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:

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Comparing:

EM-94
EM-97
EM-462
EM-598
EM-617
EM-712
EM-863
EM-945

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
EM Short Name em.detail.shortNameHelp ?	Reduction in pesticide runoff risk, Europe	AnnAGNPS, Kaskaskia River watershed, IL, USA	Value of finfish, St. Croix, USVI	DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland	RBI Spatial Analysis Method	ESII Tool method	SLAMM, Tampa Bay, FL, USA	Air pollution removal by green roofs, Chicago, USA
EM Full Name em.detail.fullNameHelp ?	Reduction in pesticide runoff risk, Europe	AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA	Relative value of finfish (on reef), St. Croix, USVI	DeNitrification-DeComposition simulation of N2O flux Ireland	Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method	ESII (Ecosystem Services Identification & Inventory) Tool method	SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA	Air pollution removal by green roofs, Chigago, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	US EPA	US EPA	None	None	None	None	None
EM Source Document ID	255	137	335	358	367	391 ? Comment: Website for online user support	415 ? Comment: Secondary sources: Documents 412 and 413.	438 ? Comment: Document 439 is an additional source for this EM.
Document Author em.detail.documentAuthorHelp ?	Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C.	Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	Bousquin, J., Mazzotta M., and W. Berry	EcoMetrix Solutions Group (ESG)	Sherwood, E. T. and H. S. Greening	Yang, J., Q. Yu and P. Gong
Document Year em.detail.documentYearHelp ?	2012	2011	2014	2010	2017	2016	2014	2008
Document Title em.detail.sourceIdHelp ?	Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction	AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture	Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual.	ESII Tool	Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats	Quantifying air pollution removal by green roofs in Chicago
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Other or unclear (explain in Comment)	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published EPA report	Website	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/	Not applicable	http://www.dndc.sr.unh.edu	Not applicable	https://www.esiitool.com/	http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html	Not applicable
Contact Name em.detail.contactNameHelp ?	Sven Lautenbach	Yongping Yuan	Susan H. Yee	M. Abdalla	Justin Bousquin	Not reported	Edward T. Sherwood	Jun Yang
Contact Address	Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany	U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561	Not reported	Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA	Department of Landscape Architecture and Horticulture, Temple University, 580 Meetinghouse Road, Ambler, PA 19002, USA.
Contact Email	sven.lautenbach@ufz.de	yuan.yongping@epa.gov	yee.susan@epa.gov	abdallm@tcd.ie	bousquin.justin@epa.gov	Not reported	esherwood@tbep.org	juny@temple.edu

EM Description

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality."	AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198"	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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish,"	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC.	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. "	AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance."	ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…"	ABSTRACT: "The level of air pollution removal by green roofs in Chicago was quantified using a dry deposition model. The result showed that a total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs in one year with O3 accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2 (7%). The highest level of air pollution removal occurred in May and the lowest in February. The annual removal per hectare of green roof was 85 kg/ha/yr. The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs. Although costly, the installation of green roofs could be justified in the long run if the environmental benefits were considered. The green roof can be used to supplement the use of urban trees in air pollution control, especially in situations where land and public funds are not readily available."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	European Commission Water Framework Directive (WFD, Directive 2000/60/EC)	Not reported	None identified	climate change	None identified	None identified	None identified	None identified
Biophysical Context	Not applicable	Upper Mississipi River basin, elevation 142-194m,	No additional description provided	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	wetlands	Not applicable	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target	No scenarios presented	fertilization	N/A	No scenarios presented	Varying sea level rise (baseline - 2m), and two habitat adaption strategies	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method Only	Method Only	Method + Application (multiple runs exist) View EM Runs	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	Application of existing model	Application of existing model	New or revised 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.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-254 \| Doc-256 ? Comment: Document 254 was also used as a source document for this EM	Doc-142	None	None	None	None	Doc-412 \| Doc-413	Doc-439
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	EM-593	None	EM-713	EM-857	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
EM Temporal Extent em.detail.tempExtentHelp ?	2000	1980-2006	2006-2007, 2010	1961-1990	Not applicable	Not applicable	2002-2100	July 2006 to July 2007
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	both	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	1
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Day	Not applicable	Not applicable	Not applicable	Month

