Transcript Modeling Biocomplexity - Actors, Landscapes and

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
Actors
Decision-makers managing the
landscape by selecting policies
responsive to their objectives
Landscape Production Models
Landscape
Feedbacks
Multiagent
Decision-making
Scenario
Definition
Select policies and
generate land
management decision
affecting landscape
pattern
Generating Landscape Metrics Reflecting
Ecosystem Service Productions
Landscape
Spatial Container in
which landscape
changes, ES
Metrics are
Landscape
depicted
Feedbacks
Policies
Fundamental Descriptors of constraints and
actions defining land use management
decisionmaking
Autonomous Change Processes
Models of Non-anthropogenic Landscape
Change
ENVISION
– Triad of Relationships
Goals
•Economic Services
•Ecosystem Services
•Socio-cultural Services
Provide a common frame of reference
for actors, policies and landscape productions
Landscapes
Metrics of Production
Policy Definition
Landscape policies are decisions or plans of
action for accomplishing desired outcomes.
from:

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
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
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:
1)
Autonomous Processes: Represent processes
causing landscape changes independent of human
decision-making – e.g. climate change, vegetative
succession, forest growth, fire, flooding, ???
2)
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
representation

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
Models
Rural Residential
Expansion
ENVISION
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
Extraction
Scenario Results – Fish IBI
Envision Puget Sound Application
Data Sources
Evaluative Models
IDU’s – GSU/LULC/…
Impervious Surfaces
Agent Descriptors
Autonomous Process
Models
Rural/Urban Development
ENVISION
Policy Set(s)
Water Quality/Loading
(SPARROW)
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
more info at:
http://envision.bioe.orst.edu