EMDS History - February 19, 2019

Over the past 20 years, EMDS (Ecosystem Management Decision Support) was a system of tools for Land and Resource Management. The application framework is designed for maximum flexibility and extensibility. The knowledge-based decision support is organized for evaluation and planning to help the resource manager and analysts, and is scalable for any geographic area.

Since 2015, EMDS has continued to evolve with the creation of the EMDS Consortium. The EMDS Consortium is a partnership with Mountain View Business Group, USDA Forest Service, Rules of Thumb, InfoHarvest, LPA, and BayesFusion to extend EMDS. Mountain View Business Group is the primary developer and architect for EMDS expansion.

EMDS Components - February 18, 2019

The EMDS Project environment consists of an add-in to ArcMap whose components consist of the EMDS Framework and its four primary engines: NetWeaver, CDP, LPA, BayesFusion. For practical purposes, the EMDS Project Environment can be thought of as a supervisory component of the add-in that manages the activities and interactions of the other components within the ArcMap environment. The EMDS Project Environment also is the primary component of the add-in with which a user interacts to set up various assessments, analyses, scenarios, and workflows. This data is organized within a tree view of project structure.

The engines of EMDS are:

  1. NetWeaver from Rules of Thumb allows for the evaluation of data against a logic model that provides a formal specification for interpreting data and synthesizing information. The model can be thought of as a type of meta-database, allowing for the evaluation of ecosystem states.
  2. CDP from InfoHarvest assists users with developing strategic priorities for management activities in landscape elements of the assessment area. This MCDA component rates management units not only with respect to their condition, but also with respect to logistical factors important to resource managers, such as the feasibility, efficacy, or social acceptability of choices.
  3. VisiRule from LPA allows for the implementation of Prolog-based decision trees. These decision models are particularly useful to support tactical planning decisions for selecting optimal actions in specific management units, given the state of the unit, and managers’ and scientists’ understanding of the effectiveness of potential actions in specific contexts.
  4. GeNIe from Bayesfusion allows for the creation of Bayesian networks. Bayesian networks have been particularly popular among wildlife biologists for decision making related to wildlife habitat suitability and population viability.