RT Journal Article
SR Electronic
T1 The structure of probabilistic networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 016485
DO 10.1101/016485
A1 T. Poisot
A1 A.R. Cirtwill
A1 K. Cazelles
A1 D. Gravel
A1 M.-J. Fortin
A1 D.B. Stouffer
YR 2015
UL http://biorxiv.org/content/early/2015/08/20/016485.abstract
AB There is a growing realization among community ecologists that interactions between species vary across space and time, and that this variation needs be quantified. Our current numerical framework to analyze the structure of species interactions, based on graph-theoretical approaches, usually do not consider the variability of interactions. Since this variability has been show to hold valuable ecological information, there is a need to adapt the current measures of network structure so that they can exploit it.We present analytical expressions of key measures of network structured, adapted so that they account for the variability of ecological interactions. We do so by modeling each interaction as a Bernoulli event; using basic calculus allows expressing the expected value, and when mathematically tractable, its variance. When applied to non-probabilistic data, the measures we present give the same results as their non-probabilistic formulations, meaning that they can be generally applied.We present three case studies that highlight how these measures can be used, in re-analyzing data that experimentally measured the variability of interactions, to alleviate the computational demands of permutationbased approaches, and to use the frequency at which interactions are observed over several locations to infer the structure of local networks. We provide a free and open-source implementation of these measures.We discuss how both sampling and data representation of ecological networks can be adapted to allow the application of a fully probabilistic numerical network approach.