Trait matching without traits: using correspondence analysis to analyze the latent structure of interaction networks

Species interactions occurring in ecological communities are often represented as networks. The structure of these networks is thought to be linked to species’ interaction niches (or Eltonian niches), which are intimately related to the notion of trait matching, which posits that a species interacts preferentially with partners whose traits are complementary to their own.

Multivariate methods are commonly used to quantify species environmental niches (or Grinnellian niches). More recently, they have also been used to study the interaction niche, but these methods consider only the niche optimum and require trait data.

In this paper, we use the correspondence analysis (CA) framework to study interaction networks and investigate trait matching without requiring traits data, using the notion of latent traits. We use reciprocal scaling, a method related to CA, to estimate niche optima and breadths, defined respectively as the mean and standard deviation of latent traits of interacting partners in CA space. We present the method, test its performance using a simulation model we designed, and analyze a real mutalistic bird-fruits interaction network.

The simulation study shows that the method is able to recover niche breadths and optima for data generated with parameters values typical of ecological networks. The bird-fruit network analysis shows strong relationships between species niche optima and niche breadths: a posteriori correlation with species traits suggest that this latent structure is related to measured traits. In this network, birds and plants of intermediate size tend to have the widest niches and birds with pointed wings (preferentially foraging in the canopy) have smaller niches than birds with rounded wings (preferentially foraging in the understory).

CA and reciprocal scaling are described as fruitful methods to characterize species interaction profiles, provide an ecologically meaningful graphical representation of interaction niches and allow to explore the effect of latent traits on network structure.