Identifying changing interspecific associations along gradients at multiple scales using wavelet correlation networks.

Abstract

Identifying interspecific associations is very important for understanding the community assembly process. However, most methods provide only an average association and assume that the association strength does not vary along the environmental gradient or with time. The scale effects are generally ignored. We integrated the idea of wavelet and network topological analysis to provide a novel way to detect non-random species associations across scales and along gradients using continuous or presence-absence ecological data. We first used a simulated species distribution dataset to illustrate how the wavelet correlation analysis builds an association matrix and demonstrates its statistical robustness. Then, we applied the wavelet correlation network to a presence-absence dataset of soil invertebrates. We found that the associations of invertebrates varied along an altitudinal gradient. We conclude by discussing several possible extensions of this method, such as predicting community assembly, utility in the temporal dimension, and the shifting effects of highly connected species within a community. The combination of the multi-scale decomposition of wavelet and network topology analysis has great potential for fostering an understanding of the assembly and succession of communities, as well as predicting their responses to future climate change across spatial or temporal scales.