To inform the U.S. Fish and Wildlife Service decision, whether or not to list polar bears as threatened under the Endangered Species Act (ESA), we projected the status of the world's polar bears (Ursus maritimus) for decades centered on future years 2025, 2050, 2075, and 2095. We defined four ecoregions based on current and projected sea ice conditions: seasonal ice, Canadian Archipelago, polar basin divergent, and polar basin convergent ecoregions. We incorporated general circulation model projections of future sea ice into a Bayesian network (BN) model structured around the factors considered in ESA decisions. This first-generation BN model combined empirical data, interpretations of data, and professional judgments of one polar bear expert into a probabilistic framework that identifies causal links between environmental stressors and polar bear responses. We provide guidance regarding steps necessary to refine the model, including adding inputs from other experts. The BN model projected extirpation of polar bears from the seasonal ice and polar basin divergent ecoregions, where ≈2/3 of the world's polar bears currently occur, by mid century. Projections were less dire in other ecoregions. Decline in ice habitat was the overriding factor driving the model outcomes. Although this is a first-generation model, the dependence of polar bears on sea ice is universally accepted, and the observed sea ice decline is faster than models suggest. Therefore, incorporating judgments of multiple experts in a final model is not expected to fundamentally alter the outlook for polar bears described here.

Testing your knowledge base

A Bayesian Network Modeling Approach to Forecasting the 21st Century Worldwide Status of Polar Bears

Developers of decision support systems (DSS) often fail to present empirical data supporting the claimed merits of their systems. Discussions with developers indicate that they often do not consider or know how to perform the required empirical evaluations. That problem is addressed by reviewing the issues inherent in using experiments, quasi-experiments, and case studies to evaluate DSSs. The discussion revolves around the issues of reliability and four types of validity: internal, construct, statistical conclusion, and external. The discussion is focused upon but not restricted to expert systems. >

Experiments, quasi-experiments, and case studies: A review of empirical methods for evaluating decision support systems

https://www.fs.fed.us/pnw/pubs/journals/pnw_2005_curran001.pdf

Progress towards more uniform assessment and reporting of soil disturbance for operations, research, and sustainability protocols

Handbook for evaluating knowledge-based systems

Validation and verification of expert systems or knowledge-based systems is a critical issue in the development and deployment of robust systems. This article is a comprehensive survey of the developments and trends in this field. More than 300 references are included in the References and Additional Readings at the end of article.

Testing your knowledge base

Citations

A Bayesian Network Modeling Approach to Forecasting the 21st Century Worldwide Status of Polar Bears

Experiments, quasi-experiments, and case studies: A review of empirical methods for evaluating decision support systems

Progress towards more uniform assessment and reporting of soil disturbance for operations, research, and sustainability protocols

Handbook for evaluating knowledge-based systems

Validation and Verification of Knowledge-Based Systems: A Survey

Related Papers (5)

Testing your knowledge of pedonontics

Test Your Knowledge

2.4.3. Test Your Knowledge

3.2.3. Test Your Knowledge

1.2.8. Test Your Knowledge