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-12
EM-418
EM-428
EM-480
EM-493
EM-700
EM-895
EM-992

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
EM Short Name em.detail.shortNameHelp ?	Evoland v3.5 (bounded growth), Eugene, OR, USA	SIRHI, St. Croix, USVI	Retained rainwater, Guánica Bay, Puerto Rico	Yasso07 - Land use SOC dynamics, China	EnviroAtlas-Carbon sequestered by trees	Mallard recruits, CREP wetlands, Iowa, USA	HWB indicator-College degree, Great Lakes, USA	DAISY model, Taastrup, Copenhagen
EM Full Name em.detail.fullNameHelp ?	Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA	SIRHI (SImplified Reef Health Index), St. Croix, USVI	Retained rainwater, Guánica Bay, Puerto Rico, USA	Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China	US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA	Mallard duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Human well being indicator-College degree, Great Lakes waterfront, USA	Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	Envision	US EPA	US EPA	None	US EPA \| EnviroAtlas \| i-Tree	None	US EPA	None
EM Source Document ID	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	335	338	344	223 ? Comment: Additional source: I-tree Eco (doc# 345).	372 ? Comment: Document 373 is a secondary source for this EM.	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers	464
Document Author em.detail.documentAuthorHelp ?	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu	US EPA Office of Research and Development - National Exposure Research Laboratory	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick	Ghaley, B. B., & Porter, J. R.
Document Year em.detail.documentYearHelp ?	2008	2014	2017	2015	2013	2010	None	2014
Document Title em.detail.sourceIdHelp ?	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement	EnviroAtlas - Featured Community	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Human well-being and natural capital indictors for Great Lakes waterfront revitalization	Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark
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 but unpublished (explain in Comment)	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published on US EPA EnviroAtlas website	Published report	Journal manuscript submitted or in review	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	http://evoland.bioe.orst.edu/ ? Comment: Software is likely available.	Not applicable	Not applicable	http://en.ilmatieteenlaitos.fi/yasso-download-and-support	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Michael R. Guzy	Susan H. Yee	Susan H. Yee	Xing Wu	EnviroAtlas Team	David Otis	Ted Angradi	Bhim Bahadur Ghaley
Contact Address	Oregon State University, Dept. of Biological and Ecological Engineering	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Chinese Academy of Sciences, Beijing 100085, China	Not reported	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804	Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Højbakkegård Allé 30, DK-2630 Taastrup, Denmark.
Contact Email	Not reported	yee.susan@epa.gov	yee.susan@epa.gov	xingwu@rceesac.cn	enviroatlas@epa.gov	dotis@iastate.edu	tedangradi@gmail.com	bbg@life.ku.dk

EM Description

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
Summary Description em.detail.summaryDescriptionHelp ?	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: "...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 indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati	AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…"	ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau."	The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group."	ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2Rn where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = HZB/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).	ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. "	With inevitable link between ecosystem function (EF), ecosystem services (ES) and agricultural productivity, there is a need for quantification and valuation of EF and ES in agro-ecosystems. Management practices have significant effects on soil organic matter (SOM), affecting productivity, EF and ES provision. The objective was to quantify two EF: soil water storage and nitrogen mineralization and three ES: food and fodder production and carbon sequestration, in a conventional winter wheat production system at 2.6% SOM compared to 50% lower (1.3%) and 50% higher (3.9%) SOM in Denmark by DAISY model. At 2.6% SOM, the food and fodder production was 6.49 and 6.86 t ha−1 year−1 respectively whereas carbon sequestration and soil water storage was 9.73 t ha−1 year−1 and 684 mm ha−1 year−1 respectively and nitrogen mineralisation was 83.58 kg ha−1 year−1. At 2.6% SOM, the two EF and three ES values were US$ 177 and US$ 2542 ha−1 year−1 respectively equivalent to US$ 96 and US$1370 million year−1 respectively in Denmark. The EF and ES quantities and values were positively correlated with SOM content. Hence, the quantification and valuation of EF and ES provides an empirical tool for optimising the EF and ES provision for agricultural productivity.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	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	Meeting water demands for agriculture and domestic purposes.	None identified	None identified	None identified	None identified	None identified
Biophysical Context	No additional description provided	No additional description provided	No additional descriptions provided	Agricultural plain, hills, gulleys, forest, grassland, Central China	No additional description provided	Prairie Pothole Region of Iowa	Waterfront districts on south Lake Michigan and south lake Erie	Agro-ecosystem test farm, Copenhagen, Denmark.
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ? Comment: Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios.	No scenarios presented	No scenarios presented	Land use change	No scenarios presented	No scenarios presented	N/A	A soil organic matter value of 1.3% was used for this model run

