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Citizen science is rapidly advancing as a field, engaging the public in scientific research and outcomes, while obtaining large quantities of data. Within the natural sciences, identification of organisms by citizen scientists is an important and increasing source of biodiversity records. However, to ensure the reliability and quality of citizen science data, including species identifications, various strategies have been employed. Crowdsourcing is commonly used to increase confidence in data quality, where results are an aggregate of multiple responses. Projects utilising crowdsourcing techniques have focused on image-based datasets, and are taxonomically biased towards large and charismatic taxa. We explored the utility of crowdsourcing species identification from audio files of calling frogs. We developed a series of audio analysis trials, inviting members of the public to identify frog species from audio recordings submitted to the FrogID project. By comparing the identifications made by citizen scientists with those made by experts in frog identification, we evaluated the suitability of using crowdsourcing to identify frog species calling in 20–60-second audio files. Citizen scientists identified frog species with 57% accuracy, although this varied depending on species, task complexity, and the individual volunteer. By aggregating responses, the accuracy of citizen scientist responses increased to up to 66% when we required a consensus of two volunteers from five responses. Our results indicate that crowdsourcing the identification of multiple frog species from audio recordings is challenging, requiring further exploration into the benefits of training volunteers, providing detailed feedback, and weighting volunteer responses according to individual skill.

Not all fossil sites preserve microfossils that can be extracted using acid digestion, which may leave knowledge gaps regarding a site's age or environmental characteristics. Here we report on a citizen science approach that was developed to identify microfossils in situ on the surface of sedimentary rocks. Samples were collected from McGraths Flat, a recently discovered Miocene rainforest lake deposit located in central New South Wales, Australia. Composed entirely of iron-oxyhydroxide, McGraths Flat rocks cannot be processed using typical microfossil extraction protocols e.g., acid digestion. Instead, scanning electron microscopy (SEM) was used to automatically acquire 25,200 high-resolution images from the surface of three McGraths Flat samples, covering a total area of 1.85 cm2. The images were published on the citizen science portal DigiVol, through which 271 citizen scientists helped to identify 300 pollen and spores. The microfossil information gained in this study is biostratigraphically relevant and can be used to constrain the environmental characteristics of McGraths Flat. Our findings suggest that automated image acquisition coupled with an evaluation by citizen scientists is an effective method of determining the age and environmental characteristics of fossiliferous rocks that cannot be investigated using traditional methods such as acid digestion.