Estimating interaction matrices from performance data for diverse systems

Abstract

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.

A Novel Simulation Framework for Validation of Ecological Network Inference

“…Aligning with previous recommendations for choice of network inference approach , we find that network inference accuracy is governed by (1) spatial biodiversity input type along a spectrum of co-occurrence, abundance, and performance which confers increasing accuracy onto networks inferred therefrom, and (2) environmental considerations in network inference approaches with network inference approaches which account for environmental conditions rendering more accurate inferences of true networks than those approaches who don't. These findings confirm previous criticisms of co-occurrence based network inference approaches failing to capitalise on existing information in abundance and performance data (Bimler et al, 2022;Kusch et al, 2023;Ovaskainen et al, 2017). In addition, our findings also highlight the previously suggested fallacy of neglecting environmental gradients in network inference leading to erroneous assignment of positive and negative because of differences in environmental preferences rather than actual interactions (Blanchet et al, 2020).…”

“…Within these matrices, columns and rows represent species-identities while cells contain the link weight (interaction/association strength) between corresponding species pairs. Network inference approaches can detect network matrices as either representing directed (Bimler et al, 2022) or undirected networks (Morueta-Holme et al, 2016; Tikhonov et al, 2020; Veech, 2013).…”

“…This has led to the development of interaction inference approaches, most of which utilize spatial patterns of species co-occurrence (e.g. ; Griffith, Veech, & Marsh, 2016;Blonder & Morueta-Holme, 2017;Damgaard, Ehlers, Ransijn, Schmidt, & Svenning, 2018;Lamonica, Pagel, & Schurr, 2021;Swain et al, 2021;Tikhonov et al, 2021;Weiss-Lehman et al, 2021;Bimler, Mayfield, Martyn, & Stouffer, 2022) to identify ecological networks. Due to their demonstrated usefulness in studying and predicting…”

“…This has led to the development of interaction inference approaches, most of which utilize spatial patterns of species co-occurrence (e.g. ; Griffith, Veech, & Marsh, 2016; Blonder & Morueta-Holme, 2017; Damgaard, Ehlers, Ransijn, Schmidt, & Svenning, 2018; Lamonica, Pagel, & Schurr, 2021; Swain et al, 2021; Tikhonov et al, 2021; Weiss-Lehman et al, 2021; Bimler, Mayfield, Martyn, & Stouffer, 2022) to identify ecological networks. Due to their demonstrated usefulness in studying and predicting (1) community assembly patterns (Freilich et al, 2018; Ovaskainen et al, 2017), (2) ecosystem processes (Dongli et al, 2022) and services (Keyes et al, 2021) as well as (3) ecosystem stability (Korkmazhan & Dunn, 2022; Thébault & Fontaine, 2010), it is pivotal that networks inferred via these approaches are accurate representations of realised biological interactions.…”

Ecological Network Resilience & Extinction Proxies - Updating Projections of Ecological Networks

“…Predicting novel interactions is complex, and experimentation may be needed 62 rather than deriving rewiring potential from functional trait approaches as we have done. Exploring already demonstrated effects of phylogenetic conservation of interaction preferences 63 and powerful practices of ecological interaction inference 64,65 may help improve the practice of studying rewiring potential in ecological networks.…”