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-87 | EM-449 | EM-838 |
EM-948 
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EM Short Name
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Area & hotspots of soil accumulation, South Africa | Decrease in erosion (shoreline), St. Croix, USVI | Eastern meadowlark abundance, Piedmont region, USA | Global forest stock, biomass and carbon downscaled |
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EM Full Name
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Area and hotspots of soil accumulation, South Africa | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Eastern meadowlark abundance, Piedmont ecoregion, USA | Global forest growing stock, biomass and carbon downscaled map |
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EM Source or Collection
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None | US EPA | None | None |
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EM Source Document ID
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271 | 335 | 405 | 442 |
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Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Riffel, S., Scognamillo, D., and L. W. Burger | Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner |
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Document Year
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2008 | 2014 | 2008 | 2008 |
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Document Title
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Mapping ecosystem services for planning and management | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | A global forest growing stock, biomass and carbon map based on FAO statistics |
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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 |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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| Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Benis Egoh | Susan H. Yee | Sam Riffell | Georg Kindermann |
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Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | International Institute for Applied Systems Analysis, Laxenburg, Austria |
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Contact Email
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Not reported | yee.susan@epa.gov | sriffell@cfr.msstate.edu | kinder(at)iiasa.ac.at |
EM Description
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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Summary Description
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AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover." | 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:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | 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/. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | None reported | None identified |
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Biophysical Context
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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | Conservation Reserve Program lands left to go fallow | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | 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 | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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Document ID for related EM
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Doc-271 | Doc-335 | Doc-405 | None |
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EM ID for related EM
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EM-85 | EM-86 | EM-88 | EM-447 | EM-448 | EM-831 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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EM Temporal Extent
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Not reported | 2006-2007, 2010 | 2008 | 1999-2005 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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Bounding Type
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Geopolitical | Physiographic or ecological | Physiographic or ecological | No location (no locational reference given) |
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Spatial Extent Name
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South Africa | Coastal zone surrounding St. Croix | Piedmont Ecoregion | Global |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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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 lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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Distributed across catchments with average size of 65,000 ha | 10 m x 10 m | Not applicable | 0.5 x 0.5 degrees |
EM Structure and Computation Approach
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | 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-87 | EM-449 | EM-838 |
EM-948
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Model Calibration Reported?
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No | Yes | Yes | No |
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Model Goodness of Fit Reported?
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No | No | No |
Yes ?Comment:For the 0.5 grid level equation where the country forest level is missing. |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | Yes | No | Yes |
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Model Uncertainty Analysis Reported?
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No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | Yes | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Unclear | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-87 | EM-449 | EM-838 |
EM-948
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-87 | EM-449 | EM-838 |
EM-948
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| None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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Centroid Latitude
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-30 | 17.73 | 36.23 | 44.51 |
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Centroid Longitude
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25 | -64.77 | -81.9 | -123.51 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Grasslands | Forests |
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Specific Environment Type
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Not applicable | Coral reefs | grasslands | Forests |
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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 and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-87 | EM-449 | EM-838 |
EM-948
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EM Organismal Scale
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Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-87 | EM-449 | EM-838 |
EM-948
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| None Available | None Available |
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None Available |
EnviroAtlas URL
| EM-87 | EM-449 | EM-838 |
EM-948
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| None Available | National Hydrography Dataset Plus (NHD PlusV2) | GAP Ecological Systems, U.S. EPA (Omernik) ecoregions | GAP Ecological Systems |
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)
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| EM-87 | EM-449 | EM-838 |
EM-948
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None |
<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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-87 | EM-449 | EM-838 |
EM-948
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| None |
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None |