Daily and seasonal dynamics of ecological interaction networks: temporal structure and its functional consequences

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Since most species are involved in complex networks of interactions, network analysis promises to allow a more complete understanding of how nature works. Species interaction networks are known to be highly variable over time. However, most theory and analysis assumes static network structure, and the influence of sampling effects is not well understood. Therefore, the drivers of temporal changes and the functional consequences for the interacting populations and for biodiversity and stability are largely unknown.In this project, I aim to tackle these challenges by integrating empirical and theoretical approaches through a conceptual framework. This framework builds on the idea that (temporal) variation in quantitative networks results from different types of consumer responses to resource availability. Integration between theoretical models and empirical data is facilitated by using the same explicit timescales and by accounting for sampling effects in comparisons. I will develop consistent concepts, theory and analytical techniques for understanding the dynamics and functional consequences of temporal structure in species interaction networks on multiple timescales (within a day, within a year and among years).I will apply this new framework for plant-pollinator data, describing network dynamics on multiple temporal scales and testing how far quantitative variation in interaction patterns and species frequencies can be explained by consumer-resource processes. I will build models of indirect interactions among species with temporal asynchrony and theoretically evaluate short- and long-term effects in networks with both mutualistic and antagonistic consumers. A specific model will focus on the daily temporal dynamics of plants linked by pollinators and the implications for plant species coexistence. This model will be evaluated with a finely resolved description of an empirical pollination network.This project will help develop and synthesize the temporal dimension of ecology and contribute to making network ecology more quantitatively predictive, which is ultimately needed for informed decisions in conservation and ecosystem service management.