Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa.

Louise Mair,Alejandro Ruete,Judi Hewitt

doi:10.1371/journal.pone.0147796

Louise Mair, Alejandro Ruete + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0147796

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Abstract

The collation of citizen science data in open-access biodiversity databases makes temporally and spatially extensive species’ observation data available to a wide range of users. Such data are an invaluable resource but contain inherent limitations, such as sampling bias in favour of recorder distribution, lack of survey effort assessment, and lack of coverage of the distribution of all organisms. Any technical assessment, monitoring program or scientific research applying citizen science data should therefore include an evaluation of the uncertainty of its results. We use ‘ignorance’ scores, i.e. spatially explicit indices of sampling bias across a study region, to further understand spatial patterns of observation behaviour for 13 reference taxonomic groups. The data is based on voluntary observations made in Sweden between 2000 and 2014. We compared the effect of six geographical variables (elevation, steepness, population density, log population density, road density and footpath density) on the ignorance scores of each group. We found substantial variation among taxonomic groups in the relative importance of different geographic variables for explaining ignorance scores. In general, road access and logged population density were consistently important variables explaining bias in sampling effort, indicating that access at a landscape-scale facilitates voluntary reporting by citizen scientists. Also, small increases in population density can produce a substantial reduction in ignorance score. However the between-taxa variation in the importance of geographic variables for explaining ignorance scores demonstrated that different taxa suffer from different spatial biases. We suggest that conservationists and researchers should use ignorance scores to acknowledge uncertainty in their analyses and conclusions, because they may simultaneously include many correlated variables that are difficult to disentangle.

Highlights

Species’ observation data collected through citizen science projects provide an increasingly valuable resource in conservation and research due to the extensive spatial and temporal coverage that can be achieved through volunteer participation [1, 2]
Ignorance scores for each species were calculated for each 10 km grid square according to the so-called half-ignorance algorithm: O0.5/(Ni+ O0.5), where N is the number of records in a grid square i and O0.5 is the number of observations considered to be enough to reduce the ignorance score by half (Lepidoptera are shown as an example in Fig 1, for maps of all 13 taxonomic groups see Fig A in S1 File)[26]
No single geographic variable consistently explained the most variation across taxonomic groups, road density, the log of population density and elevation were consistently the top three variables explaining the most variation in ignorance across all 13 taxonomic groups

Summary

Introduction

Species’ observation data collected through citizen science projects provide an increasingly valuable resource in conservation and research due to the extensive spatial and temporal coverage that can be achieved through volunteer participation [1, 2]. Extensive observation data for a taxonomically diverse range of species across large spatial and temporal extents are available on online databases (e.g. GBIF, eBird). Such citizen science observation data have a wide range of applications. A widespread application is in species’ distribution modelling [13, 14], as relatively little expense is required to obtain extensive citizen science data whereas, in contrast, the rigorous collection of presence-absence data in planned scientific surveys is costly and time consuming [15]

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