bingham³

et al. 2017

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“…Participants to CS projects can provide experimental data and facilities for researchers, raise new questions and hypotheses and co-create a new scientific culture (Chandler et al 2017). While adding value, volunteers acquire new learning and skills and gain understanding of scientific work (EC 2014).…”

Section: Introductionmentioning

confidence: 99%

“…While adding value, volunteers acquire new learning and skills and gain understanding of scientific work (EC 2014). CS has now become a mainstream approach for collecting data on ecosystems and biodiversity (Newman et al 2012, Chandler et al 2016, McKinley et al 2017). …”

Section: Introductionmentioning

confidence: 99%