EcoService Models Library (ESML)
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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-374 | EM-438 |
EM-467 
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EM-603 |
EM-632
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EM-660
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EM Short Name
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InVEST carbon storage and sequestration (v3.2.0) | InVESTv3.0 Nutrient retention, Guánica Bay | Yasso07 v1.0.1, Switzerland | Chinook salmon value, Yaquina Bay, OR | Waterfowl pairs, CREP wetlands, Iowa, USA | RUM: Valuing fishing quality, Michigan, USA |
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EM Full Name
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InVEST v3.2.0 Carbon storage and sequestration | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA |
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EM Source or Collection
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InVEST | US EPA | InVEST | None | US EPA | None | None |
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EM Source Document ID
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315 | 338 | 343 | 324 | 372 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
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Document Author
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The Natural Capital Project | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson |
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Document Year
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2015 | 2017 | 2014 | 2012 | 2010 | 2014 |
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Document Title
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Carbon storage and sequestration - InVEST (v3.2.0) | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Validating tree litter decomposition in the Yasso07 carbon model | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Valuing recreational fishing quality at rivers and streams |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript |
Software and Access
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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| https://www.naturalcapitalproject.org/invest/ | http://www.naturalcapitalproject.org/invest/ | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | Not applicable | |
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Contact Name
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The Natural Capital Project | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
Stephen Jordan | David Otis | Richard Melstrom |
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Contact Address
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371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA |
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Contact Email
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invest@naturalcapitalproject.org | yee.susan@epa.gov | markus.didion@wsl.ch | jordan.steve@epa.gov | dotis@iastate.edu | melstrom@okstate.edu |
EM Description
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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Summary Description
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Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " |
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Specific Policy or Decision Context Cited
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None identified | Improving water quality | None identified | None reported | None identified | None identified |
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Biophysical Context
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Not applicable | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Yaquina Bay estuary | Prairie pothole region of north-central Iowa | stream and river reaches of Michigan |
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EM Scenario Drivers
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Optional future scenarios for changed LULC and wood harvest | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
N/A | No scenarios presented | targeted sport fish biomass |
EM Relationship to Other EMs or Applications
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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Method Only, Application of Method or Model Run
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Method Only | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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Document ID for related EM
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Doc-309 | Doc-309 | Doc-205 | Doc-342 | Doc-344 | None | None | None |
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EM ID for related EM
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EM-349 | EM-363 | EM-112 | EM-466 | EM-469 | EM-480 | EM-485 | EM-604 | EM-397 | EM-705 | EM-703 | EM-702 | EM-701 | EM-700 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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EM Temporal Extent
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Not applicable | 1980 - 2013 | 1993-2013 | 2003-2008 | 2002-2007 | 2008-2010 |
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EM Time Dependence
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time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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future time | other or unclear (comment) | future time | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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discrete | discrete | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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1 | 1 | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Year | Year | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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Bounding Type
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Not applicable | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC |
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Spatial Extent Name
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Not applicable | Guanica Bay Study Area | Switzerland | Pacific Northwest | CREP (Conservation Reserve Enhancement Program) wetland sites | HUCS in Michigan |
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Spatial Extent Area (Magnitude)
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Not applicable | 1000-10,000 km^2. | 10,000-100,000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
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application specific | 30 m x 30 m | 5 sites | Not applicable | multiple, individual, irregular shaped sites | reach in HUC |
EM Structure and Computation Approach
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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EM Computational Approach
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Analytic | Numeric | Numeric | Numeric | Analytic | Numeric |
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EM Determinism
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deterministic | deterministic | stochastic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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Model Calibration Reported?
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Not applicable | No | No | No | Unclear | No |
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Model Goodness of Fit Reported?
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Not applicable | No | No | No | No | Yes |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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Not applicable | No | Yes |
Yes ?Comment:Compared to a second methodological approach |
Unclear | No |
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Model Uncertainty Analysis Reported?
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Not applicable | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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Not applicable | No | No | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
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| EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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| None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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| None | None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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Centroid Latitude
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-9999 | 17.97 | 46.82 | 44.62 | 42.62 | 45.12 |
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Centroid Longitude
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-9999 | -66.93 | 8.23 | -124.02 | -93.84 | 85.18 |
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Centroid Datum
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Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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EM Environmental Sub-Class
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Not applicable | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | Forests | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams |
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Specific Environment Type
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Terrestrial environments, but not specified for methods | 13 LULC were used | forests | Yaquina Bay | Wetlands buffered by grassland set in agricultural land | stream reaches |
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EM Ecological Scale
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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EM Organismal Scale
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Not applicable | Not applicable | Community | Individual or population, within a species | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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| None Available | None Available | None Available |
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EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
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| EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-374 | EM-438 |
EM-467
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EM-603 |
EM-632
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EM-660
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| None | None | None | None |
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