A framework for the description and analysis of multilayer networks is established in statistical physics, and calls are increasing for their adoption by community ecologists. Multilayer networks in community ecology will allow space, time and multiple interaction types to be incorporated into species interaction networks. While the multilayer network framework is applicable to ecological questions, it is one thing to be able to describe ecological communities as multilayer networks and another for multilayer networks to actually prove useful for answering ecological questions. Importantly, documenting multilayer network structure requires substantially greater empirical investment than standard ecological networks. In response, we argue that this additional effort is worthwhile and describe a series of research lines where we expect multilayer networks will generate the greatest impact. Inter‐layer edges are the key component that differentiate multilayer networks from standard ecological networks. Inter‐layer edges join different networks—termed layers—together and represent ecological processes central to the species interactions studied (e.g., inter‐layer edges representing movement for networks separated in space). Inter‐layer edges may take a variety of forms, be species‐ or network‐specific, and be measured with a large suite of empirical techniques. Additionally, the sheer size of ecological multilayer networks also requires some changes to empirical data collection around interaction quantification, collaborative efforts and collation in public databases. Network ecology has already touched on a wide swath of ecology and evolutionary biology. Because network stability and patterns of species linkage are the most developed areas of network ecology, they are a natural starting place for multilayer investigations. However, multilayer networks will also provide novel insights to niche partitioning, the connection between traits and species’ interactions, and even the geographic mosaic of co‐evolution. Synthesis. Multilayer networks provide a formal way to bring together the study of species interaction networks and the processes that influence them. However, describing inter‐layer edges and the increasing amounts of data required represent challenges. The pay‐off for added investment will be ecological networks that describe the composition and capture the dynamics of ecological communities more completely and, consequently, have greater power for understanding the patterns and processes that underpin diversity in ecological communities. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.13237/suppinfo is available for this article.

Although the structure of empirical food webs can differ between ecosystems, there is growing evidence of multiple ways in which they also exhibit common topological properties. To reconcile these contrasting observations, we postulate the existence of a backbone of interactions underlying all ecological networks—a common substructure within every network comprised of species playing similar ecological roles—and a periphery of species whose idiosyncrasies help explain the differences between networks. To test this conjecture, we introduce a new approach to investigate the structural similarity of 411 food webs from multiple environments and biomes. We first find significant differences in the way species in different ecosystems interact with each other. Despite these differences, we then show that there is compelling evidence of a common backbone of interactions underpinning all food webs. We expect that identifying a backbone of interactions will shed light on the rules driving assembly of different ecological communities.

Ecological communities often show changes in populations and their interactions over time. To date, however, it has been challenging to effectively untangle the mechanisms shaping such dynamics. One approach that has yet to be fully explored is to treat the varying structure of empirical communities-i.e. their network of interactions-as time series. Here, we follow this approach by applying a network-comparison technique to study the seasonal dynamics of plant-pollinator networks. We find that the structure of these networks is extremely variable, where species constantly change how they interact with each other within seasons. Most importantly, we find the holistic dynamic of plants and pollinators to be remarkably coherent across years, allowing us to reveal general rules by which species first enter, then change their roles, and finally leave the networks. Overall, our results disentangle key aspects of species' interaction turnover, phenology, and seasonal assembly/disassembly processes in empirical plant-pollinator communities.

The relationship between niche and distribution, and especially the role of biotic interactions in shaping species' geographic distributions, has gained increasing interest in the last two decades. Most ecological research has focused on negative species interactions, especially competition, predation and parasitism. Yet the relevance of positive interactions – mutualisms and commensalisms – have been brought to the fore in recent years by an increasing number of empirical studies exploring their impact on range limits. Based on a review of 73 studies from a Web of Science search, we found strong evidence that positive interactions can influence the extent of species' geographic or ecological ranges through a diversity of mechanisms. More specifically, we found that while obligate interactions, and especially obligate mutualisms, tend to constrain the ranges of one or both partners, facultative positive interactions tend to widen ranges. Nonetheless, there was more variation in effects of facultative interactions on range limits, pointing to important context‐dependencies. Therefore, we propose that conceptual development in this field will come from studying ecological interactions in the context of networks of many species across environmental gradients, since pairwise interactions alone might overlook the indirect and environmentally‐contingent effects that species have on each other in communities of many interacting species. Finally, our study also revealed key data gaps that limit our current understanding of the pervasiveness of effects that positive interactions have on species' ranges, highlighting potential avenues for future theoretical and experimental work.