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-326
EM-333
EM-414
EM-456
EM-604
EM-718
EM-875
EM-941
EM-942

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
EM Short Name em.detail.shortNameHelp ?	Area and hotspots of soil retention, South Africa	ARIES flood regulation, Puget Sound Region, USA	Evoland v3.5 (unbounded growth), Eugene, OR, USA	SAV occurrence, St. Louis River, MN/WI, USA	Reef dive site favorability, St. Croix, USVI	Chinook salmon value (household), Yaquina Bay, OR	WESP: Riparian & stream habitat, ID, USA	Valuing environmental ed., New York, New York	ESTIMAP - Pollination potential, Iran	Pollutant dispersion by vegetation barriers
EM Full Name em.detail.fullNameHelp ?	Area and hotspots of soil retention, South Africa	ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA	Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Dive site favorability (reef), St. Croix, USVI	Economic value of Chinook salmon per household method, Yaquina Bay, OR	WESP: Riparian and stream habitat focus projects, ID, USA	Valuing environmental education, Hudson River Park, New York, New York	ESTIMAP - Pollination potential, Iran	Pollutant dispersion by vegetation barriers
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	ARIES	Envision	US EPA	US EPA	US EPA	None	None	None	US EPA
EM Source Document ID	271	302	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	330	335	324	393 ? Comment: Additional data came from electronic appendix provided by author Chris Murphy.	416	434	435
Document Author em.detail.documentAuthorHelp ?	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar	Murphy, C. and T. Weekley	Hutcheson, W. Hoagland, P., and D. Jin	Rahimi, E., Barghjelveh, S., and P. Dong	Hashad, K. B. Yang, J. T. Steffens, R. W. Baldauf, P. Deshmukh, K. M. Zhang
Document Year em.detail.documentYearHelp ?	2008	2014	2008	2013	2014	2012	2012	2018	2020	2021
Document Title em.detail.sourceIdHelp ?	Mapping ecosystem services for planning and management	From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management	Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context.	Valuing environmental education as a cultural ecosystem service at Hudson River Park	Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran)	Parameterizing pollutant dispersion downwind of roadside vegetation barriers
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 but unpublished (explain in Comment)
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 report	Published journal manuscript	Published journal manuscript	Journal manuscript submitted or in review

Software and Access

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	http://aries.integratedmodelling.org/	http://evoland.bioe.orst.edu/	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Benis Egoh	Ken Bagstad	Michael R. Guzy	Ted R. Angradi	Susan H. Yee	Stephen Jordan	Chris Murphy	Walter Hutcheson	Ehsan Rahini	K. Max Zhang
Contact Address	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Geosciences and Environmental Change Science Center, US Geological Survey	Oregon State University, Dept. of Biological and Ecological Engineering	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	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA	Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID	New York University, United States	Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran	Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA
Contact Email	Not reported	kjbagstad@usgs.gov	Not reported	angradi.theodore@epa.gov	yee.susan@epa.gov	jordan.steve@epa.gov	chris.murphy@idfg.idaho.gov	wwh235@nyu.edu	ehsanrahimi666@gmail.com	kz33@cornell.edu

EM Description

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
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: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures."	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."	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."	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...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and	ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico.	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.	ABSTRACT: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. "	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."	ABSTRACT: "Communities living and working in near-road environments are exposed to elevated levels of traffic-related air pollution (TRAP), causing adverse health effects. Roadside vegetation may help reduce TRAP through enhanced deposition and mixing….there are no studies that developed a dispersion model to characterize pollutant concentrations downwind of vegetation barriers. To account for the physical mechanisms, by which the vegetation barrier deposits and disperses pollutants, we propose a multi-region approach that describes the parameters of the standard Gaussian equations in each region. The four regions include the vegetation, a downwind wake, a transition, and a recovery zone. For each region, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition which is a major factor in pollutant reduction by vegetation barriers. We generated data from 75 (CFD)-based simulations, using the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model, to parameterize the Gaussian-based equations. The simulations used reflected a wide range of vegetation barriers, with heights from 2-10 m, and various densities, represented by leaf area index values from 4-11, and evaluated under different urban conditions, represented by wind speeds from 1-5 m/s. The CTAG model has been evaluated against two field measurements to ensure that it can properly represent the vegetation barrier’s pollutant deposition and dispersion. The proposed multi-region Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing deposition. The multi-region model’s normalized mean error (NME) ranged between 0.18-0.3, the fractional bias (FB) ranged between -0.12-0.09, and R2 value ranged from 0.47-0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable range. Even though the multi-region model is parameterized for coniferous trees, our sensitivity study indicates that the parameterized Gaussian-based model can provide useful predictions for hedge/bushes vegetative barriers as well." ADDITIONAL DESCRIPTION: Detailed variable relationships are described in the source document. The VRD associated with the ESML entry provides variables in a simplified form.
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 identified	None identified	None identified	None reported	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.	No additional description provided	No additional description provided	submerged aquatic vegetation	No additional description provided	Yaquina Bay estuary	restored, enhanced and created wetlands	N/A	None additional	Communities living and working in near-road environments
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	N?A	N/A	None scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
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 + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	Application of existing model	New or revised model	Application of existing model	New or revised model

