EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.

This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:

Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
Intermediate (Computed) Variables
Response Variables
- Computed Response Variables
- Measured Response Variables

EM Variables by Category

A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:

Policy Regarding Use or Management of Ecosystem Resources
Land Surface (or Water Body Bed) Cover, Use or Substrate
Human Demographic Data
Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
Monetary Values

Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.

All EM Descriptors

This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:

EM Identity and Description
EM Modeling Approach
EM Locations, Environments, Ecology
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

EM Descriptors by Modeling Concepts

This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:

Conceptual Model
Modeling Objective
Modeling Context
Potential for Model Linkage
Feasibility of Model Use
Model Certainty
Model Structural Information

Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.

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Comparing:

EM-122
EM-397
EM-428
EM-660
EM-701
EM-702
EM-709
EM-788
EM-841
EM-862

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
EM Short Name em.detail.shortNameHelp ?	Land-use change and crop-based production, Europe	Wetland shellfish production, Gulf of Mexico, USA	Retained rainwater, Guánica Bay, Puerto Rico	RUM: Valuing fishing quality, Michigan, USA	Blue-winged Teal recruits, CREP wetlands, IA, USA	Northern Shoveler recruits, CREP wetlands, IA, USA	Pollinators on landfill sites, United Kingdom	Wild bees over 26 yrs of restored prairie, IL, USA	Brown-headed cowbird abundance, Piedmont, USA	Recreational fishery index, USA
EM Full Name em.detail.fullNameHelp ?	Land-use change effects on crop-based production, Europe	Wetland shellfish production, Gulf of Mexico, USA	Retained rainwater, Guánica Bay, Puerto Rico, USA	Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA	Blue-winged Teal duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Pollinating insects on landfill sites, East Midlands, United Kingdon	Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA	Brown-headed cowbird abundance, Piedmont ecoregion, USA	Recreational fishery index for streams and rivers, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	US EPA ? Comment: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science	US EPA	None	None	None	None	None	None	US EPA
EM Source Document ID	228	324	338	382 ? Comment: Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010.	372 ? Comment: Document 373 is a secondary source for this EM.	372 ? Comment: Document 373 is a secondary source for this EM.	389	401	405	414
Document Author em.detail.documentAuthorHelp ?	Haines-Young, R., Potschin, M. and Kienast, F.	Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson	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	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	Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin	Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree	Riffel, S., Scognamillo, D., and L. W. Burger	Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold
Document Year em.detail.documentYearHelp ?	2012	2012	2017	2014	2010	2010	2013	2017	2008	2021
Document Title em.detail.sourceIdHelp ?	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	Valuing recreational fishing quality at rivers and streams	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects	Wild bee community change over a 26-year chronosequence of restored tallgrass prairie	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers.
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	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 report	Published report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Marion Potschin	Stephen J. Jordan	Susan H. Yee	Richard Melstrom	David Otis	David Otis	Sam Tarrant	Sean R. Griffin	Sam Riffell	Gregg Lomnicky
Contact Address	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K.	Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A.	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	200 SW 35th St., Corvallis, OR, 97333
Contact Email	marion.potschin@nottingham.ac.uk	jordan.steve@epa.gov	yee.susan@epa.gov	melstrom@okstate.edu	dotis@iastate.edu	dotis@iastate.edu	sam.tarrant@rspb.org.uk	srgriffin108@gmail.com	sriffell@cfr.msstate.edu	lomnicky.gregg@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
Summary Description em.detail.summaryDescriptionHelp ?	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."	ABSTRACT: "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 … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico."	AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…"	ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. "	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).	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).	ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each	ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities."	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. "	ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.]
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Meeting water demands for agriculture and domestic purposes.	None identified	None identified	None identified	None identified	None identified	None reported	None identified
Biophysical Context	No additional description provided	Estuarine environments and marsh-land interfaces	No additional descriptions provided	stream and river reaches of Michigan	Prairie Pothole Region of Iowa	Prairie Pothole Region of Iowa	No additional description provided	The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season.	Conservation Reserve Program lands left to go fallow	None
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030)	Shellfish type; Changes to submerged aquatic vegetation (SAV)	No scenarios presented	targeted sport fish biomass	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	N/A	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs ? Comment: Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas.	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-238 \| Doc-239 \| Doc-240 \| Doc-241 \| Doc-242 \| Doc-228	None	None	None	Doc-372 \| Doc-373	Doc-372 \| Doc-373	Doc-389	None	Doc-405	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	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	EM-604 \| EM-603	None	None	EM-705 \| EM-704 \| EM-703 \| EM-702 \| EM-700 \| EM-632	EM-705 \| EM-704 \| EM-703 \| EM-701 \| EM-700 \| EM-632	EM-697	None	EM-831 \| EM-838 \| EM-839 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-846 \| EM-847	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
EM Temporal Extent em.detail.tempExtentHelp ?	1990-2030	1950 - 2050	2006 - 2012	2008-2010	1987-2007	1987-2007	2007-2008	1988-2014	2008	2013-2014
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	future time	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	6, 10, and 30	Varies by Run	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Year	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Year

