Biophysical modeling of ecosystem services: Module 2D: Building Causal Models Objectives: Compare several causal models, and understand where their application will be more or less appropriate in your country. Understand the steps in building, parameterizing, testing, calibrating/validating, and running sensitivity analysis, using causal models. Understand the role of uncertainty in models and in communicating the results of an ecosystem services modeling exercise. Begin to build a simple causal model using group/expert knowledge. Discuss cultural ecosystem services here? WAVES Training Module

Building causal models

Types of causal models Binary lookup tables Qualitative lookup tables Aggregated statistics lookup tables Multiple layer lookup tables Causal relationships Spatial interpolation Environmental regression models #5: Expert or experimentally developed causal models. #6: GIS operations, e.g., Kriging (this is a Tier 3 topic). #7: Using regression model results in model development. 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 It may be overkill to repeat this again – no problem to remove it if so. 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 Hypothesized relationships between three environmental variables (B-D) to predict ecosystem service (A) These could be experimentally or expert-opinion derived or a combination. They may be called different names - causal models, belief networks, means-ends models, etc. ? B C D

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) For some measures, point data will be collected (for rain gages, species presence, etc.). Statistical methods can be used to generate a map based on point data. Results are very sensitive to the methods selected (as shown here); applying spatial interpolation well is definitely a Tier 3 topic. Sherrouse, B.C., et al. 2014. An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming. Ecological Indicators 36:68-79. Sumarga, E. and L. Hein. 2014. Mapping ecosystem services for land use planning, the case of central Kalimantan. Environmental Management 54:84-97.

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 The points marked on a map shows places people value for recreation. Using MaxEnt and environmental layers for land cover types, distance to infrastructure and shoreline, elevation, etc. we can use the points and the relationship between environmental layers to generate a map of recreational value.

Steps to develop causal models

Step 1: Define model elements/variables Which variables matter? Which are most important? Importance of scientific consensus, theory (or not?) E A Identify the variables that matter. For a hydrologic model, for instance, soil type, slope, precipitation, and vegetation type might matter (other variables might be important too). In deductive modeling (standard scientific method), models are built around theory/scientific consensus. In data-driven or inductive modeling, which is occurring more in the newer field of “big data,” recognizing emergent patterns becomes important, and theory secondary. C B D

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 Hypothesized relationships between three environmental variables (B-D) to predict ecosystem service (A) ? B C D

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” Proxy data are used when a specific dataset is not available, but another dataset that’s related to it is, and can be used instead. For instance, we might not have precipitation data available for a single rain event, but we might have mean monthly precipitation, so could use that.

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 Deterministic/mechanistic Explanation why Explanatory power (e.g., r2) Based on deductive reasoning Probabilistic Explanatory power (e.g., r2) Explanation why Based on inductive reasoning Good for: Exploring patterns Seeing if real-world patterns conform to theory Incomplete datasets or situations with high uncertainty Testing/understanding why something works the way it does When you have a strong understanding of how something works Inductive approach (probabilistic): starts with observations – gaining increasing credibility in use of big data/data mining Deductive approach (deterministic/mechanistic): starts with theory – traditional scientific approach Dangers (among others): Putting too much faith into patterns found in the data that lack a reasonable theoretical foundation 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) This is generally a Tier 3 topic. Vigerstol, K.L. and J.E. Aukema. 2011. A comparison of tools for modeling freshwater ecosystem services. Journal of Environmental Management 92:2403-2409. 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) Ruckelshaus, M., et al. In press. Notes from the field: Lessons learned from using ecosystem service approaches to inform real-world decisions. Forthcoming in: Ecological Economics.

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 Deterministic models (ARIES, InVEST, other process-based models): good data availability, systems well-understood Probabilistic approaches (ARIES, other Bayesian/Monte Carlo approaches) weaker data availability & systems knowledge, benefit of carrying explicit uncertainty “One model fits all” an unrealistic paradigm (Vigerstol et al. 2011, Bagstad et al. in press) PPGIS/Social values mapping (incl. SolVES): cultural ecosystem services & non-use values Different model approaches will be useful under different circumstances. Biophysical models are likely to work well for predicting provisioning and regulating services; public participatory GIS (PPGIS) is more appropriate for cultural ecosystem service mapping.

Cultural ecosystem service mapping Aesthetic Biodiversity Cultural Economic Future Historic Intrinsic Learning Life Sustaining Recreation Spiritual Therapeutic This is a typology that (mostly) corresponds to cultural and non-use values, and has been used in the past with PPGIS to map these services, sometimes in tandem with biophysical models for provisioning and regulating services.

Cultural ecosystem services surveys Can survey: Recreational visitors Residents Focus groups Using: In-person Mail surveys Internet-based mapping Mapping: Points Polygons Survey methods, which can vary widely, are used to understand overall preferences for cultural ecosystem services (relative values) and spatially valued locations.

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 These are then combined with spatial interpolation methods to produce a map of cultural ecosystem services.

Mapping cultural ecosystem services Social Values for Ecosystem Services (SolVES) is one commonly used tool for cultural ecosystem services mapping. Brown & Brabyn similarly use relationships between valued locations and LULC & other variables, producing the first national map of cultural ecosystem services, for New Zealand. (Brown, G. and L. Brabyn. 2012. The extrapolation of social values to a national level in New Zealand using landscape character classification. Applied Geography 35(1-2):84-94.) Finland commissioned a national PPGIS software for use by local governments & public agencies (Brown, G. and N. Fagerholm. In press. Empirical PPGIS/PGIS mapping of ecosystem services: A review and evaluation. Forthcoming in: Ecosystem Services.). Brown & Brabyn 2012

Exercise 5: Building causal models 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. What final output/metric do you want to use for the ecosystem service? What input datasets could you use to help quantify that service, and to calibrate the model? Where are you more and less certain about the elements included in the model, and its overall structure? Who else would you want to review your causal model before moving ahead to test and refine it? Each group presents their causal model to the full group for discussion. This is simply building a conceptual model, a “mindmap,” “influence diagram,” or “belief network.” In reality we’d draw from the literature and discussions with various experts in each field.

Could add slides based on Section 2. 3 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)