Citizen science is growing in wildlife biology, providing an inexpensive, efficient way to collect large amounts of data. But how should researchers use it effectively?
In a presentation titled, “Population Level Inferences Improve with Integration of Opportunistic Presence-Absence Data and Systematic Capture-Recapture Data,” Cornell University PhD candidate Catherine Sun of the New York Cooperative Fish and Wildlife Research Unit discussed ways to use opportunistic data combined with data collected by researchers.
She received the first place for best student presentation at the 2018 annual TWS conference in Cleveland.
“We often want to understand what’s going on with wildlife populations across large regions and timeframes, but it can be hard to collect enough high-quality data with limited resources,” Sun said. “And so we were looking at citizen science approaches, which is an emerging way to collect data, and how we can use that to augment ongoing data collection efforts.”
She conducted simulations to examine how both citizen science and sampling data could be used together to get better population estimates to improve conservation efforts.
Sun used research she and her colleagues are collecting on the growing black bear (Ursus americanus)population in New York as an example. They were collecting spatial capture-recapture data but were also supplementing it with data from the iSeeMammals citizen science project Sun created as part of her graduate work. Citizens reported and submitted information on observations or sightings from trail cameras and hikes they took.
But some unexpected patterns arose with citizen science data. Much of the iSeeMammals data consisted of opportunistic sightings, which do not contain information about effort or detection probability. “People want to report when they see a bear, but they’re not as often going to tell us when they didn’t see a bear,” she said. So in addition to showing how data from sources like citizen science trail cameras and hikes can improve population estimates, Sun’s presentation also emphasized “the importance of further model development that also uses data from sightings.”
Her work with co-authors Angela Fuller and Andy Royle highlighted the importance of combining citizen science data with existing datasets to make the data more valuable.
The increasing adoption of citizen science approaches, saves money, brings in more data over space and time and offers better estimates of the ecological parameters biologists care about, Sun said.
“It’s important to think carefully about what kinds of data researchers need to collect in order to be able to answer their research questions,” she said. Designed thoughtfully, systematic sampling and citizen science can be used together, she said, to collect the data they need to better understand wildlife populations.
Sun said she was flattered to win first place for her presentation in Cleveland. “I hope that it represents within our scientific community the recognition that it’s important to advance quantitative ways for using citizen science and expanding what we can do with the limited resources we have,” she said.
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