Citizen science can complement professional invasive plant surveys and improve estimates of suitable habitat

Abstract

Aim: Citizen science is a cost-effective potential source of invasive species occurrence data. However, data quality issues due to unstructured sampling approaches may discourage the use of these observations by science and conservation professionals. This study explored the utility of low-structure iNaturalist citizen science data in invasive plant monitoring. We first examined the prevalence of invasive taxa in iNaturalist plant observations and sampling biases associated with these data. Using four invasive species as examples, we then compared iNaturalist and professional agency observations and used the two datasets to model suitable habitat for each species. Location: Hawai'i, USA. Methods: To estimate the prevalence of invasive plant data, we compared the number of species and observations recorded in iNaturalist to botanical checklists for Hawai'i. Sampling bias was quantified along gradients of site accessibility, protective status and vegetation disturbance using a bias index. Habitat suitability for four invasive species was modelled in Maxent, using observations from iNaturalist, professional agencies and stratified subsets of iNaturalist data. Results: iNaturalist plant observations were biased towards invasive species, which were frequently recorded in areas with higher road/trail density and vegetation disturbance. Professional observations of four example invasive species tended to occur in less accessible, native-dominated sites. Habitat suitability models based on iNaturalist versus professional data showed moderate overlap and different distributions of suitable habitat across vegetation disturbance classes. Stratifying iNaturalist observations had little effect on how suitable habitat was distributed for the species modelled in this study. Main Conclusions: Opportunistic iNaturalist observations have the potential to complement and expand professional invasive plant monitoring, which we found was often affected by inverse sampling biases. Invasive species represented a high proportion of iNaturalist plant observations, and were recorded in environments that were not captured by professional surveys. Combining the datasets thus led to more comprehensive estimates of suitable habitat.