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-88
EM-224
EM-333
EM-492
EM-706
EM-948
EM-979
EM-991

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
EM Short Name em.detail.shortNameHelp ?	i-Tree Eco: Carbon storage & sequestration, USA	Area and hotspots of carbon storage, South Africa	FORCLIM v2.9, West Cascades, OR, USA	Evoland v3.5 (unbounded growth), Eugene, OR, USA	EnviroAtlas - Restorable wetlands	WESP Method	Global forest stock, biomass and carbon downscaled	Predicting ecosystem service values, Bangladesh	Atlantis ecosystem harvest submodel
EM Full Name em.detail.fullNameHelp ?	i-Tree Eco carbon storage and sequestration (trees), USA	Area and hotspots of carbon storage, South Africa	FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA	Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA	US EPA EnviroAtlas - Percent potentially restorable wetlands, USA	Method for the Wetland Ecosystem Services Protocol (WESP)	Global forest growing stock, biomass and carbon downscaled map	Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh	Lessons in modelling and management of marine ecosystems: the Atlantis experience
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	i-Tree \| USDA Forest Service	None	US EPA	Envision	US EPA \| EnviroAtlas	None	None	None	None
EM Source Document ID	195	271	23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	262	390	442	457	463
Document Author em.detail.documentAuthorHelp ?	Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E.	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	US EPA Office of Research and Development - National Exposure Research Laboratory	Adamus, P. R.	Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner	Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y.	Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C.
Document Year em.detail.documentYearHelp ?	2013	2008	2007	2008	2013	2016	2008	2022	2011
Document Title em.detail.sourceIdHelp ?	Carbon storage and sequestration by trees in urban and community areas of the United States	Mapping ecosystem services for planning and management	Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	EnviroAtlas - National	Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3.	A global forest growing stock, biomass and carbon map based on FAO statistics	Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh	Lessons in modelling and management of marine ecosystems: the Atlantis experience
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 report	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	http://evoland.bioe.orst.edu/	https://www.epa.gov/enviroatlas	http://people.oregonstate.edu/~adamusp/WESP/ ? Comment: This is an Excel spreadsheet calculator	Not applicable	Not applicable	https://research.csiro.au/atlantis/home/links/
Contact Name em.detail.contactNameHelp ?	David J. Nowak	Benis Egoh	Richard T. Busing	Michael R. Guzy	EnviroAtlas Team	Paul R. Adamus	Georg Kindermann	Syed Riad Morshed	Elizabeth Fulton
Contact Address	USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA	Oregon State University, Dept. of Biological and Ecological Engineering	Not reported	6028 NW Burgundy Dr. Corvallis, OR 97330	International Institute for Applied Systems Analysis, Laxenburg, Austria	Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh	Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas.
Contact Email	dnowak@fs.fed.us	Not reported	rtbusing@aol.com	Not reported	enviroatlas@epa.gov	adamus7@comcast.net	kinder(at)iiasa.ac.at	riad.kuet.urp16@gmail.com	beth.fulton@csiro.au

EM Description

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
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)."	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot."	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."	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."	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."	Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."]	ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. "	Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability.	Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Not reported	None identified	None Identified	Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…"	None Identified	None identified	None identified	N/A	None identified
Biophysical Context	Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000.	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions	No additional description provided	No additional description provided	None	No additional description provided	Jashore city, Bangladesh	NA
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Two scenarios modelled, forests with and without fire	Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves.	No scenarios presented	N/A	No scenarios presented	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	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 (multiple runs exist) View EM Runs	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	New or revised 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-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-271	Doc-22 \| Doc-23	Doc-183 \| Doc-47 \| Doc-313 \| Doc-314 ? Comment: Doc 183 is a secondary source for the Evoland model.	None	None	None	None	Doc-456 \| Doc-459 \| Doc-461 \| Doc-463
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-85 \| EM-86 \| EM-87	EM-146 \| EM-208 \| EM-186	EM-12 \| EM-369	None	EM-718	None	None	EM-978 \| EM-981 \| EM-983 \| EM-985 \| EM-990

