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Envisioning Future
Landscape Trajectories
An Alternative Futures Approach to
Understanding Dynamics of
Landscape Change
John Bolte
Biological & Ecological Engineering Department
Oregon State University
Today’s Discussion
Overview of alternative futures approach to
understanding landscape dynamics
Description of one approach using Envision
Example applications
H.J. Andrews LTER
Puget Sound
Alternative Futures Projects
Examine multiple scenarios of trends and assumptions
about future conditions, generally using one or more
models of change,
Assist in incorporating stakeholder interactions to
define goals, constraints, trajectories, drivers, outcomes
Allow visualization of the results in a variety of types
and formats
Ultimately are intended to assist in improving land
management decision-making
Approach: Multi-agent Modeling
Based on modeling behavior and actions of
autonomous, adaptive agents (actors)
Our approach: spatially explicit, represents land
management decisions of entities (actors) with
authority over parcels of land
Actor decisions implemented through policies that
guide & constrain potential actions
Autonomous processes (e.g. succession)
simultaneously modeled
Envision – Conceptual Structure
Decision-makers managing the
landscape by selecting policies
responsive to their objectives
Landscape Production Models
Select policies and
generate land
management decision
affecting landscape
Generating Landscape Metrics Reflecting
Ecosystem Service Productions
Spatial Container in
which landscape
changes, ES
Metrics are
Fundamental Descriptors of constraints and
actions defining land use management
Autonomous Change Processes
Models of Non-anthropogenic Landscape
– Triad of Relationships
•Economic Services
•Ecosystem Services
•Socio-cultural Services
Provide a common frame of reference
for actors, policies and landscape productions
Metrics of Production
Policy Definition
Landscape policies are decisions or plans of
action for accomplishing desired outcomes.
Lackey, R.T. 2006. Axioms of ecological
policy. Fisheries. 31(6): 286-290.
Policies in ENVISION
Policies are a decision or plan of action for accomplishing a
desired outcome; they are a fundamental unit of computation in
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
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]
Models in ENVISION
Models are “plug-ins” of two types:
Autonomous Processes: Represent processes
causing landscape changes independent of human
decision-making – e.g. climate change, vegetative
succession, forest growth, fire, flooding, ???
Evaluative Models – Generate production statistics
and report back how well the landscape is doing a
producing metrics of interest – e.g. carbon
sequestration, habitat production, land availability,
risk, ???
Models in ENVISION
A well-defined, relatively simple, yet robust interface
specification is defined for both Autonomous
Processes and Evaluative Models.
Models can expose input and output variables
Models have full access to the underlying spatial
representation, policy sets, exposed variables, actor
representation, and spatial engine
Models can make changes to the underlying landscape
Envision automatically manages all exposed model data
Envision Andrews Application
Data Sources
Evaluative Models
Parcels (IDU’s)
Mean Age at Harvest
Agent Descriptors
Autonomous Process
Rural Residential
Policy Set(s)
Carbon Sequestration
Forest Products Extraction
Harvested Acreage
Fish Habitat (IBI)
Vegetative Succession
Climate Change
Resource Lands Protection
Envision Andrews - Scenarios
Conservation - no Climate Change
Development - no Climate Change
Conservation - with Climate Change
Development - with Climate Change
Envision Andrews Study Area
Scenario Results – Forest Carbon
Scenario Results – Forest Product
Scenario Results – Fish IBI
Envision Puget Sound Application
Data Sources
Evaluative Models
Impervious Surfaces
Agent Descriptors
Autonomous Process
Rural/Urban Development
Policy Set(s)
Water Quality/Loading
Nearshore Habitat
(Controlling Factors Model)
INVEST Tier 1 Carbon
Expansion of
Nearshore Modifications
Resource Lands Protection
Population Growth
Residential Land Supply
Envision Puget Sound- Scenarios
Status Quo – continue current trends
Managed Growth – adopt a suite of additional
policies aimed at conserving/restoring habitats,
protecting resource lands, emphasizing denser
development pattern near urban areas
Unconstrained Growth – allow lower density
patterns, less habitat protection, less resource
land protection
Puget Sound
South Sound
Bainbridge Island
Ferry Terminal Area
Lessons Learned
Alternative future assessments are fundamentally place-based
and client-dependent: Each application is different.
Commonalities do exist and should be exploited within an
extensible, adaptable DSS framework
Good software design is critical
Engagement with stakeholders is critical to define decision
processes, desired outcomes endpoints
Defensible, place-specific models are critical
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