EcoService Models Library (ESML)

Compare EMs

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 Criteria

Comparing:

EM-177
EM-449
EM-838
EM-1020

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
EM Short Name em.detail.shortNameHelp ?	Salmon habitat values, west coast of Canada	Decrease in erosion (shoreline), St. Croix, USVI	Eastern meadowlark abundance, Piedmont region, USA	EPIC agriculture model, Baden-Wurttemberg, Germany
EM Full Name em.detail.fullNameHelp ?	Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada	Decrease in erosion (shoreline) by reef, St. Croix, USVI	Eastern meadowlark abundance, Piedmont ecoregion, USA	Carbon sequestration in soils of SW-Germany as affected by agricultural management—Calibration of the EPIC model for regional simulations
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	US EPA	None	None
EM Source Document ID	286	335	405	482
Document Author em.detail.documentAuthorHelp ?	Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Riffel, S., Scognamillo, D., and L. W. Burger	Billen, N., Röder, C., Gaiser, T. and Stahr, K.,
Document Year em.detail.documentYearHelp ?	2003	2014	2008	2009
Document Title em.detail.sourceIdHelp ?	Valuing freshwater salmon habitat on the west coast of Canada	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Carbon sequestration in soils of SW-Germany as affected by agricultural management—calibration of the EPIC model for regional simulations
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 journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	https://epicapex.tamu.edu/epic/
Contact Name em.detail.contactNameHelp ?	Duncan Knowler	Susan H. Yee	Sam Riffell	Norbert Billen
Contact Address	School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	University of Hohenheim, Institute of Soil Science and Land Evaluation, Emil Wolff Strasse 27, D-70593 Stuttgart, Germany
Contact Email	djk@sfu.ca	yee.susan@epa.gov	sriffell@cfr.msstate.edu	billen@uni-hohenheim.de

EM Description

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
Summary Description em.detail.summaryDescriptionHelp ?	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."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef."	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. "	Global emissions trading allows for agricultural measures to be accounted for the carbon sequestration in soils. The Environmental Policy Integrated Climate (EPIC) model was tested for central European site conditions by means of agricultural extensification scenarios. Results of soil and management analyses of different management systems (cultivation with mouldboard plough, reduced tillage, and grassland/fallow establishment) on 13 representative sites in the German State Baden-Württemberg were used to calibrate the EPIC model. Calibration results were compared to those of the Intergovernmental Panel on Climate Change (IPCC) prognosis tool. The first calibration step included adjustments in (a) N depositions, (b) N2-fixation by bacteria during fallow, and (c) nutrient content of organic fertilisers according to regional values. The mixing efficiency of implements used for reduced tillage and four crop parameters were adapted to site conditions as a second step of the iterative calibration process, which should optimise the agreement between measured and simulated humus changes. Thus, general rules were obtained for the calibration of EPIC for different criteria and regions. EPIC simulated an average increase of +0.341 Mg humus-C ha−1 a−1 for on average 11.3 years of reduced tillage compared to land cultivated with mouldboard plough during the same time scale. Field measurements revealed an average increase of +0.343 Mg C ha−1 a−1 and the IPCC prognosis tool +0.345 Mg C ha−1 a−1. EPIC simulated an average increase of +1.253 Mg C ha−1 a−1 for on average 10.6 years of grassland/fallow establishment compared to an average increase of +1.342 Mg humus-C ha−1 a−1 measured by field measurements and +1.254 Mg C ha−1 a−1 according to the IPCC prognosis tool. The comparison of simulated and measured humus C stocks was r2 ≥ 0.825 for all treatments. However, on some sites deviations between simulated and measured results were considerable. The result for the simulation of yields was similar. In 49% of the cases the simulated yields differed from the surveyed ones by more than 20%. Some explanations could be found by qualitative cause analyses. Yet, for quantitative analyses the available information from farmers was not sufficient. Altogether EPIC is able to represent the expected changes by reduced tillage or grassland/fallow establishment acceptably under central European site conditions of south-western Germany.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None reported	Impact of different agricultural management strategies
Biophysical Context	No additional description provided	No additional description provided	Conservation Reserve Program lands left to go fallow	Central Europe agricultural sites
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Habitat quality	No scenarios presented	N/A	NA

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	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 em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-335	Doc-405	Doc-478
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-179 \| EM-183 \| EM-180 \| EM-181	EM-447 \| EM-448	EM-831 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-846 \| EM-847	EM-1012 \| EM-1021

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
EM Temporal Extent em.detail.tempExtentHelp ?	1989-1999	2006-2007, 2010	2008	4-20 years ? Comment: This paper compares agricultural plots that have used specific types of management practices over various periods ranging from 4-20 years. The beginning and end dates of those periods are not provided.
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	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	other or unclear (comment) ? Comment: This paper compares agricultural plots that have used specific types of management practices over various periods ranging from 4-20 years. The beginning and end dates of those periods are not provided.
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or ecological	Physiographic or ecological	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)
Spatial Extent Name em.detail.extentNameHelp ?	South Thompson watershed	Coastal zone surrounding St. Croix	Piedmont Ecoregion	Baden-Wurttemberg
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	1000-10,000 km^2.	100-1000 km^2	100,000-1,000,000 km^2	10,000-100,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	Not applicable	area, for pixel or radial feature	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	Not applicable	10 m x 10 m	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Yes	Yes	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	Yes
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	r-squared \| 0.6416 \| NA
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Yes	No	Yes	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	No	Not applicable	Unclear	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-177	EM-449	EM-838	EM-1020
North America Canada	None	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	Europe Germany

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

EM-177	EM-449	EM-838	EM-1020
Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
Centroid Latitude em.detail.ddLatHelp ?	49.29	17.73	36.23	48.62
Centroid Longitude em.detail.ddLongHelp ?	-123.8	-64.77	-81.9	9.03
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Grasslands	Agroecosystems
Specific Environment Type em.detail.specificEnvTypeHelp ?	Rivers and streams	Coral reefs	grasslands	Agriculture plots
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-177	EM-449	EM-838	EM-1020
EM Organismal Scale em.detail.orgScaleHelp ?	Other (Comment) ? Comment: Coho salmon stock	Not applicable	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-177	EM-449	EM-838	EM-1020
Species Oncorhynchus kisutch	None Available	Species Eastern meadowlark	None Available

EnviroAtlas URL

EM-177	EM-449	EM-838	EM-1020
The National Hydrography Dataset (NHD), GAP Ecological Systems, Ecosystem Markets: Imperiled Species and Habitats, Big game hunting recreation demand	National Hydrography Dataset Plus (NHD PlusV2)	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Agricultural water use (million gallons/day), Acres of Land Enrolled in the Conservation Reserve Program (CRP)

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-177	EM-449	EM-838	EM-1020
[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes

<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-177	EM-449	EM-838	EM-1020
Rivers and Streams (111) Fauna (4) Fish Stock Indicator Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	Agroecosystems (22) Soil (2) Specific types of soil Stock Indicator

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM Modeling Approach

Comment:

Comment:

EM Locations, Environments, Ecology

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Comment:

Taxonomic level and name of organisms or groups identified

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)

<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)