Abstract
AbstractAs 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.
Highlights
WEINHABITa world fundamentally shaped by the deep history of geobiological interactions
As modern ecosystems head towards uncharted territories, geohistorical data are our only record of ecosystems undisturbed by human activities and of biotic responses to global change in the past (Dietl et al 2015)
We will argue that these time series beg for causal explanations that are not restricted to unique events, but accommodate dynamical, temporally extensive modes of causality
Summary
Rock Fossil Record (Melott & Bambach 2017; Erlykin et al 2018). Similar controversies surrounding the causal implications of matching periodicities are simmering in related fields, such as the existence of Milankovitch cycles in Palaeozoic sedimentary sequences (Hinnov et al 2016; Smith et al 2016) or in seafloor bathymetry (Huybers et al 2016; Olive et al 2016). One way to break the impasse, is to confront our time series with methods that seek to distinguish causative from correlative relationships
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