Data from: Biological invasion modifies the co-occurrence patterns of insects along a stress gradient
Cite this dataset
Carbonell, José Antonio et al. (2018). Data from: Biological invasion modifies the co-occurrence patterns of insects along a stress gradient [Dataset]. Dryad. https://doi.org/10.5061/dryad.1v035
Biological invasions have become one of the most important drivers of biodiversity loss and ecosystem change world-wide. However, it is still unclear how invasions may interact with local abiotic stressors, which are expected to increase as global change intensifies. Furthermore, we know little about the response to biological invasions of insects, despite their disproportionate contribution to global animal biodiversity. The aim of the present work is to investigate the impact of an invasive aquatic insect on the co-occurrence patterns of native species of insects along a salinity gradient, and determine which assembly rules are driving these patterns. First, we characterised the habitat specialisation and functional niches of each species from physiological and biological traits, respectively, and their degree of overlap. Second, we used field data to compare the co-occurrence patterns of native and invasive species in invaded and non-invaded areas of southern Iberia and northern Morocco. Finally, we tested if habitat filtering or niche differentiation assembly rules mediate their co-occurrence. In non-invaded areas, habitat filtering drives habitat segregation of species along the salinity gradient, with a lower contribution of niche differentiation. The presence of the invasive insect modifies the distribution and co-occurrence patterns of native species. In invaded areas, niche differentiation seems to be the main mechanism to avoid competition among the invasive and native species, enabling coexistence and resource partitioning. The combined study of functional niche similarity and abiotic stressor tolerance of invasive and native species can improve our understanding of the effects of invasive species along abiotic stress gradients. This approach may increase our capacity to predict the outcomes of biological invasion in a global change context.