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-88
EM-105
EM-113
EM-122
EM-195
EM-376
EM-430
EM-492
EM-603
EM-703
EM-760

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
EM Short Name em.detail.shortNameHelp ?	Area and hotspots of carbon storage, South Africa	Benthic habitat associations, Willapa Bay, OR, USA	Wetland conservation for birds, Midwestern USA	Land-use change and crop-based production, Europe	C Sequestration and De-N, Tampa Bay, FL, USA	MIMES: For Massachusetts Ocean (v1.0)	Carbon sequestration, Guánica Bay, Puerto Rico	EnviroAtlas - Restorable wetlands	Chinook salmon value, Yaquina Bay, OR	Gadwall duck recruits, CREP wetlands, Iowa, USA	WESP: Marsh & wet meadow, ID, USA
EM Full Name em.detail.fullNameHelp ?	Area and hotspots of carbon storage, South Africa	Benthic macrofaunal habitat associations, Willapa Bay, OR, USA	Prioritizing wetland conservation for birds, Midwestern USA	Land-use change effects on crop-based production, Europe	Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA	Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0)	Carbon sequestration, Guánica Bay, Puerto Rico, USA	US EPA EnviroAtlas - Percent potentially restorable wetlands, USA	Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR	Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	WESP: Seasonally flooded marsh & wet meadow, Idaho, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	US EPA	None	EU Biodiversity Action 5	US EPA	US EPA	US EPA	US EPA \| EnviroAtlas	US EPA	None	None
EM Source Document ID	271	39	122	228	186	316	338	262	324	372 ? Comment: Document 373 is a secondary source for this EM.	393 ? Comment: Additional data came from electronic appendix provided by author Chris Murphy.
Document Author em.detail.documentAuthorHelp ?	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Ferraro, S. P. and Cole, F. A.	Thogmartin, W. A., Potter, B. A. and Soulliere, G. J.	Haines-Young, R., Potschin, M. and Kienast, F.	Russell, M. and Greening, H.	Altman, I., R.Boumans, J. Roman, L. Kaufman	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	US EPA Office of Research and Development - National Exposure Research Laboratory	Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar	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	Murphy, C. and T. Weekley
Document Year em.detail.documentYearHelp ?	2008	2007	2011	2012	2013	2012	2017	2013	2012	2010	2012
Document Title em.detail.sourceIdHelp ?	Mapping ecosystem services for planning and management	Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA	Bridging the conservation design and delivery gap for wetland bird habitat maintenance and restoration in the midwestern United States	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Estimating benefits in a recovering estuary: Tampa Bay, Florida	Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0)	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	EnviroAtlas - National	Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context.
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	Documented, not peer reviewed	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published on US EPA EnviroAtlas website	Published journal manuscript	Published report	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	http://www.afordablefutures.com/orientation-to-what-we-do	Not applicable	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Benis Egoh	Steve Ferraro	Wayne Thogmartin, USGS	Marion Potschin	M. Russell	Irit Altman	Susan H. Yee	EnviroAtlas Team	Stephen Jordan	David Otis	Chris Murphy
Contact Address	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365	Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA	Boston University, Portland, Maine	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Not reported	U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID
Contact Email	Not reported	ferraro.steven@epa.gov	wthogmartin@usgs.gov	marion.potschin@nottingham.ac.uk	Russell.Marc@epamail.epa.gov	iritaltman@bu.edu	yee.susan@epa.gov	enviroatlas@epa.gov	jordan.steve@epa.gov	dotis@iastate.edu	chris.murphy@idfg.idaho.gov

