Do members of organized groups submit better data to citizen science projects than individual participants?

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Monday, 5 January 2015: 4:45 PM
226C (Phoenix Convention Center - West and North Buildings)
Theresa M. Crimmins, USA National Phenology Network, Tucson, AZ; and A. H. Rosemartin, J. Weltzin, and L. Barnett

Engaging non-scientists in data collection through formally organized participatory programs has great potential to provide data critical to managing natural resources and to improving scientific understanding of relationships among weather, climate and physical and biological processes. These programs -- referred to as Public Participation in Scientific Research (PPSR), or more broadly Citizen Science--have already yielded data at spatial and temporal scales far beyond what science or management budgets could allow. The long-term success of such programs hinges on their abilities to maintain participants in generating high-quality, reliable data. We propose that participants' retention rates, activity level, and data quality may be related to the level of support and contact they receive from program staff or representatives.

The USA National Phenology Network's (USA-NPN) national-scale plant and animal phenology observation program, Nature's Notebook, has been active since 2009. This program engages thousands of citizen scientists in tracking plant and animal life cycle activity over the course of the year. On-line tools enable participants to register, learn the observation protocols, and submit observations. These “Independent Participants” may contact USA-NPN staff for assistance as needed; otherwise, the primary form of contact that these participants receive is email newsletters including updates, data summaries, news tidbits, and relevant articles from USA-NPN staff.

In an alternative model of participation, many established groups and organizations, including Master Gardener chapters, nature centers, arboreta, National Parks, and National Wildlife Refuges have developed Local Phenology Projects (LPPs) designed to engage groups of individuals locally in tracking phenology using Nature's Notebook, led a Local Phenology Leader who acts as liaison to the USA-NPN National Coordinating Office and the participant. Though the LPP members receive the same support and communications from USA-NPN staff as do Independent Participants, their LPP creates additional opportunities for interaction, including programmatic communications and content, and interaction with their leader as well as other group members.

We compared the quantity and quality of observations yielded by Independent Participants and members of Local Phenology Projects, recognizing that these two models are common within the field of PPSR. We evaluated the number of volunteer observers recruited, the observer activity level, and the year-to-year retention rates between 2009-2014.

After six years, we are still seeing growth in the number total number of participants in Nature's Notebook, though the rate of growth in Local Phenology Projects is greater than that for Independent Participants, indicating that this model is still catching on in a lot of locations. On a per-capita basis, members of Local Phenology Projects (LPPs) submit more observations than Independent Participants. However, the frequency of observations over the course of the year does not vary among the two groups, suggesting that the higher data submission rate among members of LPPs is the result of these individuals monitoring more organisms. Finally, the proportion of individuals that go on to participate in Nature's Notebook in a second and third year is greater among LPPs as compared to independent observers.

In terms of staff time, the costs associated with supporting Local Phenology Projects are greater than that of independent observers. However, these individuals submit more records and more of them remain active beyond their first year in the program than independent observers, suggesting that both models are worthwhile to support. In this presentation, we will articulate the costs and benefits of both models in terms of data yielded, staff time necessary to support, science outcome, and other costs and benefits.