In the era of Big Data and global biodiversity decline, there is a pressing need to transform data and information into findable and actionable knowledge. We propose a conceptual classification scheme for invasion science that goes beyond hypothesis networks and allows to organize publications and datasets, guide research directions, and identify knowledge gaps. Combining expert knowledge with literature analysis, we identified five major research themes in this field: (1) introduction pathways, (2) invasion success and invasibility, (3) impacts of invasion, (4) managing biological invasions and (5) meta-invasion science. We divided these themes into ten broader research questions and linked them to 39 major hypotheses forming the theoretical foundation of invasion science. As artificial intelligence advances, such classification schemes will become important references for organizing scientific information. Our approach can be extended to other research fields, fostering cross-disciplinary connections to leverage the scientific knowledge needed to address Anthropocene challenges. The two datasets show the final classification scheme presented in this paper and the literature corpus used in this study. 

To develop a conceptual classification scheme encompassing all overarching themes and research questions in the field of invasion science, we combined a top-down with a bottom-up classification approach (please, see the manuscript for further information). We focused on 39 major hypotheses forming the theoretical base of the discipline. This list is not exhaustive or final but serves as a starting point, comprising hypotheses selected through a prior consensus among experts.

The literature corpus is from the Hi-Knowledge initiative https://hi-knowledge.org/

The included supplemental files contain all of the information necessary to support research findings.