My co-authors and I come from a mathematical background, and are passionate about creating useful and innovative tools for the study of biodiversity. Species association networks are perfect for that. They are really interesting objects, both prominent in ecology and linked to an active mathematical research area. So our first objective for this work was to clearly and precisely transfer the mathematical notions to the ecological world. We quickly discovered that this is easier said than done, as the two disciplines share a lot of vocabulary but with very different meanings! So we actually first had to learn a lot from the ecological literature, which was a very formative and enriching experience.
Species association networks gather a lot of different types of tasks in the literature. To set clear basis for the future, we separate our work from what we call network reconstruction, which is the prediction of links in the network from observed species interactions. We tackle the task of network inference instead, which aims the building of the whole network from observed species census data only. Network inference is about identifying the statistical links (associations) between the species of an ecosystem. This might seem frustrating at first, as we do not have access to the true nature of the ecological interaction between the species (parasitism, trophic relationship, mutualism, etc). However, this formalism allows for a general view on species interactions which can be transposed to other fields, and help unravel interactions that are hard to observe.
Between two species, conditional dependence can be understood as the statistical dependence that exists once having controlled for the effect of all other observed species. Conditional dependence thus can only represent direct statistical links between species, which is a big advantage for the sparsity and interpretability of the network. Such network can answer the question “which species would be directly affected by the variation in population of this particular species?”. In studying conditional dependencies, taking environmental effects into account is paramount, as they could create spurious links in the network. Our approach infers the species conditional dependence network, and accounts for both available covariates and experimental offsets. You can download the R package we developed, called EMtree.