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-99
EM-104
EM-106
EM-319
EM-333
EM-345
EM-450
EM-630
EM-845
EM-936
EM-941

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
EM Short Name em.detail.shortNameHelp ?	Landscape importance for crops, Europe	SPARROW, Northeastern USA	Value of Habitat for Shrimp, Campeche, Mexico	Redfish and cold water coral (EFH), Norway	Evoland v3.5 (unbounded growth), Eugene, OR, USA	InVEST habitat quality, Puli Township, Taiwan	Decrease in wave runup, St. Croix, USVI	WaterWorld v2, Santa Basin, Peru	Red-winged blackbird abun, Piedmont region, USA	i-Tree species selector v. 4.0	ESTIMAP - Pollination potential, Iran
EM Full Name em.detail.fullNameHelp ?	Landscape importance for crop-based production, Europe	SPARROW (SPAtially Referenced Regressions On Watershed Attributes), Northeastern USA	Value of Habitat for Shrimp, Campeche, Mexico	Linkage between redfish and cold water coral, Norway (essential fish habitat model)	Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA	InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan	Decrease in wave runup (by reef), St. Croix, USVI	WaterWorld v2, Santa Basin, Peru	Red-winged blackbird abundance, Piedmont ecoregion, USA	i-Tree species selector v. 4.0	ESTIMAP - Pollination potential, Iran
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	US EPA	None	None	Envision	InVEST	US EPA	None	None	i-Tree	None
EM Source Document ID	228	86	227	259	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	308	335	368	405	426 ? Comment: Doc# 427 is an additional source for this EM.	434
Document Author em.detail.documentAuthorHelp ?	Haines-Young, R., Potschin, M. and Kienast, F.	Moore, R. B., Johnston, C.M., Smith, R. A. and Milstead, B.	Barbier, E. B., and Strand, I.	Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Van Soesbergen, A. and M. Mulligan	Riffel, S., Scognamillo, D., and L. W. Burger	i-Tree	Rahimi, E., Barghjelveh, S., and P. Dong
Document Year em.detail.documentYearHelp ?	2012	2011	1998	2010	2008	2014	2014	2018	2008	None	2020
Document Title em.detail.sourceIdHelp ?	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Source and delivery of nutrients to receiving waters in the northeastern and mid-Atlantic regions of the United States	Valuing mangrove-fishery linkages: A case study of Campeche, Mexico	Estimating linkages between redfish and cold water coral on the Norwegian coast	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	i-Tree Species Selector User's Manual v. 4.0	Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran)
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	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 journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Webpage	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	http://evoland.bioe.orst.edu/	https://www.naturalcapitalproject.org/invest/	Not applicable	www.policysupport.org/waterworld	Not applicable	https://species.itreetools.org/	Not applicable
Contact Name em.detail.contactNameHelp ?	Marion Potschin	Richard Moore	E.B. Barbier	Naomi S. Foley	Michael R. Guzy	Yu-Pin Lin ? Comment: Tel.: +886 2 3366 3467; fax: +866 2 2368 6980	Susan H. Yee	Arnout van Soesbergen	Sam Riffell	Not reported ? Comment: send comments through any of the means listed on the i-Tree support page: http://www.itreetools.org/support/.	Ehsan Rahini
Contact Address	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	U.S. Environmental Protection Agency, 27 Tarzwell Drive, Narragansett, Rhode Island 02882	Environment Department, University of York, York YO1 5DD, UK	Dept. of Economics and Management, Univeristy of Tromso, Norway	Oregon State University, Dept. of Biological and Ecological Engineering	Not reported	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	Not reported	Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
Contact Email	marion.potschin@nottingham.ac.uk	rmoore@usgs.gov	Not reported	naomifoley@gmail.com	Not reported	yplin@ntu.edu.tw	yee.susan@epa.gov	arnout.van_soesbergen@kcl.ac.uk	sriffell@cfr.msstate.edu	info@itreetools.org	ehsanrahimi666@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
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 methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture."	AUTHOR'S DESCRIPTION: "SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid-Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries"	AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153)	ABSTRACT: "…This paper applies the production function approach to estimate the link between cold water corals and redfish in Norway. Both the carrying capacity and growth rate of redfish are found to be functions of cold water coral habitat and thus cold water corals can be considered an essential fish habitat…The essential habitat model shows the best fit to the data…" AUTHOR'S DESCRIPTION: "…the EFH model presented by Barbier and Strand (1998), in which the habitat is considered essential to the stock; i.e., if the habitat declines to zero the fish stock will perish…based on the Gordon-Schaefer model, which is a single-species biomass model, where effort is the control variable and fish stock is the state variable. In the case of habitat-fisheries interactions, such as in our case, a second state variable is introduced, the habitat (CWC)…Scientists have stimated that 30-50% of CWC habitat has been damaged (Fossa, Mortensen, and Furevik 2002. Working within these bounds, we empirically estimate the relationship between CWC as a habitat and a fish stock..."	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."	Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the landscape.	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))."	ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r	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: "The Species Selector is a free-standing i-Tree utility that ranks tree species based on their environmental benefits at maturity. As such, it complements existing tree selection programs that rank species based on esthetics or other features. Species are selected based on three types of information. First, hardiness is considered. The hardiness zone is determined based on state and city, and all species that are not sufficiently hardy are eliminated from consideration. Second, mature height is considered. Users are asked to specify minimum and maximum heights, and species outside of that range are eliminated. Finally, eight environmental factors are considered in the rankings created by the Species Selector: • Air pollution removal • Air temperature reduction • Ultraviolet radiation reduction • Carbon storage • Pollen allergenicity • Building energy conservation • Wind reduction • Stream flow reduction (stormwater management). Users are asked to rank the importance of each of these factors on a scale of 0 to 10. The combination of hardiness, mature height, and desired functionality produces a ranked list of appropriate species from an initial database of about 1,600 species. The large species database covers a broad range of native, naturalized and exotic trees, some of which are commonly planted in urban areas. Since only city hardiness zone, tree height and user functional preferences are used to produce the list, there may well be many species on the list that are unsuitable to the local context for a variety of reasons. A species may have particular structural, drainage, sun, pest, or soil pH limitations that should exclude it from use. Furthermore, since many native and exotic species are included, items may appear that are simply not available in the local trade. For these reasons, the list should be considered a beginning rather than an end. The list will need to be whittled down to meet local needs and limitations. Relevant cultural needs should be taken into account as well. The result will be a list of recommended species suited for local use that maximizes environmental services."	Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	water-quality assessment, total maximum daily load(TMDL) determination	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…"	Environmental effects of Highway 6 construction on Puli Township, Taiwan	None identified	None identified	None reported	None identified	None reported
Biophysical Context	No additional description provided	Norteneastern region (U.S.); Mid-Atlantic region (U.S.)	Gulf of Mexico; mangrove-lagoon system	Continental slope	No additional description provided	26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere.	No additional description provided	Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal.	Conservation Reserve Program lands left to go fallow	No additional description provided	None additional
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area.	Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves.	Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas.	No scenarios presented	Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100	N/A	No scenarios presented	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist)	Method + Application	Method Only	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	New or revised model	Application of existing model	New or revised model	Application of existing model	Application of existing model	Application of existing 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-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-231 \| Doc-228	None	None	Doc-227	Doc-183 \| Doc-47 \| Doc-313 \| Doc-314 ? Comment: Doc 183 is a secondary source for the Evoland model.	Doc-278	Doc-335	None	Doc-405	Doc-427	Doc-432
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-119 \| EM-120 \| EM-121 \| EM-162 \| EM-164 \| EM-165 \| EM-122 \| EM-123 \| EM-124 \| EM-125 \| EM-166 \| EM-170 \| EM-171	None	EM-185 \| EM-319	EM-106	EM-12 \| EM-369	EM-143	EM-447	None	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-846 \| EM-847	None	EM-939

