Aim: Red wood ants (Formica rufa group) mitigate invertebrate pest outbreaks, alter invertebrate communities, and contribute to nutrient cycling. The IUCN lists these insects as near threatened. We investigated whether disturbances of Red Wood Ant (RWA) forest habitats related to recreation, infrastructure, and forest management were associated with RWA occurrence. We also investigated the habitat associations of this group.
Location: Switzerland.
Methods: We trained random forest models to predict RWA occurrence using data from 6,341 plots distributed throughout Swiss forests. Our explanatory variables included descriptions of vegetation, terrain, climate and human disturbance of the forest. A model trained using all of these variables (Model 1) was compared to another trained using all of these variables except those describing human disturbance (Model 2). We compared the abilities of these models to differentiate between RWA presences and absences using areas under receiver operator curves (AUC). The nature of the associations between the probability of RWA occurrence predicted by Model 1 and the explanatory variables that made the greatest contributions to the AUC of this model were investigated using individual conditional expectation plots.
Results: No significant difference in AUC was detected between Models 1 and 2. RWA occurrence was positively associated with elevation, conifers, canopy gaps, ground cover vegetation and solar insolation while negatively associated with air temperature and a soil wetness index.
Main conclusions: The distribution of RWA within Swiss forests appeared unassociated with the human disturbances we investigated. To conserve RWA in Switzerland, we recommend the conservation of forests with high proportions of conifers, particularly those at high elevations. We also recommend forest management that promotes ground cover vegetation and open canopy structures. The negative associations between RWA occurrence and temperatures raise concerns for the prospects of RWA in the context of predicted climate warming.