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-337
EM-345
EM-660
EM-698
EM-709
EM-788
EM-862
EM-892
EM-1020

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
EM Short Name em.detail.shortNameHelp ?	Rate of Fire Spread	InVEST habitat quality, Puli Township, Taiwan	RUM: Valuing fishing quality, Michigan, USA	Fish species richness, St. Croix, USVI	Pollinators on landfill sites, United Kingdom	Wild bees over 26 yrs of restored prairie, IL, USA	Recreational fishery index, USA	VELMA v. 2.1 contaminant modeling	EPIC agriculture model, Baden-Wurttemberg, Germany
EM Full Name em.detail.fullNameHelp ?	Rate of Fire Spread	InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan	Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA	Fish Species Richness, Buck Island, St. Croix , USVI	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	VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling	Carbon sequestration in soils of SW-Germany as affected by agricultural management—Calibration of the EPIC model for regional simulations
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	InVEST	None	None	None	None	US EPA	US EPA	None
EM Source Document ID	306	308	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.	355	389	401	414	423 ? Comment: Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430.	482
Document Author em.detail.documentAuthorHelp ?	Rothermel, Richard C.	Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T.	Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson	Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco	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	McKane	Billen, N., Röder, C., Gaiser, T. and Stahr, K.,
Document Year em.detail.documentYearHelp ?	1972	2014	2014	2007	2013	2017	2021	None	2009
Document Title em.detail.sourceIdHelp ?	A Mathematical model for predicting fire spread in wildland fuels	Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan	Valuing recreational fishing quality at rivers and streams	Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean	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.	Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation	Carbon sequestration in soils of SW-Germany as affected by agricultural management—calibration of the EPIC model for regional simulations
Document Status em.detail.statusCategoryHelp ?	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 USDA Forest Service report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published EPA report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	http://firelab.org/project/farsite	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355	https://epicapex.tamu.edu/epic/
Contact Name em.detail.contactNameHelp ?	Charles McHugh	Yu-Pin Lin ? Comment: Tel.: +886 2 3366 3467; fax: +866 2 2368 6980	Richard Melstrom	Simon Pittman	Sam Tarrant	Sean R. Griffin	Gregg Lomnicky	Robert B. McKane	Norbert Billen
Contact Address	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	1305 East-West Highway, Silver Spring, MD 20910, 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	US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333	University of Hohenheim, Institute of Soil Science and Land Evaluation, Emil Wolff Strasse 27, D-70593 Stuttgart, Germany
Contact Email	cmchugh@fs.fed.us	yplin@ntu.edu.tw	melstrom@okstate.edu	simon.pittman@noaa.gov	sam.tarrant@rspb.org.uk	srgriffin108@gmail.com	lomnicky.gregg@epa.gov	mckane.bob@epa.gov	billen@uni-hohenheim.de

EM Description

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Summary Description em.detail.summaryDescriptionHelp ?	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.	Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the landscape.	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: "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."	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: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters."	Global emissions trading allows for agricultural measures to be accounted for the carbon sequestration in soils. The Environmental Policy Integrated Climate (EPIC) model was tested for central European site conditions by means of agricultural extensification scenarios. Results of soil and management analyses of different management systems (cultivation with mouldboard plough, reduced tillage, and grassland/fallow establishment) on 13 representative sites in the German State Baden-Württemberg were used to calibrate the EPIC model. Calibration results were compared to those of the Intergovernmental Panel on Climate Change (IPCC) prognosis tool. The first calibration step included adjustments in (a) N depositions, (b) N2-fixation by bacteria during fallow, and (c) nutrient content of organic fertilisers according to regional values. The mixing efficiency of implements used for reduced tillage and four crop parameters were adapted to site conditions as a second step of the iterative calibration process, which should optimise the agreement between measured and simulated humus changes. Thus, general rules were obtained for the calibration of EPIC for different criteria and regions. EPIC simulated an average increase of +0.341 Mg humus-C ha−1 a−1 for on average 11.3 years of reduced tillage compared to land cultivated with mouldboard plough during the same time scale. Field measurements revealed an average increase of +0.343 Mg C ha−1 a−1 and the IPCC prognosis tool +0.345 Mg C ha−1 a−1. EPIC simulated an average increase of +1.253 Mg C ha−1 a−1 for on average 10.6 years of grassland/fallow establishment compared to an average increase of +1.342 Mg humus-C ha−1 a−1 measured by field measurements and +1.254 Mg C ha−1 a−1 according to the IPCC prognosis tool. The comparison of simulated and measured humus C stocks was r2 ≥ 0.825 for all treatments. However, on some sites deviations between simulated and measured results were considerable. The result for the simulation of yields was similar. In 49% of the cases the simulated yields differed from the surveyed ones by more than 20%. Some explanations could be found by qualitative cause analyses. Yet, for quantitative analyses the available information from farmers was not sufficient. Altogether EPIC is able to represent the expected changes by reduced tillage or grassland/fallow establishment acceptably under central European site conditions of south-western Germany.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	Environmental effects of Highway 6 construction on Puli Township, Taiwan	None identified	None provided	None identified	None identified	None identified	None identified	Impact of different agricultural management strategies
Biophysical Context	Not applicable	26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere.	stream and river reaches of Michigan	Hard and soft benthic habitat types approximately to the 33m isobath	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	Central Europe agricultural sites
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas.	targeted sport fish biomass	No scenarios presented	No scenarios presented	No scenarios presented	N/A	No scenarios presented	NA

