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-99 | EM-379 | EM-463 | EM-875 |
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EM Short Name
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Landscape importance for crops, Europe | VELMA soil temperature, Oregon, USA | Curios/jewelry production, St. Croix, USVI | Valuing environmental ed., New York, New York |
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EM Full Name
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Landscape importance for crop-based production, Europe | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Relative curios/jewelry production (on reef), St. Croix, USVI | Valuing environmental education, Hudson River Park, New York, New York |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | None |
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EM Source Document ID
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228 | 317 | 335 | 416 |
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Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Hutcheson, W. Hoagland, P., and D. Jin |
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Document Year
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2012 | 2013 | 2014 | 2018 |
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Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Valuing environmental education as a cultural ecosystem service at Hudson River Park |
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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-99 | EM-379 | EM-463 | EM-875 |
| Not applicable | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | |
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Contact Name
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Marion Potschin | Alex Abdelnour | Susan H. Yee | Walter Hutcheson |
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Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | New York University, United States |
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Contact Email
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marion.potschin@nottingham.ac.uk | abdelnouralex@gmail.com | yee.susan@epa.gov | wwh235@nyu.edu |
EM Description
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EM ID
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EM-99 | EM-379 | EM-463 | EM-875 |
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Summary Description
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ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | 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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(6) production of curios and jewelry associated with each habitat." | ABSTRACT: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified |
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Biophysical Context
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No additional description provided | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional description provided | N/A |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | N?A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-99 | EM-379 | EM-463 | EM-875 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application |
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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 |
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-99 | EM-379 | EM-463 | EM-875 |
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Document ID for related EM
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Doc-231 | Doc-228 | Doc-13 | Doc-317 | None | None |
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EM ID for related EM
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EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-99 | EM-379 | EM-463 | EM-875 |
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EM Temporal Extent
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2000 | 1969-2008 | 2006-2007, 2010 | 2015 |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-99 | EM-379 | EM-463 | EM-875 |
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
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The EU-25 plus Switzerland and Norway | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Hudson River Park |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10-100 ha | 100-1000 km^2 | 10-100 ha |
Spatial Distribution of Computations
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EM ID
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EM-99 | EM-379 | EM-463 | EM-875 |
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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) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | volume, for 3-D feature | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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1 km x 1 km | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-99 | EM-379 | EM-463 | EM-875 |
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EM Computational Approach
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Logic- or rule-based | Numeric | Analytic | Numeric |
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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-99 | EM-379 | EM-463 | EM-875 |
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Model Calibration Reported?
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No | No | Yes | No |
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Model Goodness of Fit Reported?
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No | No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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Yes | No | Yes | No |
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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 | No | No |
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Model Sensitivity Analysis Include Interactions?
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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])
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| EM-99 | EM-379 | EM-463 | EM-875 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-99 | EM-379 | EM-463 | EM-875 |
| None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-99 | EM-379 | EM-463 | EM-875 |
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Centroid Latitude
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50.53 | 44.25 | 17.73 | 40.73 |
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Centroid Longitude
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7.6 | -122.33 | -64.77 | -74.01 |
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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 | Provided | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-99 | EM-379 | EM-463 | EM-875 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Forests | Near Coastal Marine and Estuarine | Created Greenspace |
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Specific Environment Type
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Not applicable | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coral reefs | Park |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of 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-99 | EM-379 | EM-463 | EM-875 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-99 | EM-379 | EM-463 | EM-875 |
| None Available | None Available | None Available | None Available |
EnviroAtlas URL
| EM-99 | EM-379 | EM-463 | EM-875 |
| None Available | Average Annual Precipitation | Hectares of cotton crops | Enabling Conditions |
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-99 | EM-379 | EM-463 | EM-875 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-99 | EM-379 | EM-463 | EM-875 |
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None |
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