Drivers of temporal bias in biodiversity recording by citizen scientists

Citizen science data is increasingly important for ecological research, biodiversity conservation and monitoring. However, these data often suffer from biases due to uneven recording efforts by citizen scientists. Biases caused by intra-annual differences in recording activity can be particularly severe, hindering the use of citizen science data in research areas such as population dynamics and phenology. Therefore, understanding the factors driving recording activity is essential.

In this study, we provide a detailed assessment of how weather and calendar-related factors influence biodiversity recording activity by citizen scientists at a daily resolution. To perform this, we analyse the recording patterns for six tree species in the Iberian Peninsula, which maintain a fairly consistent appearance throughout the year. Observation data were collected from iNaturalist, a leading platform for citizen-science data. We used boosted regression trees (BRT) to compare observed recording activity patterns with those expected by chance. Our analysis included a comprehensive set of explanatory variables, such as day of the week, month, holidays, temperature, accumulated precipitation, wind intensity, and snow depth.

The BRT models effectively identified the drivers of recording activity, with the correlation between predicted and observed temporal patterns (left out of model training) ranging from 0.47 to 0.96, depending on the species. The day of the week, daily temperature, and month of the year consistently emerged as the main drivers. Recording activity was higher on weekends, to some extent on Fridays, and during the spring months. Extreme low and high temperatures generally correlated with lower recording activity, although there were exceptions. Wind speed and precipitation had a moderate influence, with higher wind intensity and accumulated precipitation leading to decreased activity. Holidays and accumulated snow had very minor relevance across species.

Our findings show that citizen scientists record more frequently on weekends, during mild weather, and in spring. By addressing these biases, we can maximize the utility of citizen-collected data for research and applied purposes, ensuring robust and reliable conclusions that enhance ecological understanding and conservation efforts.