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.

Compare Criteria:

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Comparing:

EM-12
EM-65
EM-83
EM-194
EM-491
EM-657
EM-838
EM-875
EM-936

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
EM Short Name em.detail.shortNameHelp ?	Evoland v3.5 (bounded growth), Eugene, OR, USA	Green biomass production, Central French Alps	Soil carbon and plant traits, Central French Alps	Coral and land development, St.Croix, VI, USA	EnviroAtlas - Crops with no pollinator habitat	REQI (River Ecosystem Quality Index), Italy	Eastern meadowlark abundance, Piedmont region, USA	Valuing environmental ed., New York, New York	i-Tree species selector v. 4.0
EM Full Name em.detail.fullNameHelp ?	Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA	Green biomass production, Central French Alps	Soil carbon potential estimated from plant functional traits, Central French Alps	Coral colony density and land development, St.Croix, Virgin Islands, USA	US EPA EnviroAtlas - Acres of pollinated crops with no nearby pollinator habitat, USA	REQI (River Ecosystem Quality Index), Marecchia River, Italy	Eastern meadowlark abundance, Piedmont ecoregion, USA	Valuing environmental education, Hudson River Park, New York, New York	i-Tree species selector v. 4.0
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	Envision	EU Biodiversity Action 5	EU Biodiversity Action 5	US EPA	US EPA \| EnviroAtlas	None	None	None	i-Tree
EM Source Document ID	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	260	260	96	262	378	405	416	426 ? Comment: Doc# 427 is an additional source for this EM.
Document Author em.detail.documentAuthorHelp ?	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	US EPA Office of Research and Development - National Exposure Research Laboratory	Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi	Riffel, S., Scognamillo, D., and L. W. Burger	Hutcheson, W. Hoagland, P., and D. Jin	i-Tree
Document Year em.detail.documentYearHelp ?	2008	2011	2011	2011	2013	2014	2008	2018	None
Document Title em.detail.sourceIdHelp ?	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands	EnviroAtlas - National	Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy)	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Valuing environmental education as a cultural ecosystem service at Hudson River Park	i-Tree Species Selector User's Manual v. 4.0
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
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 journal manuscript	Published journal manuscript	Published journal manuscript	Webpage

Software and Access

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	http://evoland.bioe.orst.edu/ ? Comment: Software is likely available.	Not applicable	Not applicable	Not applicable	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	Not applicable	https://species.itreetools.org/
Contact Name em.detail.contactNameHelp ?	Michael R. Guzy	Sandra Lavorel	Sandra Lavorel	Leah Oliver	EnviroAtlas Team	Elisa Morri	Sam Riffell	Walter Hutcheson	Not reported ? Comment: send comments through any of the means listed on the i-Tree support page: http://www.itreetools.org/support/.
Contact Address	Oregon State University, Dept. of Biological and Ecological Engineering	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	National Health and Environmental Research Effects Laboratory	Not reported	Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	New York University, United States	Not reported
Contact Email	Not reported	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	leah.oliver@epa.gov	enviroatlas@epa.gov	elisa.morri@uniurb.it	sriffell@cfr.msstate.edu	wwh235@nyu.edu	info@itreetools.org

