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Uncertainty about ecological variables can affect risk designations for species at risk of extinction. This study evaluated the effect of quantitatively characterizing ecological uncertainty on species at risk decision-making, using the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) as a case study. Fifty senior authors of COSEWIC assessments of vertebrate species were invited to use a confidential web-based survey to quantitatively characterize uncertainty in their expert opinions for 17 COSEWIC ecological variables. Probability distributions for the 17 variables provided by each of 16/50 (32.0%) respondents were used with Monte Carlo sampling to generate sets of point estimates used as input for a computer algorithm that emulated COSEWIC decision-making for risk designation. The effect of uncertainty on risk designation was measured as a Monte Carlogenerated probability for the same risk designation as that determined by the mean point estimates only. Analysis of uncertainty revealed plausible alternative designations for seven of the 16 species. Although the majority of these cases were affected in a relatively minor way, there were cases where the explicit characterization of uncertainty caused major differences in risk designation. From these results, it can be concluded that characterization of uncertainty can have important effects on species at risk decision-making. Responsible agencies should explicitly incorporate uncertainty in their decision-making by (1) requiring explicit characterization of uncertainty for input ecological variables and output risk designations and (2) developing rational methods to incorporate the uncertainty in decision-making.

Ontology-Driven Compositional Systems (ODCSs) are designed to assist a user with semi-or fully automatic composition of a desired system. Current research with ODCSs has been conducted around the discovery and composition of web services and alternatively a bottom-up resource management approach to automatic system composition. This paper argues that current ODCSs do not truly satisfy user expectations as the semantic knowledge required to make proper discovery, decision-making and composition has not been fully represented. The authors introduce the beginning of their work of utilizing the inheritance of multiple ontologies to fully represent the functional, data, quality & trust, and execution of compositional units within an ODCS. Furthermore, a case study of fish population modeling is presented.

Compositional systems offer a unique opportunity to users who have domain expertise but lack the necessary skills to develop software solutions in their own domain. A subset of these systems are ontology driven compositional systems (ODCS). ODCS use ontological knowledge to help facilitate composition between individual compositional units. Since an ODCS is a technologically complex system where a majority of the emphasis is placed on the inner workings of the system, often the user interface is an afterthought. This paper focuses on the human issues related to developing a workflow management application by investigating the design principles behind an ODCS interface prototype.