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A Multipatch Metapopulation Model with “Human” Agents Injected

A Multipatch Metapopulation Model with “Human” Agents Injected. Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University. Motivation. Complex Individual-based Model. Complex Individual-based Model. Socio-Ecological Model. Combined Complex Model. Social Model.

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A Multipatch Metapopulation Model with “Human” Agents Injected

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  1. A MultipatchMetapopulation Model with “Human” Agents Injected Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University

  2. Motivation Complex Individual-based Model Complex Individual-based Model Socio-Ecological Model Combined Complex Model Social Model SimpleSystem-dynamics Model SimpleSystem-dynamics Model Ecological Model

  3. Model of Ecology Slow random rate of migration between patches Each individual represented separately A well-mixed patch • A wrapped 2D grid of well-mixed patches with: • energy (transient) • bit string of characteristics • Organisms represented individually with its own characteristics, including: • bit string of characteristics • energy • position • stats recorders

  4. How Dominance is Decided

  5. Model sequence each simulation tick • Input energy equally divided between patches. • Death. A life tax is subtracted, some die, age incremented • Initial seeding. until a viable is established, random new individual • Energy extraction from patch. energy divided among the individuals there with positive score when its bit-string is evaluated against patch • Predation. each individual is randomly paired with a number of others on the patch, if dominate them, get a % of their energy, other removed • Maximum Store. energy above a maximum level is discarded. • Birth. Those with energy > “reproduce-level” gives birth to a new entity with the same bit-string as itself, with a probability of mutation, Child has an energy of 1, taken from the parent. • Migration. randomly individuals move to one of 4 neighbours • Statistics. Various statistics are calculated.

  6. Key advantages of this • Produces a complex space of changing food webs • Model creates endogenous shocks and unpredictable adaptions (to, for example, man) • Species arms-races, predator-prey dynamics, waves of species spread etc. all exhibited • Levels of environmental variety, size, granularity etc. are determined by paramters • Mutation and migration happen in parallel • “Equilibrium” almost never observed • Diversity of ecologies are emergent outcomes • “Human” agents can be embedded in food chains and the combined complexity of this explored

  7. Non-Viable Ecology Typical Non-Viable Ecologythe world state (left) Number of Species (centre) Log, 1 + Number of Individuals at each trophic level (right)

  8. Dominant Species Ecology TypicalDominant Species Ecology the world state (left) Number of Species (centre) Log, 1 + Number of Individuals at each trophic level (right)

  9. Rich Plant Ecology Typical Rich Plant Ecologythe world state (left) Number of Species (centre) Log, 1 + Number of Individuals at each trophic level (right)

  10. Mixed Ecology Typical Mixed Ecology the world state (left) Number of Species (centre) Log, 1 + Number of Individuals at each trophic level (right)

  11. Longer-Term Trends in Num. Species

  12. Neutral patches, random migration, plants only • As predicted by Hubble’s “Neutral Theory” • “skewed s-shaped” relative species abundance curve • “Multinomial distribution” of log2 species distribution • Except, maybe, species-area scatter chart

  13. Neutral Patches, local migration, plants only • unchanged “skewed s-shaped” relative species abundance curve • “Multinomial distribution” of log2 species distribution but with more lower abundance species • Pretty flat species-area scatter plot

  14. Selective patches, random migration, plants only • “Linear” drop in log 2 species abundance • “Exponential” shaped relative species abundance graph • Flatter species area scatter plot

  15. Neutral Patches, random migration, predators • Lots of new species plus flatter log 2 species distribution • flat or curved species abundance distribution • a variety of species-area curves

  16. Other combinations

  17. Adding “Human” Agents • The agents representing humans are “injected” (as a group) into the simulation once an ecology of other species has had time to evolve • The state of the ecology is then evaluated some time later or over a period of time • These agents are the same as other individual in most respects, including predation but “humans”: • can change their bit-string of skills by imitating others on the same patch (who are doing better than them) • might have a higher “innovation” rate than mutation • might share excess food with others around • might have different migration rates etc. • Could have many other learning, reasoning abilities

  18. Extinction due to Consuming all Others

  19. Waves of (Human) Predator-Prey Patterns

  20. Example run with: selective environment, local migration, predators, and “humans” @ tick 2000 @tick2000 – before “humans” @tick5000 – after “humans”

  21. Some of the dynamics of the example run

  22. Effect of humans vs. environmental complexity proportion of ecology types, red=plant, blue=mixed, purple=single species, green=non-viable diversity of ecology, blue=with humans, red=without

  23. Effect of humans vs. general migration rate proportion of ecology types, red=plant, blue=mixed, purple=single species, green=non-viable diversity of ecology, blue=with humans, red=without

  24. Effect of humans vs. food input to world proportion of ecology types, red=plant, blue=mixed, purple=single species, green=non-viable diversity of ecology, blue=with humans, red=without

  25. Num. Species vs. Num. Variants (all with humans)

  26. Innovation vs. migration rate (all with humans)

  27. Migration vs. food rate (all with humans)

  28. Human migr. rate vs. diversity (all with humans, other entities having 0.1 migration rate)

  29. Starting from plant ecology (with humans) Starting from a Plant Ecology the effects of innovation rate/mutation rate (left) and people migration rate/entity migration rate (right) , red=plant, blue=mixed, purple=single species, green=non-viable

  30. Starting from mixed ecology (with humans) Starting from a Mixed Ecology the effects of innovation rate/mutation rate (left) and people migration rate/entity migration rate (right) , red=plant, blue=mixed, purple=single species, green=non-viable

  31. Starting from single species ecology (with humans) Starting from a Single Species Ecology the effects of innovation rate/mutation rate (left) and people migration rate/entity migration rate (right) , red=plant, blue=mixed, purple=single species, green=non-viable

  32. The End! Bruce Edmonds:http://bruce.edmonds.name Centre for Policy Modelling:http://cfpm.org A paper describing some of this, the model, these slides at:http://cfpm.org/cpmrep222.html

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