Connectivity and complex systems: learning from a multi-disciplinary perspective

Turnbull, L.; Hütt, M.; Ioannides, A.A.; Kininmonth, S.; Poeppl, R.; Tockner, K.; Bracken, L.J.; Keesstra, S.; Liu, L.; Masselink, R.; Parsons, A.J.

Abstract

In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. However, any implementation of the concept of connectivity carries with it both ontological and epistemological constraints, which leads us to ask if there is one type or set of approach(es) to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges in using connectivity to understand complex systems from the standpoint of widely different disciplines. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon discipline-specific insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. We evaluate how a connectivity-based approach has generated new understanding of structural-functional relationships that characterise complex systems and propose a ‘common toolbox’ underpinned by network-based approaches that can advance connectivity studies by overcoming existing constraints.

Citation

Turnbull, L., Hütt, M., Ioannides, A., Kininmonth, S., Poeppl, R., Tockner, K., Bracken, L., Keesstra, S., Liu, L., Masselink, R., & Parsons, A. (2018). Connectivity and complex systems: learning from a multi-disciplinary perspective. Applied Network Science, 3, Article 11. https://doi.org/10.1007/s41109-018-0067-2

• Journal Article Type: Article
• Acceptance Date: May 29, 2018
• Online Publication Date: Jun 18, 2018
• Publication Date: Jun 18, 2018
• Deposit Date: Jun 5, 2018
• Publicly Available Date: Jun 5, 2018
• Journal: Applied Network Science
• Electronic ISSN: 2364-8228
• Publisher: SpringerOpen
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Article Number: 11
• DOI: https://doi.org/10.1007/s41109-018-0067-2
• Public URL: https://durham-repository.worktribe.com/output/1329553