Local-scale drivers of opportunistic community science activity in a recreational natural area

Link To Share This Poster: https://cdmcd.co/Rrm9Jy
Live Discussion Link: https://cdmcd.co/w3QnRR

Caitlin P. Mandeville and Anders G. Finstad, Department of Natural History, Norwegian University of Science and Technology, Trondheim, Norway, Erlend B. Nilsen, Norwegian Institute for Nature Research, Trondheim, Norway

Background/Question/Methods
Biodiversity data collected through opportunistic community science (also known as citizen science) are increasingly relied upon for ecological research and conservation. But the analysis of opportunistic community science data is associated with several challenges due to the frequent lack of absence data, unstructured data sampling, and hard-to-quantify spatial and temporal biases. Several common broad-scale spatial biases have been identified in community science datasets, including a bias towards roadsides and population centers. But less is known about the drivers of community science activity at a local scale. We used a small, heavily used natural area adjacent to a mid-sized city as a case study to model landscape characteristics that are associated with community science activity at a local scale. We then compared the distribution of community science activity with the distribution of general foot traffic in the area to identify drivers that are distinct to community science.
Results/Conclusions
Our results confirm that community science data are spatially uneven, and indicate several landscape characteristics that are positively associated with community science activity. These findings can be used to develop models that account for spatial biases in analysis of community science data at relatively fine spatial scales. Further, they can be used by the coordinators of opportunistic community science initiatives to identify areas that are currently underrepresented in community science datasets and develop efforts to increase sampling in these regions. And finally, our results may be of interest to the managers of natural areas seeking to identify landscape features that are attractive to users interested in nature-based activities such as community science.