Turning observations into biodiversity data: Broadscale spatial biases in community science

Biodiversity community science projects are growing rapidly in popularity. The enormous amounts of data generated by these programs are transforming how we conduct ecological research and conservation management. However, as with other biodiversity surveys, community science datasets suffer from biases in time and locations of observations. To better use these data, we modeled the spatial biases present in the popular community science platform, iNaturalist. iNaturalist uses crowdsourcing to collect georeferenced and time‐stamped observations of all taxa worldwide. With its wealth of biodiversity data, iNaturalist is now being used to answer a broad range of questions in ecology and conservation, but little is known about the platform's spatial biases. We focus on the more than 1.75 million iNaturalist observations available (as of December 2021) from British Columbia, Canada, a region with a strong community science presence and diversity of ecosystems. Using machine learning and species distribution modeling, we examined which landscape factors (e.g., protected areas, roads, human population density, habitat zones, elevation) were most important in determining where observations are taken, and we created a predicted probability map revealing how likely different regions are to be sampled by community scientists. We found strong road biases for observations in iNaturalist, with over 94% of observations within 1 km of roads. In addition, human population density and broad habitat ecosystem zones played a large role in predicting where iNaturalist observations occur across the landscape. These methods demonstrate tools for modeling the effects of spatial biases in large opportunistic datasets that can then be used to produce more accurate species distribution and biodiversity models from community science data.