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

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Comparing:

EM-119
EM-121
EM-130
EM-131
EM-178
EM-275
EM-367
EM-418
EM-437
EM-456
EM-617
EM-661
EM-683
EM-702

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

EM Short Name

Landscape importance for wildlife products, Europe

Landscape importance for recreation, Europe

KINEROS2, River Ravna watershed, Bulgaria

InVEST marine water quality, Hood Canal, WA, USA

Natural attenuation by soil, The Netherlands

SWAT, Aixola watershed, Spain

InVEST Coastal Blue Carbon

SIRHI, St. Croix, USVI

InVESTv3.0 Water yield, Guánica Bay, Puerto Rico

Reef dive site favorability, St. Croix, USVI

RBI Spatial Analysis Method

Alwife phosphorus flux in lakes, Connecticut, USA

Estuary visitation, Cape Cod, MA

Northern Shoveler recruits, CREP wetlands, IA, USA

EM Full Name

Landscape importance for wildlife products, Europe

Landscape importance for recreation, Europe

KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria

InVEST (Integrated Valuation of Envl. Services and Tradeoffs) marine water quality, Hood Canal, WA, USA

Natural attenuation capacity of the soil, The Netherlands

SWAT (Soil and Water Assessment Tool), Aixola watershed, Spain

InVEST v3.0 Coastal Blue Carbon

SIRHI (SImplified Reef Health Index), St. Croix, USVI

InVEST (Integrated Valuation of Environmental Services and Tradeoffs) v3.0 Water yield, Guánica Bay, Puerto Rico, USA

Dive site favorability (reef), St. Croix, USVI

Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method

Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA

Value of recreational use of an estuary, Cape Cod, Massachusetts

Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA

EM Source or Collection

EU Biodiversity Action 5

InVEST

None

InVEST

US EPA

US EPA | InVEST

US EPA

None

US EPA

None

EM Source Document ID

228

248 ?

205

287

295

310

335

338

335

367

383

387

372 ?

Document Author

Haines-Young, R., Potschin, M. and Kienast, F.

Nedkov, S., Burkhard, B.

Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M.

van Wijnen, H.J., Rutgers, M., Schouten, A.J., Mulder, C., de Zwart, D., and Breure, A.M.

Zabaleta, A., Meaurio, M., Ruiz, E., and Antigüedad, I.

Natural Capital Project

Yee, S. H., Dittmar, J. A., and L. M. Oliver

Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher

Yee, S. H., Dittmar, J. A., and L. M. Oliver

Bousquin, J., Mazzotta M., and W. Berry

West, D. C., A. W. Walters, S. Gephard, and D. M. Post

Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta

Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever

Document Year

2012

2013

2012

2014

2017

2014

2017

2010

2019

2010

Document Title

Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs

Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria

From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA

How to calculate the spatial distribution of ecosystem services - Natural attenuation as example from the Netherlands

Simulation climate change impact on runoff and sediment yield in a small watershed in the Basque Country, Northern Spain

Blue Carbon model - InVEST (v3.0)

Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI

Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico

Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI

Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual.

Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts

Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA

Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt

Document Status

Peer reviewed and published

Documented, not peer reviewed

Peer reviewed and published

Peer reviewed but unpublished (explain in Comment)

Peer reviewed and published

Comments on Status

Published journal manuscript

other

Published journal manuscript

Published EPA report

Published journal manuscript

Draft manuscript-work progressing

Published report

Software and Access

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

EM Software Access info Exit

Not applicable

http://www.tucson.ars.ag.gov/agwa/

https://www.naturalcapitalproject.org/invest/

Not applicable

http://swat.tamu.edu/software/arcswat/

http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model

Not applicable

http://www.naturalcapitalproject.org/invest/

Not applicable

Contact Name

Marion Potschin

David C. Goodrich

J.E. Toft

H.J. van Wijnen

Ane Zabaleta

Gregg Verutes

Susan H. Yee

Justin Bousquin

Derek C. West

Mulvaney, Kate

David Otis

Contact Address

Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom

USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719

Not reported

National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands

Hydrogeology and Environment Group, Science and Technology Faculty, University of the Basque Country, 48940 Leioa, Basque Country (Spain)

Stanford University

US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561

Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA

US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI

U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University

Contact Email

marion.potschin@nottingham.ac.uk

agwa@tucson.ars.ag.gov

jetoft@stanford.edu

harm.van.wijnen@rivm.nl

ane.zabaleta@ehu.es

gverutes@stanford.edu

yee.susan@epa.gov

bousquin.justin@epa.gov

derek.west@yale.edu

None reported

dotis@iastate.edu

EM Description

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Summary Description

ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Wildlife Products” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas."

ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Recreation” ... The potential to deliver services is assumed to be influenced by land-use ... and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Recreation… is broadly defined as all areas where landscape properties are favourable for active recreation purposes."

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."

Marine Water Quality Model. Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We used outputs from the freshwater models as inputs to the marine water quality model.We adapted a box model that has been successfully applied in Puget Sound (Babson et al., 2006; Sutherland et al., 2011) to simulate seasonal and interannual variations in salinity, water temperature, and nitrates in the Canal." (p. 4)

ABSTRACT: "Maps play an important role during the entire process of spatial planning and bring ecosystem services to the attention of stakeholders' negotiation more easily. As example we show the quantification of the ecosystem service ‘natural attenuation of pollutants’, which is a service necessary to keep the soil clean for production of safe food and provision of drinking water, and to provide a healthy habitat for soil organisms to support other ecosystem services. A method was developed to plot the relative measure of the natural attenuation capacity of the soil in a map. Several properties of Dutch soils were related to property-specific reference values and subsequently combined into one proxy for the natural attenuation of pollutants." AUTHOR'S DESCRIPTION: "The natural attenuation capacity that is modeled in this study must be seen as a measure that describes the ‘biodegradation capacity’ of the soil, including biodegradation of all types of contaminants"

ABSTRACT: "We explored the potential impact of climate change on runoff and sediment yield for the Aixola watershed using the Soil and Water Assessment Tool (SWAT). The model calibration (2007–2010) and validation (2005–2006) results were rated as satisfactory. Subsequently, simulations were run for four climate change model–scenario combinations based on two general circulation models (CGCM2 and ECHAM4) under two emissions scenarios (A2 and B2) from 2011 to 2100." AUTHOR'S DESCRIPTION: "The results were grouped into three consecutive 30-yr periods (2011-2040, 2041-2070, and 2071-2100) and compared with the values simulated for the baseline period (1961-1990)."

Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools.

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...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati

Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Stakeholders identified an objective of meeting water demands for agriculture and domestic purposes, including irrigation, drinking water, or hydropower production…Geomorphology, climate, and vegetation determine the amount of water runoff from the landscape that could be available for consumptive uses. Long-term average water yield was estimated for each HUC12 sub-watershed as the difference between total precipitation and the amount absorbed by the different land cover classes using a reservoir hydropower production model (InVEST 3.0.0; Tallis et al., 2013)."

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...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and

AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. "

ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model."

[ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ]

ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).

Specific Policy or Decision Context Cited

None identified

Land use change

None identified

Transport of solids for characterizing rivers in the European Water Framework Directive (WFD)

None identified

Meeting water demands

None identified

Restoration and management of diadromous fish runs in coastal New England

None identified

Biophysical Context

No additional description provided

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

Five soil types including Löss, Fluvial clay, Peat, Sand, and Silty Loam. Five land-use types including Pasture, Arable farming, Semi-natural grassland, Heathland, and Forest.

