6209742a-bfd6-4fbc-9b40-29d7d75d57b4 English ISO/IEC 8859-1 (also known as Latin 1) dataset dataset NERC EDS Environmental Information Data Centre Lancaster Environment Centre, Library Avenue, Bailrigg Lancaster LA1 4AP UK info@eidc.ac.uk https://eidc.ac.uk/ EIDC website The Environmental Information Data Centre (EIDC) is the UK's national data centre for terrestrial and freshwater sciences. information pointOfContact 2025-11-21T10:30:08 UK GEMINI 2.3 OSGB 1936 / British National Grid Simulations of Emerald Ash Borer (EAB) spread in Great Britain with optimised surveillance 2024-02-27 publication https://catalogue.ceh.ac.uk/id/6209742a-bfd6-4fbc-9b40-29d7d75d57b4 10.5285/6209742a-bfd6-4fbc-9b40-29d7d75d57b4 doi: Milne, A., Alonso Chavez, V., Brown, N., Parnell, S. (2024). Simulations of Emerald Ash Borer (EAB) spread in Great Britain with optimised surveillance. NERC EDS Environmental Information Data Centre 10.5285/6209742a-bfd6-4fbc-9b40-29d7d75d57b4 This dataset contains a model, input data and outputs of the Emerald Ash Borer (EAB; Agrilus planipennis) lifecycle and spread across Great Britain. Nine different scenarios are considered related to how certain we are that EAB will arrive through known pathways related to wood imports (70%, 50%, 30%) and the probability that EAB would escape at port rather than at the onwards depots (25%, 50%, 75%). The model outputs can be used to predict the best places to locate surveillance technologies (i.e., girdled trees or traps) and included in this dataset are optimised trap locations for 27 scenarios (three trapping types for each of the nine different scenarios). Full details about this dataset can be found at https://doi.org/10.5285/6209742a-bfd6-4fbc-9b40-29d7d75d57b4 Milne, A. Rothamsted Research alice.milne@rothamsted.ac.uk https://orcid.org/0000-0002-4509-0578 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Alonso Chavez, V. Rothamsted Research vasthi.alonso-chavez@rothamsted.ac.uk https://orcid.org/0000-0003-3506-7603 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Brown, N. Forest Research nathan.brown@forestresearch.gov.uk https://orcid.org/0000-0003-3590-0538 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Parnell, S. University of Warwick stephen.parnell@warwick.ac.uk https://orcid.org/0000-0002-3039-1159 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Milne, A. Rothamsted Research alice.milne@rothamsted.ac.uk https://orcid.org/0000-0002-4509-0578 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information pointOfContact Rothamsted Research alice.milne@rothamsted.ac.uk owner NERC EDS Environmental Information Data Centre info@eidc.ac.uk custodian NERC EDS Environmental Information Data Centre info@eidc.ac.uk publisher Environmental Monitoring Facilities theme GEMET - INSPIRE themes, version 1.0 2008-06-01 publication Agrilus planipennis Wikidata 2012-10-29 creation trade activity GEMET - Concepts, version 4.1.3 2019-01-28 creation Invasive species Great Britain simulated spread optimal sampling locations otherRestrictions no limitations otherRestrictions This resource is available under the terms of the Open Government Licence otherRestrictions If you reuse this data, you should cite: Milne, A., Alonso Chavez, V., Brown, N., Parnell, S. (2024). Simulations of Emerald Ash Borer (EAB) spread in Great Britain with optimised surveillance. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/6209742a-bfd6-4fbc-9b40-29d7d75d57b4 textTable 1000 English utf8 environment -8.648 1.768 49.864 60.861 Comma-separated values (CSV) Text Header file (.h) CPP NERC EDS Environmental Information Data Centre info@eidc.ac.uk distributor https://catalogue.ceh.ac.uk/datastore/eidchub/6209742a-bfd6-4fbc-9b40-29d7d75d57b4 Download the data Download a copy of this data fileAccess https://data-package.ceh.ac.uk/sd/6209742a-bfd6-4fbc-9b40-29d7d75d57b4.zip Supporting information Supporting information available to assist in re-use of this dataset information dataset dataset Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 The model requires the entry point to be initialized. We developed maps to identify the most likely first incursion point at locations across Great Britain on a 1km x 1km grid. These maps were informed by data from the forestry commission on likely entry points, and data on firewood use. When creating these maps, we considered nine scenarios related to how certain we are that EAB will arrive through known pathways related to wood imports (70%, 50%, 30%) and the probability that EAB would escape at port rather than at the onwards depots (25%, 50%, 75%). For each scenario we sampled 10000 realizations for likely entry points. These form the inputs to our model. The model is set up to run for annual time steps for 8 years. For each simulated year the larval density and tree health is recorded in infested cells and reported in output files. The model is currently set up to do this for up to 10000 realizations of entry point. The data in the output file can then be used to optimize surveillance. We did this using simulated annealing for three trapping types (Girdled, sticky straps and under bark sampling) in the nine different scenarios. The model code was developed in Visual Studio, but plain text files have been provided so that the code can be compiled and run outside of Visual Studio. The model code was checked for errors and the outputs were validated, as far as possible, against spread characteristics from the US.