a multipatch metapopulation model with “human” agents injected

A Multipatch Metapopulation Model with “Human” Agents Injected. Manchester Complexity Seminar, 18th Oct 2013. slide 1

A Multipatch Metapopulation Model with “Human” Agents Injected

Bruce EdmondsCentre for Policy Modelling

Manchester Metropolitan University


Motivation

Social Model

Ecological Model

Complex Individual-based Model

SimpleSystem-dynamics

ModelComplex Individual-based Model

SimpleSystem-dynamics

Model

Combined Complex Model

Socio-Ecological Model


Model of Ecology

• 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

A well-mixed patch

Each individual

represented separately

Slow random rate of migration

between patches


How Dominance is Decided


Model sequence each simulation tick

1. Input energy equally divided between patches.2. Death. A life tax is subtracted, some die, age incremented3. Initial seeding. until a viable is established, random new individual4. Energy extraction from patch. energy divided among the

individuals there with positive score when its bit-string is evaluated against patch

5. Predation. each individual is randomly paired with a number of others on the patch, if dominate them, get a % of their energy, other removed

6. Maximum Store. energy above a maximum level is discarded.7. 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.

8. Migration. randomly individuals move to one of 4 neighbours9. Statistics. Various statistics are calculated.


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


Non-Viable Ecology

Typical Non-Viable Ecology the world state (left) Number of Species (centre) Log, 1 + Number of Individuals at

each trophic level (right)


Dominant Species Ecology

Typical Dominant Species Ecology the world state (left) Number of Species (centre) Log, 1 + Number of

Individuals at each trophic level (right)


Rich Plant Ecology

Typical Rich Plant Ecology the world state (left) Number of Species (centre) Log, 1 + Number of Individuals at each

trophic level (right)


Mixed Ecology

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

trophic level (right)


Longer-Term Trends in Num. Species


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


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


Selective patches, random migration, plants only

• “Linear” drop in log 2 species abundance

• “Exponential” shaped relative species abundance graph

• Flatter species area scatter plot


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


Other combinations


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


Extinction due to Consuming all Others


Waves of (Human) Predator-Prey Patterns


Example run with: selective environment, local migration, predators, and “humans” @ tick 2000

@tick2000 – before “humans”

@tick5000 – after “humans”


Some of the dynamics of the example run


Effect of humans vs. environmental complexity

diversity of ecology, blue=with humans,

red=without

proportion of ecology types, red=plant, blue=mixed, purple=single species,

green=non-viable


Effect of humans vs. general migration rate


red=without


green=non-viable


Effect of humans vs. food input to world


red=without


green=non-viable


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


Innovation vs. migration rate (all with humans)


Migration vs. food rate (all with humans)


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


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


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


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


The End!

Bruce Edmonds:http://bruce.edmonds.nameCentre for Policy Modelling:

http://cfpm.orgA paper describing some of this, the model, these slides at:

http://cfpm.org/cpmrep222.html

a multipatch metapopulation model with “human” agents injected

Documents

manchester complexity

model of ecologya

combined complexity

number of individuals

emergent outcomeshuman

waves of species

simulation tickinput

maximum level