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Watershed/Catchment/HUC	Physiographic or ecological	Point or points	Not applicable	Not applicable	Watershed/Catchment/HUC	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	EU-27	East Fork Kaskaskia River watershed basin	Coastal zone surrounding St. Croix	Oak Park Research centre	Not applicable	Not applicable	Tampa Bay estuary watershed	Chicago
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	100-1000 km^2	1-10 ha	Not applicable	Not applicable	1000-10,000 km^2.	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: map units delineated by user based on project.	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	length, for linear feature (e.g., stream mile)	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	10 km x 10 km	1 km^2	10 m x 10 m	Not applicable	Not reported	map units	10 x 10 m	plot (green roof) size

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Analytic	Numeric	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Bayesian statistics	None	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Yes	Yes	Not applicable	Not applicable	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	Yes ? Comment: Actual value was not given, just that results were very poor. Simulation results were 258% of observed	Not applicable	Not applicable	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	r squared \| 00.0 \| unitless	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	Yes	Yes	Yes	Not applicable	Not applicable	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	No	No	Not applicable	Not applicable	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Unclear	No	No	Not applicable	Not applicable	No	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	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-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia Bulgaria France Lithuania Romania Hungary United Kingdom Spain Czech Republic Belgium Finland Denmark Italy Germany Estonia	North America United States Illinois	None	Europe Ireland	None	None	North America United States Florida	North America United States Illinois

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

EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
Centroid Latitude em.detail.ddLatHelp ?	50.53	38.69	17.73	52.86	Not applicable	Not applicable	27.76	41.88
Centroid Longitude em.detail.ddLongHelp ?	7.6	-89.1	-64.77	6.54	Not applicable	Not applicable	-82.54	87.65
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	None provided	Not applicable	Not applicable	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Estimated	Provided	Not applicable	Not applicable	Estimated	Provided

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland	Agroecosystems	Near Coastal Marine and Estuarine	Agroecosystems	Inland Wetlands	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Created Greenspace
Specific Environment Type em.detail.specificEnvTypeHelp ?	Streams and near upstream environments	Row crop agriculture in Kaskaskia river basin	Coral reefs	farm pasture	Restored wetlands	Not applicable	Esturary and associated urban and terrestrial environment	urban green roofs
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser 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 is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Not applicable	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-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
None Available	None Available	other finfish	None Available	None Available	None Available	None Available	None Available

EnviroAtlas URL

EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
Stream Length Impaired by Pesticides	National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Agricultural water use (million gallons/day), Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	None Available	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	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)	GAP Ecological Systems, The National Hydrography Dataset (NHD), Average Annual Precipitation, Agricultural water use (million gallons/day)	National Hydrography Dataset Plus (NHD PlusV2)	Green Space per Capita, Average Annual Daily Potential Solar Energy, Average Annual Precipitation, Average Annual Daily Potential Wind Energy

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-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
[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 Water conditions Chemical condition of freshwaters	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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 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] Provisioning Materials Water Surface water for non-drinking purposes [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Micro and regional climate regulation Global climate regulation by reduction of greenhouse gas concentrations Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance Gaseous / air flows Ventilation and transpiration Mass flows Buffering and attenuation of mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals Mediation by ecosystems Mediation of smell/noise/visual impacts Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

EM-94	EM-97	EM-462	EM-598	EM-617	EM-712	EM-863	EM-945
None	Rivers and Streams (111) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Agroecosystems (22) Flora (5) Green biomass Stock 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	Rivers and Streams (111) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Forests (21) Composite (8) Presence of environmental class/subclass Stock Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Stock Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Stock Indicator Lakes and Ponds (112) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Wetlands (12) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Stock Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Stock Indicator Soil (2) Specific types of soil Quality Indicator	None	None

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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)