Spatial bias in the GBIF database and its effect on modeling species' geographic distributions

By Jan Beck, Marianne Böller, Andreas Erhardt, Wolfgang Schwanghart. Ecological Informatics, 2014

656 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/abbbd10811f2814994c2af4294a4a837fe8849ff

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In Ecological Modeling

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Citations in the corpus

Sampling biases shape our view of the natural world (2020, background): “However, if occurrence records are biased in their collection, models may be unrealistic (Beck et al. 2014, Costello et al. 2015a, Qiao et al. 2015).”
Outstanding plant endemism levels strongly support the recognition of campo rupestre provinces in mountaintops of eastern South America (2019, methodology): “regionalization of campo rupestre As our data are derived from GBIF, unprecise or dubious records are expected (Beck et al., 2014; Maldonado et al., 2015; Yesson et al., 2007) and some records could not be removed or corrected even through our data cleaning procedures.”
Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions (2021, result): “In line with our results, previous studies have found that spatial bias can have surprisingly little impact on estimated species distributions (Beck et al., 2014; Higa et al., 2014; Johnston et al., 2020).”
Big data for forecasting the impacts of global change on plant communities (2017, background): “Strategies are being developed for reducing the effect of bias on analyses using aggregated species occurrence (Phillips et al. , 2009; Beck et al. , 2014) and vegetation plot data (Lengyel et al. , 2011; Wiser & De Cáceres, 2013).”
The use of opportunistic data for IUCN Red List assessments (2015, background): “At relatively large scale (e.g., global, continental), data are often very patchy (e.g., GBIF - Beck et al., 2014), but this can also be the case on national or regional levels when survey intensity is low.”
What we (don’t) know about global plant diversity (2018, neutral): “…and data availability; this diversity, while valid within each effort, creates global-scale biases in terms of which species feature in a particular database and which do not, which in turn effects the usability of data (Beck et al. , 2014, 2013; Hinchliff & Smith, 2014; Gratton et al. , in press).”