Regarding Dick Wiegert's work on modeling, and understanding ecosystems
- much of his work & publications dealt w/ this general topic of
integrating field measurements w/ ecological models - aquiring data
suitable for parameterizing (& validating etc) relatively simple models
of trophic interactions. He worked in thermal springs, salt marshes,
and a variety of other systems. (I was a student of his; he died a
number of years ago).
Another of his papers that gets into the topic of the tradeoffs between
model generality, realism, and precision:
Wieger, R.G. 1979. Population models: experimental tools for analysis
of ecosystems. In: Horn, D.J, G.R. Stairs, and R.D. Mitchell.
Analysis of Ecological Systems. Ohio State University Press, Columbus.
pp. 233 -275.
Another paper of interest on this topic:
Costanza, R., and T. Maxwell. 1994. Resolution and predictability: an
approach to the scaling problem. Landscape Ecology 9:47-57.
-carl fitz
Bill Silvert wrote:
After I mentioned a paper by "Weigert" in a posting I have received numerous
requests for the reference. The correct spelling is Wiegert. My apologies to all of you,
especially Prof. Wiegert.
I am currently travelling and do not have the reference with me, but I found
his website and the paper may be
Wiegert, R.G. 1975. Simulation models of ecosystems. Ann. Rev. Ecol. Syst.
6:311-338.
although I thought it was earlier. The paper describes about half a dozen
models of a simple salt spring ecosystem, and as I wrote earlier, he found that
the best performance came with an intermediate level of complexity.
I've been asked in another posting why complex models do not work too well.
There are many reasons for this, including the need for too many parameters and
resultant magnification of errors, but in ecological modelling when you get too
specific and try to model individual species you need to describe the factors
behind zonation and succession, which is hard to do. In general I find that
fairly aggregated models work well, especially when aggregated on the basis of
function rather than taxonomy.
Another problem is that very precise models are more susceptible to problems
arising from discontinuous processes, such as insect outbreaks and blooms
(algal, jellyfish, etc.). Unless we know precisely what triggers these events
and know how to predict the events, the models will not perform well.
I recall a modelling exercise where the components of an estuarine ecosystem
were modelled by separate groups of scientists. All went well, except that the
head of the zooplankton group insisted that all four of the Acartia species he
was studying be modelled individually. They never managed to do this (again,
problems of zonation and succession) and the project would have been aborted if
the rest of us hadn't thrown together a simpler but working submodel.
Not everything can be successfully predicted. This is a property of natural
systems, not just ecological models. I have already mentioned the cod-haddock
issue on this list. Modelling fish recruitment is tricky because it depends on
the overlap between larval emergence and plankton blooms, which we generally
cannot predict. We cannot do very well at predicting earthquakes either. And in
mathematics, remember Gödel's famous proof that not all true theorems can be
proven. Failure to do the impossible is not really failure (unless you are a
Marine!).
Ciência Silvert
www.ciencia.silvert.org
--
H. Carl Fitz, Assistant Professor, Landscape Ecology
Soil and Water Science Dept.
Ft. Lauderdale Research & Ed Center
IFAS, University of Florida
954-577-6363
[email protected]
http://ecolandmod.ifas.ufl.edu
