Counting Cats: The integration of expert and citizen science data for unbiased inference of population abundance.

ABSTRACT: Free-roaming animal populations are hard to count, and professional experts are a limited resource. There is vast untapped potential in the data collected by nonprofessional scientists who volunteer their time to population monitoring, but citizen science (CS) raises concerns around data quality and biases. A particular concern in abundance modeling is the presence of false positives that can occur due to misidentification of nontarget species. Here, we introduce Integrated Abundance Models (IAMs) that integrate citizen and expert data to allow robust inference of population abundance meanwhile accounting for biases caused by misidentification. We used simulation experiments to confirm that IAMs successfully remove the inflation of abundance estimates caused by false-positive detections an

SUBMITTER: McDonald JL 

PROVIDER: S-EPMC8093703 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature