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(opens in a new window)https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13985: this is the full publication in Methods in Ecology of Evolution of the arxiv pre-print(opens in a new window)https://arxiv.org/abs/2106.13565. In the paper, we review and synthesize a set of maximum-entropy methods that allow for fluctuating (‘soft’) constraints, offering a new addition to the classical toolkit of the ecologist. We illustrate the methods with some practical examples, pointing to currently available open-source computer codes, which you can find here:(opens in a new window)https://doi.org/10.6084/m9.figshare.20531655.v1. We clarify how a maximum entropy approach with maximum likelihood to estimate model parameters can be used by experimental ecologists to detect non-random patterns with null models that not only rewire, but also redistribute interaction strengths by allowing fluctuations in the enforced constraints. The method we review and synthesise offers a statistically robust and expanded (e.g. including weighted links) set of tools to understand the assembly and resilience of ecological networks.

(opens in a new window)https://arxiv.org/abs/2106.13565: How can we model fluctuations in structural properties of ecological networks? For example, networks show fluctuations in global network properties such as total number and intensity of interactions in the network, but also in the local properties of individual nodes, such as the number and intensity of species-level interactions. In this preprint, Tancredi, Matthias and Diego synthesised a set of methods based on the statistical mechanics of networks, which they illustrate with some practical examples.

(opens in a new window)https://doi.org/10.1002/ece3.8278: What makes complex soil ecological networks stable? In this paper, which involved Tancredi, Matthias and Diego, the authors propose that the organization of soil ecological networks by functional blocks stabilise these complex networks characterised by very many species and different types of interactions.