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-79 | EM-379 |
EM-660 ![]() |
EM Short Name
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Divergence in flowering date, Central French Alps | VELMA soil temperature, Oregon, USA | RUM: Valuing fishing quality, Michigan, USA |
EM Full Name
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Functional divergence in flowering date, Central French Alps | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None |
EM Source Document ID
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260 | 317 |
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. |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson |
Document Year
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2011 | 2013 | 2014 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Valuing recreational fishing quality at rivers and streams |
Document Status
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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 |
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
Not applicable | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | |
Contact Name
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Sandra Lavorel | Alex Abdelnour | Richard Melstrom |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | abdelnouralex@gmail.com | melstrom@okstate.edu |
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | 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: " 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. " |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | 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. | stream and river reaches of Michigan |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | targeted sport fish biomass |
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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New or revised model | Application of existing 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-79 | EM-379 |
EM-660 ![]() |
Document ID for related EM
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Doc-260 | Doc-269 | Doc-13 | Doc-317 | None |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | None |
EM Modeling Approach
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
EM Temporal Extent
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2007-2008 | 1969-2008 | 2008-2010 |
EM Time Dependence
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time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable |
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
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Central French Alps | H. J. Andrews LTER WS10 | HUCS in Michigan |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 ha | 100,000-1,000,000 km^2 |
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
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) |
Spatial Grain Type
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area, for pixel or radial feature | volume, for 3-D feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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20 m x 20 m | 30 m x 30 m surface pixel and 2-m depth soil column | reach in HUC |
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
EM Computational Approach
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Analytic | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
Model Calibration Reported?
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No | No | No |
Model Goodness of Fit Reported?
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Yes | No | Yes |
Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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No | No | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-79 | EM-379 |
EM-660 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-379 |
EM-660 ![]() |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
Centroid Latitude
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45.05 | 44.25 | 45.12 |
Centroid Longitude
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6.4 | -122.33 | 85.18 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Provided | Estimated |
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Forests | Rivers and Streams |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | stream reaches |
EM Ecological Scale
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Ecological scale is coarser 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 of differentiation of organisms modeled
EM ID
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EM-79 | EM-379 |
EM-660 ![]() |
EM Organismal Scale
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Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-379 |
EM-660 ![]() |
None Available | None Available |
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EnviroAtlas URL
EM-79 | EM-379 |
EM-660 ![]() |
None Available | Average Annual Precipitation | The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate |
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-79 | EM-379 |
EM-660 ![]() |
None | 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-79 | EM-379 |
EM-660 ![]() |
None | None |
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