Multiple interactions networks in ecology

Probably, the first study of ecological networks explicitly considering different interaction types was the classic study by May (1972), in which he assembled in-teraction matrices with random coefficients from a Gaussian distributionN∼(0, σ) , thereby allowing for negative and positive pairwise interactions to be consid-ered. May’s results showed that in theoretical communities assembled randomly, complexity (measured as connectance and species richness) was inversely related to the local stability of the system. But natural communities are highly complex, diverse and, nonetheless, seem to persist. After that seminal study, there has been a flurry of studies trying to uncover the processes and structural patterns that

confer stability to empirical communities (e.g. Saint-B´eat et al. 2015). However, comparable data on different interaction types is extremely difficult to acquire, and the focus for most of the second half of the 20th century has been on how competitive and antagonistic interactions drive population and community pat-terns (e.g. Connell 1961; Paine 1966). As predator-prey interactions are easiest to observe and document in the field, the analysis of empirical networks relied almost entirely on food webs for a few decades. Pioneering works by Jordano (1987), or Fonseca and Ganade (1996), amongst others, paved the way for the study of mutualistic networks, but the first studies considering more than one interaction type in the same network only appeared in the last decade (Table 2.1).

Developing a theoretical framework for multiple interactions networks in-volves the integration of a variety of interaction types and effects, direct and indirect, taking place at different temporal and spatial scales. In this review, we propose two main criteria for classifying such frameworks. The first is the classification system applied to interactions (Abrams, 1987). Interactions can be defined based on the effect they produce on each member or, alternatively, on the mechanism by which the interaction is produced. Regarding effects, each interactor can be affected positively, negatively, or not affected at all by a pairwise interaction, regardless of the actual mechanisms by which the effect occurs. For example, a (-,-) interaction, defined as competition, could actually be realized through mechanisms as different as territorial, chemical or consumptive compe-tition (Schoener, 1983). By defining all interactions with respect to the effect on each member (0,-,+), every effect-based classification is complete, in the sense that no interaction, however idiosyncratic, is left unclassified. Regarding mechanisms, interactions are defined according to the mechanism by which they take place, regardless of the effect on the interacting species. Thus, consumptive compe-tition would be defined as an interaction in which each member is affected by consumption of a common limited set of resources, territorial competition would represent limitations on the available space for each interactor, and so on. A virtually unlimited number of interaction categories can be theoretically defined under this scheme, depending on the questions addressed.

The second criterion distinguishes network analyses based on whether the strengths of different interaction types have common units (i.e., their effects are comparable, acting upon the same population property) or not. This criterion only applies to classification schemes based on effects as, by definition, strengths of interactions acting explicitly through different mechanisms have different units, and are thus not amenable to being homogenized. For example, chemical competition between two plant species may be reflected on the mortality rates of the interacting populations, while a mutualistic interaction between a plant species and a seed disperser bird may affect the dispersal rate of the plant and the population growth rate of the bird. In an effect-based classification with the same units for every interaction, on the contrary, these and every other interaction could be taken to affect a single property (e.g. long-term population size), and hence would be comparable. Based on the two criteria proposed here, we distinguish three conceptual frameworks, already found in the literature, to construct and analyse multiple interactions networks: expanded food webs, multilayer networks, and equal footing networks (Fig. 2.1).

2.3.ExpandedFoodW

Study Framework Type of clas-sification

Type of data Main findings

Arditi are a majority in food webs, and their inclusion

Synthetic Interaction webs that in-clude trophic and non-trophic interactions are ex-pected to have a lower local richness, biomass,

N.A. N.A. Lines of research to

in-tegrate parasitic

N.A. N.A. N.A. Conceptual framework

for including non-trophic interactions in food web studies.

ultipleInteractionsNetworks

Study Framework Type of clas-sification

Type of data Main findings

Donadi signifi-cant effects on the vari-ables measured,

N.A. N.A. Integration of ecosystem engineering effects into a balance of different inter-action types increases

2.3.ExpandedFoodW

N.A. N.A. N.A. N.A. Conceptual study on the

consequences and chal-lenges of merging two sub-networks

ultipleInteractionsNetworks

Study Framework Type of clas-sification

Type of data Main findings

Sander mutual-ism do not increase com-munity robustness, but a

N.A. N.A. N.A. N.A. Framework and examples

for applying multilayer networks to ecological questions

2.3.ExpandedFoodW

Synthetic Nested structure of mu-tualist networks increases pre-dicts the amount of popu-lation variance explained are the only ones that can be arbitrarily large

ultipleInteractionsNetworks

Study Framework Type of clas-sification

Type of data Main findings

Mougi

Synthetic Moderate mixing of the three interaction types, and food web structure in hybrid communities,

Synthetic Interaction mixing per se does not stabilize model

Synthetic Stability is enhanced by mixing interaction types

Synthetic Increasing levels of plant-animal mutualistic inter-actions generally result in comparatively more stable communities

2.3.ExpandedFoodW

Synthetic A mix of unilateral inter-actions (i.e.

Synthetic Adaptive shifting of inter-action partners in hybrid

Synthetic The frequency of func-tional extinctions is higher in mixed than single-type interaction networks