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Analyses | Ecosystem Trends Risk Assessment |
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Coarse filter approaches to maintaining ecological integrity are needed because there are myriad species about which we know little or nothing. Our ability to predict ecosystem processes is, at best, very limited. A coarse filter approach involves maintaining representative ecosystems in suitable abundance and distribution across watersheds, landscapes, and regions to maintain these species and processes. It is often paired with a fine filter approach to capture those elements that the coarse filter misses (e.g., rare species and ecosystems). The Central Coast Coarse Filter Ecosystem Trends Risk Assessment—Base Case (ETRA) uses the abundance and extent of representation of old forest ecosystems as the basic indicator of the probability of maintaining coarse filter biological diversity and function over space and time. It rests on the hypothesis that relative abundance of old forest is linked to the probability of maintaining ecological integrity in the Central Coast region. The analysis predicts how much of each forest ecosystem would be present under “natural” conditions based on natural disturbance regimes, and compares these predictions with the current and future landscapes predicted based on current management rules (as modelled in the base case scenario of the Central Coast EGSA-Timber, from the present through 200 years in the future). The Central Coast ETRA follows the methodology developed
for a similar risk assessment for the North Coast LRMP
(see North
Coast LRMP Environmental Risk Assessment Base Case–Coarse Filter
Biodiversity [ The “risk” calculated in the Central Coast ETRA is the probability that coarse filter functions will not be maintained, and that species/processes/ecosystems will eventually be lost or degraded. The designation of “high risk” means a high probability that ecological integrity, as indicated by representation of old forest ecosystems, will not be maintained. Results Central Coast ETRA results are presented as graphical outputs of percent old forest from the Central Coast EGSA-Timber model, and as risk levels and risk categories output from a Bayesian Belief Network. Both an overview of the raw data and an interpretation in terms of risk are presented. The base case shows that the abundance of old forest among ecosystems through time for the Central Coast region is highly variable:
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http://www.citbc.org. |