Transcript Modeling Biocomplexity - Actors, Landscapes and

Modeling Landscape Change in
the Willamette Basin
– A Biocomplexity Approach
John Bolte
Oregon State University
Department of Bioengineering
Collaborators

Dave Hulse, Department of Landscape Architecture,
Institute for a Sustainable Environment, University of
Oregon

Court Smith, Department of Anthropology, OSU

Stan Gregory, Department of Fish and Wildlife, OSU

Michael Guzy, Department of Bioengineering, OSU

Frank Miller, Department of Bioengineering, OSU

And a host of others…
Topics Covered Today

An “biocomplexity” approach to landscape change
modeling and analysis

Multi-agent simulation models

An example MAS modeling framework for landscape
change analysis: Evoland

Application in the Willamette Basin, Oregon
To start - a definition of
biocomplexity

The term “biocomplexity” is used to describe the
complex structures, interactions, adaptive capabilities
and (frequently nonlinear) dynamics of a diverse set
of biological and ecological systems, often operating
at multiple spatial and temporal scales

Many Approaches!!! Some focusing on capturing
richness of system dynamics, others more focused on
complex adaptive systems approaches
Biocomplexity Analyses
Typical Traits

Rich representation of interactions in the system

System response is characterized in terms of statespaces that reflect these interactions

Focus on system properties like:





Vulnerability
Resilience
Connectedness
Capacity for adaptation and innovation
Challenge – How to make these operational?
WRB Alternative Futures II –
Incorporating Biocomplexity
Rationale:

Large number of scenarios (100’s – 1000’s) necessary to
characterize range, likelihoods of landscape change
outcomes

Need to incorporate explicit decision behaviors,
actions/constraints, feedback loops

Need more flexible mechanisms for incorporating
additional models, processes in a transferable, interactive
framework
Willamette
Alternatives II –
Study Areas
Willamette Alternative Futures
Revisited: Assumptions

Patterns of natural resources and human systems
emerge through the interplay of policy and pattern in
coupled human/riverine systems as production
(expressed in multiple forms) becomes scarce.

We hypothesize that as resources become scarce or
impaired, a human/riverine system becomes more
tightly coupled (connections become more
important).

The system as a whole develops policy responses that
feed back into emergent spatial and temporal patterns
of both cultural and biophysical functions.
Evoland - A Biocomplexity Model
Evoland (Evolving Landscapes) is a tool for conducting alternative
futures analyses using:

A spatially explicit, GIS-based approach to landscape
representation

Actor-based (multiagent-based) approach to human
decisionmaking that explicitly represents real-world decisionmakers with attributes and behaviors within the model

Actor decisions are guided by “policies” that define, constrain
potential behaviors

Autonomous landscape process models produce non-human
induced (natural) landscape change
Evoland – General Structure
Policies: Fundamental
Descriptors of
constraints and actions
defining land use
management
decisionmaking
Actors:
Decisionmakers
making landscape
change by selecting
policies responsive to
their objectives
Policy Metaprocess: Manages existing
policies, generation of new policies
Landscape:
Spatial Container
in which land use
changes are
depicted
Landscape
Evaluators:
Generate
landscape metrics
reflecting scarcity
Exogenous
Drives: External
“program”
defining key
assumptions
Autonomous
Change
Processes:
Models of
nonhuman change
Cultural Metaprocess: Manages the
behavior of actors
Policies in Evoland

Describe actions available to actors

Primary Characteristics:

Applicable Site Attributes (Spatial Query)

Effectiveness of the Policy (determined by evaluative models)

Outcomes (possible multiple) associated with the selection and
application of the Policy

Policies are a fundamental unit of computation in Evoland
(Note: this has important consequences for representing
adaptation!)

Example: [Purchase conservations easement to allow
revegetation of degraded riparian areas] in [areas with no built
structures and high channel migration capacity] when [native
fish habitat becomes scarce]
Actor Value Mapping
Ecosystem Health
ACTORWT_1
< -2.33333 (4350)
-2.33333 to -1.66667 (4680)
-1.66667 to -1 (1741)
-1 to -0.333333 (1459)
-0.333333 to 0.333333 (1167)
0.333333 to 1 (312)
1 to 1.66667 (846)
1.66667 to 2.33333 (311)
> 2.33333 (268)
No Data
Range: -3-3
Economics
ACTORWT_1
< -2.33333 (4350)
-2.33333 to -1.66667 (4680)
-1.66667 to -1 (1741)
-1 to -0.333333 (1459)
-0.333333 to 0.333333 (1167)
0.333333 to 1 (312)
1 to 1.66667 (846)
1.66667 to 2.33333 (311)
> 2.33333 (268)
No Data
Range: -3-3
ACTORWT_0
< -2.33333 (585)
-2.33333 to -1.66667 (648)
-1.66667 to -1 (836)
-1 to -0.333333 (266)
-0.333333 to 0.333333 (1198)
0.333333 to 1 (2273)
1 to 1.66667 (1897)
1.66667 to 2.33333 (4139)
> 2.33333 (3292)
No Data
Range: -3-3
ACTO
<
-2
-1
-1
-0
0.
1
1.
>
N
Rang
Evoland Agent Properties
Property
Meaning
Evoland
Reactive
Responds to environment
Yes
Autonomous
Controls own actions
Yes
Goal-oriented
More than responsive to environment
Yes
Temporally continuous
Agent behavior continuous
Communicative
Communicates with other agents
No
Mobile
Can transport self to other locations
No
Flexible
Actions not scripted
Yes
Learning
Changes based on experience
No
Character
Believable personality or emotions
No
Once/step
Adapted from Benenson and Torrens (2004:156)
Evoland Framework for WRB
Evaluative Models
Data Sources
Fish Abundance/Distributions
IDU Coverage
Floodplain Habitat
Small-Stream Macroinvertabrates
Actor Descriptors
Upslope Wildlife Habitat
Autonomous Process
Models
Vegetative Succession
Evoland
Policy Set(s)
Parcel Market Values
Agricultural Land Supply
Forest Land Supply
Residential Land Supply
Flood Event
Conservation Set-Asides
Analysis

Resilience – determined by generating a large number of runs
(Monte Carlo) and identifying characteristics of attractor
basins in state space

Vulnerability – identify those portions of landscape likely to
experience reversible, irreversible change of ecological
function through frequency analysis

Connectedness – experiment with turning on/off feedback
loops associated with:



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Policy Generation
Actor Association Building
Time Lags in evaluative model feedback
Adaptive Capacity – Enable/Disable/Throttle policy evolution
Next Steps
Still in development, but most major pieces are in place…

Validation of Evoland-generated landscape trajectories

Richer representation of actor networks (Associations), social
processes relating to land use change

More explicit understanding of outputs, pattern/policy
relationships

More explicit incorporation of adaptive policy generation

Interactive actors and role-playing
For more information on
EvoLand
http://biosys.bre.orst.edu/evoland/
Support from the National Science Foundation, Program In
Biocomplexity in the Environment