Title Date1 WAVES © 2014 Wealth Accounting and the Valuation of Ecosystem Services Biophysical...

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


Building causal models


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.


Methods for mapping ecosystem services

Schröter et al. in press


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)


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)


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


Steps to develop causal models


Step 1: Define model elements/variablesWhich variables matter?

Which are most important?

Importance of scientific consensus, theory (or not?)

A

B

C

D

E


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)


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”


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


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


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


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)


Scaling up ecosystem services


(can add material here if we need to cover scaling up of modeling results)


Quantifying and mapping cultural ecosystem services


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


Cultural ecosystem service mapping

AestheticBiodiversityCulturalEconomicFutureHistoricIntrinsicLearningLife SustainingRecreationSpiritualTherapeutic


Cultural ecosystem services surveysCan survey:

Recreational visitors

Residents

Focus groups

Using:

In-person

Mail surveys

Internet-based mapping

Mapping:

Points

Polygons


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

http://www.landscapevalues.org/


Mapping cultural ecosystem services

Brown & Brabyn 2012


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.


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)

Title Date1 WAVES © 2014 Wealth Accounting and the Valuation of Ecosystem Services Biophysical...

Documents

Transcript of Title Date1 WAVES © 2014 Wealth Accounting and the Valuation of Ecosystem Services Biophysical...