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