Beyond species: why ecological interaction networks vary through space and time

Abstract

Community ecology is tasked with the considerable challenge of predicting the structure, and properties, of emerging ecosystems. It requires the ability to understand how and why species interact, as this will allow the development of mechanism-based predictive models, and as such to better characterize how ecological mechanisms act locally on the existence of inter-specific interactions. Here we argue that the current conceptualization of species interaction networks is ill-suited for this task. Instead, we propose that future research must start to account for the intrinsic variability of species interactions, then scale up from here onto complex networks. This can be accomplished simply by recognizing that there exists intra-specific variability, in traits or properties related to the establishment of species interactions. By shifting the scale towards population-based processes, we show that this new approach will improve our predictive ability and mechanistic understanding of how species interact over large spatial or temporal scales.Although species interactions are the backbone of ecological communities, we have little insights on how (and why) they vary through space and time. In this article, we build on existing empirical literature to show that the same species may happen to interact in different ways when their local abundances vary, their trait distribution changes, or when the environment affects either of these factors. We discuss how these findings can be integrated in existing frameworks for the analysis and simulation of species interactions. Synthesis

The use of DNA barcodes in food web construction—terrestrial and aquatic ecologists unite!

“…While studies of ecological networks to date have mostly been concerned with the distribution of interactions within locations, and less so with the variation among locations, there is now ample evidence that ecological interactions vary in space and time (Poisot et al 2012;Trøjelsgaard et al 2015). It is in resolving how interaction structure varies with the environment (see Poisot et al 2012Poisot et al , 2015 that DNA barcodes will reveal their true potential. For generating the data needed to test and parameterize this new type of "species interaction modelling", we see DNAbased techniques as a prime tool.…”

Trophic level, successional age and trait matching determine specialization of deadwood-based interaction networks of saproxylic beetles

“…Therefore, it has been recently established that networks are dynamic structures and are modulated by the temporal and spatial variability of ecological interactions [6]. To address this aspect, we tested if the investigated network indices differ between study regions.…”

“…We tested whether interaction networks might vary among years and at different locations due to changes of species composition [6]. The NMDS analyses revealed strong successional changes in community composition of xylophagous (MRPP: A ¼ 0.0407; p ¼ 0.001), fungivorous (MRPP: A ¼ 0.118; p ¼ 0.001) and predatory beetles (MRPP: A ¼ 0.0545; p ¼ 0.001) with ongoing deadwood decay (electronic supplementary material, figure S4) and also differ among the three study regions [25].…”

“…So far, almost all empirical and theoretical research on ecological networks deals with mutualistic and antagonistic bipartite networks [3][4][5]. These studies seek to understand the causes and consequences of observed patterns of species interactions [6]. An often-used tool for this purpose was to examine structural differences between network types, such as specialization, nestedness, compartmentalization, robustness and generality.…”

“…A new approach to explain the structure and dynamics of mutualistic networks incorporates traits of interaction partners [9]. Furthermore, recent trait-based studies reveal high levels of temporal and spatial dynamics in mutualistic interaction networks due to varying co-occurrences of interaction partners [6].…”