Species Interaction Network
To construct separate Fall and Spring nondirectional species interaction
networks, we adopted the methods of EMtree as proposed by Momal et
al. (2020). Generally, EMtree uses both (i) PLN models to represent the
joint distribution of species abundances and (ii) spanning trees to
create species interaction networks, where a spanning tree is defined as
a subset of a network that connects all nodes with the minimum number of
possible connections (Dale & Fortin 2014).
All network inference approaches try to reconstruct the underlying true
network configuration, but are impeded by the vast number of
configurations that a network may have (Momal et al. 2020). To
overcome this issue, EMtree employs a tree-based approach to reduce the
number of possible network configurations. EMtree constructs fitted
species interaction networks by averaging across the spanning trees and
employs an advanced tree-based algorithm to maximize the likelihood of
the inferred species interactions from the PLN models. The EMtree
approach combines both pairwise potential direct (e.g. predator-prey
interactions) and indirect (e.g. indirect competition) interactions,
represented as a single undirected connection between species’ nodes.
Each connection within the network was weighted with a value between
zero and one, representing the conditional probability of each
connection being part of the true underlying network. We assumed that if
an interaction had a non-zero weight, it existed in the network.
To create the network, we had to select a minimum threshold as a cut-off
for inferring species connections. This threshold can be used as a
metric for assessing the reliability of connections with higher
thresholds indicating higher reliability. We evaluated our network using
consecutive thresholds of 0.2 between 0.1 and 0.9 (0 is the minimum
possible threshold assuming virtually all connections and 1 is the
maximum possible threshold producing 0 connections). We increased
network robustness by iteratively resampling the network 100 times. The
EMtree approach was implemented using the EMtree package (Momal et
al. 2020) in R version 3.6.0 (R Core Team 2020).