Uncovering biases in volunteered geographic information through multi-scale analyses of citizen science species observation patterns

Authors: Daniel LeVine*, University of Texas - Austin
Topics: Spatial Analysis & Modeling, Biogeography, Geographic Information Science and Systems
Keywords: Spatial Ecology, Volunteered Geographic Information, Citizen Science, Geographically Weighted Regression
Session Type: Virtual Paper
Day: 4/7/2021
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 18
Presentation File: No File Uploaded

The recent widespread adoption of opportunistic species observation data platforms represents a significant source of occurrence data with great utility for spatial ecological analyses. Many opportunities for the conservation of rare species and species assemblages have arisen from the wealth of data made available by citizen science platforms such as iNaturalist, eBird, and MonarchWatch. However, inconsistencies have been found to exist in the spatial locations and distributions of these data, such as biases towards large population centers, road/path networks, and charismatic, easily-identifiable species. These biases require further analyses to understand the implications of the uses of these data in conservation planning. This research identifies spatial patterns in opportunistic data occurrences across Texas and the critically sensitive Texas Blackland Prairies, an ecoregion harboring remnants of the rarest prairie ecosystem in North America. Specifically, the occurrences of iNaturalist citizen science data were discerned and mapped across Texas and the Texas Blackland Prairies ecoregion. The distribution of these data was investigated with regards to both large population centers and accessibility (path networks and public vs. private land). The influence of socio-economic variables collected from US census data (percent agricultural/forestry jobs, mean household income, and population age variables) was then quantified using ordinary least squares and geographically weighted regressions. Preliminary findings suggest accessibility variables serve as the strongest predictor of opportunistic species occurrence data presence. This work further identifies opportunities to target large gaps and inconsistencies in the seemingly vast datasets offered by citizen science platforms and supports recommendations for land managers and landowners.

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