Predicting the unpredictable: the role of eco-evolutionary experience in species interactions across species and habitats.

Invasive alien species (IAS), range shifts, re-invasions by recovering native species and rewilding are among the major drivers of the current and future redistributions of the world's biota (e.g. Seebens et al. 2017). We thus face unprecedented challenges in our ability to predict the outcomes of new species interactions and understand their impact on biodiversity. This project will develop recent advances in predictions of species interaction outcomes in ecological and evolutionary contexts across a range of species and habitats.

The degree of eco-evolutionary experience (EEE) among interacting species is emerging as a major predictor of outcomes, such as species replacements after invasion (e.g. Penk et al. 2017). Indeed, recent advances in metrics that predict the ecological impacts of IAS are consistent with the hypothesis that EEE drives outcomes, such as dramatic decreases in native fauna due to invasive predators (Dick et al. 2017) and the impact of recovering native predators on modified ecosystems.

Here, we propose to develop a predictive framework that allows forecasting of the outcomes of species interactions under scenarios ranging from recolonisations by natives to completely novel pairs of interacting species. This will be based on combinations of metrics that show great promise in predicting outcomes of competitive and predatory interactions across taxa and trophic groups (Dick et al. 2017). In particular, combining the classical "functional response" (resource uptake rate) and numerical response (e.g. abundance, reproduction) into the Relative Impact Potential (RIP) metric has led to prediction of the degree of invasive species impacts and has promise for wider species interactions across the spectrum of species' EEE (Dick et al. 2017). Further, by incorporating various context-dependencies such as climate change and parasitism/disease into the RIP metric, further predictive potential can be achieved. We thus propose to utilise various study systems in Northern Ireland and Scotland to address this overall challenge by: (1) testing our metrics for predictive power in individual study systems, incorporating context-dependencies and reconciling small scale data with ecosystem wide impacts and (2) collating existing/new study systems data to test generality with meta-analyses. For example, recent invasions in N. Ireland by Ponto-Caspian species have resulted in novel interactions among taxa with little eco-evolutionary history, while range shifts of e.g. pine marten, goshawk and buzzard in Scotland are resulting the re-establishment of species interactions. This range of EEE provides a rich source of systems to test specific outcomes and generality of the methodology across habitats, taxonomic groups, trophic levels and predicted environmental change that provides context-dependencies.