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

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-271 ? Comment: Document 273 used for source information on soil erosion potential variable	Doc-303 \| Doc-305	Doc-183 \| Doc-47 \| Doc-313 \| Doc-314 ? Comment: Doc 183 is a secondary source for the Evoland model.	None	None	Doc-324	Doc-390	None	Doc-432	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-85 \| EM-87 \| EM-88	None	EM-12 \| EM-369	None	None	EM-603 \| EM-397	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	None	EM-939	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	1971-2006	1990-2050	2010 - 2012	2006-2007, 2010	2003-2008	2010-2011	2015	2020	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	Not applicable
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	future time	Not applicable	Not applicable	Not applicable	past time	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	2	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Geopolitical	Physiographic or ecological	Physiographic or ecological	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Geopolitical	Geopolitical	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	South Africa	Puget Sound Region	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	St. Louis River Estuary	Coastal zone surrounding St. Croix	Pacific Northwest	Wetlands in idaho	Hudson River Park	Iran	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	10,000-100,000 km^2	10-100 km^2	10-100 km^2	100-1000 km^2	>1,000,000 km^2	100,000-1,000,000 km^2	10-100 ha	>1,000,000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
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) ? 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.	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially lumped (in all cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases) ? Comment: Varies by inputs, but results are for areas of country	spatially distributed (in at least some 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	Not applicable	area, for pixel or radial feature	length, for linear feature (e.g., stream mile)
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed across catchments with average size of 65,000 ha	200m x 200m	varies	0.07 m^2 to 0.70 m^2	10 m x 10 m	Not applicable	Not applicable	Not applicable	ha^2	user defined

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic	Numeric	Numeric	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Bayesian statistics	Other or unclear (comment)	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-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Unclear	Yes	Yes	No	No	No	No	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	Yes	No	No	No	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	Yes	Yes	Yes	No	No	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	No	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
Africa South Africa	North America United States	North America United States Oregon	North America United States Minnesota Wisconsin	None	North America United States Oregon Washington	North America United States Idaho	North America United States New York	Europe ? Comment: Model for Iran - no form preset id for country	None

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

EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
Centroid Latitude em.detail.ddLatHelp ?	-30	48	44.11	46.72	17.73	44.62	44.06	40.73	32.29	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	25	-123	-123.09	-96.13	-64.77	-124.02	-114.69	-74.01	53.68	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands	Created Greenspace	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not reported	Terrestrial environment surrounding a large estuary	Agricultural-urban interface at river junction	Freshwater estuarine system	Coral reefs	Yaquina Bay estuary and ocean	created, restored and enhanced wetlands	Park	terrestrial land types	Communities living and working in near-road environments
EM Ecological Scale em.detail.ecoScaleHelp ?	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	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Guild or Assemblage	Other (multiple scales)	Not applicable	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
None Available	None Available	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	Species Chinook salmon (Onchorhyncus tshawytscha)	None Available	None Available	Species Apis mellifera Apis florea	None Available

EnviroAtlas URL

EM-86	EM-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
None Available	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Percent Impervious Area	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	Average Annual Precipitation	None Available	Dasymetric Allocation of Population	Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass	Enabling Conditions	The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Precipitation, Waterbody area	GAP Ecological Systems, Average Annual Precipitation

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-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[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] 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] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings	[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	[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 Intellectual and representative interactions Educational	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance 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-326	EM-333	EM-414	EM-456	EM-604	EM-718	EM-875	EM-941	EM-942
None	Rivers and Streams (111) Composite (8) Regulation of extreme events Flow Indicator Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Flow Indicator Forests (21) Composite (8) Regulation of extreme events Flow 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	None	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Site Indicator	None	Forests (21) Flora (5) Vegetation Site Indicator	Forests (21) Fauna (4) Wildlife Stock Indicator Scrubland/Shrubland (24) Fauna (4) Wildlife Stock Indicator Urban/Surburban (27) Fauna (4) Wildlife Stock Indicator	None

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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)