The structure of probabilistic networks [article]

Timothée Poisot, Alyssa Cirtwill, Kévin Cazelles, Dominique Gravel, Marie-Josée Fortin, Daniel Stouffer
2015 bioRxiv   pre-print
1. There is a growing realization among community ecologists that interactions between species vary in space and time. Yet, our current numerical framework to analyze the structure of interactions, largely based on graph-theoretical approaches, is unsuited to this type of data. Since the variation of species interactions holds much information, there is a need to develop new metrics to exploit it. 2. We present analytical expressions of key network metrics, using a probabilistic framework. Our
more » ... pproach is based on modeling each interaction as a Bernoulli event, and using basic calculus to express the expected value, and when mathematically tractable, its variance. We provide a free and open-source implementation of these measures. 3. We show that our approach allows to overcome limitations of both neglecting the variation of interactions (over-estimation of rare events) and using simulations (extremely high computational demand). We present a few case studies that highlight how these measures can be used. 4. We conclude this contribution by discussing how the sampling and data representation of ecological network can be adapted to better allow the application of a fully probabilistic numerical framework.
doi:10.1101/016485 fatcat:dtm7uqgn5vazxoop6p22na2tde