Upscaling Invasion Impact Prediction

This fellowship proposal plans to increase our capacity to predict the ways and magnitude in which non-native invasive species will cause damaging ecological impact and under what environmental scenarios. This will drastically enhance the efficiency of conservation interventions and proactive policy making to benefit biodiversity and human livelihoods. Biodiversity loss is occurring rapidly in all ecosystems across the globe. Freshwater systems have the highest extinction rates in any system and are losing populations at an alarming rate. This is due to the interacting effects of climate change, non-native invasive species, and habitat loss. Around 10% of established non-native species populations are thought to cause negative impact, however, this changes under different climatic scenarios as environmental change can trigger benign populations to become damaging. Due to the speed of change it is not practical to wait until a species is present and causing damage in the environment. Pre-emptive action will vastly benefit global biodiversity goals and economies which lose US$162.7 billion annually due to biological invasions. These costs are incurred through damage to infrastructure, flooding, loss of ecosystem services, damage to fisheries, as well as management costs undertaken to control species of concern and mitigate damage. To act pre-emptively to reduce these costs, we need accurate methods to predict which species will cause what negative impact, and under which environmental scenarios. Current methods are insufficient as they do not account for the complexity of natural environments nor the rate of global change. We need to use interdisciplinary approaches to unravel and predict the interacting twin threats of non-native invasive species and climate change at a global scale. Contemporary methods have not yet resolved this level of complexity which makes cohesive management action impossible. Food web interactions determine the structure and composition of biological communities. If interactions in the food web change, this can drastically alter the functioning of the ecosystem and have knock on effects for all levels of biodiversity, as well as human livelihoods. Food web interactions can be measured in a standardised manner across all species. I will use interaction strength as a currency to track change across communities over space and time. Foraging efficiency varies depending on physiology and morphology of both consumer and resource, and the outcomes are governed by of responses of both to environmental characteristics. Multi-disciplinary methods will be paired and tested in the laboratory and then upscaled to field campaigns under natural conditions. This will generate ecologically relevant data which will be used to develop a hypothesis testing model which will be used to predict the conditions in which an invasive species will cause negative ecological impact on native biodiversity regardless of invasion location or time since invasion. This will allow us to find general rules which control invasion dynamics and ecological impact regardless of climate and time, to be able to predict ecological outcome in advance and "future proof" legislation. I am an aquatic ecologist who has developed and pioneered multi-disciplinary impact prediction methodology. The current project builds on my previous innovations and leverages my international partnerships. My vision is that the capacity of my partnerships are built up to function as an International Freshwater Invasions Network to tackle future pressing challenges which can only be solved through global cooperation. This fellowship will provide the foundation for the formation of this network and contribute to guiding more effective legislation and targeted invasive species management approaches which account for global climate change. In doing so, the outcomes of this research will directly combat rapid biodiversity loss in freshwater ecosystems.