Analysing ecological networks of species interactions

Abstract

Network approaches to ecological questions have been increasingly used, particularly in recent decades. The abstraction of ecological systems - such as communities - through networks of interactions between their components indeed provides a way to summarize this information with single objects. The methodological framework derived from graph theory also provides numerous approaches and measures to analyze these objects and can offer new perspectives on established ecological theories as well as tools to address new challenges. However, prior to using these methods to test ecological hypotheses, it is necessary that we understand, adapt, and use them in ways that both allow us to deliver their full potential and account for their limitations. Here, we attempt to increase the accessibility of network approaches by providing a review of the tools that have been developed so far, with - what we believe to be - their appropriate uses and potential limitations. This is not an exhaustive review of all methods and metrics, but rather, an overview of tools that are robust, informative, and ecologically sound. After providing a brief presentation of species interaction networks and how to build them in order to summarize ecological information of different types, we then classify methods and metrics by the types of ecological questions that they can be used to answer from global to local scales, including methods for hypothesis testing and future perspectives. Specifically, we show how the organization of species interactions in a community yields different network structures (e.g., more or less dense, modular or nested), how different measures can be used to describe and quantify these emerging structures, and how to compare communities based on these differences in structures. Within networks, we illustrate metrics that can be used to describe and compare the functional and dynamic roles of species based on their position in the network and the organization of their interactions as well as associated new methods to test the significance of these results. Lastly, we describe potential fruitful avenues for new methodological developments to address novel ecological questions.

Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research

“…4b, 5a). Network sciences in ecology are still in development (Borrett et al 2014, Pilosof et al 2017, Delmas et al 2019). Analyses of temporal networks (Masuda and Lambiotte 2016) and network robustness are particularly important (Grass et al 2018, Guardiola et al 2018, since species go extinct at different rates during relaxation time.…”

Coexistence theory as a tool to understand biological invasions in species interaction networks: Implications for the study of novel ecosystems

“…A common ground for all these recent developments combining trophic networks with invasion biology is to understand to what extent the structure of trophic interactions allows invasion, and if that occur, which particular network properties are correlated with the ability of exotic species to invade native communities. The problem with this approach is that there are indeed many network properties that can be analysed (see Delmas et al () for a recent review), and therefore, it is not theoretically justified why some network properties such as nestedness, connectance or modularity are more extensively studied than others. Moreover, such an approach does not provide in general a clear predictive framework for why the same network characteristics might produce opposed invasion outcomes or why we should expect linear relationships between a given network characteristic (e.g.…”

EcologicalNetworks.jl: analysing ecological networks of species interactions

“…The EcologicalNetworks package offers functions to perform the majority of common ecological networks analyses -we follow the recommendations laid out in Delmas et al (2018). The key functions include (but are not limited to) species richness (richness); connectance (connectance) and linkage density (linkage_density); degree (degree) and specificity (specificity); null models (null1, null2, null3, null4); constrained network permutations (shuffle); random networks (rand); nestedness (η and nodf); shortest path (number_of_paths, shortest_path); information theoretic analysis (entropy, conditional_entropy, mutual_information, information_decomposition); centrality measures (centrality_katz, centrality_closeness, centrality_degree); resilience (symmetry, heterogeneity, resilience); motif counting (find_motif); modularity (Q), realized modularity (Qr) and functions to optimize them (lp and salp for label propagation without or with simulated annealing, brim); β-diversity measures (βs, βos, βwn); trophic level analysis (fractional_trophic_level, trophic_level); complementarity analysis (AJS; EAJS; overlap); structural random models (cascademodel, mpnmodel, nichemodel, nestedhierarchymodel).…”

“…Although the names are not necessarily the most descriptive, they have been used this way in the ecological networks literature, and the way they work is documented (see also Delmas et al 2018 for an overview of their assumptions). Although the names are not necessarily the most descriptive, they have been used this way in the ecological networks literature, and the way they work is documented (see also Delmas et al 2018 for an overview of their assumptions).…”

“…We will use four null models (as per Delmas et al 2018), null1 (all interactions have equal probability), null2 (interactions probability depends on the degree of both species) and null3 (interactions probability depends on the in-degree or out-degree of the species). This function will take a network, a type of null model, and a number of replicates and return the random draws.…”