Milne, A.; Alonso Chavez, V.; Brown, N. ; Parnell, S.
Simulations of Emerald Ash Borer (EAB) spread in Great Britain with optimised surveillance
Cite this dataset as:
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
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This dataset is available under the terms of the Open Government Licence
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wget --user=YOUR_USERNAME --password=YOUR_PASSWORD --auth-no-challenge https://catalogue.ceh.ac.uk/datastore/eidchub/6209742a-bfd6-4fbc-9b40-29d7d75d57b4
https://doi.org/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).
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).
Publication date: 2024-02-27
View numbers valid from 27 February 2024 Download numbers valid from 20 June 2024 (information prior to this was not collected)
Formats
CPP, Comma-separated values (CSV), Text file (.txt), Header file (.h)
Spatial information
Study area
Spatial representation type
Tabular (text)
Spatial reference system
OSGB 1936 / British National Grid
Provenance & quality
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.
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.
Licensing and constraints
This dataset is available under the terms of the Open Government Licence
Cite this dataset as:
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
Correspondence/contact details
Authors
Other contacts
Rights holder
Rothamsted Research
Custodian
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
Publisher
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
Additional metadata
Keywords
Funding
Natural Environment Research Council Award: NE/T007729/1
Last updated
05 November 2024 12:24