Quantitative evolutionary patterns in bipartite networks: Vicariance, phylogenetic tracking or diffuse co-evolution?

Laura Russo, Adam D. Miller, John Tooker, Ottar N. Bjornstad, Katriona Shea

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Many pairwise interactions in ecological communities are thought to be structured by co-evolution, a process difficult to study on a community level. Traditional methods can reveal correlated phylogenies between interacting organisms, suggesting a co-evolutionary association. However, several processes could lead to cophylogeny, including (1) vicariance, (2) phylogenetic tracking and (3) co-evolution. We present a framework to investigate diffuse co-evolution between groups of interacting taxa. Our methodology incorporates two complementary statistical methods to test for evolutionary signals. First, we test for correlated taxonomies between interacting species, and then we test for an association between the pattern of interactions and taxonomy of interacting species. We use these methods to clearly distinguish between three possible explanations of cophylogenies, when present. Our methodology is designed to explore diffuse evolutionary signals among many interacting taxa: it is robust to polytomies and generalization and can use taxonomic trees in place of phylogenies. Thus, we use taxonomic distances to identify groups of associated taxa, as opposed to specific co-evolutionary relationships. In a case study, we find significant evidence for phylogenetic tracking, vicariance and asymmetry. Generally, our method can be applied whenever relevant taxonomy is well resolved and includes sufficient variation at multiple taxonomic ranks between two groups of interacting taxa. This adaptable method can incorporate many types of data on species traits or behaviours, such as phylogenetic relatedness, pollination efficacy, interaction strengths and visitation rates. The analysis we provide here may be of use to a broad range of ecologists interested in evaluating diffuse evolutionary patterns in groups of interacting species.

Original languageEnglish (US)
Pages (from-to)761-772
Number of pages12
JournalMethods in Ecology and Evolution
Issue number3
StatePublished - Mar 2018

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Ecological Modeling


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