Object-based Models in Landscape Ecology

Hazel Parry

Complexity Self-organization Emergence

Ecosystems are Complex Systems

“The ecosystem is greater than the sum of its parts” (Odum). Distributed systems Auto adaptation

Complex System

Global level entity Emergent higher level entities Locally interacting heterogeneous components

Objects

 Objects have attributes:  Data  a type e.g. Bees: Bumble bee (

Bombus

)

,

Honey bee (

A. mellifera

)  Variables e.g. size of the Bee  Behaviours  Behaviour is a function of type  Types can form a taxonomy  e.g. a bee is a Hymenoptera

Bee

Nectar

Objects

Insect

Feed

Hymenoptera Ant

Leaves

Object-based models in Ecology

 Individual-based models  Large collection of interacting organisms. (not necessarily spatial)  Cellular Automata  Cells on a grid of specific dimension, undergo transition by global rules.  Multi-agent simulation  Intelligent agents, with ability to learn about their environment and adapt their behaviour accordingly.

Individual-based modelling

 The properties of the system are derived from the individuals that make up the system.  Typically use random generation of attributes, behaviour and responses of individuals to generate population distributions.

 For example, random walk simulation:

NTSS System (EGBE)

3000 2500 2000 1500 1000 500 0

Date

Ground Beetle Adults Ground Beetle Larvae Ground Beetle Pupae Ground Beetle Eggs

Cellular Automata

Von Neumann Moore Uniform t=0

Von Neumann

t=0

Cellular Automata

Von Neumann Moore Uniform t=0

Von Neumann

t=1

Forest Fire

Le Page and Bousquet (Cirad) “FireAutomata” Cellular Automata model for the spread of forest fire

Gloucester Rabies Simulation

Month Graham Smith, CSL (WEMA)

y

Multi-Agent Systems

 Comprises a set of mobile agents evolving in a common environment, communicating and interacting.  Less rigid structure than CA  Interactions between distant individuals at a variety of scales  Facilitate investigation of lower level mechanisms leading to global structural and dynamical features “Neighbourhood” e.g. defined by nearest neighbours Agent x Location (x,y)

Agents

 Agents have:  Internal data representations (

memory

or

state

)  Means for modifying their internal data representations (

perceptions

)  Means for modifying their environment (

behaviours

)  Ecological agents may interact by:  Direct spatial interaction  e.g. mating, consumption  Indirect spatial interaction  e.g. resource depletion, pheromone dispersal  Communication

matrix

LOTR

From Reality to Multi-Agent Model

The advantages of a MAS approach

 Increased realism  Capable of modelling complex systems   Provides solutions for scaling issues Reduced ‘randomness’  Agents have the capacity to evolve or adapt their behaviour  Increased flexibility  Incorporation of differential equations and stochastic modelling procedures still possible  Utilization of experimental biological data  Enable greater understanding of how and why landscape patterns emerge

Simple interaction of agents with environment

“SugarScape” (Epstein and Axtell)

Interaction of agents with a more complex environment

“SugarScape” with pollution occuring following gathering and consumption of sugar by agents (Epstein and Axtell)

Simple interaction between agents

prey predator empty

“Pursuit” predator-prey simulation (Le Page and Bousquet (Cirad))

Simulation of bird-cherry oat aphid: daily time step

3.

4.

5.

1.

2.

Adult alate aphids may immigrate into region.

Adult alate aphids may move across landscape (in relation to density and wind speed/direction).

Aphids age.

Aphids die.

Adults may reproduce and nymphs are born.

Interactions

 Aphids perceive the environment:  To search for suitable habitat  To migrate using wind  To reproduce (depends upon temperature and habitat suitability)  Aphids modify the environment:  By feeding  Aphids interact with one another:  Density determines morph and therefore likelihood of migration.

Bird-cherry oat aphid simulation

1000 100 10 1 266 286 306 326

Julian Day

346 366 386

MAS and Geographical Information Systems (GIS)

+  MAS combined with GIS will allow:  Examination of real landscapes  Realistic parameterisation of models  Dynamic updating of parameters  Generation of place-specific scenarios  Creation of simulations that can be effectively used as landscape management tools

A unifying methodology for integrated environmental management?

 Environmental management needs to be more integrated and flexible.

Dynamic environmental model Ecological Model Social model  For example:  SIMDELTA  MODULUS

SIMDELTA

Biotopes Fishermen Shoals of fish Village 

The artificial world of SIMDELTA (Bosquet and Cambier)

   Dynamics of fish population Biological and topological factors affecting the evolution of the fish Decision making of the fishermen

Multi-agent simulation at CSL?

 Modelling of insect and animal population dynamics (e.g. for the spread of disease, wildlife survival in changing habitats and dispersal of invasive species)  Modelling complex behaviour of social insect species such as honey bees and animals such as foxes.

 Examination of policy implications for farmers and landscape (e.g. innovation diffusion via farmer ‘agents’, and implications for landscape and wildlife agents)  Whole landscape or ecosystem models

References

 CORMAS http://cormas.cirad.fr/  For the FireAutomata model and the Pursuit model  Epstein, JM and Axtell, R. 1996

Growing Artificial Societies

MIT Press, London  For the SugarScape models  Gimblett, HR. 2001

Integrating Geographic Information Systems and Agent-based Modeling Techniques for Simulating Social and Ecological Processes

Santa Fe Institute

THANK YOU!

ANY QUESTIONS?

Hazel Parry PhD research postgraduate, CSL Seedcorn funded Derek Morgan (CSL), Steve Carver (UoL), Andy Evans (UoL) Email: [email protected]

Websites: http://www.geog.leeds.ac.uk/people/h.parry/ http://www.geog.leeds.ac.uk/groups/mass/