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
EM Temporal Extent em.detail.tempExtentHelp ?	1989-2010	Not reported	>650 yrs	1990-2050	2006-2013	Not applicable	1999-2005	2000-2050	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	past time	future time	Not applicable	Not applicable	Not applicable	both	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	discrete	discrete	Not applicable	Not applicable	Not applicable	discrete	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	1	2	Not applicable	Not applicable	Not applicable	10	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Not applicable	Year	Year	Not applicable	Not applicable	Not applicable	Year	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Geopolitical	Physiographic or ecological	Geopolitical	Geopolitical	Not applicable	No location (no locational reference given)	Geopolitical	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	United States	South Africa	West Cascades, Oregon	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	conterminous United States	Not applicable	Global	Jashore city, Bangladesh	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	>1,000,000 km^2	100-1000 km^2	10-100 km^2	>1,000,000 km^2	Not applicable	>1,000,000 km^2	1000-10,000 km^2.	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
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 distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	Not applicable
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	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	area, for pixel or radial feature	map scale, for cartographic feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 m^2	Distributed across catchments with average size of 65,000 ha	0.08 ha	varies	irregular	not reported	0.5 x 0.5 degrees	30m	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Numeric	Numeric	Analytic	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Other or unclear (comment)	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Machine learning	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Unclear	No	Not applicable	No	Yes	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	No	Not applicable	Yes ? Comment: For the 0.5 grid level equation where the country forest level is missing.	Yes	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	None	r2 \| Varies by run; view runs to see values \| unitless	Accuracy assessment value \| 93.33-96.67 \| % Kappa statistic \| 0.9143-0.9577 \| N/A	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Yes	No	No	No	Yes	Yes	Not applicable
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	Not applicable	No	Unclear	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	Not applicable	No	Unclear	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
North America United States ? Comment: EM presents carbon storage and sequestration rates for country and by individual state	Africa South Africa	North America United States Oregon	North America United States Oregon	North America United States	None	None	Asia India	None

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

EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
None	None	None	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
Centroid Latitude em.detail.ddLatHelp ?	40.16	-30	44.24	44.11	39.5	Not applicable	44.51	23.95	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-99.79	25	-122.24	-123.09	-98.35	Not applicable	-123.51	89.12	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84	other	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable	Estimated	Provided	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Forests \| Created Greenspace	Terrestrial Environment (sub-classes not fully specified)	Forests	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Agroecosystems	Inland Wetlands	Forests	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Open Ocean and Seas
Specific Environment Type em.detail.specificEnvTypeHelp ?	Urban forests	Not applicable	Primarily conifer forest	Agricultural-urban interface at river junction	Terrestrial	Wetlands	Forests	Urban city	Multiple
EM Ecological Scale em.detail.ecoScaleHelp ?	Zone within an ecosystem	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	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-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
EM Organismal Scale em.detail.orgScaleHelp ?	Species ? Comment: Trees were identified to species for the differential growth and biomass estimates part of the analysis.	Not applicable	Species	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
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	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	None Available	None Available	None Available

EnviroAtlas URL

EM-24	EM-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
GAP Ecological Systems, Average Annual Precipitation	None Available	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions	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, The Watershed Boundary Dataset (WBD)	None Available	GAP Ecological Systems	GAP Ecological Systems	Big game hunting recreation demand

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-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Provisioning 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	[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 Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	None	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops Energy Biomass-based energy sources Plant-based resources 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 Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems [Ecosystem] Cultural Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings] Spiritual and/or emblematic Sacred and/or religious Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings	[Ecosystem] 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

<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-88	EM-224	EM-333	EM-492	EM-706	EM-948	EM-979	EM-991
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	None	Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	None	Wetlands (12) Water (3) Liquid water Flow Indicator Quality Indicator Fauna (4) Wildlife Site Indicator Pollinators Quality Indicator Fish Stock Indicator Birds Site Indicator	None	Scrubland/Shrubland (24) Composite (8) Regulation of extreme events Site Indicator Urban/Surburban (27) Composite (8) Presence of environmental class/subclass Site Indicator	None

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

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Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

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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)