# Presentation: 209544

## Structured Decision Making for a Multi-Objective, Spatially-Explicit Problem

- Multi-species conservation planning

## Road Map

- The Problem
- Willamette River Basin, Oregon
- Components of SDM
- Application of rapid prototyping and SDM to WRB

## Problem

- Create a land-use plan that simultaneously maximizes biodiversity and economic value

## WRB

- Over 250 species in the basin
- 15 or more land cover types
- 10,000+ individual parcels of land
- Economics important
- Biodiversity important

## WRB

- Over 250 species in the basin
- 15 or more land cover types
- 10,000+ individual parcels of land
- Economics important
- Biodiversity important

- Finding a solution seems impossible...
- Must trust SDM

## Structured Decision Making

- Actions

- Costs

- Benefits

- Biological Model

- Objectives

## Structured Decision Making

- Step 1: Define our Problem/Goal:
- Maximize Biodiversity
- Maximize Economic Value
- Step 2: What are the key inputs/variables that will be included in the criteria
- Land Cover

## Questions arise after steps 1 and 2

- Should we use umbrella or indicator species?
- Should we weight species differently?
- How do we account for connectivity –
- Should we use “fragstats”?
- Perhaps Hanski’s Metapopulation Capacity

## Step 3:

- Organize the problem with a conceptual model (influence diagram)?
- Have a structured discussion on key factors, assumptions, uncertainty and lingering questions.
- If needed, identify alternative conceptual models, parameter values and scenarios to evaluate.

## Step 4: turn the conceptual model into a predictive, quantitative model using rapid prototyping

Step 5: Apply model predictions to your objectives
Interact with the model(s)
Evaluate a subset or hypothetical “easier” example to work out the kinks and increase our understanding of the problem
If the model output doesn’t relate well to your objectives it may not be the model for you.

- Step 5: Apply model predictions to your objectives
- Interact with the model(s)
- Evaluate a subset or hypothetical “easier” example to work out the kinks and increase our understanding of the problem
- If the model output doesn’t relate well to your objectives it may not be the model for you.

- Step 6:
- Decide what decision technique to use with your model relative to your objectives
- If it is to identify parameter values that meet a criteria, e.g. minimum habitat that allows persistence,
- then use optimization
- If determining what model to use:
- Bayesian analysis
- Choosing action based a single objective:
- decision tree
- Multiple objectives:
- SMART

- Step 7: Use the insights to address the full problem

## Back to the WRB

- Step 1: Define our Problem/Goal:
- Maximize Biodiversity
- Maximize Economic Value
- Step 2: What are the key inputs/variables that will be included in the criteria
- Land cover

## Step 3: Conceptual Model

- Land Cover

- Economic Suitability

- Habitat Suitability

- Economic Value

- Effective Area

- Abundance and Persistence

## Biological Model

- Define which parcels contain habitat for a species by using information about species-habitat compatibility and land cover
- Calculate upper and lower bounds for the number of breeding pairs on the landscape if all habitat patches were completely connected or completely isolated

## Biological Model

- Calculate a connectivity score for the landscape based on the distance between patches and the dispersal ability for the species
- Calculate the number of breeding pairs for a species on the landscape using the connectivity score to scale between upper and lower bounds
- Convert the number of breeding pairs for a species on the landscape into the probability that a species will be sustained on the landscape through use of a saturating function.

## First Goal

- Measure the values of a single landscape
- If we can do this, then we can compare any landscape and choose among a suite of them.

## Step 4: Rapid Prototype

- Applied Model to a “Toy Landscape”
- 14 x 14 grid
- Size chosen because 196 total cells fit easily into MS excel
- 3 main landcover types – Ag, Forestry and Natural (conserved)
- Used 97 species

## Biological Model

- Predict a land use pattern’s ability to support viable populations of a large set of species
- Each species’ appraisal of a land use pattern depends on three species-specific traits:
- habitat suitability
- the amount of habitat required for a breeding pair
- dispersal ability between suitable patches of habitat

## Simplifications

- Objective is habitat pattern for many species
- No genetics –
- Simply intractable and inappropriate given our objectives
- Habitat suitability simplified into three categories
- Unsuitable, partially suitable, completely suitable)

## Simplifications

- Objective is habitat pattern for many species
- Dispersal simplified into four categories
- Larger animals disperse farther
- Area requirement simplified to four categories
- Larger animals require more habitat
- Persistence requirement the same for all species

## Steps 5 and 6:

- Apply model predictions to your objectives
- Used heuristics to determine solutions rather than employ more formal optimization techniques
- Ran sensitivity analyses

## Optimizing Heuristics

- Start with a landscape with everything in all conservation
- Change one parcel to one that has an economic use
- Recalculate biodiversity and economic value
- Compare all possible changes and keep the parcel that minimizes the loss of biodiversity and maximizes the economic gain

## Results

## Results

- View different toy landscapes along the frontier
- May not be “optimal” but we understand the heuristics very well (not a black box) and we know that it can’t get much better

## Sensitivity Analyses

- Can use the prototype to do test the results against the assumptions in the model
- In our case, the interpretations were robust with regard to frontier shape.

## Step 7: Apply model to “real” landscape

## WRB

- Over 250 species in the basin
- 15 or more land cover types
- 10,000+ individual parcels of land
- Economics important
- Biodiversity important

## Step 7: Apply model to “real” landscape

## Further Application

- Frontiers with different budget constraints
- Different objectives – carbon sequestration

## Insights

- Modeling many species requires identifying common life-history characteristics (simplification)
- Having persistence as a metric required us to make additional assumptions
- Had to convert area into population size and population size into persistence
- Using persistence assumes you are modeling a closed system
- Using area does not make this assumption

## Number of Breeding Pairs on a Parcel

- The number of breeding pairs of species *s* that parcel *j* can support is:
- where *A**j** *is the area of parcel *j*
*C**sj*(*X**j*), is the habitat compatibility score of parcel *j *for species *s*, and
- AR*s** *is the amount of area needed by a breeding pair of species *s*.

## Parcel to Landscape:  Connectivity

- Maximum score (full connected landscape):  *ZMax**s* = sum *Z**sj* over all parcels
- Minimum score (no connections among patches): *ZMin**s* =  sum over patches that have self-sustaining populations (threshold number of breeding pairs)

## Connectivity

- Connectivity measure uses distances from one parcel to all habitat parcels
- where *d m**s**n**s** *is the distance between suitable habitat patch *m**s** *and suitable habitat patch *ns*, and
-  *s** *represents the reciprocal of the mean dispersal ability of species *s*.

## Connectivity

## Number of Breeding Pairs on a Landscape