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-86
EM-126
EM-333
EM-376
EM-414
EM-434
EM-718

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
EM Short Name em.detail.shortNameHelp ?	Area and hotspots of soil retention, South Africa	Annual profit from agriculture, South Australia	Evoland v3.5 (unbounded growth), Eugene, OR, USA	MIMES: For Massachusetts Ocean (v1.0)	SAV occurrence, St. Louis River, MN/WI, USA	Land capability classification	WESP: Riparian & stream habitat, ID, USA
EM Full Name em.detail.fullNameHelp ?	Area and hotspots of soil retention, South Africa	Annual profit from agriculture, South Australia	Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA	Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0)	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Land capability classification	WESP: Riparian and stream habitat focus projects, ID, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	None	Envision	US EPA	US EPA	None	None
EM Source Document ID	271	243	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	316	330	340	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.	Crossman, N. D., Bryan, B. A., and Summers, D. M.	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	Altman, I., R.Boumans, J. Roman, L. Kaufman	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	United States Department of Agriculture - Natural Resources Conservation Service	Murphy, C. and T. Weekley
Document Year em.detail.documentYearHelp ?	2008	2011	2008	2012	2013	2013	2012
Document Title em.detail.sourceIdHelp ?	Mapping ecosystem services for planning and management	Carbon payments and low-cost conservation	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0)	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	National Soil Survey Handbook - Part 622 - Interpretative Groups	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	Documented, not peer reviewed	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 report	Published journal manuscript	Published report	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	http://evoland.bioe.orst.edu/	http://www.afordablefutures.com/orientation-to-what-we-do	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Benis Egoh	Neville D. Crossman	Michael R. Guzy	Irit Altman	Ted R. Angradi	United States Department of Agriculture	Chris Murphy
Contact Address	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia	Oregon State University, Dept. of Biological and Ecological Engineering	Boston University, Portland, Maine	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	Not reported	Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID
Contact Email	Not reported	neville.crossman@csiro.au	Not reported	iritaltman@bu.edu	angradi.theodore@epa.gov	http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/	chris.murphy@idfg.idaho.gov

EM Description

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
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…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover."	ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns from agriculture and from carbon plantings." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep."	Note: A more recent version of this model exists. See Related EMs below for links to related models/applications. ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses."	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.	ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."	AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2]	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	Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…"	None identified	None identified	None provided	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.	Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm.	No additional description provided	No additional description provided	submerged aquatic vegetation	No additional description provided	restored, enhanced and created wetlands
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves.	No scenarios presented	No scenarios presented	No scenarios presented	Sites, function or habitat focus

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method Only	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

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

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-271 ? Comment: Document 273 used for source information on soil erosion potential variable	Doc-244	Doc-183 \| Doc-47 \| Doc-313 \| Doc-314 ? Comment: Doc 183 is a secondary source for the Evoland model.	None	None	None	Doc-390
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-85 \| EM-87 \| EM-88	None	EM-12 \| EM-369	None	None	None	EM-706 \| EM-729 \| EM-730 \| EM-734 \| EM-743 \| EM-749 \| EM-750 \| EM-756 \| EM-757 \| EM-758 \| EM-759 \| EM-760 \| EM-761 \| EM-763 \| EM-764 \| EM-766 \| EM-767 \| EM-732 \| EM-737 \| EM-738 \| EM-739 \| EM-741 \| EM-742 \| EM-751 \| EM-768

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	2002-2008	1990-2050	Not applicable	2010 - 2012	Not applicable	2010-2011
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-dependent	time-stationary	Not applicable	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	future time	future time	Not applicable	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	discrete	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	2	1	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Year	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Physiographic or ecological	Not applicable	Multiple unrelated locations (e.g., meta-analysis)
Spatial Extent Name em.detail.extentNameHelp ?	South Africa	Agricultural districts of the state of South Australia	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	Massachusetts Ocean	St. Louis River Estuary	Not applicable	Wetlands in idaho
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100,000-1,000,000 km^2	10-100 km^2	1000-10,000 km^2.	10-100 km^2	Not applicable	100,000-1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
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 distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively.	Not applicable	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed across catchments with average size of 65,000 ha	1 ha	varies	1 km x1 km	0.07 m^2 to 0.70 m^2	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Numeric	Numeric	Analytic	Not applicable	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	None	Other or unclear (comment)	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Unclear	No	Yes	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	Yes	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	Yes	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	Not applicable	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	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-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
Africa South Africa	Oceania Australia	North America United States Oregon	None	North America United States Minnesota Wisconsin	None	North America United States Idaho

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

EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
None	None	None	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian Gulf of Maine/Bay of Fundy	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
Centroid Latitude em.detail.ddLatHelp ?	-30	-34.9	44.11	41.72	46.72	Not applicable	44.06
Centroid Longitude em.detail.ddLongHelp ?	25	138.7	-123.09	-69.87	-96.13	Not applicable	-114.69
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not reported	Agricultural land for annual crops, annual legumes, and grazing of sheep and cows	Agricultural-urban interface at river junction	None identified	Freshwater estuarine system	None identified	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 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-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Guild or Assemblage	Not applicable	Species	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
None Available	Genus Triticum Species Ovis aries Bos primigenius Pisum sativum	other Fish Species Red-Legged Frog Osprey Western Terrestrial Garter Snake Little Brown Myotis Belted Kingfisher Wood Duck Northern River Otter Great Blue Heron Roughskin Newt Pacific Water Shrew American Beaver Mink Western Pond Turtle Killdeer Northwestern Salamander	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	None Available

EnviroAtlas URL

EM-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
None Available	GAP Ecological Systems	Dasymetric Allocation of Population, GAP Ecological Systems, The National Hydrography Dataset (NHD), Percent Natural Land Cover in Stream Buffer , Hectares of Vegetable Crops, Employment Rate	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	Average Annual Precipitation	Average Annual Precipitation	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-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Provisioning Nutrition Biomass Reared animals and their outputs Cultivated crops	[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	[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 Reared animals and their outputs Cultivated crops	[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-86	EM-126	EM-333	EM-376	EM-414	EM-434	EM-718
None	None	Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator Mammals Stock Indicator	None	Agroecosystems (22) Soil (2) Specific types of soil Quality Indicator	None

EcoService Models Library (ESML)

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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)