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 biashow spatial bias has changed over time -has been largely overlooked. We examined the spatial 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 spatial bias through time.
Citizen scientists play an increasingly important role in biodiversity monitoring. Most of the data, however, are unstructured—collected by diverse methods that are not documented with the data. Insufficient understanding of the data collection processes presents a major barrier to the use of citizen science data in biodiversity research. We developed a questionnaire to ask citizen scientists about their decision-making before, during and after collecting and reporting species observations, using Germany as a case study. We quantified the greatest sources of variability among respondents and assessed whether motivations and experience related to any aspect of data collection. Our questionnaire was answered by almost 900 people, with varying taxonomic foci and expertise. Respondents were most often motivated by improving species knowledge and supporting conservation, but there were no linkages between motivations and data collection methods. By contrast, variables related to experience and knowledge, such as membership of a natural history society, were linked with a greater propensity to conduct planned searches, during which typically all species were reported. Our findings have implications for how citizen science data are analysed in statistical models; highlight the importance of natural history societies and provide pointers to where citizen science projects might be further developed.
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