IAP2-22-427

Building a biological invasion risk network for UK Caribbean Overseas Territories

Biological invasions pose a continuing threat to the conservation of the unique biodiversity and ecosystems of the Caribbean Overseas Territories (OTs), which are islands and archipelagos [1]. These islands are linked to each other, to other islands and to continents via air and sea transport networks. Transport links are the source of current and future introductions of invasive species [2], and need to be quantified and understood, so that we can better predict invasion risk and preventing introductions of high-risk species in the future [3]. This PhD project aims to quantify the risk of biological invasions to the Caribbean OTs from the pools of species represented across the trade and transport network of the islands. The project provides a unique opportunity to combine network and distribution modelling, overseas fieldwork and experimental approaches that will yield results directly applicable to Caribbean OT biosecurity measures.

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Image Captions

A native plant community on the island of Anguilla; invasive non-native plants and animals pose a threat to the biodiversity of Anguilla and other Caribbean Overseas Territories. Pre-border and at-biorder biosecurity are crucial to prevent future invasions. Biosecurity works best when we have a clear understanding of invasion risk of different species associated with different pathways of introduction (Credit: Wayne Dawson).

Methodology

The project has four main objectives:

1) Build an invasion risk network for the Caribbean OTs, identify the most high-risk sources and pathways of invasive species. To achieve this objective, the PhD student will first create a dataset describing the transport links centred on the OTs and their magnitude, from open access sources and from the OT authorities themselves. This dataset will then be used to create a risk map, identifying the links and source regions posing the highest risk of species arrival, and then establishment (based on climate suitability) and invasion (based on known native and introduced ranges of high-impact invasive species globally) [4]. The predictions from this risk map will also be compared to known established and invasive non-native species on the OTs for validation (as in [2]), and to data collected on OT biosecurity capacity.

2) Collect interceptions data for at least two selected Caribbean OTs, to establish which types of INNS and which origins and pathways of introduction pose the highest biological invasion risk. This objective will involve visiting the OTs and working with local biosecurity authorities to identify and catalogue organisms that arrive at major ports of entry. These data will then form the basis of an interceptions database for the OTs which we will use to model risk based on trade, trait and interceptions data [5; 6]. We will also validate recently constructed lists of species considered to pose a high risk of imminent invasion to the OTs [3], and quantitative risk scores based on the CABI Horizon Scanning Tool.

3) Assess the ability of species distribution modelling methods to predict climatic suitability of invasive species in the Caribbean OTs. The islands and archipelagos are small in area, and this may limit reliable prediction of climate suitability for introduced species [7]. The student will assess the scale of this limitation by applying species distribution models to species already present and established on the OTs, providing an opportunity to validate and optimise methods to be applied to species not yet introduced. The student will also explore the role of other data layers in explaining the distribution of non-native species.

4) Experimentally assess the survivorship and performance of species intercepted and already present in the selected OTs. This objective will focus on plants that are i) intercepted as seed contaminants, in imported goods, transport and on people/luggage and ii) already introduced but not invasive in the exemplar OTs. Attempts to germinate and grow these seeds of these species will be carried out in climatic and light conditions simulating those found in the OTs, using climate growth chambers at Durham. For species that successful germinate and grow, their competitive ability versus candidate native plants will also be assessed under current and near-future climates, as a measure of potential impact on native vegetation.

Project Timeline

Year 1

Oct-Jan: Literature Review, establish structure of transport network dataset to be constructed, identify data sources. Attend training course on modelling/network analysis.

Jan-June: Obtain transport data and construct transport network. Align network dataset with existing data-sets on sources (ranges) of known high-impact invasive species, and ranges of species known to be established/invasive on OTs. Conduct species distribution modelling for species already established on Caribbean OTs, and for species likely to arrive through transport links.

June-Oct: Analyse transport network data-set and SDM outputs to achieve Objectives 1 and 2.

Year 2

Oct-Jan: Write up chapter/paper from network analysis; plan for field visits to 2 OTs this year.

Jan-June: Field Visits in Caribbean, collation and collection of interceptions data. Establish local protocols/connections with biosecurity officials to secure data collection longer term.

June-Oct: Analysis of interceptions data and comparison with horizon-scanning high-risk lists of species.

Year 3

Oct-Jan: Write up chapter/paper of interceptions analysis. Present project findings at national conference. Planning for simulated climate experiment on intercepted plant species.

Jan-June: Conduct simulated climate germination trials and growth/competition experiment.

June-Oct: analysis of data collected from simulated climate experiments.

Year 3.5

Oct-Mar: Write up chapter/paper from simulated climate experiment. Present findings at international conference. Project Placement at University of Cordoba, to build Shiny app.

Training
& Skills

The PhD student will obtain skills in data science, building databases, advanced statistical and distribution modelling, and analyses of networks, as well as in the design and execution of seed germination and plant growth experiments. The applied nature of the project objectives will also ensure the PhD student gains experience and training in biosecurity, and in taxonomic identification of a wide range of taxa. The PhD project will involve considerable collaboration with authorities in the OTs, which will enhance the student’s communication skills. The student will gain experience in stakeholder engagement, including engaging with policy, and expert elicitation approaches. Other transferrable skills obtained will include project management and presentation skills. Moreover, the student will gain key skills and experience in producing a Shiny app during their placement (University of Cordoba) that visualises the invasion risks identified by the project. The project will provide the PhD student with excellent skills-set for a career in biological invasion science, applied ecology, biosecurity or policy.

References & further reading

[1] Varnham K (2006) Non-native Species in UK Overseas Territories: A Review. JNCC, https://data.jncc.gov.uk/data/bdb47e73-aa8b-4958-8658-b2e7f758e5bb/JNCC-Report-372-FINAL-WEB.pdf

[2] Seebens H. et al. (2015) Global trade will accelerate plant invasion in emerging economies under climate change. Global Change Biology 21: 4128-4140. https://doi.org/10.1111/gcb.13021

[3] Roy H.E. et al. (2019). Prioritising Invasive Non-Native Species through Horizon Scanning on the UK Overseas Territories. Technical Report, UKCEH. doi: 10.13140/RG.2.2.18951.34726

[4] Chapman D. et al. (2017) Global trade networks determine the distribution of invasive non-native species. Global Ecology and Biogeography 26: 907-917. https://doi.org/10.1111/geb.12599

[5] Maclachlan M. et al. (2021) Hidden patterns of insect establishment risk revealed from two centuries of alien species discoveries. Science Advances 7: eabj1012. https://doi.org/10.1126/sciadv.abj1012

[6] Early R. et al. (2018) Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. NeoBiota 40: 25-50. https://doi.org/10.3897/neobiota.40.28165

[7] Chapman D. et al. (2019) Improving species distribution models for invasive non-native species with biologically informed pseudo-absence selection. Journal of Biogeography 46: 1209-1040. https://doi.org/10.1111/jbi.13555

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