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Title Date 1WAVES © 2014
Wealth Accounting and the Valuation of Ecosystem Services www.wavespartnership.org
Biophysical modeling of ecosystem services:Module 2D: Building Causal Models
WAVES Training Module
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Building causal models
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Types of causal models
1. Binary lookup tables
2. Qualitative lookup tables
3. Aggregated statistics lookup tables
4. Multiple layer lookup tables
5. Causal relationships
6. Spatial interpolation
7. Environmental regression models
Schröter, M., et al. In press. Lessons learned for spatial modeling of ecosystem services in support of ecosystem accounting. Forthcoming in: Ecosystem Services.
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Methods for mapping ecosystem services
Schröter et al. in press
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Causal relationships
Causal relationships (i.e., ecological production functions) can be used to model ecosystem services
Based on relationship between input data and predicted, quantified output
A
B C D?
Hypothesized relationships between three environmental variables (B-D) to predict ecosystem service (A)
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Spatial interpolation
Predict ecosystem services based on spatial autocorrelation of measured data points, sometimes using additional environmental layers
Kriging, Inverse Distance Weighting (GIS methods; Sumarga and Hein 2014)
Interpolated biodiversity data using ArcGIS: Inverse distance weighting (left); Empirical Bayesian Kriging (right)
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Environmental regression models
Use environmental layers as independent variables to predict ecosystem service values
Maximum entropy modeling (MaxEnt) software, e.g., Sherrouse et al. 2014, Sumarga and Hein 2014
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Steps to develop causal models
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Step 1: Define model elements/variablesWhich variables matter?
Which are most important?
Importance of scientific consensus, theory (or not?)
A
B
C
D
E
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Step 2: Build the conceptual model
Define the model structure:
What’s the relationship between the variables?
How do values of input variables influence outputs?
Data & math come later
A
B C D?
Hypothesized relationships between three environmental variables (B-D) to predict ecosystem service (A)
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Step 3: Collect & prepare data to parameterize model
Collect, clean, and otherwise prepare input data
Develop and document model assumptions and proxy data
A model is only as good as your data and your assumptions about model structure
“Garbage in, garbage out”
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Step 4: Testing, calibration & validation, sensitivity analysis
Calibration: Compare results of your model runs to an existing dataset
Validation: Set aside part of your dataset, develop & run the model on your remaining data, then go back and see how the model performs using the data you held back during model development
Sensitivity analysis: Test to see which variables have the biggest influence on the model outputs
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Probabilistic vs. deterministic modeling approaches
Probabilistic Explanatory power (e.g., r2) Explanation whyBased on inductive reasoning
Deterministic/mechanistic Explanation why Explanatory power (e.g., r2)Based on deductive reasoning
Good for:• Exploring patterns• Seeing if real-world patterns conform to theory• Incomplete datasets or situations with high uncertainty
Good for:• Testing/understanding why something works the way it does• When you have a strong understanding of how something works
Dangers (among others):• Putting too much faith into patterns found in the data that lack a reasonable theoretical foundation
Dangers (among others):•Sloppy model construction
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Calculating & communicating uncertainty
The same input data and equations will produce the same results every time unless something changes
Change input parameters, use stochastic inputs, and run repeatedly to generate a distribution of results (Monte Carlo simulation)
Some probabilistic models have built-in uncertainty estimates (e.g., Bayesian models, see Vigerstol & Aukema 2011)
INSERT UNCERTAINTY MAP FROM ARIES
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Uncertainty in decision making
Clear communication of uncertainty has been challenging to achieve (Ruckelshaus et al. in press)
People generally tend to:
Prefer a sure thing over a gamble (be risk averse) when outcomes are good (gains)
Reject the sure thing and accept the gamble (be risk-seeking) when choosing between multiple negative outcomes (Kahneman, D. 2011. Thinking, fast and slow. Farr, Stras, and Giroux: New York)
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Scaling up ecosystem services
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(can add material here if we need to cover scaling up of modeling results)
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Quantifying and mapping cultural ecosystem services
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Mixed methods modeling“One model
fits all” an unrealistic paradigm (Vigerstol et al. 2011, Bagstad et al. in press)
Deterministic models
(ARIES, InVEST, other process-based models): good data availability, systems
well-understood
PPGIS/Social values mapping
(incl. SolVES): cultural ecosystem services & non-use
values
Probabilistic approaches (ARIES, other
Bayesian/Monte Carlo approaches)
weaker data availability & systems knowledge, benefit of
carrying explicit uncertainty
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Cultural ecosystem service mapping
AestheticBiodiversityCulturalEconomicFutureHistoricIntrinsicLearningLife SustainingRecreationSpiritualTherapeutic
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Cultural ecosystem services surveysCan survey:
Recreational visitors
Residents
Focus groups
Using:
In-person
Mail surveys
Internet-based mapping
Mapping:
Points
Polygons
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Mapping cultural ecosystem services
MaxEnt or Kernel Density methods – extrapolate values across the landscape using relationship between points and underlying biophysical environment (land cover, landforms, elevation, slope, distance to roads/water/shoreline/infrastructure) – a spatial interpolation method
Social Values for Ecosystem Services – GIS tool (Sherrouse et al. 2014)
Extensive “Public Participatory GIS” work (Greg Brown and colleagues; http://www.landscapevalues.org
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Mapping cultural ecosystem services
Brown & Brabyn 2012
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Exercise 5: Building causal models
1. Starting with the ecosystem services and data sources identified in Exercise 1, build a causal model to quantify and map capacity for one ecosystem service.
1. What final output/metric do you want to use for the ecosystem service?
2. What input datasets could you use to help quantify that service, and to calibrate the model?
3. Where are you more and less certain about the elements included in the model, and its overall structure?
4. Who else would you want to review your causal model before moving ahead to test and refine it?
2. Each group presents their causal model to the full group for discussion.
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Could add slides based on Section 2.3.3 of Hein 2014 “Guidance for the biophysical mapping and analysis of ecosystem services in an ecosystem accounting context”
Add a few slides at the end of Level 2 training re: where can you get level 3 training. Also need a few general slides on advanced modeling topics – ABM, Monte Carlo simulation & uncertainty (see if Ioannins has good overview papers on env. Modeling)