Abstract
Observations of living organisms by citizen scientists that are reported to online portals are a valuable source of information. They are also a special kind of volunteered geographic information (VGI). VGI data have issues of completeness, which arise from biases caused by the opportunistic nature of the data collection process. We examined the completeness of bird species represented in citizen science observation data from eBird and iNaturalist in US National Parks (NPs). We used approaches for completeness estimation which were developed for data from OpenStreetMap, a crowdsourced map of the world. First, we used an extrinsic approach, comparing species lists from citizen science data with National Park Service lists. Second, we examined two intrinsic approaches using total observation numbers in NPs and the development of the number of new species being added to the data-set over time. Results from the extrinsic approach provided appropriate completeness estimations to evaluate the intrinsic approaches. We found that total observation numbers are a good estimator of species completeness of citizen science data from US NPs. There is also a close relationship between species completeness and the ratio of new species added to observation data vs. observation numbers in a given year.
Highlights
The advent of web-based citizen science portals collecting observations of living organisms from the general public triggered the production of large amounts of a special kind of volunteered geographic information (VGI) (Goodchild 2007)
The question we ask is: how can we use approaches developed for feature completeness assessment of OSM data to estimate the species completeness of opportunistic citizen science data? Several different approaches to assess completeness of OSM have been suggested
The IRMA bird species list used in this analysis for The National Park of American Samoa has just 45 bird species, the very low number of eBird observations resulted in the low completeness we found for this park
Summary
The advent of web-based citizen science portals collecting observations of living organisms from the general public triggered the production of large amounts of a special kind of volunteered geographic information (VGI) (Goodchild 2007). Reports about sightings of plants, animals, or other organisms are generated in large numbers and over a broad geographic range They are a potentially valuable source of information for present and future biological and ecological research (Dickinson and Bonney 2012). Other studies discussed more factors, like uneven observation effort in time due to season or overall decline in volunteer effort (Bird et al 2014), and observer quality in terms of skill, experience, and training received (Dickinson, Zuckerberg, and Bonter 2010). These biases lead to an incomplete representation of species distribution in space and time. Such data often record only the presence, but not the absence of species. Snäll et al (2011) compared opportunistic presence-only observation data with data from a standardized monitoring scheme in Sweden and pointed out problems caused by reporter behavior and reporting bias. Boakes et al (2010) demonstrated in their study that biases are present in data from other sources, like museum collections, literature, ringing data etc. Bird et al (2014) suggested a close collaboration of statisticians and conservation scientists to develop new statistical solutions accounting for bias when using opportunistic citizen science data
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