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
EM Temporal Extent em.detail.tempExtentHelp ?	2000	2002 ? Comment: Several nationwide database development and modeling efforts were necessary to create models consistent with 2002 conditions.	1980-1990	1986-2002	1990-2050	2010-2025	2006-2007, 2010	1950-2071	2008	Not applicable	2020
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-dependent	time-stationary	Not applicable	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	future time	Not applicable	Not applicable	both	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	2	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Not applicable	Year	Not applicable	Not applicable	Month	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Geopolitical	Geopolitical	Physiographic or ecological	Watershed/Catchment/HUC	Physiographic or ecological	Not applicable	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	The EU-25 plus Switzerland and Norway	NE U.S. Regions	Laguna de Terminos Mangrove system	Norwegian Sea (ICES areas I and II)	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	Puli Township, Nantou County	Coastal zone surrounding St. Croix	Santa Basin	Piedmont Ecoregion	Not applicable	Iran
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	>1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	10-100 km^2	100-1000 km^2	100-1000 km^2	10,000-100,000 km^2	100,000-1,000,000 km^2	Not applicable	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	Not applicable	spatially distributed (in at least some cases) ? Comment: Varies by inputs, but results are for areas of country
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	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	Not applicable	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 km x 1 km	30 x 30 m	1 km x 1 km	Not applicable	varies	40 m x 40 m	10 m x 10 m	1 km2	Not applicable	Not applicable	ha^2

