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-63
EM-208
EM-337
EM-434
EM-660
EM-709
EM-788
EM-862
EM-904
EM-984
EM-993

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
EM Short Name em.detail.shortNameHelp ?	EnviroAtlas - Natural biological nitrogen fixation	FORCLIM v2.9, Santiam watershed, OR, USA	Rate of Fire Spread	Land capability classification	RUM: Valuing fishing quality, Michigan, USA	Pollinators on landfill sites, United Kingdom	Wild bees over 26 yrs of restored prairie, IL, USA	Recreational fishery index, USA	Drag coefficient Laminaria hyperborea	Salmonid toxicity to heavy metals, USA	Velma- 6PPD-Q concentrations, Seattle, WA
EM Full Name em.detail.fullNameHelp ?	US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA	FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA	Rate of Fire Spread	Land capability classification	Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA	Pollinating insects on landfill sites, East Midlands, United Kingdon	Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA	Recreational fishery index for streams and rivers, USA	Drag coefficient Laminaria hyperborea	Chinook salmon and steelhead toxicity to heavy metals, USA	VELMA: 6PPD-Quinone stormwater concentrations , Seattle, Washington
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA \| EnviroAtlas	US EPA	None	None	None	None	None	US EPA	None	US EPA	US EPA
EM Source Document ID	262 ? Comment: EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM.	23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	306	340	382 ? Comment: Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010.	389	401	414	424	462	465
Document Author em.detail.documentAuthorHelp ?	US EPA Office of Research and Development - National Exposure Research Laboratory	Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.	Rothermel, Richard C.	United States Department of Agriculture - Natural Resources Conservation Service	Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson	Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin	Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree	Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold	Mendez, F. J. and I. J. Losada	Chapman, G.	Halama JJ, McKane RB, Barnhart BL, Pettus PP, Brookes AF, Adams AK, Gockel CK, Djang KS, Phan V, Chokshi SM, Graham JJ, Tian Z, Peter KT and Kolodziej,EP
Document Year em.detail.documentYearHelp ?	2013	2007	1972	2013	2014	2013	2017	2021	2004	1978	2024
Document Title em.detail.sourceIdHelp ?	EnviroAtlas - National	Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model	A Mathematical model for predicting fire spread in wildland fuels	National Soil Survey Handbook - Part 622 - Interpretative Groups	Valuing recreational fishing quality at rivers and streams	Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects	Wild bee community change over a 26-year chronosequence of restored tallgrass prairie	Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers.	An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields	Toxicities of Cadmium, Copper, and Zinc to Four Juvenile Toxicities of Cadmium, Copper, and Zinc to Four Juvenile Stages of Chinook Salmon and Steelhead	Watershed analysis of urban stormwater contaminant 6PPD-Quinone hotspots and stream concentrations using a process-based ecohydrological model
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Documented, not peer reviewed	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 on US EPA EnviroAtlas website	Published journal manuscript	Published USDA Forest Service report	Published report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.epa.gov/enviroatlas	Not applicable	http://firelab.org/project/farsite	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not reported
Contact Name em.detail.contactNameHelp ?	EnviroAtlas Team ? Comment: Additional contact: Jana Compton, EPA	Richard T. Busing	Charles McHugh	United States Department of Agriculture	Richard Melstrom	Sam Tarrant	Sean R. Griffin	Gregg Lomnicky	F. J. Mendez	Gary Chapman	Jonathan Halama
Contact Address	Not reported	U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA	RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808	Not reported	Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA	RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K.	Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A.	200 SW 35th St., Corvallis, OR, 97333	Not reported	Corvallis Environmental Research Laboratory, Western Fish Toxicology Station U.S. Environmental Protection Agency, Corvallis, Oregon 97330	U.S. Environmental Protection Agency, Corvallis, OR
Contact Email	enviroatlas@epa.gov	rtbusing@aol.com	cmchugh@fs.fed.us	http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/	melstrom@okstate.edu	sam.tarrant@rspb.org.uk	srgriffin108@gmail.com	lomnicky.gregg@epa.gov	mendezf@unican.es	N/A	Halama.Jonathan@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
Summary Description em.detail.summaryDescriptionHelp ?	DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM."	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. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045.	ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.	AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2]	ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. "	ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each	ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities."	ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.]	ABSTRACT: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." AUTHOR'S DESCRIPTION: "Therefore, a relation between C˜D and some nondimensional flow parameters is desirable to characterize hydrodynamically the L. hyperborea model plants for predictable purposes."	ABSTRACT: "Continuous-flow toxicity tests were conducted to determine the relative tolerances of newly hatched alevins, swim-up alevins, parr, and smolts of chinook salmon (Oncorhynchus tshawytscha) and steelhead (Salmo gairdneri) to cadmium, copper, and zinc. Newly hatched alevins were much more tolerant to cadmium and, to a lesser extent, to zinc than were later juvenile forms. However, the later progression from swim-up alevin, through parr, to smolt was accompanied by a slight increase in metal tolerance. The 96-h LC50 values for all four life stages ranged from 1.0 to >27ug Cd/liter, 17 to 38ug Cu/liter, and 93 to 815ug Zn/liter. Steelhead were consistently more sensitive to these metals than were chinook salmon. When a sensitive life stage for acute toxicity tests with metals is sought, the more resistant newly hatched alevins should be avoided. Although tolerance may increase with age, all later juvenile life stages are more sensitive and should give similar results.	ABSTRACT: "Coho salmon (Oncorhynchus kisutch) are highly sensitive to 6PPD-Quinone (6PPD-Q). Details of the hydrological and biogeochemical processes controlling spatial and temporal dynamics of 6PPD-Q fate and transport from points of deposition to receiving waters (e.g., streams, estuaries) are poorly understood. To understand the fate and transport of 6PPD and mechanisms leading to salmon mortality Visualizing Ecosystem Land Management Assessments (VELMA), an ecohydrological model developed by US Environmental Protection Agency (EPA), was enhanced to better understand and inform stormwater management planning by municipal, state, and federal partners seeking to reduce stormwater contaminant loads in urban streams draining to the Puget Sound National Estuary. This work focuses on the 5.5 km2 Longfellow Creek upper watershed (Seattle, Washington, United States), which has long exhibited high rates of acute urban runoff mortality syndrome in coho salmon. We present VELMA model results to elucidate these processes for the Longfellow Creek watershed across multiple scales–from 5-m grid cells to the entire watershed. Our results highlight hydrological and biogeochemical controls on 6PPD-Q flow paths, and hotspots within the watershed and its stormwater infrastructure, that ultimately impact contaminant transport to Longfellow Creek and Puget Sound. Simulated daily average 6PPD-Q and available observed 6PPD-Q peak in-stream grab sample concentrations (ng/L) corresponds within plus or minus 10 ng/L. Most importantly, VELMA’s high-resolution spatial and temporal analysis of 6PPD-Q hotspots provides a tool for prioritizing the locations, amounts, and types of green infrastructure that can most effectively reduce 6PPD-Q stream concentrations to levels protective of coho salmon and other aquatic species. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None Identified	None identified	None identified	None provided	None identified	None identified	None identified	None identified	None identified	NA	Not reported
Biophysical Context	No additional description provided	No additional description provided	Not applicable	No additional description provided	stream and river reaches of Michigan	No additional description provided	The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season.	None	No additional description provided	Microcosms	6PPD deposition from vehicle tire wear particles.
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Land Management (3); Climate Change (3)	No scenarios presented	No scenarios presented	targeted sport fish biomass	No scenarios presented	No scenarios presented	N/A	No scenarios presented	Life stage	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: Runs differentiated by scenario combination.	Method Only	Method Only	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	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	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-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-346 \| Doc-347 ? Comment: EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM.	Doc-22 \| Doc-23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	None	None	None	Doc-389	None	None	Doc-424	None	Doc-366 \| Doc-423 \| Doc-430
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-146 \| EM-186 \| EM-224	None	None	None	EM-697	None	None	EM-896 \| EM-897	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
EM Temporal Extent em.detail.tempExtentHelp ?	2006-2010	1990-2050	Not applicable	Not applicable	2008-2010	2007-2008	1988-2014	2013-2014	Not applicable	1978	9/2020-6/2021
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	Not applicable	Not applicable	time-stationary	time-stationary	time-stationary	time-dependent	Not applicable	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	past time	Not applicable	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable	1
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Year	Not applicable	Not applicable	Day

