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Article: Causality from palaeontological time series

Palaeontology Cover Image - Volume 61 Part 4
Publication: Palaeontology
Volume: 61
Part: 4
Publication Date: July 2018
Page(s): 495 509
Author(s): Bjarte Hannisdal, and Lee Hsiang Liow
Addition Information

How to Cite

HANNISDAL, B., LIOW, L.H. 2018. Causality from palaeontological time series. Palaeontology, 61, 4, 495-509. DOI: 10.1111/pala.12370

Author Information

  • Bjarte Hannisdal - Department of Earth Science University of Bergen PO Box 7803 5020 Bergen Norway
  • Bjarte Hannisdal - K.G. Jebsen Centre for Deep Sea Research University of Bergen Norway
  • Bjarte Hannisdal - Bjerknes Centre for Climate Research University of Bergen Norway
  • Lee Hsiang Liow - Centre for Ecological & Evolutionary Synthesis Department of Biosciences University of Oslo PO Box 1066 Blindern 0316 Oslo Norway
  • Lee Hsiang Liow - Natural History Museum University of Oslo Norway

Publication History

  • Issue published online: 12 June 2018
  • Manuscript Accepted: 05 April 2018
  • Manuscript Received: 29 January 2018

Funded By

The Research Council of Norway. Grant Numbers: 235073, 231259
Bergen Research Foundation

Online Version Hosted By

Wiley Online Library (Open Access)
Get Article: Wiley Online Library [Open Access]


As custodians of deep time, palaeontologists have an obligation to seek the causes and consequences of long‐term evolutionary trajectories and the processes of ecosystem assembly and collapse. Building explicit process models on the relevant scales can be fraught with difficulties, and causal inference is typically limited to patterns of association. In this review, we discuss some of the ways in which causal connections can be extracted from palaeontological time series and provide an overview of three recently developed analytical frameworks that have been applied to palaeontological questions, namely linear stochastic differential equations, convergent cross mapping and transfer entropy. We outline how these methods differ conceptually, and in practice, and point to available software and worked examples. We end by discussing why a paradigm of dynamical causality is needed to decipher the messages encrypted in palaeontological patterns.

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