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| Aim To understand how the integration of contextual spatial data on land
cover and human infrastructure can help reduce spatial bias in sampling effort,
and improve the utilization of citizen science-based species recording schemes.
By comparing four different citizen science projects, we explore how the sampling
design’s complexity affects the role of these spatial biases.
Location Denmark, Europe.
Methods We used a point process model to estimate the effect of land cover
and human infrastructure on the intensity of observations from four different
citizen science species recording schemes. We then use these results to predict
areas of under- and oversampling as well as relative biodiversity ‘hotspots’ and
‘deserts’, accounting for common spatial biases introduced in unstructured
sampling designs.
Results We demonstrate that the explanatory power of spatial biases such as
infrastructure and human population density increased as the complexity of the
sampling schemes decreased. Despite a low absolute sampling effort in agricultural
landscapes, these areas still appeared oversampled compared to the
observed species richness. Conversely, forests and grassland appeared undersampled
despite higher absolute sampling efforts. We also present a novel and
effective analytical approach to address spatial biases in unstructured sampling
schemes and a new way to address such biases, when more structured sampling
is not an option.
Main conclusions We show that citizen science datasets, which rely on
untrained amateurs, are more heavily prone to spatial biases from infrastructure
and human population density. Objectives and protocols of mass-participating
projects should thus be designed with this in mind. Our results suggest
that, where contextual data is available, modelling the intensity of individual
observation can help understand and quantify how spatial biases affect the
observed biological patterns. | |
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