Summary1. Significant advances in both mathematical and molecular approaches in ecology offer unprecedented opportunities to describe and understand ecosystem functioning. Ecological networks describe interactions between species, the underlying structure of communities and the function and stability of ecosystems. They provide the ability to assess the robustness of complex ecological communities to species loss, as well as a novel way of guiding restoration. However, empirically quantifying the interactions between entire communities remains a significant challenge. 2. Concomitantly, advances in DNA sequencing technologies are resolving previously intractable questions in functional and taxonomic biodiversity and provide enormous potential to determine hitherto difficult to observe species interactions. Combining DNA metabarcoding approaches with ecological network analysis presents important new opportunities for understanding large-scale ecological and evolutionary processes, as well as providing powerful tools for building ecosystems that are resilient to environmental change. 3. We propose a novel 'nested tagging' metabarcoding approach for the rapid construction of large, phylogenetically structured species-interaction networks. Taking tree-insect-parasitoid ecological networks as an illustration, we show how measures of network robustness, constructed using DNA metabarcoding, can be used to determine the consequences of tree species loss within forests, and forest habitat loss within wider landscapes. By determining which species and habitats are important to network integrity, we propose new directions for forest management. 4. Merging metabarcoding with ecological network analysis provides a revolutionary opportunity to construct some of the largest, phylogenetically structured species-interaction networks to date, providing new ways to: (i) monitor biodiversity and ecosystem functioning; (ii) assess the robustness of interacting communities to species loss; and (iii) build ecosystems that are more resilient to environmental change.

Summary 1.Biological invasions have an anthropogenic origin, and although many species are able to spread on their own within the newly invaded area, long-distance dispersal events shown to accelerate rates of spread are frequently associated with human activities. In a previous study, the performances of several invasion models of the spread of the horse chestnut leafminer Cameraria ohridella in Germany were compared, demonstrating that the best model in qualitative and quantitative terms was a stratified dispersal model taking into account the effect of human population density on the probability of long-distance dispersal events. 2. Similar data were collected in France over 4 years (2000 -2004, 5274 observation points). These data were used to assess the performance of the best-fit models from Germany using the original parameters and to model the spread of the leafminer in France. 3. The stratified dispersal model accounting for variations in human population density developed in Germany, predicted the invasion of France with a similar level of predictive power as in the area where it was developed. This suggests that an equivalent level of predictability can be expected in a newly invaded country with similar environmental conditions. 4. We applied the model to forecast the future invasion dynamics in the UK from 2005 to 2008, based on the first observations of Cameraria in the country in [2002][2003][2004]. Predictions are discussed in the light of different prevailing environmental conditions. 5. Synthesis and application . The model and predictions developed in this study provide one of the few examples of an a priori model of invasion in a newly invaded country, and provide a simple modelling framework that can be used to explore the spread of other invading organisms. In the case of Cameraria , little can be done to prevent or slow its spread but our model, by predicting changes in distribution and rates of spread, provides fore-warning of where and when damaging pest populations are likely to appear.

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Determining the host-parasitoid interactions and parasitism rates for invasive species entering novel environments is an important first step in assessing potential routes for biocontrol and integrated pest management. Conventional insect rearing techniques followed by taxonomic identification are widely used to obtain such data, but this can be time-consuming and prone to biases. Here, we present a next-generation sequencing approach for use in ecological studies which allows for individual-level metadata tracking of large numbers of invertebrate samples through the use of hierarchically organised molecular identification tags. We demonstrate its utility using a sample data set examining both species identity and levels of parasitism in late larval stages of the oak processionary moth (Thaumetopoea processionea-Linn. 1758), an invasive species recently established in the United Kingdom. Overall, we find that there are two main species exploiting the late larval stages of oak processionary moth in the United Kingdom with the main parasitoid (Carcelia iliaca-Ratzeburg, 1840) parasitising 45.7% of caterpillars, while a rare secondary parasitoid (Compsilura concinnata-Meigen, 1824) was also detected in 0.4% of caterpillars. Using this approach on all life stages of the oak processionary moth may demonstrate additional parasitoid diversity. We discuss the wider potential of nested tagging DNA metabarcoding for constructing large, highly resolved species interaction networks.

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