The Aixola watershed drains into the Aixola reservoir, which has a cpacity of 2.73 x 10^6 m^3, and is used for water supply. The elevation ranges from 340 m at the outlet of the watershed to 750 m at the highest peak, with a mean elevation of 511 m a.s.l. Most slopes in the watershed are less than 30%. The region is characterized by a humid and temperate climate. The mean annual precipitation is about 1480 mm, distributed fairly evenly throughout the year.; the mean annual temperature is 12 degrees C; and the mean annual discharge is 600 mm (around 0.092 m^3 s^−1). Autochthonus vegetation is limited to small patches, and commercial foresty, mostly evergreen stands composed mainly of Pinus radiata (Monterey pine), occupies more than 80% of the watershed. The lithology is highly homogenous, with most of the bedrock (94%) consisting of impervious Upper Cretaceous Calcareous Flysch. The main types of soils are relatively deep cambisols and regosols, with depths ranging from 0.8 to 10 m and a silt-loam texture. During the 2003-2008 period, mean suspended sediment yield calculated for the watershed was 36 t km^-2.

Land use land class; habitat type

No additional description provided

wetlands

Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook.

None identified

Prairie Pothole Region of Iowa

EM Scenario Drivers

No scenarios presented

future land use and land cover; Climate change

No scenarios presented

Four future climate change scenarios combining two IPCC SRES scenarios and two GCMs

Land use land cover changes; habitat disturbance

No scenarios presented

N/A

current and historical run size

N/A

No scenarios presented

EM Relationship to Other EMs or Applications

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Method Only, Application of Method or Model Run

Method + Application

Method + Application (multiple runs exist)

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method Only

Method + Application

Method Only

Method + Application (multiple runs exist) View EM Runs

Method + Application

New or Pre-existing EM?

New or revised model

Application of existing model

New or revised model

Application of existing model

New or revised 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-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Document ID for related EM

Doc-231 | Doc-228

Doc-277 | Doc-294 | Doc-249 | Doc-250 ?

None

Doc-288

None

Doc-311 | Doc-307 | Doc-280 | Doc-205

None

Doc-372 | Doc-373

EM ID for related EM

EM-99 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171

EM-99 | EM-119 | EM-120 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-170 | EM-171

EM-132 | EM-133

None

EM-368 | EM-148 | EM-344 | EM-111

None

EM-667 | EM-672 | EM-674 | EM-673

EM-682 | EM-684 | EM-685

EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632

EM Modeling Approach

EM Relationship to Time

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

EM Temporal Extent

2000

Not reported

varies by run, see runs for values

1999-2005

1961-2100

Not applicable

2006-2007, 2010

2006 - 2012

2006-2007, 2010

Not applicable

1960"s and early 2000's

Summer 2017

1987-2007

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

future time

Not applicable

past time

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

continuous

discrete

Not applicable

discrete

Not applicable

EM Temporal Grain Size Value

Not applicable

Not reported

Not applicable

EM Temporal Grain Size Unit

Not applicable

Not reported

Not applicable

Year

Not applicable

Day

Not applicable

EM Spatial Extent

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Bounding Type

Geopolitical

Watershed/Catchment/HUC

Physiographic or ecological

Geopolitical

Watershed/Catchment/HUC

Not applicable

Physiographic or ecological

Watershed/Catchment/HUC

Physiographic or ecological

Not applicable

Watershed/Catchment/HUC

Physiographic or ecological

Multiple unrelated locations (e.g., meta-analysis)

Spatial Extent Name

The EU-25 plus Switzerland and Norway

River Ravna watershed

Hood Canal

The Netherlands

Aixola watershed

Not applicable

Coastal zone surrounding St. Croix

Guanica Bay watershed

Coastal zone surrounding St. Croix

Not applicable

Bride Lake and Linsley Pond

Three Bays, Cape Cod

CREP (Conservation Reserve Enhancement Program

Spatial Extent Area (Magnitude)

>1,000,000 km^2

10-100 km^2

100-1000 km^2

10,000-100,000 km^2

1-10 km^2

Not applicable

100-1000 km^2

1000-10,000 km^2.

100-1000 km^2

Not applicable

10-100 ha

1000-10,000 km^2.

10,000-100,000 km^2

Spatial Distribution of Computations

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

EM Spatial Distribution

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

Spatial Grain Type

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

area, for pixel or radial feature

volume, for 3-D feature

area, for pixel or radial feature

Not applicable

length, for linear feature (e.g., stream mile)

other (specify), for irregular (e.g., stream reach, lake basin)

Spatial Grain Size

1 km x 1 km

25 m x 25 m

Not reported

100 m x 100 m

Average size 0.2 km^2

user-specified

10 m x 10 m

30 m x 30 m

10 m x 10 m

Not reported

Not applicable

beach length

multiple, individual, irregular sites

EM Structure and Computation Approach

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

EM Computational Approach

Logic- or rule-based

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

Statistical Estimation of EM

None

None

None

Other or unclear (comment)

Conventional (frequentist) statistics

None

None

None

None

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Model Calibration Reported?

Yes

Not applicable

Yes

Not applicable

Yes

Unclear

Model Goodness of Fit Reported?

Not applicable

Goodness of Fit (metric| value | unit)

None

Model Operational Validation Reported?

Yes

Not applicable

Yes

Not applicable

Model Uncertainty Analysis Reported?

Not applicable

Model Sensitivity Analysis Reported?

Yes

Not applicable

Yes

Model Sensitivity Analysis Include Interactions?

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-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Europe
- Cyprus
- Portugal
- Malta
- Greece
- Austria
- Latvia
- Netherlands
- Sweden
- Ireland
- Luxembourg
- Poland
- Slovakia
- Slovenia
- France
- Lithuania
- Hungary
- Switzerland
- Spain
- Czech Republic
- Belgium
- Norway
- Finland
- Denmark
- Italy
- Germany
- Estonia

Europe
- Cyprus
- Portugal
- Malta
- Greece
- Austria
- Latvia
- Netherlands
- Sweden
- Ireland
- Luxembourg
- Poland
- Slovakia
- Slovenia
- France
- Lithuania
- Hungary
- Switzerland
- Spain
- Czech Republic
- Belgium
- Norway
- Finland
- Denmark
- Italy
- Germany
- Estonia

Europe
- Bulgaria

North America
- United States
  - Washington

Europe
- Netherlands

Europe
- Spain

None

North America
- U.S. Caribbean Territories

None

North America
- United States
  - Connecticut

None

North America
- United States
  - Iowa

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

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

None

Temperate North Pacific
- Northeast Pacific
  - Cold Temperate Northeast Pacific
    - Puget Trough/Georgia Basin

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Greater Antilles

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

None

Temperate North Atlantic
- Northwest Atlantic
  - Cold Temperate Northwest Atlantic
    - Gulf of Maine/Bay of Fundy

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Centroid Latitude

50.53

42.8

47.8

52.37

-9999

17.73

17.96

17.73

Not applicable

41.33

41.62

42.62

Centroid Longitude

7.6

-122.7

4.88

-1

-9999

-64.77

-67.02

-64.77

Not applicable

-72.24

-70.42

-93.84

Centroid Datum

WGS84

NAD83

WGS84

Not applicable

WGS84

Not applicable

WGS84

Centroid Coordinates Status

Estimated

Provided

Not applicable

Estimated

Not applicable

Estimated

Environments and Scales Modeled

EM ID

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

EM Environmental Sub-Class

Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified)

Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests

Near Coastal Marine and Estuarine

Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland

Rivers and Streams | Forests | Barren

Inland Wetlands | Near Coastal Marine and Estuarine

Near Coastal Marine and Estuarine

Near Coastal Marine and Estuarine

Inland Wetlands

Rivers and Streams | Lakes and Ponds

Near Coastal Marine and Estuarine

Inland Wetlands | Agroecosystems | Grasslands

Specific Environment Type

Not applicable

Primarily forested watershed

glacier-carver saltwater fjord

Not applicable

Forested watershed used for commercial forestry

user specified

Coral reefs

13 LULC were used

Coral reefs

Restored wetlands

Coastal lakes and ponds and associated streams

Beaches

Wetlands buffered by grassland within agroecosystems

EM Ecological Scale

Ecological scale is finer than that of the Environmental Sub-class

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 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-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

