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.

Published in

PLoS ONE
2016, Volume: 11, number: 1, article number: e0147796
Publisher: Public Library of Science

SLU Authors

• Mair, Louise

  • SLU Swedish Species Information Centre, Swedish University of Agricultural Sciences
    

• Ruete, Alejandro

  • Department of Ecology, Swedish University of Agricultural Sciences
    
  • SLU Swedish Species Information Centre, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation


UKÄ Subject classification

Bioinformatics and Systems Biology
Ecology


Publication identifier

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

Permanent link to this page (URI)

https://res.slu.se/id/publ/69581