Towards a unified framework to study causality in Earth–life systems

Abstract

There is considerable interest in better understanding how earth processes shape the generation and distribution of life on Earth. This question, at its heart, is one of causation. In this article I propose that at a regional level, earth processes can be thought of as behaving somewhat deterministically and may have an organized effect on the diversification and distribution of species. However, the study of how landscape features shape biology is challenged by pseudocongruent or collinear variables. I demonstrate that causal structures can be used to depict the cause–effect relationships between earth processes and biological patterns using recent examples from the literature about speciation and species richness in montane settings. This application shows that causal diagrams can be used to better decipher the details of causal relationships by motivating new hypotheses. Additionally, the abstraction of this knowledge into structural equation metamodels can be used to formulate theory about relationships within Earth–life systems more broadly. Causal structures are a natural point of collaboration between biologists and Earth scientists, and their use can mitigate against the risk of misassigning causality within studies. My goal is that by applying causal theory through application of causal structures, we can build a systems‐level understanding of what landscape features or earth processes most shape the distribution and diversification of species, what types of organisms are most affected, and why.

More social species live longer, have higher generation times, and longer reproductive windows

“…The macroecological approach presented here identifies patterns and likely candidate species for further study (e.g., species around the periphery of the life history strategy space presented in Fig. 5) but, as any macroecological exercise [112], mine cannot pinpoint the exact mechanism nor imply causality in the relationships I report. Nonetheless, some comparative work in birds has shown that families with high proportions of cooperation have high survival values, even in noncooperative breeders in the family [13], suggesting a demography → sociality directionality.…”

A river runs through it: Causal graphs capture rivers’ complex control on the genetic structure of populations

“…Many of the physiographic features thought to be important to structuring or isolating populations (rivers, mountains, land-sea connectivity) are multifaceted, meaning their characteristics and their effect on biology can be explained in several ways. Representing individual facets within a graph structure allows their articulation and quantification, assessment of their direct and indirect relationships, and their comparison (Dolby, 2021). More than that, the earth processes that form and shape these features are fundamentally interrelated, and often these interdependencies are relatively well understood, at least at the level needed to construct such graphs.…”

“…In short, we find that the graph-based approaches reveal attributes of the interaction between rivers and population evolution beyond what can be learned from non-structured multivariate analyses. This result marks a material advancement in our ability to articulate and ascribe putatively causal relationships within Earth-life systems (Dolby, 2021;Dong, 2022;Igea & Tanentzap, 2021;Vernham et al, 2023) and integrate geological and genetic datasets more broadly-a fundamental need within convergence science (Dawson et al, 2022;Dolby et al, 2022).…”

The information signature of diverging lineages

“…Yet, core questions remain about how often lineages diverge in complete isolation (vicariance) or with gene flow (parapatry, sympatry). While speciation via sexual selection or ecological adaptation may be expected to occur in the presence of gene flow, there remains intense interest in the role geological, climatic, and geographical settings play in mediating or promoting speciation (Antonelli, 2017; Antonelli et al, 2018; Dolby, 2021; Rahbek et al, 2019; Ribas et al, 2022) particularly in the nascent field of geogenomics (Dolby et al, 2022). These settings may be expected to impact gene flow in different ways.…”