Causal Analysis/Diagnosis Decision Information System (CADDIS)

Step 4: Evaluate Data from Elsewhere

• 4.1. Overview
• 4.2. In-Depth Look
• 4.3. Results and Next Steps

4.2.3. Stressor-Response Relationships from Ecological Simulation Models

Concept
Within the case, the cause must be at levels associated with effects in mathematical models simulating ecological processes.

Figure 4-3. Stressor-Response Relationships from Ecological Simulation Models. If the impairment (dead planktivorous fish) is thought to be due to starvation rather than direct toxicity, a mathematical model can show how the loss of zooplankton is a function of exposure to the candidate cause (chloronaphthol) and the starvation of fish is a function of zooplankton abundance.
(General explanation of symbols)

Examples
Consider increased heavy metal concentrations as a candidate cause for reduced insect species richness. What findings support or weaken the case for heavy metals as a cause, based on stressor-response relationships from simulation models?

• Supporting evidence - Simulation of the case in AQUATOX, using parameter values (e.g., stressor levels, biotic assemblages) relevant to the impaired site, generates a scenario in which insect richness is similar to that observed at the impaired site.
• Weakening evidence - Simulation of the case in AQUATOX, using parameter values (e.g., stressor levels, biotic assemblages) relevant to the impaired site, generates a scenario in which insect richness is much higher than that observed at the impaired site.

How do I analyze the data?
The use of mathematical models that simulate ecological processes depends on either the availability of a model that adequately represents the case, or the development of an ad hoc model. Model development, implementation and interpretation are specialized skills. As a result this type of evidence may not be used as commonly as many other types of evidence.

Ecological simulation models can be especially helpful when a complex network of events influences the observed effect. Manipulating one potential contributing factor at a time, different scenarios can be modeling to determine how expected effects change. As models of causal processes become more complex, it is increasingly difficult to judge whether an individual model adequately represents the causes of ecological degradation at a site. In such cases, the best strategy is to generate models of each proposed causal pathway, and determine which model best explains the site data.

It is important to avoid adjusting the model's functions and parameters to obtain the observed impairment as an output. Such model calibration negates the utility of this type of evidence.

As with stressor-response from laboratory tests and from other field data, model output is interpreted by comparison to stressor-response information from the case. Although this may be accomplished by comparing point data from the case (e.g., the aqueous concentration at which the number of benthic arthropod species is reduced by 35%) to model output, it is much more informative to compare trends from the case such as changes in the relative abundances of scrapers and collectors along a concentration gradient.

What evidence would support or weaken the case for a candidate cause?
Supports

• Data showing that model simulation of the case (e.g., in terms of stressor or source levels) produces effects similar to the impairment

Weakens

• Data showing that model simulation of the case (e.g., in terms of stressor or source levels) does not produce effects similar to the impairment

How do I score the evidence?

Helpful tips

• Model results are seldom convincing alone, but they can strengthen the case for a candidate cause when combined with other types of evidence.
• Note that it is not possible to refute the case for a candidate cause using stressor-response relationships derived from simulation models.