qualitative reasoning about population and community ecology reha k. gerçeker boğaziçi...
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Qualitative Reasoning About Population and
Community Ecology
Reha K. Gerçeker
Boğaziçi University, 2005
“Qualitative Reasoning About Population and Community Ecology”
Paulo Salles, Bert Bredeweg. AI Magazine.Winter 2003. Vol. 24, Iss. 4; p. 77
http://staff.science.uva.nl/~bredeweg/pdf/aimag2003c.pdf
Simulation of Ecological Systems
Interested in population dynamics Interested in interaction between different
types of population (i.e. predation...) Tries to explain the mechanisms behind an
observed behaviour
Interested in population dynamics Interested in interaction between different
types of population (i.e. predation...) Tries to explain the mechanisms behind an
observed behaviour
Ecological modelling is equivalent to mathematical modelling— is it possible to capture accurate
mathematical models?
Interested in population dynamics Interested in interaction between different
types of population (i.e. predation...) Tries to explain the mechanisms behind an
observed behaviour
Acquiring data of good quality requires long-term observations
Data is mostly imprecise and incomplete
Why go Qualitative?
Ecological data is more qualitative than it is quantitative– Exact quantities are never available– Exact quantities are not important either
An ecologist is actually interested in qualitative simulation rather than quantitative simulation
Qualitative models easily capture the knowledge in an ecologist’s mind– Explicit and well-organized knowledge– Computer processible
A Reasoning Engine: GARP
Bredeweg in 1992 has implemented a qualitative reasoning engine called GARP
General Architecture for Reasoning about Physics
It is based on Qualitative Process Theory by Forbus
It has a compositional modelling approach like the QPT
Nof(t + 1) = Nof(t) + (B + Im) – (D + E)
The Growth Equation
inflow outflow
Variable Description Q-Space
Nof number of individuals ?
B birth rate {zero, plus}
Im immigration rate {zero, plus}
D death rate {zero, plus}
E emigration rate {zero, plus}
An Ecological Process: Natality
B NofI+ and I– constraints refer to positive and
negative direct influences respectively
P+ and P– constraints refer to positive and negative indirect influences respectively
Basic Processes
Natality– I+(Nof, B), P+(B, Nof)
Mortality– I–(Nof, D), P+(D, Nof)
Immigration– I+(Nof, Im)
Emigration– I–(Nof, E), P+(E, Nof)
Immigration rate is modeled independently from the population size.
M+(Nof, B)
M–(Nof, V) and M+(D, V) where V is an intermediate
variable
Quantity Space Resolution
In physics specific landmarks exist– i.e. a specific landmark for a temperature
variable might be the boiling point In an ecological system, there are no
specific landmarks to place inside the quantity spaces of Nof
normal maxhighlow medium max0 +∞Nof
GARP’s Transition Rules
QSIM and GARP differ in their transition rules in an interesting way– GARP is concerned with neither time
intervals nor intervals of landmarks– Transitions seem to take place between
time points only
t = t1 t = t2
<high, dec> <low, dec>
<high, dec> <low, std>
<high, dec> <high, dec>
0 +∞Nof
highlow
Ambiguities
According to the growth equation, Nof is influenced by several factors
The effects of such numerous factors are combined by what Forbus calls “influence resolution”
That is where ambiguities arise because the overall influence depends on the relative amounts of the factors (which are unknown)
Ambiguities can cause the simulation to branch enormously
Ambiguities (cont’d)
Ambiguity as a guide– Ambiguity might act as a guide for an ecologist
to acquire more information– It might direct ecologists to fields of research
where more work has to be done Ambiguity as a feature
– Ambiguity might sometimes be favorable– That is how different branches of simulation
come up after all Simplifying assumptions
– closed population (Im = <0, std>, E = <0, std>)
Interaction Between Populations
natality and mortality processes
Effects of populations on each other are modeled to be proportional with their sizes
Question marks on these influences determine the type of interaction between populations
P+
P–
P–
P+
Symbiosis
P+
P–
P+
Predation
supply
consumption
Population 1: PredatorPopulation 2: Prey
Interaction Types
Interactions– neutralism (0, 0)– amensalism (0, –) – comensalism (0, +)– predation (+, –)– symbiosis (+, +)– competition (–, –)
Another type of interaction is the “absence” of a population– when there is no prey population, the predator
population cannot survive
Modeled once and placed into the library of model fragments
a branch of simulation (behaviour) where both populations are growing
Simulating Predation
Causal Model for PredationPopulation 1: PredatorPopulation 2: Prey
natality processesmortality processesgrowth equationinteraction: predation
closed population
closed population
predator
prey
Simulating Predation (cont’d)
Start simulation with Nof1 = <normal, ?> and Nof2 = <normal, ?>There are 4 possible start states after filling in the unknown directions according to the contraints
start states
Populations to a MaximumBalanced CoexistencePopulations to ExtinctionPredator to Extinction
Cerrado Succession Hypotheses
Brazilian cerrado vegetation There are different types of cerrado
communities, characterized by the proportions of grass, shrubs and trees– grass likes bright, warm, dry microenvironments– trees like shaded, cold, moist microenvironments
These communities have well-defined composition determined by– fire frequency– soil fertility– water availability
The increases and decreases in populations of cerrado communities
is referred to as the Cerrado Succession HypothesesNo trees, no shrubs, only grass
Most dense forest, no grass
Cerrado Causal Model
Sim
ula
tin
g C
SH
Conclusion
Qualitative representation provides a rich vocabulary for describing– objects– situations– causality– mechanisms of change
Conclusions relevant to ecologists can be derived automatically using only qualitative data
Qualitative models prove to be a valuable complement to mathematical approaches in ecological modeling
Conclusion (cont’d)
Compositional approach enables reusability– lets the modeler use parts of his
previously defined models– lets the modeler to increase the
complexity of his models gradually– basic models represent fundamental
knowledge that explain more complex systems
Future Work
Apply same approach to represent and understand behaviour of other large communities
Develop tools to support educational and management activities
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