RT Journal Article
SR Electronic
T1 Estimating interaction matrices from performance data for diverse systems
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.03.28.486154
DO 10.1101/2022.03.28.486154
A1 Malyon D. Bimler
A1 Margaret M. Mayfield
A1 Trace E. Martyn
A1 Daniel B. Stouffer
YR 2022
UL http://biorxiv.org/content/early/2022/03/29/2022.03.28.486154.abstract
AB Network theory allows us to understand complex systems by evaluating how their constituent elements interact with one another. Such networks are built from matrices which describe the effect of each element on all others. Quantifying the strength of these interactions from empirical data can be difficult, however, because the number of potential interactions increases non-linearly as more elements are included in the system, and not all interactions may be empirically observable when some elements are rare.We present a novel modelling framework which estimates the strength of pairwise interactions in diverse horizontal systems, using measures of species performance in the presence of varying densities of their potential interaction partners.Our method allows us to directly estimate pairwise effects when they are statistically identifiable and approximate pairwise effects when they would otherwise be statistically unidentifiable. The resulting interaction matrices can include positive and negative effects, the effect of a species on itself, and are non-symmetrical.The advantages of these features are illustrated with a case study on an annual wildflower community of 22 focal and 52 neighbouring species, and a discussion of potential applications of this framework extending well beyond plant community ecology.Competing Interest StatementThe authors have declared no competing interest.