EM Organismal Scale

Not applicable

Guild or Assemblage

Not applicable

Guild or Assemblage

Not applicable

Individual or population, within a species

Not applicable

Individual or population, within a species

Taxonomic level and name of organisms or groups identified

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

None Available

other
- snapper
Family
- grouper
- parrotfish
- surgeonfish

None Available

Species
- alewife (Alosa pseudoharengus)

None Available

Species
- Northern Shoveler (Anas clypeata)

EnviroAtlas URL

EM-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Percent GAP Status 1 & 2

The National Hydrography Dataset (NHD), Average Annual Precipitation, Percent Impervious Area

None Available

GAP Ecological Systems

National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation

Carbon Storage by Tree Biomass

None Available

Average Annual Precipitation, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD)

None Available

Dasymetric Allocation of Population, The National Hydrography Dataset (NHD), GAP Ecological Systems, Green Space per Capita, Percent GAP Status 1 & 2, The Watershed Boundary Dataset (WBD)

Waterbody area

None Available

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-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

[Ecosystem] Provisioning
- Materials
  - Biomass
    - Fibres and other materials from plants, algae and animals for direct use or processing

[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
    - Experiential use of plants, animals and land-/seascapes in different environmental settings
  - Intellectual and representative interactions
    - Aesthetic
    - Entertainment

None

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Water conditions
    - Chemical condition of salt waters
- Mediation of flows
  - Liquid flows
    - Hydrological cycle and water flow maintenance

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Mass flows
    - Buffering and attenuation of mass flows

[Ecosystem] Provisioning
- Nutrition
  - Water
    - Surface water for drinking
- Materials
  - Water
    - Surface water for non-drinking purposes
[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Mass flows
    - Buffering and attenuation of mass flows

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Atmospheric composition and climate regulation
    - Global climate regulation by reduction of greenhouse gas concentrations

[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

[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

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Flood protection
[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
    - Experiential use of plants, animals and land-/seascapes in different environmental settings
  - Intellectual and representative interactions
    - Educational

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats
  - Water conditions
    - Chemical condition of freshwaters

[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

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs

<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-119

EM-121

EM-130

EM-131

EM-178

EM-275

EM-367

EM-418

EM-437

EM-456

EM-617

EM-661

EM-683

EM-702

Rivers and Streams (111)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Barren / Rock and Sand (28)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Forests (21)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Tundra (25)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Scrubland/Shrubland (24)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Grasslands (23)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Wetlands (12)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Lakes and Ponds (112)
- Fauna (4)
  - Wildlife
    - Stock Indicator

Rivers and Streams (111)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator
Barren / Rock and Sand (28)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator
Forests (21)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator
Tundra (25)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator
Scrubland/Shrubland (24)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator
Grasslands (23)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator
Wetlands (12)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator
Lakes and Ponds (112)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator
Urban/Suburban (27)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator

None

Near Coastal Marine/Estuarine (113)
- Water (3)
  - Liquid water
    - Quality Indicator

None

Rivers and Streams (111)
- Water (3)
  - Liquid water
    - Flow Indicator
    - Quality Indicator

None

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator

Wetlands (12)
- Water (3)
  - Liquid water
    - Extreme Event Indicator
- Composite (8)
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Quality Indicator
- Fauna (4)
  - Birds
    - Quality Indicator

None

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Indicator
    - Quality Indicator

Wetlands (12)
- Fauna (4)
  - Duck
    - Stock Indicator

US EPA

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

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)