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-422
EM-592
EM-878

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	EM-878
EM Short Name em.detail.shortNameHelp ?	Benthic habitat associations, Willapa Bay, OR, USA	HexSim v2.4, San Joaquin kit fox, CA, USA	APEX v1501	Health, safety and greening urban space, PA, USA
EM Full Name em.detail.fullNameHelp ?	Benthic macrofaunal habitat associations, Willapa Bay, OR, USA	HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA	APEX (Agricultural Policy/Environmental eXtender Model) v1501	Health, safety and greening urban vacant space, Pennsylvania, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	US EPA	None	None
EM Source Document ID	39	337 ? Comment: The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships.	357	419
Document Author em.detail.documentAuthorHelp ?	Ferraro, S. P. and Cole, F. A.	Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips	Steglich, E. M., J. Jeong and J. R. Williams	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	2015	2016	2011
Document Title em.detail.sourceIdHelp ?	Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA	Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations	Agricultural Policy/Environmental eXtender Model User's Manual Version 1501	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 report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	EM-878
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	http://www.hexsim.net/	https://epicapex.tamu.edu/manuals-and-publications/	Not applicable
Contact Name em.detail.contactNameHelp ?	Steve Ferraro	Theresa M. Nogeire	E. M. Steglich	Charles C. Branas
Contact Address	U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365	School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA	Blackland Research and Extension Center, 720 East Blackland Road, Temple, TX 76502	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	tnogeire@gmail.com	epicapex@brc.tamus.edu	cbranas@upenn.edu

EM Description

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	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: "...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: "APEX is a tool for managing whole farms or small watersheds to obtain sustainable production efficiency and maintain environmental quality. APEX operates on a daily time step and is capable of performing long term simulations (1-4000 years) at the whole farm or small watershed level. The watershed may be divided into many homogeneous (soils, land use, topography, etc.) subareas (<4000). The routing component simulates flow from one subarea to another through channels and flood plains to the watershed outlet and transports sediment, nutrients, and pesticides. This allows evaluation of interactions between fields in respect to surface run-on, sediment deposition and degradation, nutrient and pesticide transport and subsurface flow. Effects of terrace systems, grass waterways, strip cropping, buffer strips/vegetated filter strips, crop rotations, plant competition, plant burning, grazing patterns of multiple herds, fertilizer, irrigation, liming, furrow diking, drainage systems, and manure management (feed yards and dairies with or without lagoons) can be simulated and assessed. Most recent developments in APEX1501 include: • Flexible grazing schedule of multiple owners and herds across landscape and paddocks. • Wind dust distribution from feedlots. • Manure erosion from feedlots and grazing fields. • Optional pipe and crack flow in soil due to tree root growth. • Enhanced filter strip consideration. • Extended lagoon pumping and manure scraping options. • Enhanced burning operation. • Carbon pools and transformation equations similar to those in the Century model with the addition of the Phoenix C/N microbial biomass model. • Enhanced water table monitoring. • Enhanced denitrification methods. • Variable saturation hydraulic conductivity method. • Irrigation using reservoir and well reserves. • Paddy module for use with rice or wetland areas."	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	No additional description provided	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	EM-878
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	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 Only	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-105	EM-422	EM-592	EM-878
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-328 \| Doc-327 \| Doc-2	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-403 \| EM-98	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	EM-878
EM Temporal Extent em.detail.tempExtentHelp ?	1996,1998	60 yr	Not applicable	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	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	discrete	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Day	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	EM-878
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or ecological	Not applicable	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Willapa Bay	San Joaquin Valley, CA	Not applicable	Philadelphia
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10,000-100,000 km^2	Not applicable	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	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	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	Not applicable	14 ha	homogenous subareas	Point based

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	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	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	EM-878
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Unclear	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	Not applicable	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	Not applicable	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes	No	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	Not applicable	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	No	Not applicable	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-422	EM-592	EM-878
None	North America United States California	None	North America United States Pennsylvania

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

EM-105	EM-422	EM-592	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-422	EM-592	EM-878
Centroid Latitude em.detail.ddLatHelp ?	46.24	36.13	Not applicable	39.95
Centroid Longitude em.detail.ddLongHelp ?	-124.06	-120	Not applicable	-75.17
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-592	EM-878
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Created Greenspace
Specific Environment Type em.detail.specificEnvTypeHelp ?	Drowned river valley estuary	Agricultural region (converted desert) and terrestrial perimeter	Terrestrial environment associated with agroecosystems	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-422	EM-592	EM-878
EM Organismal Scale em.detail.orgScaleHelp ?	Species	Individual or population, within a species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-105	EM-422	EM-592	EM-878
Species Zostera marina Neotrypaea californiensis Manayunkia aestuarina Upogebia pugettensis Spartina alterniflora	Species Vulpes macrotis mutica	None Available	None Available

EnviroAtlas URL

EM-105	EM-422	EM-592	EM-878
None Available	Stream Length Impaired by Pesticides	The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Daily Potential Solar Energy, Average Annual Precipitation, Agricultural water use (million gallons/day), Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD), Stream Length Impaired by Pesticides, Water supply from NID reservoirs (million gallons)	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-422	EM-592	EM-878
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes Biomass Materials from plants, algae and animals for agricultural use [Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[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-422	EM-592	EM-878
Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	None	Rivers and Streams (111) Water (3) Liquid water Stock Indicator Quality Indicator Agroecosystems (22) Flora (5) Fodder Stock Indicator	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:

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

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)