Invasive Non-Native Species (INNS) are considered a major driver of biodiversity loss. There is increasing empirical evidence of the direct and indirect effects of INNS on biodiversity but the scale of such studies is often limited. We propose to extend invasion models to encompass ecological networks and explore the hypothesis that INNS weaken the strength of interactions within ecological networks with negative consequences for ecosystem function. Most studies on the impacts of invasions focus on the impacts of one species on another and are limited to the populationâ€level, thereby avoiding the complexities of a community approach whereby assemblages of species would be included. Oversimplified studies could result in misleading conclusions because of the importance of positive and negative feedback mechanisms, for example those mediated by parasites, throughout communities and consequently resultant effects on the functions they deliver. There is considerable scope to improve understanding by taking a network approach to unravelling impacts of INNS and so enhance predictions of impact and community resilience so informing decision-making tools such as risk assessments.
Using modelling approaches to explore ecological networks the student will address questions including:
In what ways do the strength of interactions change within ecological networks including INNS?
How do such changes effect ecosystem function?
What is the relevance to ecosystem services approaches and Nature’s Contributions to People (IPBES)?
This project is particularly timely because of the recent launch of the IPBES assessment on Invasive Alien Species and their Management.
The student will work alongside ecologists with expertise in compiling and analysing large datasets within the Centre for Ecology & Hydrology and the University of Stirling. The Non-Native Species Secretariat will provide policy and communication context. The student will have access to large-scale and long-term datasets on invasive non-native species alongside long-term monitoring time series from across the globe for terrestrial and freshwater systems and other relevant data layers (including climate and land-use) to explore interactions among drivers of change. The student will build a database of ecological interactions using life-history traits and occurrence datasets to underpin analysis on ecological networks to explore effects of invasive non-native species on ecosystem function and resilience.
The studentship, although within the Biodiversity Science Area, will span other science areas including Hydro-Climate Risks. This project will develop and apply analytical techniques to address NERC goals of understanding and predicting how our planet works and managing our environment responsibly.
Months 1-2: Review literature on effects of invasive non-native species on foodwebs, ecological networks and ecosystem function. PhD training including training in occupancy modelling and hierarchical mixed-effects models
Months 2-4: Select species (native and non-native within community assemblages) and systems for case study analysis (working with case partner and consulting with other relevant stakeholders), develop conceptual models and hypotheses for these case studies
Months 3-8: Construct database of interactions for selected case studies, Consider knowledge gaps and approaches to address them
Months 8-12: Fill gaps, test modelling approaches
Within first year: Public Engagement Training
Publish database of interactions
Implement approaches to address knowledge gaps
Develop models to explore changes to interaction strength within ecological networks including invasive non-native species
Placement with Non-Native Species Secretariat to consider approaches for embedding model outputs in dissemination materials and risk assessment protocols
Review developments within context of invasive non-native species and ecosystem function including Nature’s Contributions to People
Publish models and selected case studies
Propose a review manuscript for Trends in Ecology and Evolution on invasive non-native species and ecological networks
Develop models to encompass ecosystem resilience and consider context of Nature’s Contributions to People
Develop framework that can inform risk assessments based on model outputs
Consult with Non-Native Species Secretariat on evidence-based approach to incorporating ecological network analysis within risk assessment frameworks
Publish review manuscript on invasive non-native species and ecological networks
Students will receive training in a breadth of skills including: citizen science, public engagement, use of computing clusters and manipulation of large-scale and long-term datasets using R, occupancy modelling, hierarchical mixed effects models, environmental change impacts, species monitoring, ecosystem function, comparative ecology, ecology of invasive alien species, deriving evidence to inform policy (particularly through risk assessment). The student will link to policy through ongoing projects within the Population Ecology Group.
References & further reading
Roy, H. E., Rabitsch, W., Scalera, R., Stewart, A., Gallardo, B., Genovesi, P., … & Branquart, E. (2018). Developing a framework of minimum standards for the risk assessment of alien species. Journal of applied ecology, 55(2), 526-538.
Chapman, D., Purse, B. V., Roy, H. E., & Bullock, J. M. (2017). Global trade networks determine the distribution of invasive nonâ€native species. Global Ecology and Biogeography, 26(8), 907-917.
Roy, H. E., Hesketh, H., Purse, B. V., Eilenberg, J., Santini, A., Scalera, R., … & Beckmann, K. M. (2017). Alien pathogens on the horizon: Opportunities for predicting their threat to wildlife. Conservation Letters, 10(4), 477-484.
Golding, N., & Purse, B. V. (2016). Fast and flexible Bayesian species distribution modelling using Gaussian processes. Methods in Ecology and Evolution, 7(5), 598-608.
Roy, H. E., & Lawson Handley, L. J. (2012). Networking: a community approach to invaders and their parasites. Functional Ecology, 26(6), 1238-1248.
Professor Helen Roy MBE
Centre for Ecology & Hydrology