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	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
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	Application of existing model	Application of existing model	Application of existing model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-47 \| Doc-313 \| Doc-314 ? Comment: Doc 183 is a secondary source for the Evoland model.	None	None	Doc-343 \| Doc-342	Doc-345	Doc-372 \| Doc-373	Doc-422	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-333 \| EM-369	None	None	EM-466 \| EM-467 \| EM-469 \| EM-485	None	EM-705 \| EM-704 \| EM-703 \| EM-702 \| EM-701 \| EM-632	EM-886 \| EM-888 \| EM-889 \| EM-890 \| EM-891 \| EM-893 \| EM-894	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
EM Temporal Extent em.detail.tempExtentHelp ?	1990-2050	2006-2007, 2010	2006 - 2012	1969-2011	2010-2013	1987-2007	2022	2003-2013
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	past time	Not applicable	Not applicable	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	2	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Not applicable	Not applicable	Year	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Geopolitical	Physiographic or ecological
Spatial Extent Name em.detail.extentNameHelp ?	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	Coastal zone surrounding St. Croix	Guanica Bay watershed	Yangjuangou catchment	Durham NC and vicinity	CREP (Conservation Reserve Enhancement Program	Great Lakes waterfront	Taastrup experimental farm
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	100-1000 km^2	1000-10,000 km^2.	1-10 km^2	100-1000 km^2	10,000-100,000 km^2	1000-10,000 km^2.	1-10 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha.	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: Census block groups	spatially distributed (in at least some cases)	spatially lumped (in all cases)	other or unclear (comment)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	varies	10 m x 10 m	30 m x 30 m	30m x 30m	irregular	multiple, individual, irregular sites	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Analytic	Numeric	Numeric	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	stochastic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Other or unclear (comment) ? Comment: Agent based modeling results in response indices.	None	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Machine learning	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
Model Calibration Reported? em.detail.calibrationHelp ?	Unclear	Yes	No	No	No	Unclear	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	Yes ? Comment: p value: p<0.001	No	No	No	Unclear
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	R squared \| 0.219 \| unitless	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	No	No	No	No	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No ? Comment: Sensitivity analysis performed for agent values only.	No	No	No	No	No	Yes	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	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-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
North America United States Oregon	None	North America U.S. Caribbean Territories	Asia China	North America United States North Carolina	North America United States Iowa	North America United States Illinois Minnesota Ohio Wisconsin	Europe Denmark

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

EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
Centroid Latitude em.detail.ddLatHelp ?	44.11	17.73	17.96	36.7	35.99	42.62	42.26	55.4
Centroid Longitude em.detail.ddLongHelp ?	-123.09	-64.77	-67.02	109.52	-78.96	-93.84	-87.84	12.18
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	None provided	WGS84	WGS84	None provided
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Provided	Estimated	Estimated	Estimated	Provided

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Near Coastal Marine and Estuarine	Inland Wetlands \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland \| Barren	Agroecosystems	Created Greenspace \| Atmosphere	Inland Wetlands \| Agroecosystems \| Grasslands	Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland \| Barren \| Tundra \| Ice and Snow \| Atmosphere	Agroecosystems
Specific Environment Type em.detail.specificEnvTypeHelp ?	Agricultural-urban interface at river junction	Coral reefs	13 LULC were used	Loess plain	Urban and vicinity	Wetlands buffered by grassland within agroecosystems	Lake Michigan & Lake Erie waterfront	Agroecosystems
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Not applicable	Individual or population, within a species	Not applicable	Guild or Assemblage ? Comment: Microbrial biomass is lumped together, but specific crops are presented.

Taxonomic level and name of organisms or groups identified

EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
other Fish Species Red-Legged Frog Osprey Western Terrestrial Garter Snake Little Brown Myotis Belted Kingfisher Wood Duck Northern River Otter Great Blue Heron Roughskin Newt Pacific Water Shrew American Beaver Mink Western Pond Turtle Killdeer Northwestern Salamander	other snapper Family grouper parrotfish surgeonfish	None Available	None Available	None Available	Species Mallard (Anas platyrhynchos)	None Available	Genus Wheat

EnviroAtlas URL

EM-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
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	None Available	The Watershed Boundary Dataset (WBD)	Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass	Carbon Storage by Tree Biomass	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	Dasymetric Allocation of Population, GAP Ecological Systems, Enabling Conditions	Average Annual Daily Potential Solar Energy, Average Annual Precipitation, Agricultural water use (million gallons/day), Stream Length Impaired by Pesticides

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-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes	[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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	None	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes

<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-12	EM-418	EM-428	EM-480	EM-493	EM-700	EM-895	EM-992
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	None	None	None	Wetlands (12) Fauna (4) Duck Stock Indicator	None	Agroecosystems (22) Flora (5) food crops Stock Indicator Fodder Stock Indicator Soil (2) Specific types of soil Quality Indicator

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Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Comment:

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)