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

• DOI: 10.1002/ecs2.4582
• Publication Date: 2023-06-09T03:14:20Z
• Authors: Ellyne M. Geurts; John D. Reynolds; Brian M. Starzomski
• Keywords: Citizen Science; Crowdsourcing; Popularity

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