Abstract
Citizen science projects that use sensors (such as camera traps) to collect data can collect large-scale data without compromising information quality. However, project management challenges are increased when data collection is scaled up. Here, we provide an overview of our efforts to conduct a large-scale citizen science project using camera traps—North Carolina’s Candid Critters. We worked with 63 public libraries to distribute camera traps to volunteers in all 100 counties in North Carolina, USA. Candid Critters engaged 580 volunteers to deploy cameras at 4,295 locations across private and public lands, collecting 120,671 wildlife records and 2.2 million photographs. We provide eight key suggestions for overcoming challenges in study design, volunteer recruitment and management, equipment distribution, outreach, training, and data management. We found that citizen science was a successful and economical method for collecting large-scale wildlife records, and the use of sensors allowed for inspectable quality and streamlined acquisition. In three years, we collected roughly five times the number of verified mammal records than were previously available in North Carolina, and completed the work for less than the typical cost of collecting data with field assistants. The project also yielded many positive outcomes for adult and youth volunteers. Although citizen science presents many challenges, we hope that sharing our experiences will provide useful insight for those hoping to use sensors for citizen science over large scales.
Highlights
Large datasets are often required to study wildlife across geographically large areas, but collection of these data can be costly, time consuming, and logistically challenging
We describe our use of sensors to collect large-scale wildlife records with citizen scientists through the North Carolina’s Candid Critters (NCCC) Project, a partnership between NC State University, NC Wildlife Resources Commission (NCWRC), NC Museum of Natural Sciences, eMammal, and NC Cardinal Libraries
We focused sampling on ten representative counties for a deer-specific population study known as Fall Fawn Frenzy (FFF), which focused on estimating deer recruitment through fawn-doe ratios that can be used to help manage deer populations in NCWRC units
Summary
Large datasets are often required to study wildlife across geographically large areas, but collection of these data can be costly, time consuming, and logistically challenging. Though indirect measures of effort can be used (i.e., higher human population areas have proportionally more observations; Callaghan and Gawlik 2015), an alternative approach is to recruit citizens to collect data with sensors (e.g., camera traps, acoustic monitors, etc.) that record effort (e.g., sampling time/intervals) automatically. There are several examples of citizen science projects using specialized sensors or smart phone applications that record effort (e.g., bats (Barlow et al 2015), air pollution (Hyder et al 2017; Kaufman et al 2017), and noise pollution (Maisonneuve et al 2009; Maisonneuve et al 2010))
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