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-63
EM-337
EM-788
EM-841
EM-862

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
EM Short Name em.detail.shortNameHelp ?	EnviroAtlas - Natural biological nitrogen fixation	Rate of Fire Spread	Wild bees over 26 yrs of restored prairie, IL, USA	Brown-headed cowbird abundance, Piedmont, USA	Recreational fishery index, USA
EM Full Name em.detail.fullNameHelp ?	US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA	Rate of Fire Spread	Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA	Brown-headed cowbird abundance, Piedmont ecoregion, USA	Recreational fishery index for streams and rivers, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA \| EnviroAtlas	None	None	None	US EPA
EM Source Document ID	262 ? Comment: EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM.	306	401	405	414
Document Author em.detail.documentAuthorHelp ?	US EPA Office of Research and Development - National Exposure Research Laboratory	Rothermel, Richard C.	Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree	Riffel, S., Scognamillo, D., and L. W. Burger	Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold
Document Year em.detail.documentYearHelp ?	2013	1972	2017	2008	2021
Document Title em.detail.sourceIdHelp ?	EnviroAtlas - National	A Mathematical model for predicting fire spread in wildland fuels	Wild bee community change over a 26-year chronosequence of restored tallgrass prairie	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers.
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Documented, not peer reviewed	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published on US EPA EnviroAtlas website	Published USDA Forest Service report	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.epa.gov/enviroatlas	http://firelab.org/project/farsite	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	EnviroAtlas Team ? Comment: Additional contact: Jana Compton, EPA	Charles McHugh	Sean R. Griffin	Sam Riffell	Gregg Lomnicky
Contact Address	Not reported	RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808	Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A.	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	200 SW 35th St., Corvallis, OR, 97333
Contact Email	enviroatlas@epa.gov	cmchugh@fs.fed.us	srgriffin108@gmail.com	sriffell@cfr.msstate.edu	lomnicky.gregg@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
Summary Description em.detail.summaryDescriptionHelp ?	DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM."	ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.	ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities."	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: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.]
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None Identified	None identified	None identified	None reported	None identified
Biophysical Context	No additional description provided	Not applicable	The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season.	Conservation Reserve Program lands left to go fallow	None
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	N/A	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	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	New or revised model

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

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-346 \| Doc-347 ? Comment: EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM.	None	None	Doc-405	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	EM-831 \| EM-838 \| EM-839 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-846 \| EM-847	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
EM Temporal Extent em.detail.tempExtentHelp ?	2006-2010	Not applicable	1988-2014	2008	2013-2014
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	Not applicable	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	1
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Year

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Not applicable	Physiographic or ecological	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	counterminous United States	Not applicable	Nachusa Grasslands	Piedmont Ecoregion	United States
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	Not applicable	10-100 km^2	100,000-1,000,000 km^2	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Watersheds (12-digit HUCs).	Not applicable	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	length, for linear feature (e.g., stream mile)
Spatial Grain Size em.detail.spGrainSizeHelp ?	irregular	Not applicable	Area varies by site	Not applicable	stream reach (site)

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
Model Calibration Reported? em.detail.calibrationHelp ?	No	Not applicable	No	Yes	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Not applicable	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Not applicable	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Not applicable	No	Yes	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	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-63	EM-337	EM-788	EM-841	EM-862
North America United States	None	North America United States Illinois	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	North America United States North Carolina Indiana Wyoming Northern Mariana Islands Utah Guam Arizona Montana Kentucky California Kansas Delaware Florida Pennsylvania Iowa Mississippi Illinois Texas Connecticut Georgia Maryland Idaho Oregon Vermont Puerto Rico Maine Oklahoma Tennessee American Samoa Alabama Arkansas South Carolina Washington Nebraska West Virginia Colorado Massachusetts Missouri Alaska U.S. Virgin Islands Washington D.C. North Dakota Wisconsin Nevada New York Rhode Island Hawaii South Dakota Minnesota New Jersey Michigan Virgina New Mexico New Hampshire Louisiana Ohio

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

EM-63	EM-337	EM-788	EM-841	EM-862
None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
Centroid Latitude em.detail.ddLatHelp ?	39.5	-9999	41.89	36.23	36.21
Centroid Longitude em.detail.ddLongHelp ?	-98.35	-9999	-89.34	-81.9	-113.76
Centroid Datum em.detail.datumHelp ?	WGS84	Not applicable	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Not applicable	Provided	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems \| Grasslands	Grasslands	Rivers and Streams
Specific Environment Type em.detail.specificEnvTypeHelp ?	Terrestrial	Not applicable	Restored prairie, prairie remnants, and cropland	grasslands	reach
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	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-788	EM-841	EM-862
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Species	Species	Guild or Assemblage

Taxonomic level and name of organisms or groups identified

EM-63	EM-337	EM-788	EM-841	EM-862
None Available	None Available	other Bees	Species Brown headed cowbird	None Available

EnviroAtlas URL

EM-63	EM-337	EM-788	EM-841	EM-862
Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD)	Average Annual Precipitation	GAP Ecological Systems	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	None Available

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-63	EM-337	EM-788	EM-841	EM-862
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Pollination and seed dispersal	[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 [Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and 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-63	EM-337	EM-788	EM-841	EM-862
Atmosphere Soil (2) Specific types of soil Quality Indicator	None	None	Grasslands (23) Fauna (4) Birds Stock Indicator	Rivers and Streams (111) Fauna (4) Fish 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

Comment:

EM Modeling Approach

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)