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This data paper describes a compilation of 73,075 quantitative diet data records for 759 primarily North American bird species, providing standardized information not just on the diet itself, but on the context for that diet information including the year, season, location, and habitat type of each study. The methods used for collecting and cleaning these data are described, and we present tools for summarizing and visualizing diet information by bird species or prey.

 

Abstract The availability of citizen science data has resulted in growing applications in biodiversity science. One widely used platform, iNaturalist, provides millions of digitally vouchered observations submitted by a global user base. These observation records include a date and a location but otherwise do not contain any information about the sampling process. As a result, sampling biases must be inferred from the data themselves. In the present article, we examine spatial and temporal biases in iNaturalist observations from the platform's launch in 2008 through the end of 2019. We also characterize user behavior on the platform in terms of individual activity level and taxonomic specialization. We found that, at the level of taxonomic class, the users typically specialized on a particular group, especially plants or insects, and rarely made observations of the same species twice. Biodiversity scientists should consider whether user behavior results in systematic biases in their analyses before using iNaturalist data.