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Multiple unrelated locations (e.g., meta-analysis)	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	The EU-25 plus Switzerland and Norway	Gulf of Mexico (estuarine and coastal)	Guanica Bay watershed	HUCS in Michigan	CREP (Conservation Reserve Enhancement Program	CREP (Conservation Reserve Enhancement Program	East Midlands	Nachusa Grasslands	Piedmont Ecoregion	United States
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	10,000-100,000 km^2	1000-10,000 km^2.	100,000-1,000,000 km^2	10,000-100,000 km^2	10,000-100,000 km^2	1000-10,000 km^2.	10-100 km^2	100,000-1,000,000 km^2	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: Computations at this pixel scale pertain to certain variables specific to Mobile Bay.	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)
Spatial Grain Type em.detail.spGrainTypeHelp ?	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)	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	length, for linear feature (e.g., stream mile)
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 km x 1 km	55.2 km^2	30 m x 30 m	reach in HUC	multiple, individual, irregular sites	multiple, individual, irregular sites	multiple unrelated locations	Area varies by site	Not applicable	stream reach (site)

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
EM Computational Approach em.detail.emComputationalApproachHelp ?	Logic- or rule-based	Numeric	Analytic	Numeric	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	No	No	Unclear	Unclear	Not applicable	No	Yes	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	Yes	No	No	Not applicable	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	R squared \| 0.552 \| unitless	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	No	No	Not applicable	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	Not applicable	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	Not applicable	No	Yes	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	Unclear	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
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	North America United States Mississippi Texas Louisiana Alabama Florida	North America U.S. Caribbean Territories	North America United States Michigan	North America United States Iowa	North America United States Iowa	Europe United Kingdom	North America United States Illinois	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	North America United States North Carolina Indiana Wyoming Northern Mariana Islands Utah Guam Arizona Montana Kentucky California Kansas Delaware Florida Pennsylvania Iowa Mississippi Illinois Texas Connecticut Georgia Maryland Idaho Oregon Vermont Puerto Rico Maine Oklahoma Tennessee American Samoa Alabama Arkansas South Carolina Washington Nebraska West Virginia Colorado Massachusetts Missouri Alaska U.S. Virgin Islands Washington D.C. North Dakota Wisconsin Nevada New York Rhode Island Hawaii South Dakota Minnesota New Jersey Michigan Virgina New Mexico New Hampshire Louisiana Ohio

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

EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
None	Temperate North Atlantic Northwest Atlantic Warm Temperate Northwest Atlantic Northern Gulf of Mexico	None	None	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
Centroid Latitude em.detail.ddLatHelp ?	50.53	30.44	17.96	45.12	42.62	42.62	52.22	41.89	36.23	36.21
Centroid Longitude em.detail.ddLongHelp ?	7.6	-87.99	-67.02	85.18	-93.84	-93.84	-0.91	-89.34	-81.9	-113.76
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Provided	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Inland Wetlands \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland \| Barren	Rivers and Streams	Inland Wetlands \| Agroecosystems \| Grasslands	Inland Wetlands \| Agroecosystems \| Grasslands	Created Greenspace \| Grasslands	Agroecosystems \| Grasslands	Grasslands	Rivers and Streams
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	Submerged aquatic vegetation in estuaries and coastal lagoons	13 LULC were used	stream reaches	Wetlands buffered by grassland within agroecosystems	Wetlands buffered by grassland within agroecosystems	restored landfills and grasslands	Restored prairie, prairie remnants, and cropland	grasslands	reach
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Species	Not applicable	Not applicable	Individual or population, within a species	Individual or population, within a species	Individual or population, within a species	Species	Species	Guild or Assemblage

Taxonomic level and name of organisms or groups identified

EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
None Available	Family Penaeid shrimp Species Callinectes sapidus (blue crab)	None Available	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye	Species Blue-winged teal (Anas discors)	Species Northern Shoveler (Anas clypeata)	Genus Calliopum Lasioglossum Species Apis mellifera Bombus lapidarius Oedemera nobilis Episyrphus balteatus Bombus terrestris/lucorum Bombus pascuorum	other Bees	Species Brown headed cowbird	None Available

EnviroAtlas URL

EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
Hectares of Vegetable Crops	Big game hunting recreation demand	The Watershed Boundary Dataset (WBD)	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	GAP Ecological Systems	GAP Ecological Systems	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	None Available

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
[Ecosystem] Provisioning Nutrition Biomass Cultivated crops	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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 Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

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

EM-122	EM-397	EM-428	EM-660	EM-701	EM-702	EM-709	EM-788	EM-841	EM-862
Agroecosystems (22) Flora (5) food crops Stock Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Crabs Stock Indicator Shrimp Stock Indicator	None	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	Wetlands (12) Fauna (4) Duck Stock Indicator	Wetlands (12) Fauna (4) Duck Stock Indicator	None	None	Grasslands (23) Fauna (4) Birds Stock Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator

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)