“Can community structure causally determine dynamics of constituent species? A test using a host-parasite community”

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Ryan E. Langendorf and Daniel F. Doak (Sep 2019)

Read the Article (Just Accepted)

Are community structures causal? Downward causation in a host-parasite community inferred from observational data

Can community structure causally determine dynamics of constituent species?

Ecologists have long struggled with how features of an entire community – such as diversity or structural complexity – influence the functioning and dynamics of the member species. These community properties are do not exist physically, outside of the complex arrangements and dynamics of multiple species. How, if not mechanically, are they then able to influence the dynamics of the populations that constitute them? And more practically, how can ecologists test for this kind of causation down the organizational scales of an ecosystem? In this upcoming paper, Langendorf and Doak tackle these questions with observational data and causal discovery, applying the increasingly popular Convergent Cross Mapping method of inferring causality to time series data of a Slovakian host-parasite community where species abundances were recorded along with their interactions. They find that the populations of three host species were affected by how connected, clustered, and evenly-distributed interactions were across the entire community. This work offers evidence that structures in the configurations of ecological interactions affect constituent species. Just as importantly, it develops and demonstrates a method for identifying which community features are important to a given species of interest.


Structures of communities have been widely studied with the assumption that they are not only a useful bookkeeping tool, but also can causally influence dynamics of the populations from which they emerge. However, convincing tests of this assumption have remained elusive, because generally the only way to alter a community property is by manipulating its constituent populations, thereby preventing independent measurements of effects on those populations. There is a growing body of evidence that methods like Convergent Cross Mapping (CCM) can be used to make inferences about causal interactions using state space reconstructions of coupled time series, a method that relies only on observational data. Here we show that CCM can be used to test the causal effects of community properties using a well-studied Slovakian rodent-ectoparasite community. CCM identified causal drivers across the organizational scales of this community, including evidence that host dynamics were influenced by the degree to which the community at large was connected and clustered. Our findings add to the growing literature on the importance of community structures in disease dynamics and argue for a broader use of causal inference in the analysis of community dynamics.