Dr. Yvonne Buckley talks to the North Queensland Register about the development of a set of ‘simple rules’ helping both manage pests and protect endangered species, as well as managing biosecurity and disease outbreaks.
These research findings are from Chades et al. (2011).
Chades, I., Martin, T.G., Nicol, S., Burgman, M.A., Possingham, H.P. & Buckley, Y.M. General rules for managing and surveying networks of pests, diseases, and endangered species. Proc. Natl. Acad. Sci. USA. 2011 0: 1016846108v1-201016846
Link to journal article
Author: Dr. Iadine Chades
Efficiently managing diseases, pests or threatened species over time and across space is a difficult challenge. Pests and diseases can reinfect or reinvade previously managed locations, while threatened species may disappear from previously occupied areas. Managers need to know where to invest their limited resources so that they get the biggest bang for their buck. Being efficient in managing also means accounting for our inability to perfectly detect the presence of a disease or a species. The research answers the key questions: Have we managed long enough to ensure the pest or disease is eradicated? Or have we stopped too soon to protect a threatened population?
Dr Iadine Chades from CSIRO Ecosystem Sciences and colleagues, including Dr. Yvonne Buckley who jointly works with CSIRO and The University of Queensland, have provided a decision tool to answer these questions. Using typical network patterns (lines, stars, islands and clusters), they derived simple and robust management rules that outperform traditional outside-in management strategies by up to 30%. The rules take into account management success, dispersal, economic cost, and imperfect detection and offer decision-makers a practical basis for managing networks relevant to many significant environmental, biosecurity, and human health issues.
The following youtube video is an updated GUI for optimising management of invasive species on a network under perfect detection.
Dr. Iadine Chades can now design directed network as well as undirected network. The fast optimising algorithm she is using is SPUDD with ADD.
More information about SPUDD:
Dr. Iadine Chades is pleased to announce the release of the Markov Decision Process (MDP) toolbox V3 (MATLAB).
“If you are interested in solving optimization problem using stochastic dynamic programming, have a look at this toolbox. Thanks to M-J Cros, the toolbox is now available via file exchange (MATLAB website) and includes a reinforcement leaning method (Q-learning). Please feel free to give us feedback (email@example.com).”
“The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: backwards induction, value iteration, policy iteration, linear programming algorithms with some variants. The functions were developped with MATLAB (note that one of the functions requires the Mathworks Optimization Toolbox) by Iadine Chadès, Marie-Josée Cros, Frédérick Garcia, Régis Sabbadin of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France).The version 3.0 (September 2009) adds several functions related to Reinforcement Learning and improves the handling of sparse matrices. For more detail see the README file.”
Toolbox page: http://www.inra.fr/mia/T/MDPtoolbox