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-84
EM-88
EM-99
EM-186
EM-392
EM-699
EM-706
EM-729
EM-863
EM-876

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
EM Short Name em.detail.shortNameHelp ?	ACRU, South Africa	Area and hotspots of carbon storage, South Africa	Landscape importance for crops, Europe	FORCLIM v2.9, Western OR, USA	EPA H2O, Tampa Bay Region, FL,USA	Fish species richness, St. John, USVI, USA	WESP Method	WESP: Urban Stormwater Treatment, ID, USA	SLAMM, Tampa Bay, FL, USA	Neighborhood greenness and health, FL, USA
EM Full Name em.detail.fullNameHelp ?	ACRU (Agricultural Catchments Research Unit), South Africa	Area and hotspots of carbon storage, South Africa	Landscape importance for crop-based production, Europe	FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA	EPA H2O, Tampa Bay Region, FL, USA	Fish species richness, St. John, USVI, USA	Method for the Wetland Ecosystem Services Protocol (WESP)	WESP: Urban Stormwater Treament, ID, USA	SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA	Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	None	EU Biodiversity Action 5	US EPA	US EPA	None	None	None	None	None
EM Source Document ID	271	271	228	23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	321	355	390	393 ? Comment: Additional data came from electronic appendix provided by author Chris Murphy.	415 ? Comment: Secondary sources: Documents 412 and 413.	417
Document Author em.detail.documentAuthorHelp ?	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Haines-Young, R., Potschin, M. and Kienast, F.	Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.	Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy	Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco	Adamus, P. R.	Murphy, C. and T. Weekley	Sherwood, E. T. and H. S. Greening	Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik
Document Year em.detail.documentYearHelp ?	2008	2008	2012	2007	2015	2007	2016	2012	2014	2016
Document Title em.detail.sourceIdHelp ?	Mapping ecosystem services for planning and management	Mapping ecosystem services for planning and management	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model	EPA H20 User Manual	Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean	Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3.	Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context.	Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats	Neighborhood greenness and chronic health conditions in Medicare beneficiaries
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	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published EPA report	Published journal manuscript	Published report	Published report	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	http://www.epa.gov/ged/tbes/EPAH2O	Not applicable	http://people.oregonstate.edu/~adamusp/WESP/ ? Comment: This is an Excel spreadsheet calculator	Not applicable	http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html	Not applicable
Contact Name em.detail.contactNameHelp ?	Roland E Schulze	Benis Egoh	Marion Potschin	Richard T. Busing	Marc J. Russell, Ph.D.	Simon Pittman	Paul R. Adamus	Chris Murphy	Edward T. Sherwood	Scott C. Brown
Contact Address	School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA	USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561	1305 East-West Highway, Silver Spring, MD 20910, USA	6028 NW Burgundy Dr. Corvallis, OR 97330	Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID	Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA	Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136
Contact Email	schulzeR@nu.ac.za	Not reported	marion.potschin@nottingham.ac.uk	rtbusing@aol.com	russell.marc@epa.gov	simon.pittman@noaa.gov	adamus7@comcast.net	chris.murphy@idfg.idaho.gov	esherwood@tbep.org	sbrown@med.miami.edu