EM Description

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot."	AUTHOR'S DESCRIPTION: "In this paper we report the results of 2 estuary-wide studies of benthic macrofaunal habitat associations in Willapa Bay, Washington, USA. This research is part of an effort to develop empirical models of biota-habitat associations that can be used to help identify critical habitats, prioritize habitats for environmental protection, index habitat suitability (U.S. Fish and Wildlife Service, 1980; Kapustka, 2003), perform habitat equivalency and compensatory restoration analyses (Fonseca et al., 2002; Kirsch et al., 2005), and as habitat value criteria in ecological risk assessments (Obery and Landis, 2002; Ferraro and Cole, 2004; Landis et al., 2004)." (491)	ABSTRACT: "The U.S. Fish and Wildlife Service’s adoption of Strategic Habitat Conservation is intended to increase the effectiveness and efficiency of conservation delivery by targeting effort in areas where biological benefits are greatest. Conservation funding has not often been allocated in accordance with explicit biological endpoints, and the gap between conservation design (the identification of conservation priority areas) and delivery needs to be bridged to better meet conservation goals for multiple species and landscapes. We introduce a regional prioritization scheme for North American Wetlands Conservation Act funding which explicitly addresses Midwest regional goals for wetland-dependent birds. We developed decision-support maps to guide conservation of breeding and non-breeding wetland bird habitat. This exercise suggested ~55% of the Midwest consists of potential wetland bird habitat, and areas suited for maintenance (protection) were distinguished from those most suited to restoration. Areas with greater maintenance focus were identified for central Minnesota, southeastern Wisconsin, the Upper Mississippi and Illinois rivers, and the shore of western Lake Erie and Saginaw Bay. The shores of Lakes Michigan and Superior accommodated fewer waterbird species overall, but were also important for wetland bird habitat maintenance. Abundant areas suited for wetland restoration occurred in agricultural regions of central Illinois, western Iowa, and northern Indiana and Ohio. Use of this prioritization scheme can increase effectiveness, efficiency, transparency, and credibility to land and water conservation efforts for wetland birds in the Midwestern United States."	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 novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios."	AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2)	AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors.	AUTHOR'S DESCRIPTION: "In addition to affecting water quality, the ecosystem services of nitrogen retention, phosphorous retention, and sediment retention were also considered to contribute to stakeholder goals of maintaining the productivity of agricultural land and reducing soil loss. Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature."	DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map."	ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico.	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 = 2Rn 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 = HZB/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).	A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Strategic habitat conservation by USFW for Wetland Conservation Act funding	None identified	Restoration of seagrass	None identified	None identified	None Identified	None reported	None identified	None identified
Biophysical Context	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	benthic estuarine	Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods	No additional description provided	Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove	No additional description provided	No additional description provided	No additional description provided	Yaquina Bay estuary	Prairie Pothole Region of Iowa	restored, enhanced and created wetlands
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Conservation efforts for: marsh-wetland breeding birds, regional marsh and open-water for non-breeding birds, mudflat/shallows for birds during non-breeding period.	Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030)	Habitat loss or restoration in Tampa Bay Estuary	No scenarios presented	No scenarios presented	No scenarios presented	N/A	No scenarios presented	Sites, function or habitat focus

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs
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	New or revised model	Application of existing 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-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-271	None	Doc-169 \| Doc-170 \| Doc-171 \| Doc-172 \| Doc-173 \| Doc-174 \| Doc-175	Doc-238 \| Doc-239 \| Doc-240 \| Doc-241 \| Doc-242 \| Doc-228	None	None	None	None	None	Doc-372 \| Doc-373	Doc-390
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-85 \| EM-86 \| EM-87	None	None	EM-123 \| EM-124 \| EM-125 \| EM-162 \| EM-164 \| EM-165 \| EM-166 \| EM-170 \| EM-171 \| EM-99 \| EM-119 \| EM-120 \| EM-121	None	None	None	None	EM-604 \| EM-397	EM-705 \| EM-704 \| EM-702 \| EM-701 \| EM-700 \| EM-632	EM-718 \| EM-734 \| EM-743

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	1996,1998	2007	1990-2030	1982-2010	Not applicable	1978 - 2013	2006-2013	2003-2008	1987-2007	2010-2012
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	future time	Not applicable	future time	Not applicable	Not applicable	Not applicable	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	discrete	Not applicable	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	6, 10, and 30	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Year	Not applicable	Year	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Watershed/Catchment/HUC	Geopolitical	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Multiple unrelated locations (e.g., meta-analysis)
Spatial Extent Name em.detail.extentNameHelp ?	South Africa	Willapa Bay	Upper Mississippi River and Great Lakes Region	The EU-25 plus Switzerland and Norway	Tampa Bay Estuary	Massachusetts Ocean	Guanica Bay watershed	conterminous United States	Pacific Northwest	CREP (Conservation Reserve Enhancement Program	Wetlands in idaho
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	>1,000,000 km^2	>1,000,000 km^2	1000-10,000 km^2.	1000-10,000 km^2.	1000-10,000 km^2.	>1,000,000 km^2	>1,000,000 km^2	10,000-100,000 km^2	100,000-1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
EM Spatial Distribution em.detail.distributeLumpHelp ?	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)	spatially distributed (in at least some cases)	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 lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed across catchments with average size of 65,000 ha	Not applicable	1 ha	1 km x 1 km	1 ha	1 km x1 km	30 m x 30 m	irregular	Not applicable	multiple, individual, irregular sites	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Logic- or rule-based	Analytic	Numeric	Analytic	Analytic	Numeric	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	No	No	Yes	No	No	No	No	Unclear	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	No	No	No	No	No	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	R^2 \| 0.34-0.82 \| Not applicable R^2 \| 0.29-0.68 \| Not applicable R^2 \| 0.07-0.17 \| Not applicable	None	None	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	No	No	No	No	Yes ? Comment: Compared to a second methodological approach	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	No	No	No	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	No	No	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	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-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
Africa South Africa	None	North America United States Iowa Indiana Illinois Missouri Minnesota Ohio Kansas Wisconsin Michigan Nebraska	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	None	None	North America U.S. Caribbean Territories	North America United States	North America United States Oregon Washington	North America United States Iowa	North America United States Idaho

