Model simulation and Expected Value of Information code
Dates
Publication Date
2016-06-01
Start Date
2011-08
End Date
2012-09
Citation
Max Post van der Burg, 20160601, Model simulation and Expected Value of Information code: , http://dx.doi.org/10.3996/032015-JFWM-023.
Summary
This code was used in a simulated decision analysis project designed to evaluate the value of different kinds of information with regard to making optimal investments in invasive plant control programs. The code was developed in the R programming environment. The file "sim_code.R" contains the initialization of the parameters and analysis; the file "pop_sim.ccp" is a C++ program that executes the actual simulation and returns the results to R. We developed a hypothetical scenario in which a manager is tasked with control of invasive plants on 100 management units each 100 ha in size. 90 of these units were assumed to be under private management and 10 were assumed to be conservation units (i.e. under public management). For this problem [...]
Summary
This code was used in a simulated decision analysis project designed to evaluate the value of different kinds of information with regard to making optimal investments in invasive plant control programs. The code was developed in the R programming environment. The file "sim_code.R" contains the initialization of the parameters and analysis; the file "pop_sim.ccp" is a C++ program that executes the actual simulation and returns the results to R. We developed a hypothetical scenario in which a manager is tasked with control of invasive plants on 100 management units each 100 ha in size. 90 of these units were assumed to be under private management and 10 were assumed to be conservation units (i.e. under public management). For this problem we assumed that there were only two target species to control: leafy spurge (Euphorbia esula) and yellow toadflax (Linaria vulgaris). These two species were identified of examples of species that are controlled within the LCC geography. They are also species for which there is some literature to help parameterize a model. We should note that this model is gross generalization and was not intended to provide insight into invasive species biology. Because this model is stochastic, we measured this objective as the number of years out of 50 that at least 50% of the units were uninfested. We arbitrarily chose the target of 50%, which could have been any value. However, we felt that because these species are so difficult to control, target of 50% was fairly attainable. The dynamics of the model assume that management units can either be infested with one of the species, both of them or none at all. We further assumed that once a unit was infested, the species immediately achieved some average density and began producing propagules. These propagules had to disperse some arbitrary distance before they could infest another unit. Once those seeds landed in an uninfested unit, there was some probability they would lead to an infestation. To describe infestation dynamics, we developed a model that was composed of eight states to describe the management units. The model simulates the dynamics of infestation and control and then produces an Expected Value of Information analysis that shows the improvement in performance given resolution of each uncertain parameter in the model.
Click on title to download individual files attached to this item.
Simulation model.xml Original FGDC Metadata
View
26.31 KB
application/fgdc+xml
Sim_code.R
14.18 KB
text/x-rsrc
pop_sim.cpp
5.44 KB
text/x-c
Purpose
To evaluate the Epected Value of Information for resolving uncertainty in each model parameter. This analysis was intended to help prioritize which information would be most beneficial in improving invasive species management decisions.
Communities
National and Regional Climate Adaptation Science Centers