EM Description

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "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…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature."	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 study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture."	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." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e).	AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . .	ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems."	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."]	A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level.	ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…"	ABSTRACT: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None Identified	None reported	None provided	None identified	None identified	None identified	None identified
Biophysical Context	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.	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.	No additional description provided	Coastal to montane, Pacific Northwest US (Oregon) forests.	Not applicable	Hard and soft benthic habitat types approximately to the 33m isobath	None	restored, enhanced and created wetlands	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Two scenarios modelled, forests with and without fire	Land Use, EGS algorithm values,	No scenarios presented	N/A	Sites, function or habitat focus	Varying sea level rise (baseline - 2m), and two habitat adaption strategies	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
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 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	Method + Application	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	New or revised model	Application of existing model	New or revised model	Application of existing model	New or revised model	WESP - Urban Stormwater Treatment	Application of existing 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-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-272 ? Comment: Doc ID 272 was also used as a source document for this EM	Doc-271	Doc-231 \| Doc-228	Doc-22 \| Doc-23 ? Comment: Related document ID 22 provides tree species specific parameters in appendix.	None	Doc-355	None	Doc-390	Doc-412 \| Doc-413	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-85 \| EM-86 \| EM-87	EM-119 \| EM-120 \| EM-121 \| EM-162 \| EM-164 \| EM-165 \| EM-122 \| EM-123 \| EM-124 \| EM-125 \| EM-166 \| EM-170 \| EM-171	EM-146 \| EM-208 \| EM-224	None	EM-590 \| EM-698	EM-718	EM-718 \| EM-734	EM-857	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
EM Temporal Extent em.detail.tempExtentHelp ?	1950-1993	Not reported	2000	>650 yrs	Not applicable	2000-2005	Not applicable	2010-2011	2002-2100	2010-2011
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	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	Not applicable	Not applicable	past time	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Day	Not applicable	Not applicable	Year	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Geopolitical	Geopolitical	Physiographic or ecological	Geopolitical ? Comment: Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived.	Physiographic or ecological	Not applicable	Multiple unrelated locations (e.g., meta-analysis)	Watershed/Catchment/HUC	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	South Africa	South Africa	The EU-25 plus Switzerland and Norway	Western Oregon, north of 43.00 N to Washington border	Tampa Bay region	SW Puerto Rico,	Not applicable	Wetlands in idaho	Tampa Bay estuary watershed	Miami-Dade County
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	>1,000,000 km^2	>1,000,000 km^2	10,000-100,000 km^2	1000-10,000 km^2.	100-1000 km^2	Not applicable	100,000-1,000,000 km^2	1000-10,000 km^2.	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
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 lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed by catchments with average size of 65,000 ha	Distributed across catchments with average size of 65,000 ha	1 km x 1 km	0.08 ha	30m x 30m	not reported	not reported	Not applicable	10 x 10 m	Census block

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Logic- or rule-based	Numeric	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	None	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	No	No	No	Not applicable	No	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	No	Yes	Not applicable	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	R squared \| 0.56 \| unitless Cohen's Kappa statistic \| 0.53 \| unitless AUC \| 0.79 \| unitless	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Yes	Yes	No	Yes	No	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	Not applicable	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	Yes	Not applicable	No	No	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	No	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-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
Africa South Africa	Africa South Africa	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia France Lithuania Hungary Switzerland Spain Czech Republic Belgium Norway Finland Denmark Italy Germany Estonia	North America United States Oregon	North America United States Florida	None	None	North America United States Idaho	North America United States Florida	North America United States Florida

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

EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
None	None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
Centroid Latitude em.detail.ddLatHelp ?	-30	-30	50.53	44.66	28.05	17.79	Not applicable	44.06	27.76	25.64
Centroid Longitude em.detail.ddLongHelp ?	25	25	7.6	-122.56	-82.52	-64.62	Not applicable	-114.69	-82.54	-80.5
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Ground Water \| Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Forests	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Inland Wetlands	Inland Wetlands	Inland Wetlands \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Created Greenspace
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not reported	Not applicable	Not applicable	Primarily conifer forest	All terestrial landcover and waterbodies	shallow coral reefs	Wetlands	created, restored and enhanced wetlands	Esturary and associated urban and terrestrial environment	urban neighborhood greenspace
EM Ecological Scale em.detail.ecoScaleHelp ?	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 is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is coarser 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-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Not applicable	Species	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
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) Quercus garryana Pseudotsuga menziesii	None Available	other (fish class)	None Available	None Available	None Available	None Available

EnviroAtlas URL

EM-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
Average Annual Precipitation	None Available	None Available	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions	The National Hydrography Dataset (NHD), GAP Ecological Systems, Total Annual Reduced Nitrogen Deposition, Waterbody area, Percent GAP Status 1 & 2, Acres of Land Enrolled in the Conservation Reserve Program (CRP), The Watershed Boundary Dataset (WBD), Carbon Storage by Tree Biomass	None Available	None Available	Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass	National Hydrography Dataset Plus (NHD PlusV2)	Percent Population other than White, Non-Hispanic, Percent Population under 13 Years Old

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-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
[Ecosystem] Provisioning Nutrition Water Surface water for drinking Materials Water Surface water for non-drinking purposes	[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 Cultivated crops	[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 Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Mediation of flows Liquid flows Flood protection Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions 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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance 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	[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

<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-84	EM-88	EM-99	EM-186	EM-392	EM-699	EM-706	EM-729	EM-863	EM-876
Rivers and Streams (111) Water (3) Liquid water Stock Indicator	None	Agroecosystems (22) Flora (5) food crops Stock Indicator	None	Rivers and Streams (111) Composite (8) Regulation of extreme events Extreme Event Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	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	None	Urban/Surburban (27) Composite (8) Presence of environmental class/subclass Stock Indicator

EcoService Models Library (ESML)

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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

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)