Although networks provide a powerful approach to study a large variety of ecological systems, their formulation does not typically account for multiple interaction types, interactions that vary in space and time, and interconnected systems such as networks of networks. The emergent field of 'multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it specifically allows one to differentiate and model 'intralayer' and 'interlayer' connectivity. The framework provides a set of concepts and tools that can be adapted and applied to ecology, facilitating research on high-dimensional, heterogeneous systems in nature. Here, we formally define ecological multilayer networks based on a review of previous, related approaches; illustrate their application and potential with analyses of existing data; and discuss limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers largely untapped potential to investigate ecological complexity and provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.

Pathogens compete for hosts through patterns of cross-protection conferred by immune responses to antigens. In Plasmodium falciparum malaria, the var multigene family encoding for the major blood-stage antigen PfEMP1 has evolved enormous genetic diversity through ectopic recombination and mutation. With 50–60 var genes per genome, it is unclear whether immune selection can act as a dominant force in structuring var repertoires of local populations. The combinatorial complexity of the var system remains beyond the reach of existing strain theory and previous evidence for non-random structure cannot demonstrate immune selection without comparison with neutral models. We develop two neutral models that encompass malaria epidemiology but exclude competitive interactions between parasites. These models, combined with networks of genetic similarity, reveal non-neutral strain structure in both simulated systems and an extensively sampled population in Ghana. The unique population structure we identify underlies the large transmission reservoir characteristic of highly endemic regions in Africa.

Environmental conditions, including anthropogenic disturbance, can significantly alter host and parasite communities. Yet, our current knowledge is based mainly on endoparasites, while ectoparasites remain little studied. We studied the indirect effects of anthropogenic disturbance (human population density) and climate (temperature, precipitation and elevation) on abundance of highly host-specific bat flies in four Neotropical bat species across 43 localities in Venezuela. We formulated a set of 11 a priori hypotheses that included a combination of the two effectors and host species. Statistically, each of these hypotheses was represented by a zero-inflated negative binomial mixture model, allowing us to control for excess zeros in the data. The best model was selected using Akaike's information criteria. Fly abundance was affected by anthropogenic disturbance in Artibeus planirostris, Carollia perspicillata and Pteronotus parnellii, but not Desmodus rotundus. Climate affected fly abundance in all bat species, suggesting mediation of these effects via the host or by direct effects on flies. We conclude that human disturbance may play a role in shaping bat-bat fly interactions. Different processes could determine fly abundance in the different bat species.

In their competition for hosts, parasites with antigens that are novel to the host immune system will be at a competitive advantage. The resulting frequency-dependent selection can structure parasite populations into strains of limited genetic overlap. For the causative agent of malaria, Plasmodium falciparum , the high recombination rates and associated vast diversity of its highly antigenic and multicopy var genes preclude such clear clustering in endemic regions. This undermines the definition of strains as specific, temporally persisting gene variant combinations. We use temporal multilayer networks to analyze the genetic similarity of parasites in both simulated data and in an extensively and longitudinally sampled population in Ghana. When viewed over time, populations are structured into modules (i.e., groups) of parasite genomes whose var gene combinations are more similar within than between the modules and whose persistence is much longer than that of the individual genomes that compose them. Comparison to neutral models that retain parasite population dynamics but lack competition reveals that the selection imposed by host immunity promotes the persistence of these modules. The modular structure is, in turn, associated with a slower acquisition of immunity by individual hosts. Modules thus represent dynamically generated niches in host immune space, which can be interpreted as strains. Negative frequency-dependent selection therefore shapes the organization of the var diversity into parasite genomes, leaving a persistence signature over ecological time scales. Multilayer networks extend the scope of phylodynamics analyses by allowing quantification of temporal genetic structure in organisms that generate variation via recombination or other non-bifurcating processes. A strain structure similar to the one described here should apply to other pathogens with large antigenic spaces that evolve via recombination. For malaria, the temporal modular structure should enable the formulation of tractable epidemiological models that account for parasite antigenic diversity and its influence on intervention outcomes.

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.