Blanca Arroyo-Correa discusses her recent article: Individual-based plant–pollinator networks are structured by phenotypic and microsite plant traits. Find out more about what this research reveals about the drivers underlying the context dependency of plant–pollinator interactions.
The establishment of mutualistic interactions is influenced by the abiotic and biotic context in which they take place and therefore it is highly dynamic over space. Individual-based networks can be used to unveil processes at the population level and to explicitly assess the role of intraspecific trait variation in structuring ecological interactions. Within a population of an animal-pollinated plant species, such networks, technically bipartite networks, illustrate pollinator species connected to the individual plants they visit. An additional, useful way to visualize these interactions is to represent the unipartite projection of the network (i.e., with individual plants outlined as nodes and the links connecting those nodes indicating the level of pollinator sharing). By downscaling from plant communities to populations we can better predict the functional consequences of network configuration, as the pattern of shared pollinators among individual plants may be translated into potential mating events, helping us to link conceptually with the analysis of demographic and evolutionary effects.
When all other Mediterranean shrub species have finished flowering in the stabilized sand dunes of Doñana National Park in Southern Spain, Halimium halimifolium (Cistaceae) blooms.
Our previous naturalistic observations of this generalist insect-pollinated species, which often dominates the vegetation cover in this natural area, pointed out that individuals are very different in their phenotypic traits but also in the microsite in which they occur. The characteristics of this system led us to pose some interesting questions: how does this inter-individual variation in plant traits affect pollinator visitation patterns? How does it upscale to affect the overall configuration of bipartite and unipartite pollination networks? How do these influences of plant traits ultimately translate into functional outcomes for plants?
To characterize individual plants’ pollinator assemblages, we processed 120 hours of video recordings within a well-defined population over its flowering season. In addition, we performed drone flights to estimate inter-individual plant variation in phenotypic and microsite traits. A class of statistical models that has been widely applied in social sciences helped us to answer our questions. Exponential random graph models (ERGMs) allow us to identify the underlying mechanisms that explain the whole structure of a given network. We modelled the probability of our individual-based pollination networks as a function of terms that represent the network (e.g., link density), node (e.g., plant traits), or link features (e.g., spatial distance between plants). ERGMs are useful to account for the fact that ecological interactions’ probability can be dependent on the distribution of all the interactions within the network in addition to the effects of individual or species traits. Adapting this modelling approach to bipartite ecological networks was indeed the most challenging task of this study, as ERGMs are currently only well implemented for unipartite networks.
Interestingly, we found comparable effects of phenotypic and microsite plant traits on configuring both the plant-pollinator bipartite network and the plant-plant unipartite network derived from pollinator sharing. Furthermore, the position of individual plants within the network, together with the direct effects of plant traits, had important consequences for intrapopulation variation in plant fitness (estimated as fruit and seed production).
Overall, by combining individual-based pollination networks and ERGMs, our newly published study contributes to disentangling how inter-individual variation in plant traits predictably shapes mutualistic networks and shifts their functional outcomes. With this novel and flexible framework, we can move forward ecological network analyses from descriptive metrics to more powerful forecasting approaches. Our ability to infer and predict ecological interactions would be vastly improved with this kind of analytical tools, as we will better understand the evolutionary and ecological drivers of network structure.
Blanca Arroyo-Correa Estación Biológica de Doñana, Seville, Spain
Read the full article online: Individual-based plant–pollinator networks are structured by phenotypic and microsite plant traits