Abstract

AbstractCitizen science projects provide valuable ecological data owing to their capacity to collect a wide variety of data at scales that would be difficult to achieve through traditional methods. A trade‐off exists between the complexity of data collected and participation, with projects typically falling into a continuum between documenting the presence of species at a location, through to the collection of detailed ecological data using complex protocols. Researchers must balance an approach that suits their aims with maximising participation. However, the ability of different citizen science approaches to collected detailed ecological data for a target species is poorly understood. We compared the number of participants and amount of data collected from the three projects to explore different citizen science approaches for focal species research. We examine data for the Australian brush‐turkey (Alectura lathami) from two open survey citizen science projects and one focal species project: iNaturalist, eBird and BrushTurkeys. Over a period of 18 months, eBird recorded the greatest number of participants (n = 1861) and presence records of brush‐turkeys (n = 17810). Across the three projects participation varied significantly with an average of 1.95 (range = 1–65), 9.6 (range = 1–389) and 4.7 (range = 1–331) reports per citizen scientist, respectively. The targeted BrushTurkeys project collected specific ecological data including counts, sex and behaviours in a higher proportion of sightings and recorded the largest number of nest mound reports compared with eBird and iNaturalist. We confirm that engaging an active group of participants produced the largest amount of data (eBird), including reports per participant. Ecologically, the most detailed information on habitat use and behaviours came from the focal citizen science project (BrushTurkeys). We conclude that seeking opportunities to grow and actively engage existing citizen science communities to report more detailed ecological information is likely to produce more detailed and informative data set.

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