@article {Harris018861,
author = {Harris, David J.},
title = {Estimating species interactions from observational data with Markov networks},
year = {2015},
doi = {10.1101/018861},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Inferring species interactions from observational data is one of the most controversial tasks in community ecology. One difficulty is that a single pairwise interaction can ripple through an ecological network and produce surprising indirect consequences. For example, two competing species would ordinarily correlate negatively in space, but this effect can be reversed in the presence of a third species that is capable of outcompeting both of them when it is present. Here, I apply models from statistical physics, called Markov networks or Markov random fields, that can predict the direct and indirect consequences of any possible species interaction matrix. Interactions in these models can be estimated from observational data via maximum likelihood. Using simulated landscapes with known pairwise interaction strengths, I evaluated Markov networks and several existing approaches. The Markov networks consistently outperformed other methods, correctly isolating direct interactions between species pairs even when indirect interactions or abiotic environmental effects largely overpowered them. A linear approximation, based on partial covariances, also performed well as long as the number of sampled locations exceeded the number of species in the data. Indirect effects reliably caused a common null modeling approach to produce incorrect inferences, however.},
URL = {https://www.biorxiv.org/content/early/2015/05/26/018861},
eprint = {https://www.biorxiv.org/content/early/2015/05/26/018861.full.pdf},
journal = {bioRxiv}
}