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method Only	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method Only	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	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

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

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-278	None	Doc-355	Doc-389	None	None	Doc-430	Doc-478
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-143	None	EM-590 \| EM-699	EM-697	None	None	EM-883 \| EM-884 \| EM-887	EM-1012 \| EM-1021

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
EM Temporal Extent em.detail.tempExtentHelp ?	Not applicable	2010-2025	2008-2010	2000-2005	2007-2008	1988-2014	2013-2014	Not applicable	4-20 years ? Comment: This paper compares agricultural plots that have used specific types of management practices over various periods ranging from 4-20 years. The beginning and end dates of those periods are not provided.
EM Time Dependence em.detail.timeDependencyHelp ?	Not applicable	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	past time	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	discrete	other or unclear (comment) ? Comment: This paper compares agricultural plots that have used specific types of management practices over various periods ranging from 4-20 years. The beginning and end dates of those periods are not provided.
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Year	Day	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Bounding Type em.detail.boundingTypeHelp ?	Not applicable	Geopolitical	Watershed/Catchment/HUC	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Geopolitical	Not applicable	Multiple unrelated locations (e.g., meta-analysis)
Spatial Extent Name em.detail.extentNameHelp ?	Not applicable	Puli Township, Nantou County	HUCS in Michigan	SW Puerto Rico,	East Midlands	Nachusa Grasslands	United States	Not applicable	Baden-Wurttemberg
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	Not applicable	100-1000 km^2	100,000-1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	10-100 km^2	>1,000,000 km^2	Not applicable	10,000-100,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
EM Spatial Distribution em.detail.distributeLumpHelp ?	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 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)
Spatial Grain Type em.detail.spGrainTypeHelp ?	Not applicable	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	length, for linear feature (e.g., stream mile)	volume, for 3-D feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	Not applicable	40 m x 40 m	reach in HUC	not reported	multiple unrelated locations	Area varies by site	stream reach (site)	user defined	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	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-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Model Calibration Reported? em.detail.calibrationHelp ?	Not applicable	Unclear	No	No	Not applicable	No	No	Not applicable	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Not applicable	Not applicable	Yes	Yes	Not applicable	No	No	Not applicable	Yes
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	R squared \| 0.552 \| unitless	R squared \| 0.48 \| unitless Cohen's Kappa statistic \| 0.46 \| unitless AUC \| 0.76 \| unitless	None	None	None	None	r-squared \| 0.6416 \| NA
Model Operational Validation Reported? em.detail.validationHelp ?	No	Not applicable	No	Yes	Not applicable	No	No	Not applicable	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Not applicable	No	No	No	Not applicable	No	No	Not applicable	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Not applicable	No	No	Yes	Not applicable	No	No	Not applicable	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	No	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-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
None	Asia China ? Comment: Taiwan	North America United States Michigan	None	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	Europe Germany

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

EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Centroid Latitude em.detail.ddLatHelp ?	-9999	23.98	45.12	17.79	52.22	41.89	36.21	Not applicable	48.62
Centroid Longitude em.detail.ddLongHelp ?	-9999	120.96	85.18	-64.62	-0.91	-89.34	-113.76	Not applicable	9.03
Centroid Datum em.detail.datumHelp ?	Not applicable	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Not applicable	Estimated	Estimated	Estimated	Estimated	Provided	Estimated	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands	Rivers and Streams	Near Coastal Marine and Estuarine	Created Greenspace \| Grasslands	Agroecosystems \| Grasslands	Rivers and Streams	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	Predominantly an agricultural area with associated forest land	stream reaches	shallow coral reefs	restored landfills and grasslands	Restored prairie, prairie remnants, and cropland	reach	Terrestrial environment	Agriculture plots
EM Ecological Scale em.detail.ecoScaleHelp ?	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 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 is finer than that of 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-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Community	Not applicable	Guild or Assemblage	Individual or population, within a species	Species	Guild or Assemblage	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
None Available	None Available	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye	other (fish class)	Genus Calliopum Lasioglossum Species Apis mellifera Bombus lapidarius Oedemera nobilis Episyrphus balteatus Bombus terrestris/lucorum Bombus pascuorum	other Bees	None Available	None Available	None Available

EnviroAtlas URL

EM-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Average Annual Precipitation	Percent GAP Status 1 & 2	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate	None Available	GAP Ecological Systems	GAP Ecological Systems	None Available	None Available	Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Agricultural water use (million gallons/day), Acres of Land Enrolled in the Conservation Reserve Program (CRP)

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-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions 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 waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes

<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-337	EM-345	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
None	Forests (21) Composite (8) Presence of environmental class/subclass Stock Indicator Quality Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Stock Indicator Quality Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	None	None	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	None	Agroecosystems (22) Soil (2) Specific types of soil Stock Indicator

EcoService Models Library (ESML)

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

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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)