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. ; The Belgian Nature and Forest Agency (ANB), the Institute for Nature and Forest Research (INBO), and the Umbrella for Nature Research in Limburg (LIKONA) are gratefully acknowledged for providing the data used in this study and commenting on our results. In particular, we thank the following persons for their support and insights: Cecile Nagels (LIKONA), Luc Crevecoeur (LIKONA), Wouter Van Landuyt (INBO), Dirk De Beer (INBO), and Martine Waterinckx (ANB). The largest part of this study has been conducted when Thomas Neyens was funded as a postdoctoral researcher by the Flemish Research Foundation (12S7217N). ; Neyens, T ...