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-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
EM Short Name
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Runoff potential of pesticides, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | FORCLIM v2.9, West Cascades, OR, USA | InVEST crop pollination, NJ and PA, USA | Visitation to natural areas, New England, USA |
EM Full Name
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Runoff potential of pesticides, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | InVEST crop pollination, New Jersey and Pennsylvania, USA | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA |
EM Source or Collection
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None | US EPA | US EPA | InVEST | US EPA |
EM Source Document ID
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254 | 137 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
279 | 436 |
Document Author
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Schriever, C. A., and Liess, M. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin |
Document Year
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2007 | 2011 | 2007 | 2009 | 2020 |
Document Title
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Mapping ecological risk of agricultural pesticide runoff | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Modelling pollination services across agricultural landscapes | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA |
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 |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | https://github.com/USEPA/Recreation_Benefits.git | |
Contact Name
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Carola Alexandra Schriever | Yongping Yuan | Richard T. Busing | Eric Lonsdorf | Nathaniel Merrill |
Contact Address
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Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, |
Contact Email
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carola.schriever@ufz.de | yuan.yongping@epa.gov | rtbusing@aol.com | ericlonsdorf@lpzoo.org | merrill.nathaniel@epa.gov |
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
Summary Description
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ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | 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. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." | ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " |
Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None Identified | None identified | None identified |
Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | No additional description provided | Natural area water bodies |
EM Scenario Drivers
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No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Two scenarios modelled, forests with and without fire | No scenarios presented | N/A |
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | 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-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
Document ID for related EM
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Doc-255 | Doc-256 | Doc-257 | Doc-142 | Doc-22 | Doc-23 | Doc-279 | None |
EM ID for related EM
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None | None | EM-146 | EM-208 | EM-186 | EM-340 | EM-338 | None |
EM Modeling Approach
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
EM Temporal Extent
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2000 | 1980-2006 | >650 yrs | 2000-2002 | 2017 |
EM Time Dependence
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time-dependent | time-stationary | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable | past time | Not applicable | past time |
EM Time Continuity
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discrete | Not applicable | discrete | Not applicable | discrete |
EM Temporal Grain Size Value
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1 | Not applicable | 1 | Not applicable | 1 |
EM Temporal Grain Size Unit
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Day | Not applicable | Year | Not applicable | Day |
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Other | Point or points |
Spatial Extent Name
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EU-15 | East Fork Kaskaskia River watershed basin | West Cascades, Oregon | Central New Jersey and east-central Pennsylvania | Cape Cod |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. |
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
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 distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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10 km x 10 km | 1 km^2 | 0.08 ha | 30 m x 30 m | water feature edge (beach) |
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
EM Computational Approach
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Analytic | Numeric | Numeric | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
Model Calibration Reported?
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No | No | No | Unclear | Yes |
Model Goodness of Fit Reported?
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No | No | No | No |
Yes ?Comment:Random forest model performance statistics |
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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No | Yes | Yes |
Yes ?Comment:Aggregate native bee abundance on watermelon flowers was measured at 23 sites in 2005. Species richness was measured using the specimens collected from watermelon flowers at the end of the sampling period. |
Yes |
Model Uncertainty Analysis Reported?
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Yes | Yes | No | No | Unclear |
Model Sensitivity Analysis Reported?
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Yes | Unclear | No | No | Yes |
Model Sensitivity Analysis Include Interactions?
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No | Not applicable | Not applicable | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
Centroid Latitude
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50.01 | 38.69 | 44.24 | 40.2 | 41.72 |
Centroid Longitude
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4.67 | -89.1 | -122.24 | -74.8 | -70.29 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Estimated | Estimated | Estimated |
EM ID
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EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Forests | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Lakes and Ponds | Near Coastal Marine and Estuarine |
Specific Environment Type
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Arable lands in near-stream environments | Row crop agriculture in Kaskaskia river basin | Primarily conifer forest | Cropland and surrounding landscape | beaches |
EM Ecological Scale
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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 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-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
EM Organismal Scale
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Not applicable | Not applicable | Species | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
None Available | None Available |
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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-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
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-92 | EM-97 |
EM-224 ![]() |
EM-339 | EM-943 |
None |
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None |
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