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
EM Computational Approach em.detail.emComputationalApproachHelp ?	Logic- or rule-based	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic	*	Analytic	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Other or unclear (comment)	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Yes	Yes	Unclear	Unclear	Yes	No	Yes	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes ? Comment: R-squared of .97 refers to the modelled loading whereas .83 refers to yield (see table 1, pg 972 for more information)	Yes	Yes	No	Not applicable	No	No	No	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	R^2 \| 0.97 \| Not applicable Mean Square Error \| 0.12 \| Not applicable Root Mean Square Error \| 0.35 \| Not applicable R-squared \| 0.83 \| Not applicable	Adjusted R^2 \| 0.745 \| Not applicable F-stat \| 30.216 \| Not applicable SE of regression \| 639745.7 \| Not applicable	Adj R2 (30% CWC decline) \| 0.8677 \| unitless Durbin-Watson (DW) test (30% CWC decline) \| 1.392486 \| unitless P value (30% CWC decline) \| 0.0000 \| unitless Adj R2 (50% CWC decline) \| 0.8880 \| unitless Durbin-Watson (DW) test (50% CWC decline) \| 1.582468 \| unitless P-value (50% CWC decline) \| 0.0000 \| unitless	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	Yes	No	No	No	Not applicable	Yes	Yes	No	Not applicable	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Unclear	Yes	No	No	No	No	No	No	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	Yes	Yes	No	No	No	No	Yes	Not applicable	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Unclear	Unclear	Yes	Not applicable	Not applicable	Not applicable	Not applicable	Unclear	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-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
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 Vermont New York West Virginia Massachusetts Maine Connecticut New Hampshire Delaware Maryland New Jersey Pennsylvania Virgina	North America Mexico	None	North America United States Oregon	Asia China ? Comment: Taiwan	None	None	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	North America United States	Europe ? Comment: Model for Iran - no form preset id for country

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

EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Southern Gulf of Mexico	Temperate North Atlantic Northeast Atlantic Northern European Seas Northern Norway and Finnmark Southern Norway	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
Centroid Latitude em.detail.ddLatHelp ?	50.53	42	18.61	70	44.11	23.98	17.73	-9.05	36.23	Not applicable	32.29
Centroid Longitude em.detail.ddLongHelp ?	7.6	-73	-91.55	10	-123.09	120.96	-64.77	-77.81	-81.9	Not applicable	53.68
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Ground Water \| Terrestrial Environment (sub-classes not fully specified) \| Agroecosystems \| Atmosphere	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine \| Open Ocean and Seas	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Rivers and Streams \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands	Near Coastal Marine and Estuarine	None	Grasslands	Created Greenspace	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	none	Mangrove	cold water coral reefs	Agricultural-urban interface at river junction	Predominantly an agricultural area with associated forest land	Coral reefs	tropical, coastal to montane	grasslands	Urban greenspace	terrestrial land types
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is coarser 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	Other or unclear (comment)	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-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Guild or Assemblage	Guild or Assemblage	Not applicable	Community	Not applicable	Not applicable	Species	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
None Available	None Available	other Shrimp	Genus Sebastes sp. Lophelia sp. Species Lophelia pertusa	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	None Available	None Available	None Available	Species Redwinged blackbird	None Available	Species Apis mellifera Apis florea

EnviroAtlas URL

EM-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
None Available	National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, U.S. EPA (Omernik) ecoregions, Total Annual Nitrogen Deposition	GAP Ecological Systems, Big game hunting recreation demand	Big game hunting recreation demand	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	Percent GAP Status 1 & 2	National Hydrography Dataset Plus (NHD PlusV2)	Average Annual Precipitation	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2)	The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Precipitation, Waterbody area

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-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
[Ecosystem] Provisioning Nutrition Biomass Cultivated crops	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters 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 Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[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 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] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	None	[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 Atmospheric composition and climate regulation Micro and regional climate regulation Global climate regulation by reduction of greenhouse gas concentrations Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Gaseous / air flows Storm protection Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal

<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-99	EM-104	EM-106	EM-319	EM-333	EM-345	EM-450	EM-630	EM-845	EM-936	EM-941
Agroecosystems (22) Flora (5) food crops Stock Indicator	Rivers and Streams (111) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Shrimp Stock Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator Open Oceans and Seas (114) Fauna (4) Fish Stock Indicator	Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Forests (21) Composite (8) Presence of environmental class/subclass Stock Indicator Quality Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Stock Indicator Quality Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator	None	Forests (21) Fauna (4) Wildlife Stock Indicator Scrubland/Shrubland (24) Fauna (4) Wildlife Stock Indicator Urban/Surburban (27) Fauna (4) Wildlife Stock 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])

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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)