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.

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Comparing:

EM-105
EM-629
EM-843
EM-904

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-105	EM-629	EM-843	EM-904
EM Short Name em.detail.shortNameHelp ?	Benthic habitat associations, Willapa Bay, OR, USA	SolVES, Pike & San Isabel NF, WY	Mourning dove abundance, Piedmont region, USA	Drag coefficient Laminaria hyperborea
EM Full Name em.detail.fullNameHelp ?	Benthic macrofaunal habitat associations, Willapa Bay, OR, USA	SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO	Mourning dove abundance, Piedmont ecoregion, USA	Drag coefficient Laminaria hyperborea
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	None	None	None
EM Source Document ID	39	369	405	424
Document Author em.detail.documentAuthorHelp ?	Ferraro, S. P. and Cole, F. A.	Sherrouse, B.C., Semmens, D.J., and J.M. Clement	Riffel, S., Scognamillo, D., and L. W. Burger	Mendez, F. J. and I. J. Losada
Document Year em.detail.documentYearHelp ?	2007	2014	2008	2004
Document Title em.detail.sourceIdHelp ?	Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA	An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields
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-105	EM-629	EM-843	EM-904
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Steve Ferraro	Benson Sherrouse	Sam Riffell	F. J. Mendez
Contact Address	U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365	USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	Not reported
Contact Email	ferraro.steven@epa.gov	bcsherrouse@usgs.gov	sriffell@cfr.msstate.edu	mendezf@unican.es

EM Description

EM ID em.detail.idHelp ?	EM-105	EM-629	EM-843	EM-904
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "In this paper we report the results of 2 estuary-wide studies of benthic macrofaunal habitat associations in Willapa Bay, Washington, USA. This research is part of an effort to develop empirical models of biota-habitat associations that can be used to help identify critical habitats, prioritize habitats for environmental protection, index habitat suitability (U.S. Fish and Wildlife Service, 1980; Kapustka, 2003), perform habitat equivalency and compensatory restoration analyses (Fonseca et al., 2002; Kirsch et al., 2005), and as habitat value criteria in ecological risk assessments (Obery and Landis, 2002; Ferraro and Cole, 2004; Landis et al., 2004)." (491)	[ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.	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. "	ABSTRACT: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." AUTHOR'S DESCRIPTION: "Therefore, a relation between C˜D and some nondimensional flow parameters is desirable to characterize hydrodynamically the L. hyperborea model plants for predictable purposes."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None	None reported	None identified
Biophysical Context	benthic estuarine	Rocky mountain conifer forests	Conservation Reserve Program lands left to go fallow	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	N/A	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-105	EM-629	EM-843	EM-904
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	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-105	EM-629	EM-843	EM-904
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-369	Doc-405	Doc-424
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-626 \| EM-628	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-844 \| EM-845 \| EM-846 \| EM-847	EM-896 \| EM-897

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-105	EM-629	EM-843	EM-904
EM Temporal Extent em.detail.tempExtentHelp ?	1996,1998	2004-2008	2008	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	Not applicable
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
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-105	EM-629	EM-843	EM-904
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Willapa Bay	National Park	Piedmont Ecoregion	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	1000-10,000 km^2.	100,000-1,000,000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-105	EM-629	EM-843	EM-904
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	30m2	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-105	EM-629	EM-843	EM-904
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	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-105	EM-629	EM-843	EM-904
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	No	Yes	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R^2 \| 0.34-0.82 \| Not applicable R^2 \| 0.29-0.68 \| Not applicable R^2 \| 0.07-0.17 \| Not applicable	Z score \| -3.938 to -21.532 \| NA R value \| 0.412 - 0.811 \| NA	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	Yes	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	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-105	EM-629	EM-843	EM-904
None	North America United States Colorado	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	None

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

EM-105	EM-629	EM-843	EM-904
Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	None	Temperate North Atlantic Northeast Atlantic Northern European Seas

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-105	EM-629	EM-843	EM-904
Centroid Latitude em.detail.ddLatHelp ?	46.24	38.7	36.23	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-124.06	105.89	-81.9	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-105	EM-629	EM-843	EM-904
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Forests	Grasslands	Near Coastal Marine and Estuarine
Specific Environment Type em.detail.specificEnvTypeHelp ?	Drowned river valley estuary	Montain forest	grasslands	Near Coastal Marine and Estuarine
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer 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	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-105	EM-629	EM-843	EM-904
EM Organismal Scale em.detail.orgScaleHelp ?	Species	Not applicable	Species	Species

Taxonomic level and name of organisms or groups identified

EM-105	EM-629	EM-843	EM-904
Species Zostera marina Neotrypaea californiensis Manayunkia aestuarina Upogebia pugettensis Spartina alterniflora	None Available	Species Mourning dove	Species Laminaria hyporborea

EnviroAtlas URL

EM-105	EM-629	EM-843	EM-904
None Available	GAP Ecological Systems, Enabling Conditions	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	National Hydrography Dataset Plus (NHD PlusV2)

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-105	EM-629	EM-843	EM-904
[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 Wild animals and their outputs Wild plants, algae and their outputs [Ecosystem] Cultural Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings] Spiritual and/or emblematic Symbolic Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic Educational	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection

<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-105	EM-629	EM-843	EM-904
Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	Forests (21) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	None