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-91 | EM-103 | EM-119 |
EM-321 ![]() |
EM Short Name
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RHyME2, Upper Mississippi River basin, USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | Landscape importance for wildlife products, Europe | Erosion prevention by vegetation, Portel, Portugal |
EM Full Name
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RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | Landscape importance for wildlife products, Europe | Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal |
EM Source or Collection
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US EPA | US EPA | EU Biodiversity Action 5 | EU Biodiversity Action 5 |
EM Source Document ID
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123 | 275 | 228 | 281 |
Document Author
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Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Haines-Young, R., Potschin, M. and Kienast, F. | Guerra, C.A., Pinto-Correia, T., Metzger, M.J. |
Document Year
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2013 | 2014 | 2012 | 2014 |
Document Title
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Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy |
Document Status
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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 |
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Liem Tran |
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 |
Marion Potschin | Carlos A. Guerra |
Contact Address
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Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | 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 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal |
Contact Email
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ltran1@utk.edu | frazier@nceas.ucsb.edu | marion.potschin@nottingham.ac.uk | cguerra@uevora.pt |
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
Summary Description
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ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | 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: "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 … “Wildlife Products” . . . 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, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas." | 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)." |
Specific Policy or Decision Context Cited
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Not reported | None identified | None identified | None identified |
Biophysical Context
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No additional description provided | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | No additional description provided | 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. |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | 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 |
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | 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 | 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
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
Document ID for related EM
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Doc-123 | None | Doc-231 | Doc-228 | Doc-282 | Doc-283 | Doc-284 | Doc-285 |
EM ID for related EM
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None | None | EM-99 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | None |
EM Modeling Approach
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
EM Temporal Extent
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1987-1997 | December 2007 - November 2008 | 2000 | January to December 2003 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Month |
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
Bounding Type
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Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Geopolitical |
Spatial Extent Name
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Upper Mississippi River basin; St. Croix River Watershed | Yaquina Estuary (intertidal), Oregon, USA | The EU-25 plus Switzerland and Norway | Portel municipality |
Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 1-10 km^2 | >1,000,000 km^2 | 100-1000 km^2 |
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
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) |
Spatial Grain Type
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NHDplus v1 | other (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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NHDplus v1 | 0.87-104.29 ha | 1 km x 1 km | 250 m x 250 m |
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
EM Computational Approach
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Numeric | Analytic | Logic- or rule-based | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
Model Calibration Reported?
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Yes | Unclear | No | No |
Model Goodness of Fit Reported?
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Yes | No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | Yes | No |
Model Uncertainty Analysis Reported?
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No | No | No | No |
Model Sensitivity Analysis Reported?
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No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
No | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
Centroid Latitude
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42.5 | 44.62 | 50.53 | 38.3 |
Centroid Longitude
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-90.63 | -124.06 | 7.6 | -7.7 |
Centroid Datum
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WGS84 | None provided | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Estimated | Estimated |
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
EM Environmental Sub-Class
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Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland |
Specific Environment Type
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None | Estuarine intertidal | Not applicable | Silvo-pastoral system |
EM Ecological Scale
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Ecosystem | 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 coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-91 | EM-103 | EM-119 |
EM-321 ![]() |
None Available |
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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-91 | EM-103 | EM-119 |
EM-321 ![]() |
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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-91 | EM-103 | EM-119 |
EM-321 ![]() |
None |
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