Cost-effective portfolio allocation across quarantine, surveillance and eradication using info-gap theory
Data files
Jul 30, 2024 version files 78.93 KB
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Figure_2.m
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Figure_3.m
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Figure_4.m
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matlab_totalcost.xlsx
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README.md
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Abstract
Biosecurity activities primarily include pre-border and border quarantine, post-border surveillance and post-border eradication. Budget allocated to quarantine and surveillance activities ultimately influence the expenditure and success rate of eradication campaigns. Optimal portfolio allocation examined in previous research is susceptible to potential severe uncertainties existing in ecology and in the behaviour of invasive species itself. These uncertainties, together with a limited budget, make it difficult for decision makers to allocate the total management budget to each biosecurity activity in a robust manner.
Info-gap decision theory is applied to model the severe uncertainty in invasive species management, and robust optimize the total management cost.
This research shows that using a combination of pre-border and border quarantine (to reduce the incursion probability) and post-border surveillance (to enable early detection and rapid response), enables decision makers to be more robust to potential uncertainty.
Further, it is reported that investment in quarantine that is more cost-effective should outweigh that in surveillance, in line with precautionary principle.
Increasing the estimated population threshold for surveillance detection also gains more robustness.
Synthesis and applications: Portfolio allocation options developed in this research provide decision makers with a way to manage the invasive species spatially, cost-effectively, and confidently by allocating the total management budget in a robust manner. The methods outlined in this research can not only be applied to invasive species, but also the conservation of endangered species that are constrained by severe uncertainty in ecological modelling and limited resources.
Cost-effective portfolio allocation across quarantine, surveillance and eradication using info-gap theory
We have submitted our raw data (matlab+totalcost.xlsx), MATLAB script (Figure 2.m, Figure 3.m, Figure 4.m).
Description of the Data and file structure
In the excel file ‘matlab+totalcost.xlsx’:
Surface Info: The potential entry points for an Asian house gecko (AHG) incursion on Barrow Island (BWI), Western Australia. Zone 0 is the buffer area at Material Offloading Facility (MOF) (i.e. X-Blocs area). Zone 1 is the area with the highest occupancy probability, where the majority of the surveillance budget should be spent. Zone 2 is the secondary introduction area (100 m buffer area around Zone 1) and is considered the lower risk boundary for a species dispersing out of Zone 1. Zone 3 is the remaining island area where the AHG is less likely to establish prior to detection thus with no SSCs allocated. POF is Permanent Operating Facility; GTP is Gorgon LNG Plant Gorgon Liquefied Natural Gas Plant.
Entry Info: Incursion probability to each location on Barrow Island, Western Australia.
Survival Info: Survival probability in each zone (Z0, Z1, Z2) at each location on Barrow Island, Western Australia
SSC Info: Surveillance System Component (SSC) (i.e. a range of methods used to detect a potential AHG) are used for surveillance detection. This excel illustrates the sigma (𝜎), footprint and unit cost of various SSCs at different locations and zones on Barrow Island, Western Australia. Sigma is the detection probability of SSCs given invasive species present in the footprint. Footprint is the area in which an AHG can be detected with a single unit of SSC. Cost is per unit of SSCs.
Quarantine: Quarantine components units being used by Chevron, cost of each quarantine component, and total cost of all quarantine components. Definitions of the SSCs in this sheet:
(1) Mobe-mote: The Mobe-mote is an adhesive siliconised paper that has been manufactured as an aid to trapping Hemidactylus frenatus around problem, high-risk areas.
(2) Go gecko - Spray: A repellant designed to combat geckos wherever they hide.
(3) Go gecko - Tech: A suite of advanced biosecurity tools designed to detect and monitor non-indigenous species.
(4) EARs (net-worked): Environmental Acoustic Recognition Sensors (EARS). A networked device that can detect the multiple chirp calls of Hemidactylus frenatus. This device is powered by solar panels and provides notifications to an end user via a user interface when a suspect call is detected. One SSC equals 1 EAR recording for 1 night
(5) S.survey: Structured surveys. A formal biological survey of an area looking for signs of nonindigenous vertebrate or invertebrate species. Signs may include tracks, scats, auditory calls, burrows, eye shine, eggs or individuals. One SSC is approximately 5,000 m2 area.
(6) TEU(vessel&Freight): A Twenty-foot Equivalent Unit (TEU) is a standard measure for cargo container capacity, used to quantify and manage shipping and freight logistics.
Code file ‘Figure 2’: This script was used to generate Fig 2 in the manuscript, indicating the relationship between robustness and total budget limit, with the optimal portfolio allocation between quarantine and surveillance applied. This part also presented the effects of altering the estimated population threshold on the robustness.
Code files ‘Figure 3’/’Figure 4’: This script was used to generate Fig 3/Fig 4 in the manuscript. This part was to evaluate the robustness of various portfolio allocation options, with estimated population threshold set at 8 (Fig 3) and 20 (Fig 4) respectively.
Key Information Sources
Input data in excel file ‘matlab+totalcost.xlsx’ was provided by Chevron Australia Pty Ltd.
Code/Software
MATLAB is required to run Figure 2.m, Figure 3.m, Figure 4.m; the script was created using version R2018b (MathWorks 2018).
Input data, provided by Chevron Australia, can be found in excel file 'matlab+totalcost.xlsx'.
Code file 'Figure 2' is used to generate Fig 2 in the manuscript, indicating the relationship between robustness and total budget limit, with the optimal portfolio allocation between quarantine and surveillance applied. This part also presented the effects of altering the estimated population threshold on the robustness.
Code files 'Figure 3' and 'Figure 4' are used to generate Fig 3 and Fig 4 in the manuscript. This part was to evaluate the robustness of various portfolio allocation options, with estimated population threshold set at 8 and 20 respectively.
All the exploratory analyses of robustness function in the manuscript have been conducted in MATLAB R2018b (MathWorks 2018).