The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

Opportunistically collected species occurrence data are often used for species distribution models (SDMs) when high-quality data collected through standardized recording protocols are unavailable. While opportunistic data are abundant, uncertainty is usually high, e.g. due to observer effects or a lack of metadata. To increase data quality and improve model performance, we filtered species records based on record attributes that provide information on the observation process or post-entry data validation. Data filtering does not only increase the quality of species records, it simultaneously reduces sample size, a trade-off that remains relatively unexplored. By controlling for sample size in a dataset of 255 species, we were able to explore the combined impact of data quality and sample size on model performance. We applied three data quality filters based on observers' activity, the validation status of a record in the database and the detail of a submitted record, and analyzed changes in AUC, Sensitivity and Specificity using Maxent with and without filtering. The impact of stringent filtering on model performance depended on (1) the quality of the filtered data: records validated as correct and more detailed records lead to higher model performance, (2) the proportional reduction in sample size caused by filtering and the remaining absolute sample size: filters causing small reductions that lead to sample sizes of more than 100 presences generally benefitted model performance and (3) the taxonomic group: plant and dragonfly models benefitted more from data quality filtering compared to bird and butterfly models. Our results also indicate that recommendations for quality filtering depend on the goal of the study, e.g. increasing Sensitivity and/or Specificity. Further research must identify what drives species' sensitivity to data quality. Nonetheless, our study confirms that large quantities of volunteer generated and opportunistically collected data can make a valuable contribution to ecological research and species conservation

Waarnemingen.be : non-native plant and animal occurrences in Flanders and the Brussels Capital Region, Belgium

Citizen scientists make important contributions to the collection of occurrence data of non-native species. We present two datasets comprising more than 520,000 records of 1,771 non-native species from Flanders and the Brussels Capital Region in Belgium, Western Europe, collected through the website http://www.waamemingen.be hosted by Stichting Natuurinformatie and managed by the nature conservation NGO Natuurpunt. Most records were collected by citizen scientists, mainly since 2008. Waarnemingen.be aims at recording all species, native and non-native, and it is shown here that this kind of biodiversity portals are also particularly well suited to collect large amounts of data on non-native species. Both datasets presented here are also discoverable through the Global Biodiversity Information Facility (GBIF)