Detecting non-neutral modules in species co-occurrence data: principles and application to plant communities.

Inferring assembly processes from species co-occurrence data is a long-standing challenge in community ecology. Approaches that focus on detecting non-random spatial covariance between species occurrences are limited by the fact that spatial patterns can deviate from randomness for many reasons. Process-based null hypotheses are needed to overcome this limitation. Here, we explored the neutral theory of community ecology as a promising candidate. We built upon a robust property of neutral co-occurrences, the 'rank consistency': within a common regional pool, the presence probabilities of two species should be ordered similarly across local sites. We suggested performing pairwise tests of species rank consistency along ecological gradients of interest and creating a species network where rank-consistent species are connected. Network mo-dules then indicate species groups that do not co-occur neutrally with one another, hence making an important step towards the understanding of assembly processes. These modules can be further interpreted by relating their composition to species traits. We tested our framework on virtual data and successfully retrieved pre-defined functional groups without generating false positive detections. Then, we analysed two published examples on tropical trees and Mediterranean herbaceous communities. We found ecologically meaningful modules in both cases, hence illustrating the potential of our approach.