Re: Simulating Individual Behavior

[Steve Emsley]

Just some thoughts:

All of the individual-based models in ecology that I am aware of seek
to model spatial resource competition. These range between 'artificial
ecosystems", such as Gecko, and specific applications, such as Dave Green's
CA study of 'Crown-of-Thorns' outbreaks. These models belong to the 'divide
and conquer' school. Personally, my interest is in developing a model as a
'surrogate experimental system" (Olson 1995). Perhaps 'simulation' is a more
accurate description.

As such, I question Philippe Laval's statement that "in an
ecosystem there is probably no unique latent 'model' to be discovered,
against which we could compare some data and make adjustments." Although I
concur that there is no 'whole ecosystem model' surely, in terms of emergent
phenomena, one would not expect such a model to exist. However, there are
latent models for physiological & behavioural processes within organisms
which, by virtue of their interaction, determine one of many 'ecosystem'
models. 

Mike Brown says "Ecologists are concerned with aggregations of individuals; 
they have not had to know as much about individual species as, for example, 
zoologists." In my field, plankton ecology, mean-field modellers have
dominated the field for decades. Mathematically-endowed researchers will
reduce any mean field coupled system of differential equations into an
idealised excitable system. It is their goal in life. Forget individual
differences or interaction between individuals - it just muddies the water.

 "First, the ecologists ... have to 'ratchet down' and study the behavior 
of individual entities ...  it does demand a new focus for researchers."
This is exactly what is required. In order to approach a plankton SWARM
simulation this 'controversial (mad!)' researcher has had to enlist help
from researchers into cell-orientated mechanistic models of physiology and
behaviour. Even then we're only dealing with a drop in the ocean!! At least,
it's not a multicellular drop!

Jan Kreft says " If you start with a
conceptual model with all the parameters and mechanics you need and only a
rough idea of actual values (from population average measurements) you can
run the sim and compare the output with the real system to be modeled.
Then you optimize the parameters to achieve the desired output. Perhaps
call it back-calibration." Excellent. I attempted this argument with a
mean-field modeller PI on our "Testable Model" project by suggesting that
my model will be intrinsically testable. I argue that for my simulation
if I can adequately model the light field (easy) and turbulent environment
(more difficult) then seed my 'ocean' with agents having a log normal spread
of parameter values I can spin up and, assuming my mechanistic models are
realistic, evolve a population that has parameter values optimised for
fitness in their artificial environment (and comparable with organisms to
be found in real oceanonographic settings). Sort of a spatially-extended
genetic algorithm. 

Ginger says " For it suggests what kind of terrestrial ecosystems (rather 
safer and easier to manipulate than open ocean :) could conceivably offset 
CO2 emissions." Suggest you glance at Wally Broeker (1991)  Keeping global
 change honest". Anti-intuitively, calcitic organisms PRODUCE CO2 when
forming their skeletons. Although once it gets down into the deep-sea
sediments it's there for a residence time c. 1 million years you have to
find a way of increasing productivity first. Apart from dumping all our
cars into the Pacific (Martin iron-feritilisation hypothesis) chances are
that global warming transients furthur stabilise the mixed layer leading
to REDUCTION in productivity. Lovelock is probably correct - grow more
trees (and build timber houses, furniture or bury the crop!!).

With that warming thought, I'll sign off!
--
Steve Emsley
Ecosystem Analysis & Management Group,
University of Warwick, UK
address@hidden


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