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-130
EM-422
EM-878

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
EM Short Name em.detail.shortNameHelp ?	Benthic habitat associations, Willapa Bay, OR, USA	KINEROS2, River Ravna watershed, Bulgaria	HexSim v2.4, San Joaquin kit fox, CA, USA	Health, safety and greening urban space, PA, USA
EM Full Name em.detail.fullNameHelp ?	Benthic macrofaunal habitat associations, Willapa Bay, OR, USA	KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria	HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA	Health, safety and greening urban vacant space, Pennsylvania, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	EU Biodiversity Action 5	US EPA	None
EM Source Document ID	39	248 ? Comment: Document 277 is also a source document for this EM	337 ? Comment: The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships.	419
Document Author em.detail.documentAuthorHelp ?	Ferraro, S. P. and Cole, F. A.	Nedkov, S., Burkhard, B.	Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips	Branas, C. C., R. A. Cheney, J. M. MacDonald, V. W. Tam, T. D. Jackson, and T. R. Ten Havey
Document Year em.detail.documentYearHelp ?	2007	2012	2015	2011
Document Title em.detail.sourceIdHelp ?	Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA	Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria	Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations	A difference-in-differences analysis of health, safety, and greening vacant urban space
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-130	EM-422	EM-878
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	http://www.tucson.ars.ag.gov/agwa/	http://www.hexsim.net/	Not applicable
Contact Name em.detail.contactNameHelp ?	Steve Ferraro	David C. Goodrich	Theresa M. Nogeire	Charles C. Branas
Contact Address	U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365	USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719	School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA	Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 936, 423 Guardian Drive, Philadelphia, PA 19104-6021
Contact Email	ferraro.steven@epa.gov	agwa@tucson.ars.ag.gov	tnogeire@gmail.com	cbranas@upenn.edu

EM Description

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
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: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS."	ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]."	ABSTRACT: "Greening of vacant urban land may affect health and safety. The authors conducted a decade-long difference-indifferences analysis of the impact of a vacant lot greening program in Philadelphia, Pennsylvania, on health and safety outcomes. ‘‘Before’’ and ‘‘after’’ outcome differences among treated vacant lots were compared with matched groups of control vacant lots that were eligible but did not receive treatment. Control lots from 2 eligibility pools were randomly selected and matched to treated lots at a 3:1 ratio by city section. Random-effects regression models were fitted, along with alternative models and robustness checks. Across 4 sections of Philadelphia, 4,436 vacant lots totaling over 7.8 million square feet (about 725,000 m^2) were greened from 1999 to 2008. Regression adjusted estimates showed that vacant lot greening was associated with consistent reductions in gun assaults across all 4 sections of the city (P < 0.001) and consistent reductions in vandalism in 1 section of the city (P < 0.001). Regression-adjusted estimates also showed that vacant lot greening was associated with residents’ reporting less stress and more exercise in select sections of the city (P < 0.01). Once greened, vacant lots may reduce certain crimes and promote some aspects of health. Limitations of the current study are discussed. Community-based trials are warranted to further test these findings." REVIEWER'S COMMENTS: Regression models were fitted separately for point-based, tract-based, and block group-based outcomes, and for the four sections of Philadelphia separately and combined. This entry presents just the point-based outcomes for the whole of Philadelphia.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified
Biophysical Context	benthic estuarine	Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak.	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships.	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	Application of existing 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-130	EM-422	EM-878
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-277 \| Doc-294 \| Doc-249 \| Doc-250 ? Comment: Document 277 is also a source document for this EM	Doc-328 \| Doc-327 \| Doc-2	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-132 \| EM-133	EM-403 \| EM-98	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
EM Temporal Extent em.detail.tempExtentHelp ?	1996,1998	Not reported	60 yr	1998-2008
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-dependent	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	future time	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	discrete	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not reported	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not reported	Year	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Watershed/Catchment/HUC	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Willapa Bay	River Ravna watershed	San Joaquin Valley, CA	Philadelphia
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10-100 km^2	10,000-100,000 km^2	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: Point-based measures are continuous and boundary-free, assign each lot to its own unique neighborhood, and avoid aggregation effects while directly accounting for spillover and the variability of neighboring areas.
Spatial Grain Type em.detail.spGrainTypeHelp ?	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	Not applicable	25 m x 25 m	14 ha	Point based

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Yes	Unclear	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	No ? Comment: Each outcome was fitted separatly, with R2 provided. See Variable Value comment for each Response.
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	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No
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	No	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-130	EM-422	EM-878
None	Europe Bulgaria	North America United States California	North America United States Pennsylvania

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

EM-105	EM-130	EM-422	EM-878
Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
Centroid Latitude em.detail.ddLatHelp ?	46.24	42.8	36.13	39.95
Centroid Longitude em.detail.ddLongHelp ?	-124.06	24	-120	-75.17
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Rivers and Streams \| Terrestrial Environment (sub-classes not fully specified) \| Forests	Terrestrial Environment (sub-classes not fully specified)	Created Greenspace
Specific Environment Type em.detail.specificEnvTypeHelp ?	Drowned river valley estuary	Primarily forested watershed	Agricultural region (converted desert) and terrestrial perimeter	Urban and urban green space
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 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

EM ID em.detail.idHelp ?	EM-105	EM-130	EM-422	EM-878
EM Organismal Scale em.detail.orgScaleHelp ?	Species	Not applicable	Individual or population, within a species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-105	EM-130	EM-422	EM-878
Species Zostera marina Neotrypaea californiensis Manayunkia aestuarina Upogebia pugettensis Spartina alterniflora	None Available	Species Vulpes macrotis mutica	None Available

EnviroAtlas URL

EM-105	EM-130	EM-422	EM-878
None Available	The National Hydrography Dataset (NHD), Average Annual Precipitation, Percent Impervious Area	Stream Length Impaired by Pesticides	Percent Population other than White, Non-Hispanic, GAP Ecological Systems, Green Space per Capita, Total Employment, Percent Population under 13 Years Old, Employment Rate

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-130	EM-422	EM-878
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Cultural 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

<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-130	EM-422	EM-878
Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	None	None	Urban/Surburban (27) Composite (8) Presence of environmental class/subclass Quality 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

Comment:

Comment:

Comment:

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

Comment:

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

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)