# Qualitative Reasoning About Population and Community Ecology Reha K. Gerçeker Boğaziçi University, 2005.

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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 ecologists 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 inflowoutflow VariableDescriptionQ-Space Nofnumber of individuals? Bbirth rate{zero, plus} Imimmigration rate{zero, plus} Ddeath rate{zero, plus} Eemigration rate{zero, plus}

An Ecological Process: Natality BNof I+ 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 normalmaxhighlowmediummax 0 + Nof

GARPs 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 = t 1 t = t 2 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 (contd) 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 =, E = )

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+ Symbiosis P+ P– P+ Predation supply consumption Population 1: Predator Population 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 Predation Population 1: Predator Population 2: Prey natality processes mortality processes growth equation interaction: predation closed population

predator prey Simulating Predation (contd) Start simulation with Nof1 = and Nof2 = There are 4 possible start states after filling in the unknown directions according to the contraints start states Populations to a Maximum Balanced Coexistence Populations to Extinction Predator 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 Hypotheses No trees, no shrubs, only grass Most dense forest, no grass

Simulating CSH

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 (contd) 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