Evidence of novelty bias and specialization in participatory science
sampling behavior
Abstract
Participatory science (or ”citizen science”) records are becoming
increasingly useful for wildlife monitoring due to their volume and
spatiotemporal coverage. However, statistical analysis using these data
can be challenging due to the many sources of bias that need to be
corrected. Many previous studies characterize sampling biases across
entire participatory science datasets, such as spatial heterogeneity in
sampling effort or species preferences. User-level heterogeneity in
sampling behavior is less well studied, but it may be just as important
as dataset-level bias in contributing to error in downstream analyses.
Here, we investigate user-level novelty and specialization bias. Novelty
bias occurs when an individual observer preferentially reports species
that have not seen before, while specialization bias occurs when an
observer preferentially reports species they have previously observed
(i.e., they specialize in particular species). We provide the first test
of this kind of user-level sampling bias in participatory science data
by analyzing the sampling histories of more than 540 observers on the
popular participatory science platform iNaturalist in Pennsylvania, USA.
We find evidence of specialization or novelty bias in the overall
sampling behavior of 66% of the observers considered. Specialization
bias was more than 5 times more common than novelty bias, indicating
that observers reported species they had reported previously at a higher
rate than expected. Looking within taxonomic groups, 41% of observers
deviated from unbiased sampling. Novelty bias and specialization bias
were both common within taxa. These findings suggest that iNaturalist
observers often specialize in favorite taxa or species, while within
taxa some users simultaneously seek out previously unobserved species.