Aim Understanding the environmental factors determining the establishment of invasive populations is a crucial issue in the study of biological invasions. By taking into account the uncertainty of predictions, ensembles of niche-based models can provide useful information. Therefore, we explored the use of consensus techniques to generate a quantitative description of the environmental conditions favouring the establishment of four problematic invasive decapods: Cherax destructor, Eriocheir sinensis, Pacifastacus leniusculus and Procambarus clarkii.
Location Iberian Peninsula.
Methods We collected both native and invasive distribution records from multiple sources. From these data, we modelled the potential distribution of the four decapod species using eight correlative models comprising regression, classification and machine learning methods. The relative influence of the environmental variables in single models was averaged to achieve a consensus contribution of the variables. Ecological requirements were investigated by means of consensus suitability curves, a spatial analysis procedure that shows the variation of consensus suitability along the gradients of environmental variables.
Results The predictive accuracy of single models ranged from fair to very good. Still, the variability between predictions was high. Similarly, the influence of each variable in different models was also uneven. Consensus analysis identified the variables related to temperature as highly influential for all invaders. Consensus suitability curves show that C. destructor and Procambarus clarkii have reduced suitability in colder areas whereas the suitability for P. leniusculus is greatly reduced in warmer areas. The distance to the ocean was highly influential in E. sinensis models, with suitability showing an exponential decay as distance increased.
Main conclusions We show that the information about the species–environment relationships obtained from niche-based models is highly dependent on the characteristics of the models used. In this context, we demonstrate that ensembles of models and consensus approaches can be used to identify such relationships while also allowing the assessment of the uncertainty of the achieved knowledge.