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-103 |
EM-321 
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EM-491 |
EM-728
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EM Short Name
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Birds in estuary habitats, Yaquina Estuary, WA, USA | Erosion prevention by vegetation, Portel, Portugal | EnviroAtlas - Crops with no pollinator habitat | Seed mix and mowing in prairie reconstruction, USA |
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EM Full Name
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Bird use of estuarine habitats, Yaquina Estuary, WA, USA | Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | US EPA EnviroAtlas - Acres of pollinated crops with no nearby pollinator habitat, USA | Seed mix design and first year management in prairie reconstruction, IA, USA |
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EM Source or Collection
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US EPA | EU Biodiversity Action 5 | US EPA | EnviroAtlas | None |
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EM Source Document ID
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275 | 281 | 262 | 395 |
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Document Author
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Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | US EPA Office of Research and Development - National Exposure Research Laboratory | Meissen, J. C., A. J. Glidden, M. E. Sherrard, K. J. Elgersma, and L. L. Jackson |
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Document Year
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2014 | 2014 | 2013 | 2019 |
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Document Title
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Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | EnviroAtlas - National | Seed mix design and first year management influence multifunctionality and cost-effectiveness in prairie reconstruction |
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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 on US EPA EnviroAtlas website | Published journal manuscript |
Software and Access
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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| Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not applicable | |
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Contact Name
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M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Carlos A. Guerra | EnviroAtlas Team | Justin Meissen |
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Contact Address
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Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | Not reported | Tallgrass Prairie Center, 2412 West 27th Street, Cedar Falls, IA 50614-0294, USA |
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Contact Email
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frazier@nceas.ucsb.edu | cguerra@uevora.pt | enviroatlas@epa.gov | justin.meissen@uni.edu |
EM Description
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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Summary Description
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AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)." | DATA FACT SHEET: "This EnviroAtlas national map estimates the total acres of agricultural crops within each 12-digit hydrologic unit (HUC) that have varying amounts of nearby forested pollinator habitat. The crop types selected from the U.S. Department of Agriculture Cropland Data Layer (CDL) require (or would benefit from) the presence of pollinators, but crops may have no nearby native pollinator habitat. This metric is based on the average flight distance of native bees, both social and solitary, that nest in woodland habitats and forage on native plants and cultivated crops." "The metric was generated using the ESRI ArcMap Neighborhood Distance tool in conjunction with a blended landcover grid, which included the 2006 National Land Cover Dataset (NLCD) and U.S. Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (CDL). Pollinator habitat is defined as trees (fruit, nut, deciduous, and evergreen) for nesting and associated woodland for additional pollen sources. Crops that either require or benefit from pollination were selected and a distance measure of 2.8 kilometers (the average bee species’ foraging distance from the nest4) was used to assess presence or absence of nearby native pollinator habitat. The total area of crops without nearby pollinator habitat was summarized by 12-digit HUC boundaries taken from the NHDPlusV2 Watershed Boundary Dataset (WBD Snapshot)." | ABSTRACT: "Agricultural intensification continues to diminish many ecosystem services in the North American Corn Belt. Conservation programs may be able to combat these losses more efficiently by developing initiatives that attempt to balance multiple ecological benefits. In this study, we examine how seed mix design and first year management influence three ecosystem services commonly provided by tallgrass prairie reconstructions (erosion control, weed resistance, and pollinator resources). We established research plots with three seed mixes, with and without first year mowing. The grass-dominated “Economy” mix had 21 species and a 3:1 grass-to-forb seeding ratio. The forb-dominated “Pollinator”mix had 38 species and a 1:3 grass-to-forb seeding ratio. The grass:forb balanced “Diversity” mix, which was designed to resemble regional prairie remnants, had 71 species and a 1:1 grass-to-forb ratio. To assess ecosystem services, we measured native stem density, cover, inflorescence production, and floral richness from 2015 to 2018. The Economy mix had high native cover and stem density, but produced few inflorescences and had low floral richness. The Pollinator mix had high inflorescence production and floral richness, but also had high bare ground and weed cover. The Diversity mix had high inflorescence production and floral richness (comparable to the Pollinator mix) and high native cover and stem density (comparable to the Economy mix). First year mowing accelerated native plant establishment and inflorescence production, enhancing the provisioning of ecosystem services during the early stages of a reconstruction. Our results indicate that prairie reconstructions with thoughtfully designed seed mixes can effectively address multiple conservation challenges." |
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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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Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure. | No additional description provided | The site, located at the Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa, is relatively level with slopes not exceeding a 5% grade. Soil composition is primarily poorly drained Clyde clay loams with a minor component of somewhat poorly drained Floyd loams. Sub-surface tile drains exist on site and are spaced approximately 18–24m apart. The land was used for corn and soybean production prior to site establishment in 2015. |
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EM Scenario Drivers
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No scenarios presented | Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area | No scenarios presented | Seed mix design |
EM Relationship to Other EMs or Applications
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | 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 | 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.detail.relatedEmHelp
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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Document ID for related EM
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None | Doc-282 | Doc-283 | Doc-284 | Doc-285 | None | Doc-394 |
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EM ID for related EM
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None | None | None | EM-719 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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EM Temporal Extent
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December 2007 - November 2008 | January to December 2003 | 2001-2015 | 2015-2018 |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-dependent |
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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 | discrete |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Month | Not applicable | Year |
EM Spatial Extent
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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Bounding Type
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Physiographic or ecological | Geopolitical | Geopolitical | Other |
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Spatial Extent Name
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Yaquina Estuary (intertidal), Oregon, USA | Portel municipality | conterminous United States | Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa |
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Spatial Extent Area (Magnitude)
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1-10 km^2 | 100-1000 km^2 | >1,000,000 km^2 | <1 ha |
Spatial Distribution of Computations
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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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 distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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other (habitat type) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
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0.87-104.29 ha | 250 m x 250 m | irregular | 6.1 m x 8.53 m |
EM Structure and Computation Approach
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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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 | stochastic |
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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-103 |
EM-321
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EM-491 |
EM-728
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Model Calibration Reported?
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Unclear | No | No | Not applicable |
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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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No | No | No | 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-103 |
EM-321
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EM-491 |
EM-728
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-103 |
EM-321
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EM-491 |
EM-728
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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Centroid Latitude
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44.62 | 38.3 | 39.5 | 42.93 |
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Centroid Longitude
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-124.06 | -7.7 | -98.35 | -92.57 |
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Centroid Datum
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None provided | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-103 |
EM-321
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EM-491 |
EM-728
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands |
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Specific Environment Type
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Estuarine intertidal | Silvo-pastoral system | Terrestrial | prairie/grassland reconstruction at demonstration farm site |
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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 coarser than that of the Environmental Sub-class | Ecological scale is finer than that of 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-103 |
EM-321
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EM-491 |
EM-728
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EM Organismal Scale
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Guild or Assemblage | Not applicable | Guild or Assemblage | Community |
Taxonomic level and name of organisms or groups identified
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| EM-103 |
EM-321
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EM-491 |
EM-728
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None Available |
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None Available |
EnviroAtlas URL
| EM-103 |
EM-321
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EM-491 |
EM-728
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| None Available | Average Annual Precipitation | GAP Ecological Systems, The Watershed Boundary Dataset (WBD) | None Available |
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.detail.cicesHelp
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| EM-103 |
EM-321
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EM-491 |
EM-728
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-103 |
EM-321
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EM-491 |
EM-728
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None |