Completeness of citizen science biodiversity data from a volunteered geographic information perspective

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.

How Does the Diversity of Divers Affect the Design of Citizen Science Projects?

“…Other more flexible projects are open to most marine users, such as Redmap 3 or Reef Environmental Education Foundation (REEF) 4 , which use less complex methodologies. Other projects require specific gear, for example, a dive computer to report seawater temperature (Wright et al, 2016) or a camera to take photographs that are then submitted to the CS platform, e.g., iNaturalist (Jacobs and Zipf, 2017). Some projects also require a minimum level of diving experience, for example, Reef Check or RLS Roelfsema et al, 2016) or a minimum level of knowledge (verified by an exam) before volunteers can join (e.g., REEF; Wolfe and Pattengill-Semmens, 2013).…”

Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach

“…The degree of completeness of the data varied in the different parcels, ranging from 14% in parcel 5 to 34.1% in parcel 1. The variability in the degree of completeness among the plots and the sparse contribution pattern of the VGI (Figure 4) mostly originated from the opportunistic nature (for more details, see [148,149]) of the voluntary contributions and affected by the different VGI completeness impacting factors (for more details, see Section 6.1.1). For instance, a visual interpretation of Figures 1 and 4 demonstrated that generally, there were fewer contributions in areas where trees were surrounded by dense understory vegetation that had a high spectral similarity to tree crowns (i.e., low-contrast areas).…”

“…However, while conducting original research in this area fall outside the scope of the current study, it seems that exploring the feasibility of obtaining the minimum required VGI quality for conducting the proposed collective sensing framework in a large-scale orchard tree remote mapping program is still possible through reviewing and conceptually analyzing the outputs of existing relevant studies. The participation of volunteers in CS projects has an opportunistic nature [148,149]. Volunteers mostly determine their preferred times for contributing, the locations of their contributions, and the amount of effort they would like to invest in a crowdsourcing project.…”

Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence

“…Other studies have examined the ‘completeness’ of species data from points, again comparing surveys or site‐specific species lists with the presence of species in an area determined by reported occupancy points. These, too, find discrepancies, principally that not all species known to be present in an area are necessarily represented in available point of occurrence data (Yesson et al , Jacobs and Zipf , Qian et al ). Again, this effect is scale‐dependent; the discrepancy decreases as the size of the area surveyed increases.…”