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Temporal trends in the spatial bias of species occurrence records

Cite this dataset

Bowler, Diana et al. (2022). Temporal trends in the spatial bias of species occurrence records [Dataset]. Dryad.


Large-scale biodiversity databases have great potential for quantifying long-term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias - how spatial bias has changed over time - has been largely overlooked. We examined the spatial sampling bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well-known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long-term trends of species’ distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land-use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long-term trends in species’ distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the strength of spatial bias through time.


The data were downloaded from multiple species occurrence record databases:

GBIF, Naturgucker and

See the README file for information on the processing and analysis

Code for the simulations is also provided.


Deutsche Forschungsgemeinschaft, Award: DFG FZT 118, 202548816