Biological invasions are processes influenced by many different, often interacting factors. Each case of invasion has its specific history during which specific circumstances lead to the success of the invasive species. In the face of the wealth of interacting factors the question arises whether it is possible to develop some kind of 'checklist' guiding in-depth analyses of invasion cases, and allowing for systematic consideration of important factors.
Results/Conclusions
To reach this aim, a conceptual model of biological invasions has been developed: the model of invasion steps and stages (INVASS model). Based on a chronological dissection of an idealized invasion process, the key factors which influence species invasions have been analyzed systematically. For each of four invasion steps, potential problems have been identified which may arise for an invading organism, and favorable species characteristics as well as environmental conditions are named which can contribute to the avoidance or solution of these problems. The main factors influencing invasions thus are systematized and assigned to specific steps of the invasion process. Four tables summarize these factors. The model in its original version is applicable to invasions of terrestrial vascular plants, and it has been modulated for an application to mammals and freshwater fish.
In the presentation it will be shown how the INVASS model's tables can be used as checklists guiding in-depth analyses of invasion cases. Analyses based on the model allow to systematically check which of the various factors influencing invasion processes have been important during the respective case. With examples like the one of American Mink (Mustela vison) and Nutria (Myocastor coypus) invading Europe, it will be shown how the model can help to reveal the history of an invasion case and thus to explain the causes of specific invasions. It will be demonstrated that such in-depth analyses can also be used as a basis for predictions about further range expansions as well as for risk analysis.