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Watershed/Catchment/HUC	Not applicable	Not applicable	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Geopolitical	Not applicable	Geopolitical	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	counterminous United States	South Santiam watershed	Not applicable	Not applicable	HUCS in Michigan	East Midlands	Nachusa Grasslands	United States	Not applicable	Northwest	Longfellow creek
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	Not applicable	Not applicable	100,000-1,000,000 km^2	1000-10,000 km^2.	10-100 km^2	>1,000,000 km^2	Not applicable	100,000-1,000,000 km^2	1-10 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Watersheds (12-digit HUCs).	spatially distributed (in at least some cases)	Not applicable	Not applicable	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 lumped (in all cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	Not applicable	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	length, for linear feature (e.g., stream mile)	Not applicable	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	irregular	0.08 ha	Not applicable	Not applicable	reach in HUC	multiple unrelated locations	Area varies by site	stream reach (site)	Not applicable	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Analytic	Not applicable	Numeric	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Not applicable	Not applicable	No	Not applicable	No	No	Yes	No	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	Not applicable	Not applicable	Yes	Not applicable	No	No	Not applicable	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	R squared \| 0.552 \| unitless	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	No	Not applicable	No	No	Unclear	No	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	Not applicable	Not applicable	No	Not applicable	No	No	No	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	Not applicable	Not applicable	No	Not applicable	No	No	No	Yes	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	N/A	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Yes	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
North America United States	North America United States Oregon	None	None	North America United States Michigan	Europe United Kingdom	North America United States Illinois	North America United States North Carolina Indiana Wyoming Northern Mariana Islands Utah Guam Arizona Montana Kentucky California Kansas Delaware Florida Pennsylvania Iowa Mississippi Illinois Texas Connecticut Georgia Maryland Idaho Oregon Vermont Puerto Rico Maine Oklahoma Tennessee American Samoa Alabama Arkansas South Carolina Washington Nebraska West Virginia Colorado Massachusetts Missouri Alaska U.S. Virgin Islands Washington D.C. North Dakota Wisconsin Nevada New York Rhode Island Hawaii South Dakota Minnesota New Jersey Michigan Virgina New Mexico New Hampshire Louisiana Ohio	None	North America United States Oregon	North America United States Washington

