A unifying framework for fast randomization of ecological networks with fixed (node) degrees

The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (or their matrix counterpart) while preserving node degrees. Here we introduce two extensions of the procedure, making it capable to randomize also unimode directed and undirected networks. We provide formal mathematical proofs that the two extensions, as the original Curveball, are fast and unbiased (i.e. they sample uniformly from the universe of possible network configurations).

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We extend the Curveball algorithm to unimode directed and undirected networks.

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As the original Curveball, extensions are fast and unbiased.

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We provide Python and R code implementing the new procedures.

Network analysis reveals why Xylella fastidiosa will persist in Europe

The insect vector borne bacterium Xylella fastidiosa was first detected in olive trees in Southern Italy in 2013, and identified as the main culprit behind the 'olive quick decline syndrome'. Since then, the disease has spread rapidly through Italy's main olive oil producing region. The epidemiology of the outbreak is largely unstudied, with the list of X. fastidiosa hosts and vectors in Europe likely incomplete, and the role humans play in dispersal unknown. These knowledge gaps have led to management strategies based on general assumptions that require, among others, local vector control and, in certain areas, the destruction of infected plants and healthy ones around them in an attempt to eradicate or halt the spreading pest. Here we show that, regardless of epidemiological uncertainties, the mere distribution of olive orchards in Southern Italy makes the chances of eradicating X. fastidiosa from the region extremely slim. Our results imply that Southern Italy is becoming a reservoir for X. fastidiosa. As a consequence, management strategies should keep the prevalence of X. fastidiosa in the region as low as possible, primarily through vector control, lest the pathogen, that has also been detected in southern France and the island of Mallorca (Spain), continues spreading through Italy and Europe.

Erratum: Proof of uniform sampling of binary matrices with fixed row sums and column sums for the fast Curveball algorithm [Phys. Rev. E 91, 042812 (2015)]

This corrects the article DOI: 10.1103/PhysRevE.91.042812.

Proof of uniform sampling of binary matrices with fixed row sums and column sums for the fast Curveball algorithm

Randomization of binary matrices has become one of the most important quantitative tools in modern computational biology. The equivalent problem of generating random directed networks with fixed degree sequences has also attracted a lot of attention. However, it is very challenging to generate truly unbiased random matrices with fixed row and column sums. Strona et al. [Nat. Commun. 5, 4114 (2014)] introduce the innovative Curveball algorithm and give numerical support for the proposition that it generates truly random matrices. In this paper, we present a rigorous proof of convergence to the uniform distribution. Furthermore, we show the Curveball algorithm must include certain failed trades to ensure uniform sampling.