Invasion pathways of aquatic invasive non-native species using the canal network

Overview

The UK canal network is a point of vulnerability when it comes to mitigating the spread of invasive non-native species (INNS). There are approximately 2,200 miles (3,500 km) of navigable canals and rivers throughout the United Kingdom, with the highest density present in England. High connectivity of the canal system permits the rapid spread of unwanted aquatic organisms within and between connected rivers. This can lead to the spread of INNS between river catchments, mediated by canals, an often unquantified invasion pathway.

Freshwater environments are highly dynamic and prone to invasion by non-native species. The number of INNS is often associated with high human population density and as canal systems flow through urban areas they pose a high risk in mediating the spread of INNS. INNS are considered the second greatest threat to native wildlife and they cost the UK economy up to £1.7 billion a year (Williams et al, 2010). Every year the Canal and Rivers Trust spend ~ £700,000 treating invasive plant species. However, these direct control costs are likely to represent a small proportion of the wider impact of invasive species. INNS interfere with navigation and water control within the canal network, as well as reducing water quality and habitat availability for native species.

This project aims to assess the impact of canals on the transport of INNS through bulk water movements and leisure and transport craft.
The key objectives are: 1) Determine the extent of invasion by invasive alien plant and invertebrate species across the canal network using generalised linear models to derive a suitability map; 2) Identify a suite of INNS of particular concern, specifically to canals and canalised rivers based on their published traits and extent; 3) Determine the speed of invasion through chronosequence of invasion (a space-for-time substitution, e.g. Hasselquist et al 2015), for a managed section of canal; 4) Develop a network model of the UK canal system; 5) Make predictions on the future extent and speed of invasion using the network model, validated with field data.

Methodology

This project is twofold in its approach: 1) Data gathering and manipulation to inform INNS presence and spread combined with field work which will provide training in field studies and taxonomic ID, and 2) ecological and climate modelling.

1) Data will be collected from field sites across English and Scottish canals, and connected river systems (canalised or otherwise), focussing on both aquatic invasive plants and invertebrates. In order to map the occurrence of INNS across the UK in the canal system databases such as Botanical Society of Britain and Ireland Distribution Database, National Biodiversity Network, the Joint Nature Conservation Committee River Macrophytes Database and the Centre for Hydrology and Ecology UK Checklist of freshwater species.
2) The student will thereafter focus fieldwork on a specific section of the canal network and its associated canalised rivers to collect longitudinal data on INNS presence and extent, for ground truthing and model validation;
3) The modelling component of this project will use graph-theoretic approaches to build a conceptual model of the UK canal network. A mathematical graph can be considered analogous to an electrical circuit (McRae et al, 2008). Edge weights (“current”) will be assigned according to the frequency and amplitude of water flow (data sources already identified from the EA). Locks form temporary barriers, analogous to gate capacitors. The resultant model will be a dynamic representation of the canal network that can be used predictively to simulate the spread of a novel INNS. This model will be validated with the results of the field studies and collated data. It will be possible to use the model to predict the outcome of management interventions and to plan optimal strategies for the containment and/or eradication of INNS.

Project Timeline

Year 1

Months 1-3: Literature review; training in statistical programming
Months 4-6: Assemble database of INNS in canal networks and adjacent rivers and create habitat suitability models to identify waterways at risk from INNS
Months 7-9: Field work, identifying ground truthing field site, INNS data collection, flow data
Months 10-12: Development of initial modelling framework

Year 2

Months 1-3: Analysis of water flow and boat traffic data for canals and adjacent rivers; assemble model network
Months 4-9: Seasonal Field work INNS data collection, flow data;
Months 10-12: Calibration and verification of model network via sensitivity analysis

Year 3

Months 1-3: Analysis of field data; validation of models with field results
Months 4-9: Final validation of model using field results;
Months 10-12: Predictive modelling, commencing writeup of thesis

Year 3.5

Months 1-3: Finalising analyses, Continuing writeup of thesis
Months 4-6: Finalising writeup of thesis

Training
& Skills

The student will undergo the doctoral training programme instituted by Newcastle University and the School of Natural and Environmental Sciences. They will be provided with training in statistical programming as well as relevant field techniques. The student will be joining the Modelling, Evidence, and Policy Research group which has a vibrant research culture and an emphasis on knowledge sharing and continuing development.
The student will receive training from an interdisciplinary supervisory team, particularly in some of the key NERC most wanted skills:

  • Fieldwork: the large fieldwork element of this PhD means the student will be exposed to a variety of sampling and experimental techniques in the field
  • Taxonomic Identification: to create an inventory of fauna and flora the student will receive species ID training, particularly botanical.
  • Data management: curating a large database drawn together from multiple sources (including own field work) will require specialist skills.
  • Modelling: training will be provided in the R statistical programming language on graph theory and simulation modelling, as well as statistical analysis and prediction
References & further reading

Hasselquist, et al (2015). Time for recovery of riparian plants in restored northern Swedish streams: a chronosequence study. Ecological Applications, 25(5), 1373-1389.
McRae et al 2008. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology, 89(10), 2008, pp. 2712-2724
Williams, et al. The economic cost of invasive non-native species on Great Britain. CABI Proj No VM10066 (2010): 1-199.

Further Information

Dr Mark Shirley, mark.shirley@newcastle.ac.uk

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