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

EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
None	None	None	None	None	None	None	None	Temperate North Atlantic Northeast Atlantic Northern European Seas	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
Centroid Latitude em.detail.ddLatHelp ?	39.5	44.24	-9999	Not applicable	45.12	52.22	41.89	36.21	Not applicable	44.53	47.55
Centroid Longitude em.detail.ddLongHelp ?	-98.35	-122.24	-9999	Not applicable	85.18	-0.91	-89.34	-113.76	Not applicable	123.25	122.37
Centroid Datum em.detail.datumHelp ?	WGS84	None provided	Not applicable	Not applicable	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84	None provided
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Not applicable	Not applicable	Estimated	Estimated	Provided	Estimated	Not applicable	Estimated	Provided

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Forests	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams	Created Greenspace \| Grasslands	Agroecosystems \| Grasslands	Rivers and Streams	Near Coastal Marine and Estuarine	Rivers and Streams	Rivers and Streams
Specific Environment Type em.detail.specificEnvTypeHelp ?	Terrestrial	primarily Conifer Forest	Not applicable	None identified	stream reaches	restored landfills and grasslands	Restored prairie, prairie remnants, and cropland	reach	Near Coastal Marine and Estuarine	Modeling stream exposure	small stream
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Species	Not applicable	Not applicable	Not applicable	Individual or population, within a species	Species	Guild or Assemblage	Species	Species	Species

Taxonomic level and name of organisms or groups identified

EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
None Available	Species Pinus ponderosa Abies amabilis Tsuga mertensiana Thuja plicata Abies procera Abies grandis Chamaecyparis nootkatensis Tsuga heterophylla Other (trees) Pseudotsuga menziesii	None Available	None Available	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye	Genus Calliopum Lasioglossum Species Apis mellifera Bombus lapidarius Oedemera nobilis Episyrphus balteatus Bombus terrestris/lucorum Bombus pascuorum	other Bees	None Available	Species Laminaria hyporborea	Species Chinook salmon Steelhead trout	Species Coho salmon

EnviroAtlas URL

EM-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD)	GAP Ecological Systems, Average Annual Precipitation	Average Annual Precipitation	Average Annual Precipitation	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate	GAP Ecological Systems	GAP Ecological Systems	None Available	National Hydrography Dataset Plus (NHD PlusV2)	None Available	Carbon Storage by Tree Biomass

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-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[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] Provisioning Nutrition Biomass Reared animals and their outputs Cultivated crops	[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 Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Pollination and seed dispersal	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Mediation of waste, toxics and other nuisances By natural chemical and physical processes Not applicable

<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-63	EM-208	EM-337	EM-434	EM-660	EM-709	EM-788	EM-862	EM-904	EM-984	EM-993
Atmosphere Soil (2) Specific types of soil Quality Indicator	None	None	Agroecosystems (22) Soil (2) Specific types of soil Quality Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	None	None	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	None	None	Rivers and Streams (111) Fauna (4) Fish Extreme Event Indicator ? Comment: Model identifies toxicant concentrations relative to the known LC50 for coho juveniles which is 95ng/L (Spromber and Scholz, 2011;

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EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

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)

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