Different facets of the same niche: Integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers

Abstract

Citizen science initiatives have been increasingly used by researchers as a source of occurrence data to model the distribution of alien species. Since citizen science presence‐only data suffer from some fundamental issues, efforts have been made to combine these data with those provided by scientifically structured surveys. Surprisingly, only a few studies proposing data integration evaluated the contribution of this process to the effective sampling of species' environmental niches and, consequently, its effect on model predictions on new time intervals. We relied on niche overlap analyses, machine learning classification algorithms and ecological niche models to compare the ability of data from citizen science and scientific surveys, along with their integration, in capturing the realized niche of 13 invasive alien species in Italy. Moreover, we assessed differences in current and future invasion risk predicted by each data set under multiple global change scenarios. We showed that data from citizen science and scientific surveys captured similar species niches though highlighting exclusive portions associated with clearly identifiable environmental conditions. In terrestrial species, citizen science data granted the highest gain in environmental space to the pooled niches, determining an increased future biological invasion risk. A few aquatic species modelled at the regional scale reported a net loss in the pooled niches compared to their scientific survey niches, suggesting that citizen science data may also lead to contraction in pooled niches. For these species, models predicted a lower future biological invasion risk. These findings indicate that citizen science data may represent a valuable contribution to predicting future spread of invasive alien species, especially within national‐scale programmes. At the same time, citizen science data collected on species poorly known to citizen scientists, or in strictly local contexts, may strongly affect the niche quantification of these taxa and the prediction of their future biological invasion risk.

Predicting the Potential Distribution of Haloxylon ammodendron under Climate Change Scenarios Using Machine Learning of a Maximum Entropy Model

“…In comparison with the range of suitable areas under current climatic conditions, there is an increase of 74,400 km 2 in the highly suitable areas and 16,100 km 2 in the moderately suitable areas. However, the marginally suitable areas have diminished by 35,000 km 2 , resulting in an overall increment of 55,500 km 2 .…”

“…By 2050S, the area of highly suitable area will cover 782,600 km 2 , with the moderately suitable area spanning 431,400 km 2 . The marginally suitable area will include 784,100 km 2 , culminating in a total suitable area of 1.981 million km 2 . In contrast with the range of suitable areas under current climatic conditions, the moderately suitable areas have reduced by 131,200 km 2 , and the marginally suitable areas have shrunk by 32,600 km 2 .…”

“…Regarding the 2070s, the highly suitable area is anticipated to span 873,000 km 2 , with the moderately suitable area covering 429,000 km 2 . The marginally suitable area is predicted to extend over 889,400 km 2 , resulting in a total suitable area of 2,191,300 km 2 . Compared with the range of suitable areas under the current climatic conditions, there is a considerable augmentation of 383,200 km 2 in the highly suitable areas and 72,700 km 2 in the marginally suitable areas.…”

“…Looking ahead to the 2070s, the highly suitable area is predicted to extend over 770,700 km 2 , while the moderately suitable area will span 4410 km 2 . The marginally suitable area is estimated to cover 681,300 km 2 , resulting in a total suitable area of 1.88 million km 2 . In comparison with the range of suitable areas under current climatic conditions, there is a reduction of 121,700 km 2 in the moderately suitable areas and 135,400 km 2 in the less suitable areas.…”

Scale‐dependent influences of environmental, historical, and spatial processes on taxonomic and functional beta diversity of Japanese bat assemblages

“…Niche‐based processes function on the premise that individual species adapt to specific environmental conditions (Leibold et al., 2004). These processes deterministically drive biological communities alongside environmental factors such as climate and geographical features (Di Febbraro et al., 2022; Ding et al., 2023). Spatial processes shape communities via dispersal limitations and stochastic ecological drift (Dray et al., 2006; Hubbell, 2001).…”