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An Alternative Futures Approach to

Understanding Landscape Dynamics

and ServicesKellie Vache, PhD.

Biological & Ecological Engineering Oregon State University

Today’s Discussion Overview of alternative futures approach to

socio-ecological modeling

Description of one approach using Envision

Example applications Andrews Forest

Puget Sound

Socio-Ecological Modeling

To Start - A Definition of Biocomplexity

Term used to describe complex structures, interactions, adaptive capabilities and dynamics

diverse set of biological and ecological systems

multiple spatial and temporal scales

Many Approaches!!! Some focusing on capturing richness of system dynamics, some on complex adaptive systems approaches

Challenge – How to make these operational?

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 Ultimately are intended to assist in

improving land management decision-making

Software-based Alternative Futures

A mechanism to include biocomplexity in alternative Futures – to do so requires: Easy to use interface

Present results in a format useful to end users

Spatially and temporally explicit

Extensible to incorporate evolving “best” science

Internal feedback

Envision Components

Site Selection and Characterization

Aggregate Evaluation of Management Alternatives

Alternative Scenario Selection

Detailed Evaluation of

Individual Services

Datasets Visualizations Landscape Production Evaluators

Water Quality

Carbon

Other ESE’s

…

Alternatives

Analysis Framework and Architecture

Stressors Drivers

Goals Policies

Approach: Multi-Agent Modeling

Model the behavior and actions agents (actors)

represents land management decisions of actors with authority over parcels of land

Actor decisions implemented through policies that guide & constrain potential actions

Ecosystem Services (e.g. forest succession, wetland function) can be simultaneously modeled

Multiagent Decision-making

Envision – Conceptual Structure

Actors Decision-makers managing the landscape by selecting policies responsive to their objectives

Policies

Fundamental Descriptors of constraints and actions defining land use management

decisionmaking

ScenarioDefinition

Autonomous Change ProcessesModels of Non-anthropogenic Landscape Change

Ecosystem Service ModelsGenerating Landscape Metrics Reflecting

Landscape Productions

Landscape Spatial Container in which landscape

changes, ES Metrics are

depicted

LandscapeFeedbacksSelect policies and

generate land management decision affecting landscape pattern

ENVISION – Triad of Relationships

Polic

iesIn

tent

ions

Actors

Values

LandscapesService Metrics

Provide a common frame of referencefor actors, policies and landscape productions

Goals• Economic Services• Ecosystem Services• Socio-cultural Services

Policy DefinitionLandscape 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 Envision

Describe actions available to actors Primary Characteristics:

Applicable Site Attributes (Spatial Query) Effectiveness of the Policy at addressing goals 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, 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, ???

Some Examples From Northwestern US

Some Examples From Northwestern US

Puget Sound

Andrews Forest

Example 1. Andrews Forest

HJ Andrews(LOOK – 6200 ha)

WS10 (10 ha)

WS02 (60 ha)

WS03 (101 ha)WS08 (21 ha)

MACK (580 ha)

WS09(9 ha)

Photographed by Al Levno Date: 7/91

HI15

PRIMET

Envision Andrews Forest

195 km2

25 year simulation

Population growth:~10,000~18,500

Envision Andrews Forest - Scenarios

Scenario Name Key Scenario Features

Conservation – Current Climate

Discourage low-density development, Assume climate is similar to current

Conservation – Warmer Climate

Discourage low-density development, Hotter, drier summers rainier winters.

Development – Current Climate

Allow low density developmentAssume climate is similar to current.

Development – Warmer Climate

Allow low density developmentHotter, drier summers Rainier winters

EN

VIS

ION

Mean Age at Harvest

Carbon Sequestration

Forest Products Extraction

Harvested Acreage

Fish Habitat (IBI)

Resource Lands Protection

Evaluative ModelsData Sources

Autonomous ProcessModels

Landscape Data

Rural Residential Expansion

Policy Set(s)

Agent Descriptors

Vegetative Succession

Climate Change

Envision Andrews Forest

Conservation Scenario

Development Scenario

Landcover Over 25 Yrs

Scenario Results – Forest Carbon

Scenario Results – Forest Product Extraction

Scenario Results – Fish IBI

Example 2. Puget Sound

42,800 km2

60 year simulation

Population growth:~4.2 million to~7.0 million in 2060

Envision Puget Sound- Scenarios

Scenario Name Key Scenario Features

Status Quo continue current trends

Managed Growth conserving/restoring habitats,protecting resource lands, denser development pattern near urban areas

Unconstrained Growth allow lower density patternsless habitat protectionless resource land protection

Three Different Scenarios

EN

VIS

ION

Impervious Surfaces

Water Quality/Loading (SPARROW)

Nearshore Habitat (Controlling Factors Model)

Resource Lands Protection

Evaluative ModelsData Sources

Autonomous ProcessModels

Landscape Data

Rural/Urban Development

Policy Set(s)

Agent Descriptors

Expansion of Nearshore Modifications

Population Growth Residential Land Supply

INVEST Tier 1 Carbon

Envision Puget Sound

Puget Sound

Seattle Area

Seattle Area

Mt Rainier

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

Interactions between population growth, landscape development and ecosystem services drive socio-ecological systems, and need to be accommodated

Engagement with stakeholders is critical to define decision processes, desired outcomes endpoints

Thanks to Dr. John Bolte

and the Envision Development Team

Muchas Gracias!

more info at:http://envision.bioe.orst.

edu

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