Mapping species richness using opportunistic samples: a case study on ground-floor bryophyte species richness in the Belgian province of Limburg

In species richness studies, citizen-science surveys where participants
make individual decisions regarding sampling strategies provide a
cost-effective approach to collect a large amount of data. However, it is
unclear to what extent the bias inherent to opportunistically collected
samples may invalidate our inferences. Here, we compare spatial
predictions of forest ground-floor bryophyte species richness in Limburg
(Belgium), based on crowd- and expert-sourced data, where the latter are
collected by adhering to a rigorous geographical randomisation and data
collection protocol. We develop a log-Gaussian Cox process model to
analyse the opportunistic sampling process of the crowd-sourced data and
assess its sampling bias. We then fit two geostatistical Poisson models to
both data-sets and compare the parameter estimates and species richness
predictions. We find that the citizens had a higher propensity for
locations that were close to their homes and environmentally more
valuable. The estimated effects of ecological predictors and spatial
species richness predictions differ strongly between the two
geostatistical models. Unknown inconsistencies in the sampling process,
such as unreported observer’s effort, and the lack of a hypothesis-driven
study protocol can lead to the occurrence of multiple sources of sampling
bias, making it difficult, if not impossible, to provide reliable
inferences.