EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
EM Short Name
em.detail.shortNameHelp
?
|
Agronomic ES and plant traits, Central French Alps | Salmon habitat values, west coast of Canada | InVEST crop pollination, Costa Rica | SWAT, Guanica Bay, Puerto Rico, USA |
EM Full Name
em.detail.fullNameHelp
?
|
Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada | InVEST crop pollination, Costa Rica | SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
EU Biodiversity Action 5 | None | InVEST | US EPA |
EM Source Document ID
|
260 | 286 | 279 | 334 |
Document Author
em.detail.documentAuthorHelp
?
|
Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Hu, W. and Y. Yuan |
Document Year
em.detail.documentYearHelp
?
|
2011 | 2003 | 2009 | 2013 |
Document Title
em.detail.sourceIdHelp
?
|
Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Valuing freshwater salmon habitat on the west coast of Canada | Modelling pollination services across agricultural landscapes | Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model |
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report |
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | Not applicable | |
Contact Name
em.detail.contactNameHelp
?
|
Sandra Lavorel | Duncan Knowler | Eric Lonsdorf | Yongping Yuan |
Contact Address
|
Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6 | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada |
Contact Email
|
sandra.lavorel@ujf-grenoble.fr | djk@sfu.ca | ericlonsdorf@lpzoo.org | Yuan.Yongping@epa.gov |
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
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." AUTHOR'S DESCRIPTION: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | 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." | 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." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..." | AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008). |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | None identified | None Identified |
Biophysical Context
|
Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | No additional description provided | Need to fill in |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | Habitat quality | No scenarios presented | Planting type, fertilizing rate, harvest rate |
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | New or revised 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
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-260 | Doc-270 | None | Doc-279 | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-81 | EM-82 | EM-83 | EM-179 | EM-183 | EM-180 | EM-181 | EM-338 | EM-339 | None |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
Not reported | 1989-1999 | 2001-2002 | 1981-2004 |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | future time |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Day |
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Physiographic or ecological | Other | Watershed/Catchment/HUC |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Central French Alps | South Thompson watershed | Large coffee farm, Valle del General | Guanica Bay, Puerto Rico watersheds |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 |
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
20 m x 20 m | Not applicable | 30 m x 30 m | 30m x 30m |
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Analytic | Numeric |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Yes | Unclear |
Yes ?Comment:Used 1981 and 1982 data to calibrate hydrology. |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | No |
No ?Comment:Calibration for both the stream flow and Sediment concentration of the mode |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | Yes |
Yes ?Comment:Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | Yes | Yes |
Yes ?Comment:Yes for both runoff and sediment |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | No | No | No |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
|
|
|
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
None |
|
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | 49.29 | 9.13 | 18.19 |
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | -123.8 | -83.37 | -66.76 |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Estimated | Estimated | Estimated |
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows. | Rivers and streams | Cropland and surrounding landscape | watershed |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is coarser 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community |
Other (Comment) ?Comment:Coho salmon stock |
Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-80 |
EM-177 ![]() |
EM-340 | EM-417 |
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-80 |
EM-177 ![]() |
EM-340 | EM-417 |
|
|
|
|
<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-80 |
EM-177 ![]() |
EM-340 | EM-417 |
|
|
|
None |