Abstract
Volunteer citizen monitoring is an increasingly important source of scientific data. We developed a volunteer program for early detection of new invasive species by private landowners on their own land. Early detection of an invasive species, however, subjects the landowner to the potentially costly risk of government intervention to control the invasive species. We hypothesized that an adult experiential learning module could increase recruitment and retention because private landowners could learn more about and understand the social benefits of early detection and more accurately gauge the level of personal risk. The experiential learning module emphasized group discussion and individual reflection of risks and benefits of volunteering and included interactions with experts and regulatory personnel. A population of woodland owners with >2 ha of managed oak woodland in central Minnesota were randomly assigned to recruitment treatments: (a) the experiential learning module or (b) a letter inviting their participation. The recruitment and retention rates and data quality were similar for the two methods. However, volunteers who experienced the learning module were more likely to recruit new volunteers than those who merely received an invitation letter. Thus the module may indirectly affect recruitment of new volunteers. The data collection was complex and required the volunteers to complete timely activities, yet the volunteers provided sufficiently high quality data that was useful to the organizers. Volunteers can collect complex data and are willing to assume personal risk to contribute to early detection of invasive species.
Original language | English (US) |
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Pages (from-to) | 606-618 |
Number of pages | 13 |
Journal | Environmental management |
Volume | 58 |
Issue number | 4 |
DOIs | |
State | Published - Oct 1 2016 |
Bibliographical note
Publisher Copyright:© 2016, Springer Science+Business Media New York.
Keywords
- Citizen science
- Early detection
- Invasive species
- Volunteer citizen monitoring