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-65
EM-71
EM-260
EM-333
EM-604
EM-684
EM-712
EM-719
EM-964
EM-982

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
EM Short Name em.detail.shortNameHelp ?	Green biomass production, Central French Alps	Community flowering date, Central French Alps	Coral taxa and land development, St.Croix, VI, USA	Evoland v3.5 (unbounded growth), Eugene, OR, USA	Chinook salmon value (household), Yaquina Bay, OR	Beach visitation, Barnstable, MA, USA	ESII Tool method	Seed mix for native plant establishment, IA, USA	EcoSim II - method	Specific conductivity, USA
EM Full Name em.detail.fullNameHelp ?	Green biomass production, Central French Alps	Community weighted mean flowering date, Central French Alps	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA	Economic value of Chinook salmon per household method, Yaquina Bay, OR	Beach visitation, Barnstable, Massachusetts, USA	ESII (Ecosystem Services Identification & Inventory) Tool method	Cost-effective seed mix design for native plant establishment, Iowa, USA	EcoSim II - method	Specific Conductivity, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	US EPA	Envision	US EPA	US EPA	None	None	None	US EPA
EM Source Document ID	260	260	96	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	324	386	391 ? Comment: Website for online user support	394	448	460
Document Author em.detail.documentAuthorHelp ?	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.	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar	Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta	EcoMetrix Solutions Group (ESG)	Meissen, J.	Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell	Olson, J.R., and S.M. Cormier
Document Year em.detail.documentYearHelp ?	2011	2011	2011	2008	2012	2018	2016	2018	2000	2019
Document Title em.detail.sourceIdHelp ?	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	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management	Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts	ESII Tool	Cost-effective seed mix design and first-year management	Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II	Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity
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	Other or unclear (explain in Comment)	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Website	Published report	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	http://evoland.bioe.orst.edu/	Not applicable	Not applicable	https://www.esiitool.com/	Not applicable	https://ecopath.org/downloads/	(https://edg.epa.gov/ metadata/catalog/main/home.page)
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	Leah Oliver	Michael R. Guzy	Stephen Jordan	Kate K, Mulvaney	Not reported	Justin Meissen	Carl Walters	John Olson
Contact Address	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	Oregon State University, Dept. of Biological and Ecological Engineering	U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA	Not reported	Not reported	Tallgrass Prairie Center, University of Northern Iowa	Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4	California State Univ. Monterey Bay, 100 Campus Center, Seaside CA 93955
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	leah.oliver@epa.gov	Not reported	jordan.steve@epa.gov	Mulvaney.Kate@EPA.gov	Not reported	Not reported	c.walters@oceans.ubc.ca	joolson@csumb.edu

EM Description

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
Summary Description em.detail.summaryDescriptionHelp ?	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: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables."	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)	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:"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.	ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression.	AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance."	AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully."	ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores "	We developed a random forest model that predicts natural background specific conductivity (SC), a measure of total dissolved ions, for all stream segments in the contiguous United States at monthly time steps between the years 2001 to 2015. Models were trained using 11 796 observations made at 1785 minimally impaired stream segments and validated with observations from an additional 92 segments. Static predictors of SC included geology, soils, and vegetation parameters. Temporal predictors were related to climate and enabled the model to make predictions for different dates. The model explained 95% of the variation in SC among validation observations (mean absolute error = 29 μS/cm, Nash-Sutcliffe efficiency = 0.85). The model performed well across the period of interest but exhibited bias in Coastal Plain and Xeric regions (26 and 30%, respectively). National model predictions showed large spatial variation with the greatest SC predicted to occur in the desert southwest and plains. Model predictions also reflected changes at individual streams during drought.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Not applicable	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	To assess the number of people who would be impacted by beach closures.	None identified	Seed mix design and management practices for native plant restoration	None	N/A
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	nearshore; <1.5 km offshore; <12 m depth	No additional description provided	Yaquina Bay estuary	Four separate beaches within the community of Barnstable	Not applicable	The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site.	None, Ocean ecosystems	Stream segment taken from StreamCat database
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Not applicable	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	No scenarios presented	N/A	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	Method Only	Method + Application
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	New or revised model

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

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-260 \| Doc-269	None	Doc-183 \| Doc-47 \| Doc-313 \| Doc-314 ? Comment: Doc 183 is a secondary source for the Evoland model.	Doc-324	Doc-386 \| Doc-387	None	Doc-395	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	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-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	None	EM-12 \| EM-369	EM-603 \| EM-397	EM-682 \| EM-685 \| EM-683 \| EM-686	EM-713	EM-728	EM-1055	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2007-2008	2006-2007	1990-2050	2003-2008	2011 - 2016	Not applicable	2015-2017	Not applicable	2001-2015
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent	time-stationary	time-dependent	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	future time	Not applicable	past time	Not applicable	Not applicable	both	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	discrete	Not applicable	discrete	Not applicable	discrete	discrete ? Comment: Modeller dependent	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	2	Not applicable	1	Not applicable	1	1	3
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Year	Not applicable	Day	Not applicable	Year	Day	Month

