envision y el modelamiento del paisaje - en ingles
TRANSCRIPT
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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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