March 11, 2009
assumptions:
- good science uses models to generate hypotheses
- multiple hypotheses means multiple choices for management
- models can help present data and results
- models can help solicit input from involved parties
- models can be used to generate scenarios
examples:
- phosphorus pollution TMDL model for Klamath Lake
- multiple stable state model for lakes
- resiliency of population
- harvest models for fisheries or other crops
- others
limitations:
- choice of modeling approach can restrict what is considered
- P mass balance models (such as in STELLA) rarely have an "aesthetics" indicies
- simultaneous models of different types may not converge
- where convergence is considered desirable
- non-convergence maybe because of different information types (mass balance vs. network)
- don't forget that the "map is not the territory"
- problems with or within the model are not the same as real problems
- be careful using model LINGO
- r vs. K selection is only meaningful if you are talking about the "Logistic" equation
- somebody lives in that "cell" - don't just color it red
- the most powerful model can't replace primary experience in that situation/enviroment
- my life lesson from my last sabbatical
What are the model structures you used?
MM equations with different Km and Vmax related to change in S
3.