Dataset Information

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 and can provide accurate estimates of both bias and abundance. We illustrate the approach with a case study on unowned domestic cats, which are commonly confused with owned, and infer their abundance by analyzing a combination of CS data and expert data. Our case study finds that relying on CS data alone, either through simple summation or via traditional modeling approaches, can vastly inflate abundance estimates. IAMs provide an adaptable framework, increasing the opportunity for further development of the approach, tailoring to specific systems and robust use of CS data.

SUBMITTER: McDonald JL

PROVIDER: S-EPMC8093703 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Citizen science is an approach that has become increasingly popular in recent years. Despite this growing popularity, there still is widespread scepticism in the academic world about the validity and quality of data from citizen science projects. And although there might be great potential, citizen science is a rarely used approach in the field of bioacoustics. To better understand the possibilities, but also the limitations, we here evaluated data generated in a citizen science project on nightingale song as a case study. We analysed the quantity and quality of song recordings made in a non-standardized way with a smartphone app by citizen scientists and the standardized recordings made with professional equipment by academic researchers. We made comparisons between the recordings of the two approaches and among the user types of the app to gain insights into the temporal recording patterns, the quantity and quality of the data. To compare the deviation of the acoustic parameters in the recordings with smartphones and professional devices from the original song recordings, we conducted a playback test. Our results showed that depending on the user group, citizen scientists produced many to a lot of recordings of valid quality for further bioacoustic research. Differences between the recordings provided by the citizen and the expert group were mainly caused by the technical quality of the devices used-and to a lesser extent by the citizen scientists themselves. Especially when differences in spectral parameters are to be investigated, our results demonstrate that the use of the same high-quality recording devices and calibrated external microphones would most likely improve data quality. We conclude that many bioacoustic research questions may be carried out with the recordings of citizen scientists. We want to encourage academic researchers to get more involved in participatory projects to harness the potential of citizen science-and to share scientific curiosity and discoveries more directly with society.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets