EcoService Models Library (ESML)
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Compare EMs
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.
EM Identity and Description
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
EM Short Name
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Salmon habitat values, west coast of Canada | Decrease in erosion (shoreline), St. Croix, USVI | Global forest stock, biomass and carbon downscaled | EPIC agriculture model, Baden-Wurttemberg, Germany |
EM Full Name
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Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Global forest growing stock, biomass and carbon downscaled map | Carbon sequestration in soils of SW-Germany as affected by agricultural management—Calibration of the EPIC model for regional simulations |
EM Source or Collection
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None | US EPA | None | None |
EM Source Document ID
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286 | 335 | 442 | 482 |
Document Author
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Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner | Billen, N., Röder, C., Gaiser, T. and Stahr, K., |
Document Year
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2003 | 2014 | 2008 | 2009 |
Document Title
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Valuing freshwater salmon habitat on the west coast of Canada | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | A global forest growing stock, biomass and carbon map based on FAO statistics | Carbon sequestration in soils of SW-Germany as affected by agricultural management—calibration of the EPIC model for regional simulations |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
Not applicable | Not applicable | Not applicable | https://epicapex.tamu.edu/epic/ | |
Contact Name
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Duncan Knowler | Susan H. Yee | Georg Kindermann | Norbert Billen |
Contact Address
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School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | International Institute for Applied Systems Analysis, Laxenburg, Austria | University of Hohenheim, Institute of Soil Science and Land Evaluation, Emil Wolff Strasse 27, D-70593 Stuttgart, Germany |
Contact Email
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djk@sfu.ca | yee.susan@epa.gov | kinder(at)iiasa.ac.at | billen@uni-hohenheim.de |
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
Summary Description
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ABSTRACT: "In this paper, we present a framework for valuing benefits for fisheries from protecting areas from degradation, using the example of the Strait of Georgia coho salmon fishery in southern British Columbia, Canada. Our study improves upon previous methods used to value fish habitat in two major respects. First, we use a bioeconomic model of the coho fishery to derive estimates of value that are consistent with economic theory. Second, we estimate the value of changing the quality of fish habitat by using empirical analyses to link fish population dynamics with indices of land use in surrounding watersheds." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. " | Global emissions trading allows for agricultural measures to be accounted for the carbon sequestration in soils. The Environmental Policy Integrated Climate (EPIC) model was tested for central European site conditions by means of agricultural extensification scenarios. Results of soil and management analyses of different management systems (cultivation with mouldboard plough, reduced tillage, and grassland/fallow establishment) on 13 representative sites in the German State Baden-Württemberg were used to calibrate the EPIC model. Calibration results were compared to those of the Intergovernmental Panel on Climate Change (IPCC) prognosis tool. The first calibration step included adjustments in (a) N depositions, (b) N2-fixation by bacteria during fallow, and (c) nutrient content of organic fertilisers according to regional values. The mixing efficiency of implements used for reduced tillage and four crop parameters were adapted to site conditions as a second step of the iterative calibration process, which should optimise the agreement between measured and simulated humus changes. Thus, general rules were obtained for the calibration of EPIC for different criteria and regions. EPIC simulated an average increase of +0.341 Mg humus-C ha−1 a−1 for on average 11.3 years of reduced tillage compared to land cultivated with mouldboard plough during the same time scale. Field measurements revealed an average increase of +0.343 Mg C ha−1 a−1 and the IPCC prognosis tool +0.345 Mg C ha−1 a−1. EPIC simulated an average increase of +1.253 Mg C ha−1 a−1 for on average 10.6 years of grassland/fallow establishment compared to an average increase of +1.342 Mg humus-C ha−1 a−1 measured by field measurements and +1.254 Mg C ha−1 a−1 according to the IPCC prognosis tool. The comparison of simulated and measured humus C stocks was r2 ≥ 0.825 for all treatments. However, on some sites deviations between simulated and measured results were considerable. The result for the simulation of yields was similar. In 49% of the cases the simulated yields differed from the surveyed ones by more than 20%. Some explanations could be found by qualitative cause analyses. Yet, for quantitative analyses the available information from farmers was not sufficient. Altogether EPIC is able to represent the expected changes by reduced tillage or grassland/fallow establishment acceptably under central European site conditions of south-western Germany. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | Impact of different agricultural management strategies |
Biophysical Context
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No additional description provided | No additional description provided | No additional description provided | Central Europe agricultural sites |
EM Scenario Drivers
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Habitat quality | No scenarios presented | No scenarios presented | NA |
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
Document ID for related EM
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None | Doc-335 | None | Doc-478 |
EM ID for related EM
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EM-179 | EM-183 | EM-180 | EM-181 | EM-447 | EM-448 | None | EM-1012 | EM-1021 |
EM Modeling Approach
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
EM Temporal Extent
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1989-1999 | 2006-2007, 2010 | 1999-2005 |
4-20 years ?Comment:This paper compares agricultural plots that have used specific types of management practices over various periods ranging from 4-20 years. The beginning and end dates of those periods are not provided. |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | past time |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
other or unclear (comment) ?Comment:This paper compares agricultural plots that have used specific types of management practices over various periods ranging from 4-20 years. The beginning and end dates of those periods are not provided. |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
Bounding Type
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Physiographic or ecological | Physiographic or ecological | No location (no locational reference given) | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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South Thompson watershed | Coastal zone surrounding St. Croix | Global | Baden-Wurttemberg |
Spatial Extent Area (Magnitude)
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1000-10,000 km^2. | 100-1000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 |
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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Not applicable | 10 m x 10 m | 0.5 x 0.5 degrees | Not applicable |
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
Model Calibration Reported?
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Yes | Yes | No | Yes |
Model Goodness of Fit Reported?
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No | No |
Yes ?Comment:For the 0.5 grid level equation where the country forest level is missing. |
Yes |
Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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No | Yes | Yes | Yes |
Model Uncertainty Analysis Reported?
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No | No | No | Unclear |
Model Sensitivity Analysis Reported?
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Yes | No | No | Unclear |
Model Sensitivity Analysis Include Interactions?
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No | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
Centroid Latitude
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49.29 | 17.73 | 44.51 | 48.62 |
Centroid Longitude
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-123.8 | -64.77 | -123.51 | 9.03 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated |
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
EM Environmental Sub-Class
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Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Agroecosystems |
Specific Environment Type
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Rivers and streams | Coral reefs | Forests | Agriculture plots |
EM Ecological Scale
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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 corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
EM Organismal Scale
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Other (Comment) ?Comment:Coho salmon stock |
Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
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None Available | None Available | None Available |
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-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-177 ![]() |
EM-449 |
EM-948 ![]() |
EM-1020 |
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None |
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