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Geopolitical	Physiographic or ecological	Not applicable	Other	Other	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	St.Croix, U.S. Virgin Islands	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	Pacific Northwest	Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach)	Not applicable	Iowa State University Northeast Research and Demonstration Farm	Not applicable	Contiguous United States
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	10-100 ha	Not applicable	<1 ha	Not applicable	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
EM Spatial Distribution em.detail.distributeLumpHelp ?	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 lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: map units delineated by user based on project.	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	Not applicable	length, for linear feature (e.g., stream mile)	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	Not applicable	varies	Not applicable	by beach site	map units	20 ft x 28 ft	Not applicable	3.1 km2

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Other or unclear (comment)	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Yes	Unclear	No	Yes	Not applicable	Not applicable	No	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	Yes	No	No	No	Not applicable	Not applicable	No	Yes
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 69.9 \| Not applicable	R squared \| 45.2 \| Not applicable	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	None	None	None	None	None	None	R2 \| 0.92 \| unitless MAE \| 22 \| uS/cm NSE \| 0.92 \| unitless
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	No	Yes	No	Not applicable	No	Not applicable	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	Yes	No	No	No	Not applicable	Not applicable	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	Yes	Not applicable	Not applicable	Not applicable	Yes
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	Yes

EM Locations, Environments, Ecology

Location of EM Application

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

EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
Europe France	Europe France	None	North America United States Oregon	North America United States Oregon Washington	North America United States Massachusetts	None	North America United States Iowa	None	North America United States

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

EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
None	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	17.75	44.11	44.62	41.64	Not applicable	42.93	Not applicable	39.83
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	-64.75	-123.09	-124.02	-70.29	Not applicable	-92.57	Not applicable	98.58
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	NAD83	WGS84	WGS84	WGS84	Not applicable	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Estimated	Estimated	Estimated	Estimated	Not applicable	Provided	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Terrestrial Environment (sub-classes not fully specified)	Agroecosystems \| Grasslands	Open Ocean and Seas	Rivers and Streams
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	stony coral reef	Agricultural-urban interface at river junction	Yaquina Bay estuary and ocean	Saltwater beach	Not applicable	Research farm in historic grassland	Pelagic	Stream segment
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Not applicable	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	Not applicable	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Guild or Assemblage	Not applicable	Other (multiple scales)	Not applicable	Not applicable	Community	Other (Comment) ? Comment: Varied levels of taxonomic order	Not applicable

Taxonomic level and name of organisms or groups identified

EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
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 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	Species Chinook salmon (Onchorhyncus tshawytscha)	None Available	None Available	None Available	other Whiting shrimp Whales Seals Cod Phytoplankton Mackerel Benthos Zooplankton Anchovy	None Available

EnviroAtlas URL

EM-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
GAP Ecological Systems	None Available	None Available	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	Dasymetric Allocation of Population	Average Annual Precipitation	GAP Ecological Systems, The National Hydrography Dataset (NHD), Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems	Big game hunting recreation demand	GAP Ecological Systems, Average Annual Precipitation, Average Annual Daily Potential Wind Energy

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-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
None	None	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Maintenance of physical, chemical, abiotic conditions By natural chemical and physical processes Not applicable	[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 Physical use of 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] Provisioning Materials Water Surface water for non-drinking purposes [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Micro and regional climate regulation Global climate regulation by reduction of greenhouse gas concentrations Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance Gaseous / air flows Ventilation and transpiration Mass flows Buffering and attenuation of 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 Mediation by ecosystems Mediation of smell/noise/visual impacts 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 Pollination and seed dispersal	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters

<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-65	EM-71	EM-260	EM-333	EM-604	EM-684	EM-712	EM-719	EM-964	EM-982
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	Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Site Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Site Indicator	Rivers and Streams (111) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Forests (21) Composite (8) Presence of environmental class/subclass Stock Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Stock Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Stock Indicator Lakes and Ponds (112) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Wetlands (12) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Stock Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Stock Indicator Soil (2) Specific types of soil Quality Indicator	Grasslands (23) Composite (8) Presence of environmental class/subclass Quality Indicator	Open Oceans and Seas (114) Fauna (4) Fish Flow Indicator	Rivers and Streams (111) Water (3) Liquid water Quality Indicator

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EM Locations, Environments, Ecology

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

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

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)