Marine Non-native Species Network Analysis

Modelling the risk of the introduction and spread of non-indigenous species in the UK and Ireland

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Executive summary

Non-indigenous species (NIS) pose a major threat to global biodiversity, and incur significant economic costs. As a result it is necessary to prevent their introduction and spread. This is reflected in the requirement to reduce the impact of marine NIS under the EU Marine Strategy Framework Directive (MSFD) (descriptor 2). The ability to predict where, and by which pathway, an NIS is most likely to arrive, establish and subsequently spread, is invaluable in reducing the impact of NIS on our marine environment. This study aimed firstly to identify potential hotspots of introduction and establishment of marine NIS across the UK and Ireland for nine broad taxonomic groups. The second main aim was to investigate the potential for internal spread within the study area, using Didemnum vexillum (DV) as a case study. DV is an invasive sea squirt that can have severe environmental and economic consequences, and in recent years has colonised several locations in the UK and Ireland.
The following steps were completed to achieve the above aims:
  1. The highest risk pathways of introduction of marine NIS were identified as: commercial shipping (via ballast water or biofouling of hulls); recreational boating (biofouling); imports of live animals as stock for aquaculture; imports of live animals for the seafood trade; natural dispersal e.g. rafting.
  2. A grid was generated over the coastline of the UK and Ireland of cell size 70 km x 70 km.
  3. Data were collected on the intensity of activity for each pathway, to allow each grid cell to be scored from 0 (low activity) to 100 (very high activity) for each pathway.
  4. Weightings for each pathway were constructed, using published data, to account for their relative importance for each of nine taxonomic groups (plankton, algae, plants, worms, arthropods, crustacea, tunicates, jellyfish and molluscs), and DV.
  5. Intensity scores and weightings were combined to create a score for each grid cell for ‘likelihood of introduction’ for each of the taxa, and for non-indigenous species generally.
  6. Environmental data was used to score each grid cell for likelihood of establishment, if introduced, for each taxonomic group.
  7. Data were collected on pathways of internal spread for DV (domestic commercial shipping, domestic recreational boating, internal aquaculture stock movements and natural spread) and plotted as network diagrams.
The following main results were obtained:
  • • Three areas have a high or very high likelihood of introduction for NIS in general, and for most of the taxonomic groups studied: the Thames Estuary, Kent coast and the Solent. These areas also score highly for likelihood of establishment. Medium risk areas for introduction and establishment include Devon, Cork and Dumfries.
  • • Four of the high or medium likelihood areas for introduction of DV correlate with known locations of the species: the north Kent coast, the Solent, Devon and Carlingford, Northern Ireland. All of the current known locations of DV are in areas with a medium or high likelihood of establishment. It can be hypothesised, based on the model, that populations found along the North Kent coast, the Solent and Plymouth were initial introductions, and the other populations were established through internal spread.
  • • The network analysis highlights several areas that may be at risk of being colonised by DV by spread from current locations by ferry movements (south west coast of Scotland, Isle of Man), movements of aquaculture stock (Thames Estuary, Essex coast), recreational boating and natural dispersal aided by currents (south Kent coast)
  • • The network analysis also demonstrates how well connected locations across the UK and Ireland are by the four pathways studied.
Our main recommendations from the results of this study are:
  • Monitoring and biosecurity programmes should prioritise the areas with high likelihoods of both introduction and establishment. Such programmes may need to focus on specific taxonomic groups to optimise the probability of detection.
  • Pathways that pose the greatest risk for a given area and/or taxonomic group should be targeted in terms of monitoring and biosecurity
  • The current study should be combined with horizon scanning (such as that conducted by the GB Non-native Species Secretariat (NISS)) to determine the most likely points of introduction of these high risk species.
  • Further work to develop the work presented here should be carried out, specifically:
    • collection of additional and finer resolution datasets (e.g. for ocean currents)
    • include a user interface to the risk model and GIS map display
    • conduct more case studies to further validate the model
    • include biosecurity measures as parameters in model
    • develop network analysis to allow quantitative outputs
In conclusion, this work has made a start in creating a model that enables a geographic analysis of risk. The process highlighted gaps in data availability, but also the benefits of collaboration between the constituent countries of the UK and Ireland. Methods have been developed that can be built on in the future to reach their full potential.

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