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-24
EM-81
EM-131
EM-224
EM-492
EM-598
EM-700
EM-858

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
EM Short Name em.detail.shortNameHelp ?	i-Tree Eco: Carbon storage & sequestration, USA	Cultural ES and plant traits, Central French Alps	InVEST marine water quality, Hood Canal, WA, USA	FORCLIM v2.9, West Cascades, OR, USA	EnviroAtlas - Restorable wetlands	DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland	Mallard recruits, CREP wetlands, Iowa, USA	ARIES Flood Reg, Santa Fe, NM
EM Full Name em.detail.fullNameHelp ?	i-Tree Eco carbon storage and sequestration (trees), USA	Cultural ecosystem service estimated from plant functional traits, Central French Alps	InVEST (Integrated Valuation of Envl. Services and Tradeoffs) marine water quality, Hood Canal, WA, USA	FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA	US EPA EnviroAtlas - Percent potentially restorable wetlands, USA	DeNitrification-DeComposition simulation of N2O flux Ireland	Mallard duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	ARIES Flood regulation, Santa Fe, New Mexico
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	i-Tree \| USDA Forest Service	EU Biodiversity Action 5	InVEST	US EPA	US EPA \| EnviroAtlas	None	None	None
EM Source Document ID	195	260	205	23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	262	358	372 ? Comment: Document 373 is a secondary source for this EM.	411
Document Author em.detail.documentAuthorHelp ?	Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M.	Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.	US EPA Office of Research and Development - National Exposure Research Laboratory	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	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	Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa.
Document Year em.detail.documentYearHelp ?	2013	2011	2013	2007	2013	2010	2010	2018
Document Title em.detail.sourceIdHelp ?	Carbon storage and sequestration by trees in urban and community areas of the United States	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA	Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model	EnviroAtlas - National	Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Towards globally customizable ecosystem service models
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
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 report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	https://www.epa.gov/enviroatlas	http://www.dndc.sr.unh.edu	Not applicable	https://integratedmodelling.org/hub/#/register ? Comment: Need to set up an account first and then can access the main integrated modelling hub page:
Contact Name em.detail.contactNameHelp ?	David J. Nowak	Sandra Lavorel	J.E. Toft	Richard T. Busing	EnviroAtlas Team	M. Abdalla	David Otis	Javier Martinez-Lopez
Contact Address	USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Not reported	U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA	Not reported	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain
Contact Email	dnowak@fs.fed.us	sandra.lavorel@ujf-grenoble.fr	jetoft@stanford.edu	rtbusing@aol.com	enviroatlas@epa.gov	abdallm@tcd.ie	dotis@iastate.edu	javier.martinez@bc3research.org

EM Description

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)."	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 Cultural 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 cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions."	Marine Water Quality Model. Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We used outputs from the freshwater models as inputs to the marine water quality model.We adapted a box model that has been successfully applied in Puget Sound (Babson et al., 2006; Sutherland et al., 2011) to simulate seasonal and interannual variations in salinity, water temperature, and nitrates in the Canal." (p. 4)	ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire."	DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map."	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC.	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: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Not reported	None identified	Land use change	None Identified	None Identified	climate change	None identified	None identified
Biophysical Context	Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000.	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	No additional description provided	West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions	No additional description provided	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	Prairie Pothole Region of Iowa	Watersheds surrounding Santa Fe and Albuquerque, New Mexico
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	future land use and land cover; Climate change	Two scenarios modelled, forests with and without fire	No scenarios presented	fertilization	No scenarios presented	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist)	Method + Application (multiple runs exist) View EM Runs ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	Method + Application	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	Application of existing model	Application of existing model	New or revised model	Application of existing model	New or revised model	Application of existing model

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

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	Doc-22 \| Doc-23	None	None	Doc-372 \| Doc-373	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-82 \| EM-83	None	EM-146 \| EM-208 \| EM-186	None	EM-593	EM-705 \| EM-704 \| EM-703 \| EM-702 \| EM-701 \| EM-632	EM-859