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

EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian Gulf of Maine/Bay of Fundy	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
Centroid Latitude em.detail.ddLatHelp ?	-30	46.24	42.05	50.53	27.95	41.72	17.96	39.5	44.62	42.62	44.06
Centroid Longitude em.detail.ddLongHelp ?	25	-124.06	-88.6	7.6	-82.47	-69.87	-67.02	-98.35	-124.02	-93.84	-114.69
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Inland Wetlands	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Inland Wetlands \| Near Coastal Marine and Estuarine \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland \| Barren	Agroecosystems	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Inland Wetlands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	Drowned river valley estuary	Not reported	Not applicable	Subtropical Estuary	None identified	13 LULC were used	Terrestrial	Yaquina Bay	Wetlands buffered by grassland within agroecosystems	created, restored and enhanced wetlands
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	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-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Species	Species	Not applicable	Not applicable	Species	Not applicable	Not applicable	Individual or population, within a species	Individual or population, within a species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
None Available	Species Zostera marina Neotrypaea californiensis Manayunkia aestuarina Upogebia pugettensis Spartina alterniflora	Species Tundra Swan (eastern) Wilson's Snipe Black Tern Sanderling Black-crowned Night-Heron Wilson's Phalarope American Black Duck Upland Sandpiper Mallard Duck Common Tern Piping Plover Wood Duck Canvasback American Golden Plover Blue-winged Teal Lesser Scaup Yellow Rail King Rail Dunlin American Woodcock Short-billed Dowitcher Killdeer	None Available	None Available	other Filter feeders Tube builders Erect structure builders Species Ocean pout Northern sand lance Cunner Longhorn sculpin Bluefish American lobster Minke whale Right whale Spiny dogfish Atlantic mackeral Bluefin tuna Atlantic cod (resident) Winter flounder Harbor seal Pollock Acadian redfish Harbor porpoise Blueback herring Fin whale Atlantic cod (migrant) Atlantic menhaden Silver hake Striped bass Humpback whale Haddock Red hake Scup Yellowtail flounder Witch flounder Windowpane flounder Atlantic herring White sided dolphin White hake Little skate American sand lance Goosefish Alewives	None Available	None Available	Species Chinook salmon	Species Gadwall (Anas strepera)	None Available

EnviroAtlas URL

EM-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
None Available	None Available	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	Hectares of Vegetable Crops	Carbon Storage by Tree Biomass	GAP Ecological Systems, Average Annual Daily Potential Solar Energy, Big game hunting recreation demand, Percent GAP Status 1 & 2, Percent potentially restorable wetlands on agricultural land, Average Annual Daily Potential Wind Energy	The Watershed Boundary Dataset (WBD), Percent Impervious Area, Carbon Storage by Tree Biomass	GAP Ecological Systems, The Watershed Boundary Dataset (WBD)	Big game hunting recreation demand	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass

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-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
[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] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Mediation of waste, toxics and other nuisances Mediation by biota Bio-remediation by micro-organisms, algae, plants, and animals Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	[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	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	None	None	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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 Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance 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

<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-88	EM-105	EM-113	EM-122	EM-195	EM-376	EM-430	EM-492	EM-603	EM-703	EM-760
None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	Wetlands (12) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator Fauna (4) Birds Stock Indicator	Agroecosystems (22) Flora (5) food crops Stock Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator Mammals Stock Indicator	None	None	None	Wetlands (12) Fauna (4) Duck Stock Indicator	None

EcoService Models Library (ESML)

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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)