EM Description

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
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: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions."	AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294)	DATA FACT SHEET: "This EnviroAtlas national map estimates the total acres of agricultural crops within each 12-digit hydrologic unit (HUC) that have varying amounts of nearby forested pollinator habitat. The crop types selected from the U.S. Department of Agriculture Cropland Data Layer (CDL) require (or would benefit from) the presence of pollinators, but crops may have no nearby native pollinator habitat. This metric is based on the average flight distance of native bees, both social and solitary, that nest in woodland habitats and forage on native plants and cultivated crops." "The metric was generated using the ESRI ArcMap Neighborhood Distance tool in conjunction with a blended landcover grid, which included the 2006 National Land Cover Dataset (NLCD) and U.S. Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (CDL). Pollinator habitat is defined as trees (fruit, nut, deciduous, and evergreen) for nesting and associated woodland for additional pollen sources. Crops that either require or benefit from pollination were selected and a distance measure of 2.8 kilometers (the average bee species’ foraging distance from the nest4) was used to assess presence or absence of nearby native pollinator habitat. The total area of crops without nearby pollinator habitat was summarized by 12-digit HUC boundaries taken from the NHDPlusV2 Watershed Boundary Dataset (WBD Snapshot)."	ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed."	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 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: "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."
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	None identified	Not applicable	None Identified	None identified	None reported	None identified	None identified
Biophysical Context	No additional description provided	Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	nearshore; <1.5 km offshore; <12 m depth	No additional description provided	No additional description provided	Conservation Reserve Program lands left to go fallow	N/A	No additional description provided
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	Not applicable	No scenarios presented	No scenarios presented	N/A	N?A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
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	Method + Application	Method + Application	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	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
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.	Doc-260	Doc-260	None	None	None	Doc-405	None	Doc-427
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-333 \| EM-369	EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82	None	None	None	EM-831 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-846 \| EM-847	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
EM Temporal Extent em.detail.tempExtentHelp ?	1990-2050	2007-2009	Not reported	2006-2007	2001-2015	1996-2003 ? Comment: All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map.	2008	2015	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	Not applicable
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	2	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Watershed/Catchment/HUC	Physiographic or ecological	Geopolitical	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	Central French Alps	Central French Alps	St. Croix, U.S. Virgin Islands	conterminous United States	Marecchia river catchment	Piedmont Ecoregion	Hudson River Park	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	10-100 km^2	10-100 km^2	>1,000,000 km^2	100-1000 km^2	100,000-1,000,000 km^2	10-100 ha	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
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 lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially lumped (in all cases)	Not applicable
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	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	Not applicable	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	varies	20 m x 20 m	20 m x 20 m	Not applicable	irregular	500 m x 1000 m	Not applicable	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	stochastic	deterministic	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.	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
Model Calibration Reported? em.detail.calibrationHelp ?	Unclear	No	No	Yes	No	Not applicable	Yes	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	Yes	No	Not applicable	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	Adjusted R \| 69.9 \| Not applicable	None	R squared (for coral density) \| 0.90 \| Not applicable p-value \| 0.0002 \| unitless	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	No	No	Yes ? Comment: R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF.	No	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Yes	No	Not applicable	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No ? Comment: Sensitivity analysis performed for agent values only.	No	No	No	No	Not applicable	Yes	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	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-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
North America United States Oregon	Europe France	Europe France	None	North America United States	Europe Italy	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	North America United States New York	North America United States

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

EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
Centroid Latitude em.detail.ddLatHelp ?	44.11	45.05	45.05	17.75	39.5	43.89	36.23	40.73	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-123.09	6.4	6.4	-64.75	-98.35	12.3	-81.9	-74.01	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	NAD83	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Inland Wetlands \| Terrestrial Environment (sub-classes not fully specified)	Grasslands	Created Greenspace	Created Greenspace
Specific Environment Type em.detail.specificEnvTypeHelp ?	Agricultural-urban interface at river junction	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	stony coral reef	Terrestrial	Riparian zone along major river	grasslands	Park	Urban greenspace
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Not applicable	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	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-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Community	Community	Guild or Assemblage	Guild or Assemblage	Species ? Comment: Bird species for faunistic index of conservation.	Species	Not applicable	Species

Taxonomic level and name of organisms or groups identified

EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
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	Genus Acropora Porites spp. Diploria spp. Species Mycetophylia lamarckiana Mycetophylia ferox Montastrea cavernosa Dichocoenia stokesii Meandrina meandrites Agaricia tenuifolia Agaricia fragilis Montastrea annularis Agaricia agaricites Dendrogyra cylindricus Millepora complanata Montastrea faveolata Porites astreoides Montastrea franksii	other bee	None Available	Species Eastern meadowlark	None Available	None Available

EnviroAtlas URL

EM-12	EM-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
Dasymetric Allocation of Population, GAP Ecological Systems, The National Hydrography Dataset (NHD), Percent Natural Land Cover in Stream Buffer , Hectares of Vegetable Crops, Employment Rate	GAP Ecological Systems	None Available	None Available	GAP Ecological Systems, The Watershed Boundary Dataset (WBD)	Ecosystem Markets: Imperiled Species and Habitats	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	Enabling Conditions	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2)

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-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	None	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[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 Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters	[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 Intellectual and representative interactions Educational	[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

<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-65	EM-83	EM-194	EM-491	EM-657	EM-838	EM-875	EM-936
Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Grasslands (23) Flora (5) Green biomass Stock Indicator Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	Agroecosystems (22) Fauna (4) Pollinators Stock Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator	Forests (21) Flora (5) Vegetation Site Indicator	None

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EM Locations, Environments, Ecology

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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)