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
EM Temporal Extent em.detail.tempExtentHelp ?	1989-2010	Not reported	varies by run, see runs for values	>650 yrs	2006-2013	1961-1990	1987-2007	1981-2015
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	past time	Not applicable	both	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	discrete	Not applicable	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	Not applicable	1	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Not applicable	Not applicable	Year	Not applicable	Day	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Physiographic or ecological	Geopolitical	Point or points	Multiple unrelated locations (e.g., meta-analysis)	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	United States	Central French Alps	Hood Canal	West Cascades, Oregon	conterminous United States	Oak Park Research centre	CREP (Conservation Reserve Enhancement Program	Santa Fe Fireshed
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	10-100 km^2	100-1000 km^2	100-1000 km^2	>1,000,000 km^2	1-10 ha	10,000-100,000 km^2	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	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)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 m^2	20 m x 20 m	Not reported	0.08 ha	irregular	Not applicable	multiple, individual, irregular sites	30 m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Analytic	Numeric	Analytic	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Other or unclear (comment)	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	No	No	Yes	Unclear	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	No	Yes ? Comment: Actual value was not given, just that results were very poor. Simulation results were 258% of observed	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	r squared \| 00.0 \| unitless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	Yes	No	Yes	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes ? Comment: An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling.	No	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	No
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-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
North America United States ? Comment: EM presents carbon storage and sequestration rates for country and by individual state	Europe France	North America United States Washington	North America United States Oregon	North America United States	Europe Ireland	North America United States Iowa	North America United States New Mexico

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

EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
Centroid Latitude em.detail.ddLatHelp ?	40.16	45.05	47.8	44.24	39.5	52.86	42.62	35.86
Centroid Longitude em.detail.ddLongHelp ?	-99.79	6.4	-122.7	-122.24	-98.35	6.54	-93.84	-105.76
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	NAD83	WGS84	WGS84	None provided	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Estimated	Estimated	Estimated	Provided	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Forests \| Created Greenspace	Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Forests	Agroecosystems	Agroecosystems	Inland Wetlands \| Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Urban forests	Subalpine terraces, grasslands, and meadows.	glacier-carver saltwater fjord	Primarily conifer forest	Terrestrial	farm pasture	Wetlands buffered by grassland within agroecosystems	watersheds
EM Ecological Scale em.detail.ecoScaleHelp ?	Zone within an ecosystem	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 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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
EM Organismal Scale em.detail.orgScaleHelp ?	Species ? Comment: Trees were identified to species for the differential growth and biomass estimates part of the analysis.	Community	Not applicable	Species	Not applicable	Not applicable	Individual or population, within a species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
None Available	None Available	None Available	Species Pinus ponderosa Abies amabilis Tsuga mertensiana Thuja plicata Picea sitchensis Abies procera Abies grandis Chamaecyparis nootkatensis Tsuga heterophylla Other (trees) Pseudotsuga menziesii	None Available	None Available	Species Mallard (Anas platyrhynchos)	None Available

EnviroAtlas URL

EM-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
GAP Ecological Systems, Average Annual Precipitation	GAP Ecological Systems	None Available	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions	GAP Ecological Systems, The Watershed Boundary Dataset (WBD)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	Dasymetric Allocation of Population, Average Annual Precipitation, Ecosystem Markets: Imperiled Species and Habitats

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-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[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 Intellectual and representative interactions Aesthetic Heritage, cultural	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of salt waters Mediation of flows Liquid flows Hydrological cycle and water flow maintenance	[Ecosystem] Provisioning Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing [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 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	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection

<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-24	EM-81	EM-131	EM-224	EM-492	EM-598	EM-700	EM-858
Forests (21) Flora (5) Trees Stock Indicator Atmosphere Composite (8) Regulation of extreme events Flow Indicator Urban/Suburban (27) Flora (5) Trees Flow Indicator Composite (8) Regulation of extreme events Quality Indicator	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	None	None	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	Wetlands (12) Fauna (4) Duck Stock Indicator	None

EcoService Models Library (ESML)

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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)