Valuing the information hidden in true long-term data for invasion science

Abstract

AbstractInvasive species pose a significant threat to global biodiversity and human well-being. Despite the widespread use of long-term biomonitoring data in many natural science fields, the analysis of long-term time series with a focus on biological invasions is uncommon. To address this gap, we used twenty macroinvertebrate time series from the highly anthropogenically altered Rhine River, collected over 32 years from 1973 to 2005. We examined the adequacy of the data in capturing non-native species trends over time and explored trends in alpha, beta, and gamma diversity of non-native species with several climatic and site-specific predictors. Our findings revealed that the data adequately captured a saturating non-native species richness over time. Additionally, we observed an increase in both alpha and gamma diversity of both native and non-native species over time, with a recent dip in trends. Beta diversity trends were more complicated, but eventually increased, contrasting trends in native species beta diversity. Our applied models indicate that in this highly altered ecosystem, climatic shifts were insignificant, while time was the primarily driving factor. Proximity to anthropogenic structures and the distance to the outlet were the only site-specific predictors facilitating non-native species diversity. These findings highlight the value and importance of long-term time series for the study of invasive species, particularly long-term invasion dynamics and once again underline that naturality of ecosystems precede the effect of climate change.