Criticality Analysis
Abstract
Prior to human disturbance, the ecological system evolved mechanisms to recover from natural disturbances and maintain a relatively stable system. The further the system is moved away from this natural state, the greater the probability that the system will be unable to respond to natural disturbances. The result is that the system may move to a new and potentially undesired state and tend to remain there. Criticality analysis calculates how far the current or future state of each watershed has been moved from the natural state (defined here as estimated pre-human conditions) to estimate the vulnerability to radical changes.
Method Details
Nonlinear change is well documented in the ecological literature. Sudden catastrophic changes have occurred in lake eutrophication, desertification, and fisheries collapses. It is not even theoretically possible to predict the point at which radical change will occur. The real problem is estimating how tightly strung the rubber band is and the risk that it will snap with the next minor impact.
As an approach to nonlinear change, ReVA focuses on estimating how far the ecological system has already been moved from its natural state. This approach assumes that systems in the natural state retain the feedback networks that permitted stable response to disturbances over the long period of evolutionary history. As human activities add stressors (e.g., chemical pollutants), extract resources (e.g., lumber), and change landcover (e.g., fragmentation) the natural feedbacks are disrupted and the system becomes more vulnerable to radical and potentially irreversible change.
The first step in applying the Criticality approach is to define the hypothetical 'natural' state. The task is simple for some variables, e.g., human population and pollutants, which can be assumed to have been zero. The task is more arbitrary for other variables such as biodiversity. The watersheds in the Mid-Atlantic range from highland forests to coastal plains. Even under pre-human conditions, it cannot be assumed that this diversity of systems was characterized by a single set of biotic variables.
To deal with the uncertainties involved in defining the natural state, the analysis is based on 'fuzzy' values. A fuzzy value is expressed not as a single number but as a range of possible values plus an assumed distribution. The range of values is selected as the lowest and highest values that can be reasonably expected to have existed in the natural state. A triangular distribution is assumed if the most reasonable value would be expected to lie toward the center of this range. A flat or rectangular distribution is assumed if our ignorance only permits us to say that the value lies somewhere within the range. Once the definition of the 'natural state' is established, it is possible to calculate a 'fuzzy' distance between each watershed and the natural state.
Limitations
The greatest weakness of the Criticality approach is the inability to predict where the threshold of criticality lies. For the same reason, the analysis cannot determine which watersheds are free of significant risk. Simply because a watershed shows up as green on the map doesn't mean it is "safe". The analysis is relatively conservative in that uncertainty tends to lump more watersheds into the less vulnerable category. As a result the analysis is biased toward giving false negatives.
To test the sensitivity of the method, various definitions of the 'natural' state have been tried. The analysis is relatively robust to the definition. This insensitivity results from the fact that whatever assumptions are made about the biotic variables, setting human activities to zero is implicit in the definition of the natural state. The robustness of the approach is strongest for the most disturbed sites. Further, the argument that these watersheds are at or beyond the critical threshold is strong.
Without the ability to specify the bifurcation point, we have only a weak argument about the watersheds that are closer to a natural state. Therefore, the results of this analysis show only the worst watersheds. By showing the worst watersheds we indicate the watersheds that are at greatest risk of catastrophic change and our argument for robustness is strong. By showing the remainder of the region in uniform grey, we acknowledge our ignorance about the critical threshold.
Because of our 'fuzzy' definitions of the natural state, the analysis appears to be biased in the direction of giving false negatives, i.e., possibly underestimating the risk of catastrophic change. This bias can be interpreted in either of two ways. First, it makes a strong case that the watersheds shown are indeed vulnerable. Second, the results should not be interpreted as meaning that other watersheds are 'safe' for further development.
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